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Eindhoven University of Technology
MASTER
Design of tracking signals for improving inventory turnover
Oomen, S.
Award date:2017
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Master Thesis Project
1CM96
Design of tracking signals for improving inventory turnover
In partial fulfillment of the requirements for the degree of
Master of Science
in Operations Management and Logistics
Author:S. [email protected]
Supervisors:dr. Z. Atan (TU/e, OPAC)dr. ir. R.J.I. Basten (TU/e, OPAC)dr. M. Udenio (TU/e, OPAC)B. van Velzen (ASML)J. Stultiens (ASML)
Eindhoven University of Technology
Industrial Engineering & Innovation Sciences
September 14, 2017
TUE. School of Industrial Engineering and Innovation Sciences
Series Master Theses Operations Management and Logistics
Subject headings: excess and obsolete inventory, overplanned inventory, inventory turnover, track-
ing signals, statistical process control, time series forecasting, material requirements planning, high-
tech industry
Preface
This report describes the master thesis project performed to fulfil my education at the Eindhoven
University of Technology. It marks the end of my master in Operations Management and Logistics
at Eindhoven University of Technology, and the end of six years of being a student.
I want to thank a number of people who helped me during the project. First of all, Zumbul, you
were a great mentor for me. You helped me with content-related discussions, critical feedback
and structuring, but most importantly you helped me by telling me not to worry too much. Your
positive approach helped me to value my work and gave me confidence. Thank you for everything.
Second, Rob, I want to thank you for the time you made for me during the holidays. Your attention
for detail helped me to become aware of all my decisions and to ensure the story I wrote down was
also the story that I wanted to tell. Thank you both for your contributions.
Next, I want to thank my supervisors at ASML for giving me the opportunity to conduct this
project. Jack, thank you for your feedback and business insights. Our biweekly meetings helped
me to remain on schedule and focus the research to what is most valuable. Bas, you helped me a
lot during every stage of the project. From picking up a badge and getting my laptop, until my
final presentation. You made me find my way within ASML, and your logic expressed in drawings
helped me to clarify my thoughts. I am afraid I even picked up some of your humor. Thank you
for all your time and input.
My road towards this master thesis project was an unconventional one. Starting at Maastricht
University, via Newcastle, Australia, I ended up in Eindhoven. I want to thank my friends in
Maastricht, Australia and the new friends I made in Eindhoven, who made me feel at ease so
quickly, for an unforgettable student time. Finally, I want to thank my family who supported
me in everything that I did. From within the Netherlands, Belgium but also further away you
always remained closely connected to what is important in my life. The main lesson I learn from
this project? To stay a bit closer to my Australian friends’ most famous quote: “No worries, mate”.
Suzan Oomen
I
Abstract
Companies in the high-tech industry face inventory inefficiencies by the existence of overplanned
raw inventory, entailing inventory without a demand. Overplanned inventory exists due to a combi-
nation of occurence of influx, when additional materials arrive or become overplanned, and lack of
reduction. Improved insight is required on the sources and drivers of influx to be able to address root
causes to prevent future influx, and guide corrective actions to stipulate outflux of the overplanned
inventory. This master thesis explains how influx monitoring using tracking signals contributes to
improved inventory turnover by reducing overplanned raw inventory levels for high-tech companies.
It describes sources of overplanned inventory in the high-tech industry and presents a method to
measure influx from these sources. Drivers of existing non-overplanned raw inventory becoming
overplanned are examined both qualitatively and quantitatively to provide an indication of what
needs to be tracked to monitor influx effectively. Then, tracking signals are designed to identify
significant influx thereby monitoring the influx. Methods to set thresholds for tracking signals are
derived from time series forecasting and statistical process control and evaluated. Monitoring influx
using tracking signals resulted in increased awareness regarding influx and helped to identify the
root cause. This facilitates improved corrective actions and recurrence prevention by improving the
underlying process. Implementing a structural improvement process to follow the signal ensures
that benefits of monitoring are grasped. At the case company, the designed tracking signals are ex-
pected to impose a direct inventory reduction of e9.6∗1 million per year, excluding the potentially
even larger indirect effects through prevention, thereby improving inventory turnover. This impact
can be further enlarged by extending the scope of monitoring.
1An asterisk (*) indicates that this value is transformed due to confidentiality.
II
Executive summary
This report describes research on improving inventory turnover in the high-tech industry by re-
ducing overplanned raw inventory levels. Overplanned inventory is defined as inventory which is
within the planning scope of Material Requirements Planning (MRP), but for which no requirement
exists in the current plan. Overplanned inventory, which is long-lasting, contributes to inventory
inefficiency by unnecessarily increasing the total net inventory with overage inventory and by in-
creasing the average time an item is on stock. This negatively impacts operational performance
indicators such as days of inventory on-hand and inventory turnover, and financial performance
due to increased operational costs.
Reducing overplanned inventory is difficult when the reason behind the material becoming over-
planned is unknown. In addition, although reduction of existing overplanned inventory improves
inventory turnover, companies’ long-term focus should be towards proactive management of over-
planned inventory where it is prevented before it occurs. Together, this calls for increased insight on
the sources and drivers of overplanned raw inventory influx, i.e. new overplanned inventory. Influx
monitoring, where tracking signals are designed to identify abnormally high influxes efficiently, is
expected to generate these insights and guide actions based on them. Consequently, the central
question researched is:
How can influx monitoring using tracking signals contribute to improved inventory turnover by
reducing overplanned raw inventory for high-tech companies?
The research is conducted at a leading manufacturer in the high-tech semiconductor industry. Its
supply chain faces challenges like other high-tech companies where supplier lead-times are long,
engineering is concurrent, relationships with suppliers and customers are important and volume is
low but highly valued with complex products.
Influx occurs when the overplanned inventory of a material stored at a plant is larger in period t
than it was in period t− 1. One source of influx represents existing non-overplanned raw inventory
which becomes overplanned in period t. Other influxes constitute new arrival of overplanned raw
inventory: newly purchased items from suppliers, and as-new items returning from suppliers. In
addition, influx occurs in the form of factory returns or returns from customers. Finally, because
overplanned raw inventory only considers stock in scope of MRP, influx can come from stock that
was not available for MRP in period t− 1 but changed to an MRP-relevant status or location.
High-tech companies can measure influx from these sources if they keep snapshot data of their
inventory position, including inventories on order, in two consecutive periods. Shorter periods
improve accuracy of measurement, while longer periods are more cost efficient. Required fields
include the material number, location, number of units, value of units, stock age, order number of
items on order and source-type, representing to which of the identified sources the instance belongs.
Three steps are necessary to measure the influx from each source.
1. Measure influx of overplanned raw inventory between period t− 1 and t
2. Measure outflux from each source between period t− 1 and t
3. Match influx to outfluxes to explain the source
III
Influx from existing non-overplanned raw inventory is analyzed further to examine what drives this
influx. This provides insight regarding what needs to be tracked to monitor overplanned influx.
Existing inventory becomes overplanned when a previously existing plan for an inventory is removed
in the next MRP run, due to a change in top level requirements or in how these are cascaded to
material level requirements through the bill of materials. The first can result from changes in
requirements by customers, service requirements, forecasts, buffer requirements, including safety
stocks and work-in-process, or manual reservations. The latter are the result of engineering changes.
Figure 1 shows the relative contribution over the analyzed periods for each of the drivers for the
case company. The driver other includes additional drivers not expected but identified in the data,
mainly constituting requirement drops for development projects, and intercompany shipments.
Figure 1: Contribution to overplanned influx per driver
No driver can be identified that explains the major part of the problem, requiring signals to be able
to monitor all. Signals are established at different aggregation levels. Signals at lower aggregation
levels serve the purpose to signal on the influx drivers and should be actionable. Examples include
signals on influx value per engineering change, per top requirement, per direct requirement and per
material number. Influx signals at a higher aggregation level provide overview and may lead to
discovery of new relevant lower aggregation levels, and to communicate to management where and
when influx creation occurs. Higher level signals are employed on total influx value, influx value
per business line and per plant.
Tracking signals are generated when one of the tracked influxes exceeds a threshold. The threshold
needs to balance excessive alerting with lack of good monitoring. Dynamic thresholds can be set
based on time series extrapolation forecasting using the Holt-method which allows a trend effect.
The preferred method for static thresholds involves a statistical process control method based on
empirical quantiles, which needs to be reset every fixed number of periods. Which of the two meth-
ods is preferred depends on the influx data and other company preferences such as computational
efforts. For variable data as found in the high-tech industry, no clear outperformance of one of the
two methods is found, and thus the static method is applied because it is most cost efficient.
Tracking signals identify the most significant influxes that need to be addressed to monitor effi-
ciently. Signals provide additional information to find the root cause of these influxes. Knowing the
root cause enables improved corrective actions to stimulate outflux of overplanned inventory and
influx prevention by addressing the root cause. When prevention is not possible, monitoring helps
to identify risk areas based on which a predictive model can be built to start outflux initiatives
before the material becomes overplanned. At the case company, the designed tracking signals are
IV
expected to impose a direct inventory reduction worth e9.6∗2 million per year, excluding the indi-
rect effects through prevention. This improves inventory turnover and provides financial benefits
for companies in reducing unnecessary acquisition and holding costs. The impact can be further
enlarged by extending the scope of monitoring. Following, recommendations to achieve similar
benefits include:
• Make inventory position snapshots
Inventory position snapshots provide the required data to analyze overplanned influx. The
frequency of snapshots needs to balance accuracy of analysis and data retrieval and storage
costs. This balance depends on the moving pace of inventory.
• Track the stock age
The stock age is a relevant field which allows companies to analyze inventory efficiency. In
addition, it is necessary as a distinctive field to identify and measure influx and outflux.
• Measure overplanned influx per source and per driver
Measuring influx provides insight on the dynamics of overplanned inventory instead of a static
view of the total amount. Measuring the influx per source helps to create overview where
in the supply chain overplanned influx is created. Driver measurement provides additional
insight on what organizational functions or processes drive overplanned creation. Presenting
measurements to management allows for informed discussions on where to focus prevention
initiatives.
• Monitor overplanned influx by signaling influxes exceeding a threshold
Monitoring influx using tracking signals helps to recognize influx at the moment it occurs, and
provides a prioritization regarding which influxes are most significant to gain maximum results
with minimum effort. The signal provides information on the timing, materials, location,
amount and reason of the influx enabling the search for the root cause, the right persons to
solve the problem, and thereby enabling improved corrective actions. Preventive initiatives
to improve the process behind the root cause can then be initiated.
• Implement a structural improvement process following signals
Assign the signaled influx to a team, which is responsible to go through all steps in the process
to ensure full improvement potential is captured.
• Enlarge scope to increase benefits
Methods and rationales of influx measuring and monitoring can be applied to the other sources
identified or types of inefficient inventory to enlarge positive benefits that can be obtained.
• Continue research towards proactive inventory management
Monitoring provides insight on risk areas that exist in which materials are likely to become
overplanned. Although these should be addressed to aim for prevention, this will not always
be possible or directly the case. Future research could investigate whether overplanned influx
can be predicted based on these variables, so that corrective actions can already be started
before the materials become overplanned, resulting in proactive outflux creation.
2An asterisk (*) indicates that this value is transformed due to confidentiality.
V
Contents
List of figures IX
List of tables X
Abbreviations XI
1 Introduction 1
1.1 Problem definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Problem analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.1 Occurence of influx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.2 Lack of outflux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.3 Roadmap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.5 Research methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.6 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Case setting high-tech industry 6
2.1 Company background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.1 Supply chain management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.2 Supply chain planning and inventory control . . . . . . . . . . . . . . . . . . 7
2.2 Data introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 Current state analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3.1 High levels of overplanned inventory . . . . . . . . . . . . . . . . . . . . . . . 9
2.3.2 Long-lasting levels of overplanned raw inventory . . . . . . . . . . . . . . . . 9
3 Influx sources of overplanned raw inventory 11
3.1 Existing inventory becomes overplanned . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2 Inventory arrives overplanned from supplier commitment . . . . . . . . . . . . . . . . 12
3.3 Inventory arrives overplanned from repair or requalification by supplier . . . . . . . . 12
3.4 Inventory returns overplanned from factory . . . . . . . . . . . . . . . . . . . . . . . 13
3.5 Inventory returns overplanned from customer . . . . . . . . . . . . . . . . . . . . . . 13
3.6 Inventory returns overplanned from non-MRP relevant status or location . . . . . . . 13
3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4 Measurement of overplanned raw inventory influx per source 15
4.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.2 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.3 Measuring method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.3.1 Measure influx of overplanned raw inventory . . . . . . . . . . . . . . . . . . 16
4.3.2 Measure outflux of all identified sources . . . . . . . . . . . . . . . . . . . . . 17
4.3.3 Matching influx and outflux . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
VI
4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
5 Choice of focal influx source 21
5.1 Qualifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
5.2 Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
5.2.1 Generality of drivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
5.2.2 Overplanned type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
5.2.3 Potential to guide actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
5.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
6 Drivers overplanned raw inventory from existing inventory 24
6.1 Qualitative analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
6.1.1 Demand changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
6.1.2 Engineering changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
6.2 Quantitative analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
6.2.1 Demand score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
6.2.2 Engineering score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
6.3 Sensitivity analysis and model tuning . . . . . . . . . . . . . . . . . . . . . . . . . . 29
6.3.1 Model tuning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
6.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
6.4.1 Driving Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
7 Design of tracking signals 32
7.1 Tracking signals design methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
7.1.1 Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
7.1.2 Statistical process control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
7.2 Tracking fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
7.3 Evaluation of tracking methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
7.3.1 Efficient signaling capability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
7.3.2 Additional considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
7.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
8 Contribution of tracking signals 39
8.1 ASML signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
8.1.1 Description of signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
8.1.2 Practical evaluation of signals . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
8.1.3 Insights from signals towards proactive inventory management . . . . . . . . 40
8.2 Making tracking signals contribute . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
8.3 Generalization to other sources and inefficient inventories . . . . . . . . . . . . . . . 42
8.4 Cost considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
8.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
9 Conclusion and recommendations 44
9.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
9.2 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
VII
10 Reflection 49
10.1 Academic contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
10.2 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
10.3 Future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
11 Bibliography 52
A Identification of source types 55
B Overview of assumptions 57
C Measurement of influx per source based on snapshots 60
D Driver quantification 63
E Results sensitivity analysis 68
F Developing efficient tracking signals 69
G Evaluating tracking signals 70
H Simulation tracking signals with fictitious data 72
VIII
List of Figures
1 Contribution to overplanned influx per driver . . . . . . . . . . . . . . . . . . . . . . IV
1.1 Roadmap to improve inventory turnover . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Research approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1 Cumulative distribution function overplanned raw inventory per TWM . . . . . . . . 10
3.1 Major influxes of overplanned raw inventory . . . . . . . . . . . . . . . . . . . . . . . 11
3.2 Influxes of raw overplanned inventory . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.1 Measuring influx and outflux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.2 Matching influx and outflux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.3 Measurement of major influxes of overplanned raw inventory over weeks . . . . . . . 20
5.1 Overplanned raw inventory per influx source . . . . . . . . . . . . . . . . . . . . . . . 21
5.2 Influx percentage explained by sources per overplanned type . . . . . . . . . . . . . . 22
6.1 DC vs EC driven influx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
6.2 Driver quantification influx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
7.1 Efficient frontier of value explained per number of signals, comparing TSF and SPC 37
8.1 Distribution of types of signals obtained . . . . . . . . . . . . . . . . . . . . . . . . . 39
8.2 Evaluation tree signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
8.3 Timeline for measuring inefficient inventory . . . . . . . . . . . . . . . . . . . . . . . 42
9.1 New roadmap to improve inventory turnover . . . . . . . . . . . . . . . . . . . . . . 45
B.1 Number of influxes linked to an EC per number of weeks since completion . . . . . . 59
D.1 Translation of fraction value to demand score per factor . . . . . . . . . . . . . . . . 65
E.1 Results sensitivity analysis varying weights and factors . . . . . . . . . . . . . . . . . 68
E.2 Results sensitivity analysis varying λ . . . . . . . . . . . . . . . . . . . . . . . . . . 68
G.1 Efficient signaling frontier per method per week . . . . . . . . . . . . . . . . . . . . . 70
G.2 Variability of total influx data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
H.1 Positive trend data with signals SPC and TSF . . . . . . . . . . . . . . . . . . . . . 72
H.2 Negative trend data with signals SPC and TSF . . . . . . . . . . . . . . . . . . . . . 73
H.3 Variable data with signals SPC and TSF . . . . . . . . . . . . . . . . . . . . . . . . . 73
IX
List of Tables
2.1 Performance buckets overplanned ratio . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.1 Measuring method influxes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.2 Summary methods outflux per source . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6.1 Weights trained model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
7.1 Summary of signaling methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
8.1 Eight Disciplines problem solving process . . . . . . . . . . . . . . . . . . . . . . . . 41
A.1 Characteristics to find source types influx for Chapter 4 . . . . . . . . . . . . . . . . 55
C.1 Notations measuring source flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
D.1 Notations driver quantification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
G.1 Parameters Holt-method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
G.2 Evaluation TSF and SPC over weeks . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
X
Abbreviations
8D Eight Disciplines
AMR Average of the Moving Ranges
BOM Bill of Materials
DIO Days of Inventory On-Hand
DC Demand Changes
DUV Deep Ultraviolet
E&O Excess & Obsolete
EC Engineering Change
EQ Empirical Quantiles
EUV Extreme Ultraviolet
FIFO First In, First Out
FSD Field Service Defect
FTE Full-Time Equivalent
LCL Lower Control Limit
MP Mature Products
MPS Master Production Schedule
MRP Material Requirements Planning
MSE Mean Squared Error
MTO Make To Order
NPV Net Present Value
OP Overplanned Percentage
PO Purchasing Order
R&D Research and Development
RO Requirement Order
SPC Statistical Process Control
SS Safety Stock
TR Top Requirement
TSF Time Series Forecasting
TWM Time without Movement
UCL Upper Control Limit
USP Used Service part
WIP Work in Process
XI
1 Introduction
This report describes research on improving inventory turnover in the high-tech industry by mea-
suring and monitoring overplanned inventory influx. Overplanned inventory is defined as inventory
which is relevant for planning by material requirements planning (MRP), but for which no require-
ment exists in the current plan. Influx occurs when overplanned inventory increases. The research
is the result of a master thesis project conducted by the Eindhoven University of Technology in
collaboration with ASML, a leading manufacturer in the high-tech semiconductor industry. This
introductory chapter defines the problem in Section 1.1, followed by a preliminary problem analysis
in Section 1.2 and definition of the scope in Section 1.3. Research questions are formulated and
presented in Section 1.4, and Section 1.5 describes the research methodology. Finally, Section 1.6
outlines the remainder of the report.
1.1 Problem definition
A problem refers to a business performance which is undesirable, either for a financial or an op-
erational performance indicator. In high-tech industries, inventory is often of high value so that
holding inefficient inventory is costly. Overplanned inventory, which is long-lasting, contributes
to inventory inefficiency by unnecessarily increasing the total net inventory with overage inventory
and by increasing the average time an item is on stock. This reduces companies’ inventory turnover
rates and increases the days of inventory on-hand (DIO), two operational performance indicators
important for companies in the high-tech industry.
To add to this, overplanned inventory has a direct impact on the operational costs of supply chains
through inventory holding and possibly scrapping costs and is thus relevant for organizations’ finan-
cial performances. Overplanned inventory uses valuable storage space, diminishes working capital,
inflates assets and leads to a decreased return on investment (Tersine & Toelle, 1984; Chung & Wee,
2008). Particularly holding excess inventory for products with short lifecycles or high value, as are
common in the high-tech industry, is expensive (Chopra & Sodhi, 2004). Moreover, overplanned
inventory indirectly impacts performance since value tied up in inventory is not readily available
for other positive net present value (NPV) investments (Crandall & Crandall, 2003).
In high-tech industries, inventory buffers should only exist to ensure enough submodule or compo-
nent availability to start the make To order (MTO) manufacturing or assembly process (De Kok,
2015). The largest number of inventory items held by high-tech companies thus entails raw stock,
defined as any material which is not produced by the company itself, but by an external supplier,
and is not yet integrated in another product. The overplanned raw inventory consists of both obso-
lete and excess items. A material is declared obsolete when it is no longer included in any product’s
bill of materials (BOM) (Balakrishnan & Chakravarty, 1996). Items that are overplanned but still
included in a BOM are declared overplanned excess. Concluding, the problem is defined as follows:
The existence of long-lasting overplanned raw inventory, including excess and obsolete stock,
contributes to inventory inefficiency and reduces financial and operational performance.
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1.2 Problem analysis
The defined problem of overplanned inventory existence is a result of the flows into the stock point,
named influx, and the flows out of the stock point, named outflux. Thus, two initial problem areas
can be defined within the main problem: 1) occurrence of overplanned inventory influx, and 2) lack
of overplanned inventory outflux. Academic literature is used as a source of potential reasons for
occurrence of influx and lack of outflux of overplanned inventory. Since overplanned inventory is
not a generic term, literature on this topic cannot be found. However, overplanned inventory is a
subset of excess and obsolete (E&O) inventory and thus literature is examined on this term.
1.2.1 Occurence of influx
Crandall and Crandall (2003) analyze the creation of E&O inventories. To identify drivers, organi-
zations have to look upstream towards suppliers, downstream towards customers but also internally
at the firm’s own operations. Excess inventories can be the effect of demand variations or supply
variations against which inventories are kept as a buffer. Moreover, E&O inventories can be the
effect of variation at internal functions. First of all, sales and marketing functions induce excess
inventories by requiring high product availability, new product introductions or overestimated fore-
casts. Second, the engineering function causes obsolete inventories when design improvements are
not phased-in to consume all existing old stock. Third, excess inventory is created by produc-
tion wishing to utilize level production strategies or when producing out-of-specification products.
Fourth, purchasing may induce excess inventories as a result of quantity discounts or economic
order quantities. Fifth, financial functions can encourage E&O inventory creation by backing per-
formance measures not considering costs of inventory. Sixth, operational execution drives E&O
inventories, including inventory record inaccuracies and inadequate software systems. Moreover,
cross-functional issues, such as lack of interdepartmental communication and coordination, lack of
usage of cross-functional teams and lack of organization-wide accountability for E&O inventory,
can result in excess and obsolete inventory. Finally, E&O inventories are created due to insufficient
supply chain coordination including lack of information sharing with customers or suppliers or opti-
mization initiatives for single parties instead of the whole supply chain (Crandall & Crandall, 2003).
Toelle and Tersine (1989) similarly find forecasting errors, inventory record inaccuracies and inad-
equate planning and execution systems as frequent causes for excess inventory, as well as long or
variable lead-times, obsolescence from design changes, distribution channel adjustments and master
schedule smoothing. Other drivers found in literature include excessive human ordering (Chung
& Wee, 2008; Willoughby, 2010), inadequate material planning systems (Willouhghby 2010), order
request transmission errors and inadequate information systems (Rosenfield, 1989).
1.2.2 Lack of outflux
Lack of outflux can result from lack of knowledge about what inventory needs to be retained and
what should be reduced. Additionally, when methods to dispose inventory are lacking, outflux
is improbable. Crandall and Crandall (2003) introduce six methods that can be used to create
outflux and thus reduce inventory levels. First, when possible, the inventory should be returned to
the supplier. Second, companies can modify the inventory so it can be used in other products, or a
third option is to use the inventory as spare parts. Fourth, companies can try to sell the inventory,
if necessary at a discount or through alternative channels. If the inventory cannot be sold, an
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option would be to barter it to exchange the inventory for other assets. Fifth, the inventory can be
donated. Even though this does not provide direct value, it can be advantageous for tax reasons
and at least costs of holding it are eliminated. Finally, if none of the above alternatives is possible,
the inventory needs to be scrapped. Due to the costs of scrapping, good reverse logistics and
excess-inventory management programs are important for the profitability of companies (Crandall
& Crandall, 2003).
1.2.3 Roadmap
Although cleaning up the existing long-lasting overplanned inventory helps to improve the inven-
tory efficiency for high-tech companies, it is better in the long-run to focus on preventing these
inventories from occurring in the first place. Inventory write-offs are found to have a negative effect
on the public’s opinion of a company (Crandall & Crandall, 2003) and are associated with negative
stock price performance (Singhal, 2005). In addition, scrapping is costly. Crandall and Crandall
(2003) highlight that businesses should emphasize the prevention of E&O inventory, rather than
account for it or dispose it. Proactive management of overplanned inventory, where it is predicted
and if possible prevented before it occurs, is preferred.
At the start of the research a roadmap was developed to move towards proactive inventory man-
agement, as shown in Figure 1.1. This roadmap consists of five stages and represents a transition
from merely reporting the total level of overplanned inventory in inventory position snapshots, to
measuring the influx into overplanned between two snapshots, monitoring different influxes so that
abnormal influxes are signaled to identify root causes, predicting influxes based on signals on root
cause indicators and preventing influx based on these predictions by proactive actions or changing
processes based on insights from monitoring.
Figure 1.1: Roadmap to improve inventory turnover
1.3 Scope
The scope of the project is defined based on the discussions of the previous sections. The research
focuses on raw inventory. Note that this only includes inventory which is consumed in the pro-
duction process, so excluding reusable assets. The research investigates all types of overplanned
inventory, including both excess and obsolete overplanned materials. Also overplanned items de-
3
fined as caused by business decisions are included.
Focus of the research is on the occurrence of influx and the creation of insight of the influxes to
aid outflux. Methods for outflux or actual outflux creation are out of scope for this research. Ad-
ditionally, MRP integrity and data integrity are out of scope as causes, as well as human mistakes
such as incorrect bookings because these cannot be identified from data.
Due to time limitations, the current research is scoped to focus on the first three steps of the
roadmap in Figure 1.1 and provide guidance on how to continue from monitoring. This includes
design of measurement methods and tracking signals to monitor influx. Analysis and design of
steps 4 and 5 is out of scope.
The research is applied in a case setting at ASML and is based on inventory data provided in
the ASML Inventory Dashboard as well as some additional SAP data provided by ASML. All raw
inventory is included in scope at all warehouse locations, both factory and field locations, and for
all business lines. Materials in transit between two locations are included in the inventory position
of the receiving location.
1.4 Research questions
Measuring and monitoring influx provide the first steps to move towards proactive inventory man-
agement. This research aims to discover methods to measure overplanned inventory influx, and
based on these insights design tracking signals that identify high value influxes to monitor influx.
It investigates how this can help to solve inventory inefficiency problems faced in the high-tech
industry. The research question for this master thesis is defined as follows:
How can influx monitoring using tracking signals contribute to improved inventory turnover by
reducing overplanned raw inventory levels for high-tech companies?
The research is divided into two phases. The first phase analyzes influx of overplanned raw inven-
tory and provides an overview of where in high-tech supply chains overplanned inventory is created.
The second phase chooses one influx to further investigate how tracking signals should be designed
so that monitoring this influx can contribute to improved inventory efficiency. The following re-
search questions are formulated to provide guidance to answer the main research question:
1. What sources of raw overplanned inventory influx exist for high-tech companies and why do
they occur?
2. How can high-tech companies measure the influxes for raw overplanned inventory?
3. What qualifiers and criteria can high-tech companies use to choose an influx to investigate
monitoring potential?
4. What drives the chosen overplanned raw inventory influx?
5. How can tracking signals be designed to monitor the chosen overplanned raw inventory influx?
6. What overplanned raw inventory influx is identified by the developed tracking signals?
7. How can monitoring influx using tracking signals contribute to reducing overall overplanned
raw inventory levels?
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1.5 Research methodology
The research was initiated by ASML and follows Van Aken, Berends and Van der Bij (2007)’s
methodology for business problem-solving projects in organizations. Business problem-solving
projects are projects started with the aim to improve the performance of a business system on
one or more criteria. For companies, the full business project consists of a design phase, a change
phase and a learning phase. The current research only covers the design phase and describes the
problem definition, as in Section 1.1, the analysis and diagnosis of the problem aiming to develop
specific understanding of the nature of the problem and to diagnose drivers, and the plan of action
which provides a solution design for the problem. Research methods for analysis and diagnosis in-
clude data analysis, literature analysis and expert interviews. The plan of action is developed based
on literature and simulation and evaluated through interviews (dynamic analysis). An academic
reflection phase is added to reflect on the academic contribution of the research to generalize the
results for the high-tech industry. The actual intervention and evaluation are not in scope of this
research
Translating the research model to this research results in the research approach and according
chapters as in Figure 1.2. The academic contribution of the research is expected to be twofold.
First, it serves as a case study to examine the applicability of drivers of overplanned inventory
as found in literature in the high-tech industry. This examination is performed in Chapter 6.
Second, it contributes by applying methods from other research domains, such as statistics, demand
forecasting and quality control, to develop and evaluate methods to monitor inventory influx. These
methods are explained in Chapter 7.
Problem definition
Chapter 1
Analysis & Diagnosis
Chapter 2, 3 (RQ1), 4
(RQ2), 5 (RQ3), 6 (RQ4)
Plan of Action
Chapter 7 (RQ5), 8
(RQ6, RQ7) and 9
Academic Reflection
Chapter 10
Figure 1.2: Research approach
1.6 Outline
The report continues by introducing the ASML case setting in Chapter 2 and verifying the problem
for the company. Chapter 3 identifies the sources of influx of overplanned raw inventory to answer
question 1. Chapter 4 describes a method for measurement of these influxes, and the results of
measurement at ASML, answering question 2. Chapter 5 represents the end of the first phase of
measuring influxes and describes the process of choosing one influx to analyze for the monitoring
part: influx from existing raw inventory, which answers question 3. Chapter 6 deepens the un-
derstanding of this influx by qualitatively and quantitatively describing the drivers of existing raw
inventory becoming overplanned, addressing question 4. Chapter 7 provides and evaluates methods
for development of tracking signals, referring to question 5, while Chapter 8 describes the results of
the tracking signals for ASML and how these tracking signals can contribute to improve inventory
efficiency at ASML to provide an answer to question 6 and 7. The report continues with conclusions
and recommendations in Chapter 9 and Chapter 10 reflects on the research.
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2 Case setting high-tech industry
The high-tech industry is characterized by complex product technologies, short product lifecycles
and global and dynamic markets. This dynamic nature stresses the importance of new product
development and continuous product improvement. Service provided to customers accompanying
the end-product is crucial in the industry, as well as buyer-supplier relationships, due to suppliers’
influence on the quality and innovativeness of the end-product (Beckman & Sinha, 2005). Since
inventory holding costs and risks of obsolescence are generally high in high-tech industries, a large
part of production is often order-driven (Perona, Saccani & Zanoni, 2009).
The research is applied to the inventory data of ASML, a high-tech company operating in the
semiconductor industry. This chapter describes the case company’s background in Section 2.1,
introducing its products, and explaining its supply chain practices relevant for the research. This
allows high-tech companies to assess the comparability of their own company to the case company
to evaluate how the research results would apply for them. Section 2.2 introduces the data used for
the research and Section 2.3 provides a current state analysis of the problem of high and long-lasting
overplanned inventory.
2.1 Company background
ASML was founded in 1984 and has grown to a well-established company with over 16,500 employ-
ees, and customers and suppliers all over the world. ASML has more than 70 offices in 17 countries,
with manufacturing and R&D facilities located in the United States, the Netherlands and Taiwan.
The corporate headquarters is located in Veldhoven, The Netherlands. In 2016, net sales added up
to e6,795 million and net income to e1,472 million. Most sales were recorded in Asia. ASML is a
publicly traded company, listed on NASDAQ and Euronext Amsterdam (ASML, 2016).
ASML is the world’s leading manufacturer of chip-making machines, supplying advanced lithogra-
phy machines for the world’s semiconductor industry. Using Moore’s law as a guiding principle,
ASML is determined to develop technology that will result in ever-smaller, cheaper, more powerful
and energy-efficient semiconductors. To achieve this, ASML needs to design, develop, integrate,
market and service advanced technology for high-tech lithography, metrology and software solu-
tions at a rapid speed to enable its customers, major international chipmakers, to produce chips
with an ever-decreasing size. This fits with ASML’s vision, “to deliver, install and service leading
lithography solutions, meeting stakeholders’ expectations, which enable great products that change
people’s lives” (ASML, 2016).
ASML sells three categories of new products: deep ultraviolet (DUV) lithography, extreme ultra-
violet (EUV) lithography, and holistic (Apps) lithography solutions. The DUV range of systems
primarily consists of the TWINSCAN DUV machines, which represents ASML’s established busi-
ness line. The EUV range includes the next-generation lithographic systems which are equipped
with a completely new EUV light source technology, relying on a new optical technology using
mirrors instead of lenses. The holistic lithography portfolio consists of software and metrology
products to complement the scanner products from DUV and EUV. Finally, ASML has some more
mature systems which are called the PAS systems (MP). In addition, ASML provides installation,
support and training services and services for upgrades and refurbishments. Product options and
6
enhancements are designed to increase throughput and improve patterning and overlay. Full sys-
tem (field) upgrade packages can be purchased by customers to upgrade existing systems to meet
tighter performance requirements. These are all part of ASML’s service business.
2.1.1 Supply chain management
ASML’s supply chain includes many tiers of suppliers, ASML factory warehouses, factories, field
warehouses and customers. Regular supply flows through these locations from supplier to customer,
but also return flows exist, for instance when defects occur.
Several challenges exist for managing ASML’s supply chain. First of all, supplier lead-times can
be as long as two years while customer lead-time is only six months or even shorter. Additionally,
ASML offers a wide range of customized products with a late customer order decoupling point
until which customers can change the product’s configuration. This means that supplies have to
be ordered long before ASML knows how demand will exactly materialize, leading to challenges
to match supply and demand. Second, in 2016 85% of the BOM of ASML’s machines came from
suppliers, to allow ASML to focus on its core competencies of design and machine integration. This
makes that management of the supplier network is crucial for ASML’s success which represents a
challenge. Long-term relationships with suppliers, based on technological capability, reliability and
transparency, are important, sharing both rewards and risks. Third, ASML’s customers are highly
demanding pace-setting companies for which system downtime is extremely costly. Therefore, very
tight service level agreements exist which ASML needs to meet. Fourth, to meet industry demands,
ASML makes complex engineering concurrent, meaning that over 5000 system redesigns are done
on a yearly basis providing a huge challenge for ASML’s factories, suppliers and customers. Finally,
ASML’s supply chain is characterized as highly complex, low volume and high value; the technology
produced and delivered to customers is very complex, making the processes in the supply chain
involving; the system throughput is low delivering 150-200 systems per year, but the value of these
systems is high, ranging from one to over a hundred million euros per system. This combination of
characteristics, together with the importance of supplier relationships, concurrent engineering and
long supplier lead-times makes supply chain management at ASML extremely challenging.
The supply chain management function is responsible to deal with these challenges. Its goal is to
ensure material availability at all times, at all locations in the right quantity and quality against
affordable cost; from first development to the mature product phase and installed base. Supply
chain management partners with key stakeholders both within and outside ASML and manages
the material and information flows.
2.1.2 Supply chain planning and inventory control
The supply chain planning department has the responsibility to define and establish a robust in-
tegral plan, where risks and opportunities are balanced. Supply chain planning is not responsible
for calculating the expected demand, but instead needs to ensure that the expected demand as
calculated by central planning can be met by supplies. Its goal is thus to satisfy all demand on
time against a minimum cost. MRP is used to calculate how much of each item is needed at what
time to satisfy a final demand. Inventory control falls within this department.
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Inventory control drives improvement projects and provides data insights for inventory manage-
ment. Although most inventory management is done decentralized by planners, the inventory
control team provides support to these planners. They are responsible for inventory management
reporting and support improvement and reduction projects.
2.2 Data introduction
The data analyzed are the data included in the weekly snapshot of ASML’s inventory: the Inventory
Dashboard. Inventory levels are measured each Sunday by the total value and total quantity lying
on stock. The Inventory Dashboard also keeps track of the stock age of inventory. The Inventory
Dashboard was introduced to gain complete data insight in ASML’s stock, work orders (WIP) and
commitment (PO) and to classify this in the primary supply chain processes. It provides a complete
picture of ASML’s inventory position, including inventories in transit and pipeline inventory. The
Inventory Dashboard combines information about where in ASML’s supply chain items are located
with information about the planned demands for the items. Inventory items are linked to MRP
elements and demands, to decide for what purpose an inventory exists. The requirement order
shows the direct demand, one hierarchical level up, to which a stock is allocated, while the top
requirement shows the highest level independent demand which the stock is planned to fulfil. In
situations where the product hierarchy only consists of one level, the requirement order is equal to
the top requirement. However, in most cases in the high-tech industry with complex products, the
requirement order represents a requirement in-between the top requirement and the raw material,
asking for assembly or shipment of the material. If an item is planned by MRP but cannot be
allocated to a demand or purpose such as safety stock over the full planning horizon, the item is
defined overplanned.
ASML does not keep track of the Inventory Dashboard over time which means no historical data
is available for the research. Therefore, analysis starts in the starting week of the research project,
set as week 1. From this moment, relevant information from the Inventory Dashboard is stored so
some historical data is available at the end of the research. Even though the problem is defined to
be in raw inventory, all instances of the Inventory Dashboard are stored, to be able to track where a
certain item was located before it became raw overplanned inventory. This also includes materials
on order. Note that due to the definition of raw inventory used, a raw item can also return from
non-raw sources.
Thorough examination of the data revealed no structural particularities or inconsistencies which
make that data instances have to be eliminated, except for items that are planned separately at
field warehouses. For these items, reorder levels are set to which items have to be stocked at these
locations to function as safety stock. However, these reorder levels are not recorded as a demand
in the Inventory Dashboard and thus all items serving the purpose of these safety stocks are con-
sidered overplanned, even though a large part of them in reality is planned. Since the amount
of inventory that belongs to this group represents less than 0.01% of the total raw inventory, the
separately planned inventories are eliminated from the data to remove the inconsistency.
During the research also some problems with the Inventory Dashboard creation occurred. In week
2 an adjustment was made to the logic of the dashboard which made that not all data was loaded
into the file correctly and some data is missing. Software problems in week 18 made it impossible
8
to obtain the data of that week. Therefore, for this week no data is available.
The Inventory Dashboard is a good source to enable internal integration within the company, and
balances ease and accuracy of analysis. A limitation is snapshot bias: only weekly snapshots are
provided, and thus no information can be extracted about movements within one week. Since the
research is aimed at overplanned inventory for which movements are slow, this limitation is not
expected to have a large impact on the results.
Due to data confidentiality, not all results can be provided in this report. Numbers noted with
an asterisk (*) in the superscript are transformed, and thus do not represent the real number. In
addition, in some graphs vertical axis labels have been removed.
2.3 Current state analysis
2.3.1 High levels of overplanned inventory
Ideally, supply is planned to match demand perfectly and thus no overplanned raw inventory would
exist at all for high-tech companies. However, due to supply chain characteristics described in
Section 2.1.2 such as concurrent engineering, long lead-times, and demand variability, this is not
realistic. Therefore, performance buckets are defined to analyze whether the overplanned raw
inventory levels at ASML are a problem. The performance buckets use a benchmark for excess
inventory by Manufacturing Alliance for Productivity and Innovation (2015) as a starting point,
but are adjusted to reflect the specific supply chain characteristics of the high-tech industry and
measurement differences between excess inventory and overplanned inventory in this research. The
buckets were verified with employees and mirror their idea of when the overplanned inventory
levels would be problematic and are shown in Table 2.1. Accordingly, the level of overplanned raw
inventory is defined to be problematic when its ratio of the total raw inventory, measured in euro
value, is larger than 20%.
Table 2.1: Performance buckets overplanned ratio
Good performance area: [0-15%)
Warning area: [15%-20%)
Problem area: >20%
The data from the ASML Inventory Dashboard show that over the weeks of this research on average
more than 20% of inventory, measured both in units and in value, is overplanned. Therefore, the
ratios are considered a problem for ASML. This also holds for all business lines and plant or
warehouse locations analyzed separately. Therefore, all remain in scope of the research.
2.3.2 Long-lasting levels of overplanned raw inventory
The Inventory Dashboard keeps track of the stock age of items, but resets it every time an item is
booked to another location, or re-evaluated with an official movement type. Therefore, it is not pos-
sible to calculate the DIO for an item. What can be calculated is the time that a raw overplanned
item has been without movement, which is the time that it has been in the same raw inventory
storage location, defined as time without movement (TWM). Although this is an underestimation
9
of the amount of time the inventory has been within ASML, it is an interesting measure to provide
insight on the amount of activity occurring for the overplanned raw inventory.
The stock age calculation in the inventory data has a cutoff point at 104 weeks meaning that stock
age remains 104 after reaching this age. Therefore, an average cannot be calculated. Instead, stock
age days are grouped in buckets of a week and the probability is calculated for an overplanned
item to have a certain TWM measured in weeks using Equation 1, where valuex,t is the value of
inventory with TWM x in week t. T is the total number of weeks of inventory data available for
the calculation.
P (TWM = x) =
∑Tt=1 valuex,t∑104
i=0
∑Tt=1 valuei,t
, for x = {0, 1, ..., 104} (1)
Next, the cumulative distribution function is found using Equation 2 and the probability that the
TWM is larger than x number of weeks using Equation 3.
FTWM (x) = P (TWM ≤ x) (2)
P (TWM > x) = 1− FTWM (x) (3)
Figure 2.1: Cumulative distribution function overplanned raw inventory per TWM
Figure 2.1 displays the cumulative distribution function for the TWM of the overplanned raw
inventory. It shows that half of the overplanned raw inventory has had no movement for the past
41 weeks. Additionally, 46.0% of overplanned raw inventory has had no movement for the past
year, and 33.5% of overplanned raw inventory has had no movement for the past two years. ASML
operates an efficiency target that raw inventory should be used within 7 days. The fact that 89.0%
of overplanned raw inventory has been lying in raw inventory without movement for more than
these 7 days represents a problem to meet this efficiency target. The problem of overplanned raw
inventories is clearly present at ASML.
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3 Influx sources of overplanned raw inventory
This chapter describes the major sources of influx that exist for raw overplanned inventory, an-
swering the first question: What sources of raw overplanned inventory influx exist for high-tech
companies and why do they occur? These influxes were first identified by thorough analysis of
ASML’s inventory data, and later validated through expert interviews. This data-driven approach
is preferred to gain objective insights.
Overplanned raw inventory influx either involves existing raw inventory which becomes overplanned,
or new arrival of overplanned raw inventory. Section 3.1 describes how existing raw inventory
becomes overplanned. Section 3.2 describes newly purchased overplanned arrivals from suppliers,
termed commitment, and Section 3.3 describes as-new items arriving from suppliers through repair
or requalification flows. In addition, raw overplanned inventory returns from factory work orders,
or returns from customers during field actions. These flows are described in Section 3.4 and 3.5,
respectively. Finally, because the definition of overplanned raw inventory only considers stock in
scope of MRP, Section 3.6 describes overplanned raw inventory arrivals from stock that was not
available for planning in MRP before. Figure 3.1 provides an overview of these influxes and Figure
3.2 shows the drivers explained by experts.
Figure 3.1: Major influxes of overplanned raw inventory
3.1 Existing inventory becomes overplanned
The first influx identified involves existing raw inventory that becomes overplanned. This happens
when the demand that was allocated to the inventory falls away, or a different supply is allocated
to that demand. Inherently, this is driven by plan changes for materials. Plans are altered in
MRP due to demand changes, which change the master production schedule (MPS), or engineering
changes (EC) which change the cascadation of the demands to raw material requirements making a
different supply necessary. Demand changes result from customer demand changes for both systems
and options or upgrades, involving changes in timing or product mix or customer cancellations.
Demand changes also occur for service items, refurbishments and projects. Additionally, plans are
changed when forecasts are changed to reflect current insights. Moreover, when buffer requirements,
such as safety stock or WIP buffers, are reduced, raw inventory that was previously kept for this
purpose becomes overplanned. Finally, planners are allowed to enter requirements manually in the
plan, through a process called manual reservations. When these are cancelled, existing inventory
becomes overplanned.
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3.2 Inventory arrives overplanned from supplier commitment
The second source of influx that was observed involves commitment. Commitment reflects the
open orders for new-buy materials arriving from first-tier suppliers. Items arriving from commit-
ment are by definition raw inventory. When no demand can be allocated to the arriving item, the
item becomes overplanned raw inventory. Overplanned raw inventory influx results from commit-
ment either when additional supply is ordered which is at that moment already known to have
no demand, or when an ordered supply becomes overplanned during the lead-time of the order.
Contracts exist with suppliers which allow rescheduling of the arrivals of these overplanned items.
When the maximum rescheduling period has expired, called the max re-out period, suppliers are
allowed to deliver the items, even when they are no longer demanded. Overplanned raw inventory
is thus expected to arrive at this contractual moment.
Additional overplanned supply is ordered for various reasons. Minimum order quantities at suppli-
ers can prompt orders with higher quantities than demand necessitates. In addition, surplus supply
is ordered to cope with uncertainties in the supply chain such as late deliveries or quality problems.
Finally, business decisions drive additional supply orders, for instance when commercial benefits
like quantity discounts exist or when a supply reaches the end of its lifecycle and supplementary
supplies are necessary to satisfy customer orders and service agreements.
Ordered supply becomes overplanned during the lead-time of the supply due to a combination of
supply characteristics and plan changes. Plan changes are associated with the driving processes
described in Section 3.1. The effects of these plan changes are amplified by supply characteristics
of the high-tech industry such as long supplier lead-times and the low volume and high value of
supplies.
3.3 Inventory arrives overplanned from repair or requalification by supplier
The third source of influx identified represents the return flow from suppliers. Defect raw materials
are returned to its supplier for repair, if benefits exceed costs. Once the item is repaired, it returns
and becomes raw inventory. When no demand is allocated to the arriving item, the item constitutes
influx of overplanned raw inventory. Similarly, items are returned to suppliers for requalification
when newer versions are desired, which creates overplanned raw inventory influx when no demand
exists for the upgraded material.
Similar to overplanned raw inventory influx from commitment, overplanned raw inventory influx
from repair or requalification is driven by two processes. Items returning from repair or requal-
ification can become overplanned during the repair/requalification time. This is again driven by
a combination of plan changes, following from the processes described in Section 3.1, and supply
characteristics such as long supplier repair or requalification times and the low volume and high
value of supplies. In addition, repair items are returned to suppliers even when it is known in
advance that the returning item has no planned demand. This involves warranty items which are
returned to the supplier in any case. In addition, due to production preferences at suppliers lead-
times can be shorter for new supplies than for repair items. Therefore, new supplies are ordered
for items also returned to suppliers to enhance on-time material availability. Since lead-times for
the repair items are longer, these are the ones to arrive overplanned because the original demand
12
is already satisfied by commitment. The timing difference is further increased due to the present
inspection process, where a new item may be ordered at the first moment the item is moved to an
inspection location and cannot be used for MRP planning, while the item is returned for repair
to the supplier only at the end of the inspection process. Requalifications are costly and generally
items are not returned to suppliers for requalification without planned demand.
3.4 Inventory returns overplanned from factory
Fourth, overplanned influx is found to arrive from factory returns. This inventory is likely to
be overplanned since the reason the material returns involves the item not being needed for the
demand anticipated. This can either be due to cancellation of the complete work order, when
demand is reduced of the top requirement, or because the material is not necessary for the work
order anticipated after all. When materials required for a work order are not completely known,
the BOM includes all materials that have a high probability to be required for that work order.
However, it is not uncommon that these probable materials are not needed for the specific work
order on hand, and thus items requested are returned to the warehouse. This mostly ensues for
refurbishment work orders where the current configuration is uncertain. Moreover, configuration
changes during system assembly change the demand and drive factory returns. Configuration
changes can be made to a system either when the system’s allocation to a customer changes,
or when the allocated customer makes changes to the configuration demanded. The customer
is allowed to change configurations until the customer order decoupling point is reached. Late
placement of the customer order decoupling point thus drives overplanned items returning from
the factory. In addition, raw inventory returns from factory due to disassembly of modules returning
from customers and from testing by the development and engineering department (D&E).
3.5 Inventory returns overplanned from customer
The fifth source of influx identified includes customers. Items return from customers in the case
of defects or field upgrades. When a defect arises at a customer, a new part is sent to replace the
defect part and the old part returns to raw inventory as a field service defect (FSD) item. These
items however are then inspected and either scrapped or repaired and thus should not become influx
for overplanned raw inventory directly. In the case of field upgrades, a customer system or option
is upgraded meaning that old materials may return to raw inventory. These items are classified as
used service parts (USP) and after inspection can become relevant overplanned raw inventory. This
flow is observed as a customer return when inspection requires less than one week. In addition,
before materials are actually installed at the customer during a repair or upgrade action they are
allocated to a service order. All materials that are expected to be required for the service order are
sent to the service engineer, and booked to customer consignment stock. When a service engineer
notices that the material is not required after all, or when the service order is cancelled, the material
returns to raw inventory. This can be identified as inventory returning from customer consignment
stock.
3.6 Inventory returns overplanned from non-MRP relevant status or location
Finally, overplanned inventroy influx can consist of materials which were not in scope of MRP
before. This is the case when an item has a non-MRP relevant status, or when it is stored at a
non-MRP relevant location. When its status or location is changed to an MRP relevant one while
13
no demand for it exists, it becomes overplanned raw inventory. These changes can involve stock
status changes from blocked to unrestricted use, or movements from quality inspection storage
locations back to regular storage locations. Before an item is decided to be defect, for example
after a customer return, it is placed at an inspection location to assess its quality. During this
time, the item is excluded from MRP planning. When the raw material’s quality is assessed to be
satisfactory, it is booked from the inspection location back into a normal storage location and can
be used in MRP again to satisfy a demand. This process is allowed to take up to 25 days, during
which a new order may be triggered to satisfy the original demand for the returning item.
3.7 Conclusion
Data-driven analysis showed that multiple sources of influxes exist. It requires a company-specific
analysis to investigate what sources apply for each individual high-tech company. However, the
case of ASML provides insight on what influxes of overplanned raw inventory may be likely for
high-tech companies like ASML, where return flows and long lead-times are common and complex-
ity is high. High-tech companies can use the ASML case as a starting point to look for similar
flows in their organization. Figure 3.2 summarizes above discussion providing an overview of the
flows and some insight on their drivers. Similar to analysis by Crandall and Crandall (2003), this
qualitative analysis indicates that overplanned inventory is created by different internal functions,
such as production, sales and marketing, engineering and purchasing and also aggravated by limited
cross-functional and supply chain coordination.
Returning materials after field
options
Planned additional supply
Raw overplanned influx
Customer demand
Requirement changes
Engineering changes
MRP changes
Ordered supply becomes overplanned
Supply Chain characteristics
Business decisions
Uncertainty
Minimum order quantity
Work order cancellations
Disassembly
Incorrect BOM s (refurbishments)
Testing
Configuration changes
MRP changes
Repair/requalification becomes overplanned
Supply Chain characteristics
Demand for returned item is already satisfied
by new item
Max re-out
Repair LT > new LT
Inspection process
Service demandReturning
materials after field upgrades
Customer defects
Unknown customer configuration by service engineer
Quality inspection
process
Blocked stockPlanned
separately in reorder points
ForecastsOther
demands
Bufferrequirements
Manual reservations
Figure 3.2: Influxes of raw overplanned inventory
14
4 Measurement of overplanned raw inventory influx per source
This chapter introduces a method developed in the research, which high-tech companies can use to
measure the influxes as introduced in Chapter 3, answering question 2: How can high-tech compa-
nies measure the influxes for raw overplanned inventory? This represents the first transition in the
roadmap in Figure 1.1 to change from reporting total levels of overplanned inventory in inventory
position snapshots to measuring influx into overplanned between two snapshots.
The method is illustrated using ASML’s Inventory Dashboard data. High-tech companies can learn
the rationale of the method and apply this to their own snapshot data. Section 4.1 explains what
data is necessary in the snapshots to enable measurement and Section 4.2 lists the assumptions on
which the method is based. The measuring method itself is explained in Section 4.3. Section 4.4
presents the limitations of the measurements while results of the method applied to ASML’s data
during the research horizon are presented in Section 4.5. Section 4.6 concludes the chapter.
4.1 Data
The data analyzed are the data included in the weekly snapshot of ASML’s inventory: the Inventory
Dashboard. Inventory levels as well as inventories on order are measured each Sunday by their total
value and total quantity. Several fields are relevant per data instance to be able to measure the
influxes:
• Material number: unique identifier for each material.
• Plant: the plant the instance is located at, or ordered to
• Number of units: number of units represented by the instance
• Value of units: value in euro represented by the instance
• Stock age (if applicable): field measuring the time since the instance was created
• Order number (if applicable): actual order number of the instance if still on order
• Source-type: field created during this research to store the source the instance belongs to: raw
overplanned stock, raw non-overplanned stock, commitment on order, repair or requalification
on order, customer stock, non-MRP relevant stock or other. The characteristics to identify
these source-types at ASML are provided in Appendix A.
4.2 Assumptions
Several key assumptions form the basis of the method for measuring influxes:
1. Items with the same material number, plant and stock age are interchangeable.
2. Items with the same material number, plant and order number are interchangeable.
3. Data reporting considers items to be consumed using a first in, first out (FIFO) strategy:
items that were stocked first are reported as moved out first.
15
4. Differences per material-plant-stock age combination in overplanned raw inventory are due
to differences for the same combination somewhere else: inventory does not magically appear
and disappear.
5. Inventory position changes that happen in between two snapshot measurement points do not
significantly change the overall results.
In addition, several assumptions are made on data consistency:
1. MRP systems matching supply and demand work correctly.
2. The creation of the Inventory Dashboard and calculation of stock age work as intended.
3. Snapshots accurately reflect inventory positions.
4. Master data is well-maintained.
The first assumptions are all valid for ASML and explained in Appendix B. Although the data
consistency assumptions do not always hold, the research did not run into problems with data
consistency other than the problems mentioned in Section 2.2. For these cases, data is left out of
the analysis.
4.3 Measuring method
This section explains the rationale for the measuring method. A more detailed description of the
measuring method is provided in Appendix C.
The method to measure influx per source involves matching total influx into overplanned raw
inventory in week t with outflux of the sources identified in week t. The measurement involves
three high level steps. First, influx into overplanned raw inventory is measured between week t− 1
and t. Second, outflux from each source is measured between week t − 1 and week t. Third, each
influx into overplanned raw inventory in week t is matched to all potential outflux in week t to
explain the source.
Table 4.1: Measuring method influxes
1: Measure influx of overplanned raw inventory between week t− 1 and t
2: Measure outflux from each source between week t− 1 and t
3: Match influx to outfluxes to explain source
4.3.1 Measure influx of overplanned raw inventory
Influx into overplanned raw inventory is measured by comparing the snapshot of the previous week
t − 1 with the snapshot of the current week t. When no influx or outflux occurs, all overplanned
raw inventory for a material at a plant in week t − 1 with a stock age of x − 1 would still exist
in week t with a stock age of x. When the level of inventory, measured in units, in week t of the
material at the plant with stock age x is higher than the respective inventory in week t − 1 with
stock age x− 1, influx has occurred. This holds for all x larger than zero. Any inventory in week t
with a stock age of zero weeks constitutes influx. Subsequently, influx is grouped per material-plant
16
combination, separated for influx for items with a stock age of zero or items with a stock age larger
than zero. Figure 4.1 visualizes the rationale to measure influx.
4.3.2 Measure outflux of all identified sources
For measurement of source outfluxes, a distinction is necessary between sources that represent al-
ready existing inventory, which consequently have a stock age, and sources that involve items on
order and are without stock age. The first type of sources can be measured using a similar method
as described above for influx, for the second type some adjustments are required.
existing raw, non-mrp relevant and customer consignment sources
Outflux per source is measured using a similar method for sources representing different internal
inventories: non-overplanned raw inventory and non-MRP relevant inventory. Outflux occurs when
the level of inventory, measured in units, in week t of a certain material at a certain plant with stock
age x is lower than the respective inventory in week t− 1 with stock age x− 1. Outflux is grouped
per material-plant combination for both sources separately. Outflux from customers is partially
measured using this method, namely for items that were allocated to customer consignment stock.
Figure 4.1 visualizes the rationale for measuring the outflux.
Figure 4.1: Measuring influx and outflux
commitment and repair or requalification flows
Sources represented by orders in the snapshots, for which the stock age field is not applicable, need
a different field to distinguish influx and outflux: the order number. Outflux of the source occurs
when a negative difference exists between the quantity in week t−1 and week t per material-plant-
order number combination. Sources following this method include commitment from suppliers and
repair or requalification from suppliers.
shipments
In addition, inventory gets shipped between plants. While in transport, the items are reported
as on order instead of stock, and are thus not part of overplanned raw inventory at a location.
Therefore, outflux of these intercompany shipments needs to be measured, to be able to attribute
influx into raw overplanned inventory to these shipments. This is also done at the material-plant-
order number aggregation level and then grouped per material-plant combination. The items that
were already overplanned during the shipment are subtracted from the overplanned raw inventory
influx, since these simply represent movements of overplanned items. The remaining items, which
17
were not overplanned during shipment, but became overplanned at arrival are added to outflux of
existing raw inventory.
factory and customer flows
Items that are part of a work order or finished system are not included separately in the inventory
position. Therefore, snapshot analysis is not sufficient to identify outflux of factory returns on
raw material level. The same holds for items returning from customers which were not part of
consignment stock, since customer inventory is not part of the inventory position of the company.
Additional data is thus required to measure outflux from factory returns or customers.
At ASML, bookings to the factory or the customer are done with a specific movement code. Items
returning from factories or customers are booked back to inventory with a related reverse movement
code, Movement codes are stored in SAP and all instances with these movement codes during week
t − 1 can be extracted. If the quantitiy of inventory booked with the reverse movement code is
larger than the quantity booked towards the factory or customer per material-plant combination
during week t−1, outflux from factories or customers has occurred. Items returning from customers
are extended with USP or FSD, depending on the type of customer return. The subset of outflux
identified using the movement types with one of these two extensions represents customer outflux.
The remaining outflux is outflux from factories. Table 4.2 summarizes per source what data fields
are used to measure outflux.
Table 4.2: Summary methods outflux per source
Source type Comparison level to find outflux
1. Existing raw inventory: Material-plant-stock age
+ Material-plant-order number for shipments
2. Commitment arrivals: Material-plant-order number
3. Repair/requalification arrivals: Material-plant-order number
4. Factory returns: Additional data movement codes not USP or FSD
5. Customer returns Material-plant-stock age for stock on customer consignment
+ Additional data movement codes USP and FSD
6. Non-MRP relevant returns: Material-plant-stock age
4.3.3 Matching influx and outflux
The final step encompasses matching influx into overplanned raw inventory with outflux of the
sources identified in Chapter 3. This is done at the aggregation level of material-plant combination,
separated for influx with a stock age of zero and influx with a stock age larger than zero. As
explained in Section 2.3.2, ASML resets the stock age field when an item is booked to another
location. Therefore, the overplanned influx for items with stock age larger than zero can only be
matched to outflux of existing raw inventory, which measures the first flow described in Section
3.1. Additional items arriving that are overplanned always have a stock age of zero. Overplanned
raw influx with stock age equal to zero is consequently matched to outflux of commitment, repair
or requalification, factories, customers and non-MRP relevant locations, without prioritization. To
avoid double counting, influx that can be matched to outflux from multiple sources is proportionally
allocated over the sources. Proportional matching is a good method, because interest is not in where
18
one exact overplanned material comes from, but instead what together leads to overplanned. For
example, when an arrival from both commitment and repair source at the same time leads to one
unit overplanned, this overplanned material results from the combination of the two arrivals and
not one in particular. Figure 4.2 shows the rationale of the matching mechanism when an influx
can be allocated to two sources, source A and source B.
Figure 4.2: Matching influx and outflux
4.4 Limitations
Above method allows to identify the sources of overplanned raw influx, and helps to track the in-
ventories over time. However, still 30.7% of influx remains unexplained, due to several limitations.
First, a limitation arises from the snapshot approach. Movements that happen in between snap-
shots cannot be tracked from the snapshots only generated once every week. This limitation could
be overcome by increasing the frequency of snapshots. However, the weekly snapshots were already
available at ASML making marginal data retrieval and storage costs for this research low. When
companies’ inventory is more fast-moving, it may be worth to incur some additional costs to im-
prove accuracy of analysis.
Second, the assumptions on data consistency do not always hold. As an example, the missing data
in week 2 makes that the influx in week 2 is understated and the influx in week 3 overstated, also
causing an abnormal amount of unexplained influx for this week since part of the source data is
missing. This problem was corrected in the next week.
Another limitation of the research arises from the fact that at ASML the stock age is reset with every
movement. Therefore, when an item is moved between storage locations within the same plant, the
stock age is reset to zero. This means that according to the measurement method an outflux exists
of the material with the old stock age, and an influx exists of the material with stock age 0, while
in reality nothing happened apart from a movement between storage locations. An analysis is done
to study the impact of this limitation: unexplained influx of overplanned raw inventory with stock
age 0 is linked per material-plant combination to outflux of overplanned raw inventory with older
stock ages. This helped to explain 64.9% of the 30.7% of unexplained overplanned raw influx. Since
these items are not actually overplanned raw influx, they are removed from the influx data. Rather
than adjusting the measurement method for this research, it is recommended for ASML to adjust
19
the stock age calculation in the Inventory Dashboard to improve the measurement. After this
correction 13.5% of influx remains unexplained, which is accepted given the first two limitations.
Large remaining unexplained influx is analyzed each week to ensure no structural flow is omitted.
4.5 Results
This section describes the results of the measuring method applied to ASML’s data over the time
horizon of the research. Influx measurement is performed using stock quantities, since prices of
items can vary over time or between locations. However, since the analysis involves all types of buy-
materials with a large variation in value, comparison in quantities does not give a representative
view of the impact that is associated with the influx from each source. Therefore, every unit is
multiplied with its standard cost price in euros to measure the impact of the different flows on
overplanned raw inventory influx in value. Figure 4.3 shows the resulting distribution of influx over
the sources measured in value per week. Because the measuring method compares two snapshots,
the first displayed week is week 2, which compares the data of week 1 and 2. Note that results for
week 18 and 19 are missing due to the missing dataset for week 18.
Figure 4.3: Measurement of major influxes of overplanned raw inventory over weeks
4.6 Conclusion
High-tech companies can measure the influxes if they keep snapshot data of their inventory position
including inventories on order, in two consecutive periods. The frequency of snapshots should
balance data retrieval and storage costs with accuracy of analysis and depends on the moving pace
of inventory. Fields required include the material number, material location, number of units,
value of units, stock age, order number for items on order, and source-type representing the source
the instance belongs to. Three steps are necessary to measure the major influxes and described
in Section 4.3. Over the weeks, the largest portion of influx at ASML results from existing raw
inventory, commitment and factory returns. Repair and requalification influx and customer influx
follow in impact. Non-MRP influx only contributes to the problem in a minor extent.
20
5 Choice of focal influx source
The remainder of the research will focus on one of the influx sources to investigate the potential
of monitoring using tracking signals to improve inventory turnover. This chapter describes the
qualifiers and criteria to choose this focal influx, answering question 3: What qualifiers and criteria
can high-tech companies use to choose an influx to investigate monitoring potential? When inves-
tigation for this influx shows positive results for using tracking signals to monitor the influx, the
insights and methods can be transferred or used as guidelines to monitor other influxes. High-tech
companies can use these qualifiers and criteria to research what influx is most beneficial for them
to start monitoring using tracking signals. Section 5.1 describes the qualifiers and assesses ASML’s
influx per source to them. Section 5.2 continues with the criteria. Section 5.3 concludes the chapter.
5.1 Qualifiers
To be considered as a candidate for the tracking signal investigation, the source influx needs to
satisfy several qualifiers. First, the value of the influx from this source needs to be large enough to
have a significant impact on the overplanned raw inventory influx and thus to allow for tracking
signals to make a significant improvement by reducing that influx. Figure 5.1 presents the influx
per source as a percentage of the total over the full research period. Second, some stability of the
influx in Figure 4.3 is required in order to be able to set a norm and have data to analyze every
week. Third, data regarding the influx needs to be available.
Figure 5.1: Overplanned raw inventory per influx source
Based on these qualifiers, non-MRP relevant influx and customer influx are eliminated as candi-
dates. The value coming from non-MRP relevant influx is not large enough for tracking signals
to make a significant impact. Influx from customer returns does not satisfy the stability qualifier.
The influx that remains unexplained cannot be explained from the available data and is thus also
not qualified.
5.2 Criteria
The influxes that satisfy the qualifiers are assessed based on several criteria to decide on the most
appropriate influx to start analysis. The first criterion involves the generality of the expected
drivers of the influx. If the drivers that are expected to be found of an influx are also applicable to
influx from other sources, the rationale behind the tracking signals developed for this can be more
easily transferred. This increases the potential total impact of the tracking signals. Second, the
research wants to address both excess and obsolete overplanned inventory, as well as overplanned
business decision inventory. Therefore, an influx is preferred in which all three are apparent. Third,
21
in order for tracking signals to be able to have an impact, the influx needs to be actionable. Simply
monitoring provides insight, but when the tracking signals can guide actions, more steps towards
inventory efficiency improvement can be made.
The qualified influxes, including existing raw influx, commitment influx, repair/requalification in-
flux and factory return influx are assessed on these criteria.
5.2.1 Generality of drivers
Figure 3.2 shows the expected drivers for each of the influxes. Existing raw influx occurs due
to changes in MRP. These same changes in MRP are the reason why ordered supply becomes
overplanned during lead time for commitment and repair and requalification flows, although these
have additional drivers, such as minimum order quantities for commitment, and the length of the
inspection and repair process in combination with high material availability targets for repair and
requalification. Factory return influx is driven by factory processes and is relatively unrelated to
the drivers of the other flows. Existing raw influx drivers are thus most general because they
are also directly relevant and help to explain part of the problem for commitment and repair and
requalification influx.
5.2.2 Overplanned type
The second criterion is that the influx represents a significant portion of obsolete, excess and
business decision overplanned raw inventory influx, so that monitoring this influx addresses all
three types. The source distributions per overplanned inventory type are displayed in Figure 5.2.
Existing raw influx largely impacts all three types of overplanned inventory, representing 21.6% of
obsolete influx, 29.5% of excess influx and 27.1% of business decision influx. Commitment influx
largely addresses excess influx, with 26.1% of excess influx coming from commitment, but only
accounts for 7.0% of obsolete influx, and 10.7% of business decision influx. On the other hand,
repair and requalification influx largely addresses business decision influx representing 31.5% of the
total, but only has a minor impact on excess and obsolete influx with a contribution of 5.7% and
11.1% respectively. Factory return influx has a significant impact on all three influxes, constituting
15.7% of obsolete, 18.3% of excess and 30.1% of business decision influx, although in a less amount
than existing raw influx.
Figure 5.2: Influx percentage explained by sources per overplanned type
22
5.2.3 Potential to guide actions
Tracking signals on all four influxes would be valuable for monitoring purposes so that actions can
be driven at the moment the influx occurs. In addition, insights from monitoring can be revealed
to stakeholders to motivate them to change processes or behaviors. Factory returns and existing
raw inventory are most actionable because they happen within the boundaries of the company
itself, and no coordination with external partners is required. However, when tracking signals are
used to guide towards influx prediction, commitment influx may be most actionable since the items
representing influx are not ASML owned yet in week t− 1.
5.3 Conclusion
Based on the above described qualifiers and criteria, the decision is made to further analyze existing
raw inventory influx at ASML. This influx satisfies all qualifiers, has drivers relevant for two other
major flows, and addresses all types of overplanned. Action potential is expected to exist and is
further addressed in Chapter 8.
23
6 Drivers overplanned raw inventory from existing inventory
This chapter deepens the understanding of what causes existing non-overplanned raw inventory
to become overplanned, answering question 4: What drives the chosen overplanned raw inventory
influx? Knowing what drives inventory to become overplanned indicates what needs to be tracked
to monitor effectively and efficiently. Section 6.1 describes a qualitative analysis of possible drivers,
while Section 6.2 presents the rationale of a method for driver quantification based on indicators.
The method is applied to ASML’s inventory data to illustrate the method and to verify drivers
in the high-tech industry. Section 6.3 describes the results of a sensitivity analysis of the model
parameters and tunes them. The results of this tuned model for ASML are provided in Section 6.4
and the chapter is concluded in Section 6.5. The method is most likely not readily generalizable to
other high-tech companies, but companies can learn the rationale and apply this similarly to their
data. Readers uninterested in the details of the quantification method can omit Sections 6.2 and
6.3 and continue reading in Section 6.4.
6.1 Qualitative analysis
Overplanned inventory only includes items in scope of MRP. The purpose of MRP is to provide a
forecasted plan for components and calculate current and planned order releases, based on several
sources of input information: the MPS, providing a time-phased plan for completion of products,
the BOM, used for explosion of the end item to find component requirements, master data such as
lot sizes, production and procurement lead times and buffer requirements, and the current inven-
tory status (Heisig, 2012).
Inventory becomes overplanned when a previously existing plan for it is removed in the next MRP
run. Since MRP plans are based on the above described input information, inventory becomes
overplanned due to a change in one of these inputs. MPS plans independent demands, coming
from the master plan for customer orders, service orders or forecasts. When these drop, inventory
becomes overplanned. The same holds for buffer requirements. Changes in the BOM impact the
propagation of independent requirements to components by MRP. Therefore, changes in the BOM
where components are removed from the BOM of a parent, or are replaced by a newer version
can make the old inventory overplanned. Changes in production and procurement lead times only
change the timing of order releases and do not make inventory overplanned considering the full
time horizon. Finally, inventory statuses are assumed to be correctly reported and items do not
magically appear or disappear. Therefore, this cannot be a source of overplanned inventory creation.
In short, changes in three inputs can drive existing inventory to become overplanned: the MPS,
buffer requirements and the BOM. The first two reflect requirement changes, while the last one is
in the high-tech industry commonly driven by ECs. Therefore, demand changes (DC) and ECs are
analyzed separately to calculate the impact by each on the overplanned inventory creation.
6.1.1 Demand changes
Demand changes reflect all changes in independent requirements, so both changes in the MPS
and the buffer requirements. MPS plans production for customer orders, service requirements
and forecasted demands, and changes can thus result from customer mix changes or cancellations,
changed service requirements or changed forecasts. Slow-moving demands, common in the high-
24
tech industry, are often the most stochastic and erratic, making them especially vulnerable (Balaji
& Kumar, 2013). Direct requirements for a component, including safety stocks and reservations
for the component made manually for various purposes, also exist. When these requirements drop,
existing raw inventory becomes overplanned.
6.1.2 Engineering changes
Due to the process of concurrent engineering, ECs are expected to be a major factor impacting
inventory inefficiency for high-tech companies. Jarratt, Eckert and Caldwell (2011) define ECs
as “alterations made to parts, drawings or software that have already been released during the
product design process and lifecycle, regardless of the scale or the type of the change”. ECs can
be necessary due to three different reasons. The first reason is to follow specific customer needs
that evolve during the lifecycle of the product. The second reason is to make improvements to
the design based on experience that is gathered during the product lifecycle. Finally, ECs can
embody design modifications that are needed to resolve identified problems in the previous design
(Veldman & Alblas, 2012). ECs lead to an alteration of the connections and dependencies between
elements of a product, affecting materials requirements for elements and their operational processes.
ECs can thus have a propagating effect, meaning that changes initially affecting one component
or product, spread to other components, or products, affecting also their manufacturing processes
and requirements (Jaratt et al., 2011). More information regarding this propagating effect can be
read in Ho and Li (1997). Mather (1977) and Ho (1994) studied the influence of ECs on MRP
performance and both found frequent ECs as an important cause of MRP scheduling instability.
6.2 Quantitative analysis
Compared to the influx measurements per source in Chapter 4, driver quantification in this chap-
ter requires additional data. First, tables regarding requirements per material at each plant are
needed. Second, information about ECs and affected materials is to be obtained. In addition,
two additional fields from the Inventory Dashboard become relevant which are a result of MRP
calculations: the highest level demand of the influx material, called top requirement, and the direct
requirement stored as the requirement order. These fields are explained in Section 2.2.
Furthermore, four additional assumptions are made:
• The indicators identified have explanatory value regarding whether an influx is demand or
engineering driven.
• Effects of ECs are mostly observed within one week after implementation.
• The probability that an influx is driven by an EC decreases with a constant rate over time
after the completion date of the EC.
• The individual influxes found in one week are independent of influxes in other weeks.
The objective of quantification is to assign value towards each driver per overplanned influx,
Influxt,m,p, if larger than zero. Influxt,m,p is defined as the net influx between week t − 1 and
t for material m ∈ M at plant p ∈ P summed over the stock ages, where M is the total set of
materials, and P the total set of plants. Because information is not complete at ASML, scores
25
will be established for an influx to be driven by DCs and ECs. The V aluet,m,p involved with the
Influxt,m,p is allocated proportionally to these scores. Summing over all plants and materials gives
the total influx value per driver per week, as in Equations 4 and 5.
V alueDCt =
∑p∈P
∑m∈M
Demand scoret,m,pEngineering scoret,m,p +Demand scoret,m,p
× V aluet,m,p for t > 1 (4)
V alueECt =
∑p∈P
∑m∈M
Engineering scoret,m,pEngineering scoret,m,p +Demand scoret,m,p
× V aluet,m,p for t > 1 (5)
The rationale to obtain values for Demand scoret,m,p and Engineering scoret,m,p is elaborated on
in this section. Appendix C provides a more detailed explanation of the score calculations.
6.2.1 Demand score
Direct requirements
For direct raw material requirements, a list of outstanding requirements can be extracted and
matched to influx. When listed requirements in week t decrease compared to requirements in week
t − 1 for m at p, overplanned influx of this m at p between these weeks is linked to the drop
in requirements and thus assigned as demand-driven. Let Rt,m,p be the quantity of requirements
for m at p at the moment the snapshot of inventory data of week t is taken, and Rt−1,m,p the
required quantity at the time of the snapshot of week t − 1. Then, the amount per influx that
is demand-driven, InfluxDCt,m,p, can be found as the minimum of the difference in requirements
Rt−1,m,p − Rt,m,p and the quantity of the Influxt,m,p for each Influxt,m,p larger than zero, as in
Equation 6. If Rt,m,p is larger than Rt−1,m,p, InfluxDCt,m,p equals zero. The demand score for the
total influx based on direct requirements, Demand scoreDRt,m,p, is then found as the fraction of the
Influxt,m,p explained by InfluxDCt,m,p, as in Equation 7. At ASML, this is possible for safety stock
requirements and manual reservations.
InfluxDCt,m,p = Min{Influxt,m,p, [Rt−1,m,p −Rt,m,p]+} ∀ m, p; t > 1|Influxt,m,p > 0 (6)
Demand scoreDRt,m,p =
InfluxDCt,m,p
Influxt,m,p∀ m, p; t > 1|Influxt,m,p > 0 (7)
Indirect requirements
For influx resulting from drops in indirect requirements, the link is more difficult. It is not possible
to extract demand plans and cascade differences through the BOM to find outflux in requirements
on the raw material level due to these higher level requirements. Instead, several indicators have to
be used together to provide a probability score that an overplanned influx is driven by a demand
change on a higher hierarchical level. Each influx obtains a score on each indicator and the scores
are then combined to provide an estimation of influx driven by demand changes.
• Top requirement (TR) drops: Define Demand scoreTRt,m,p as the fraction of dropped top re-
quirements for m at p in t. Drops are top requirements to which the material was allocated
in week t− 1 and not anymore in week t that completely disappear in week t, and for which
the current date is not passed the requirement date. Drops indicate demand-driven influx.
Only complete disappearances provide information because otherwise the dislocation of the
26
material to the top requirement can still be driven by anything on the in-between hierarchical
levels.
• Requirement orders (RO) remaining : Define Demand scoreROt,m,p as the fraction of requirement
orders to which m at p was allocated in week t − 1 and still is in week t, powered to a
factor (factor 1): a large fraction of the requirement orders remaining indicates demand-
driven influx. The power factor is applied because the indicating power of the fraction is not
expected to be linear. The factor changes how the fraction contributes to the score where
factors smaller than one flatten the relative contribution of different values and factors larger
than one amplify the differences, as in Figure D.1.
• Overplanned percentage (OP): Define Demand scoreOPt,m,p as the fraction of existing inventory
of m at p in week t−1 that remains non-overplanned in week t, powered to a factor (factor 2):
a large fraction of existing stock remaining used indicates demand-driven influx. Here, the
power factor is also applied to make contributions of fractions to scores non-linear.
• Existence of safety stock (SS) requirements: Define Demand scoreSSt,m as a binary to indicate
whether safety stock is kept for m in t. For phased-out items no safety stock should be kept
so existence of safety stock indicates demand-driven influx.
Measurement model
The demand scores are combined to assign a total score, Demand scoret,m,p, per Influxt,m,p to
indicate a probability that the influx is driven by a demand change. Direct requirement drops
compose a direct measurement and therefore Demand scoreDRt,m,p is fully added to the total demand
score. The indicator demand scores have to be multiplied with weights between zero and one
(w1, w2, w3, w4) to take into account potential differences in explanatory power for the indicator
scores. Accordingly, Demand scoret,m,p is calculated as the minimum of 1 and the sum of the
individual demand scores defined, multiplied with weights, as in Equation 8.
Demand scoret,m,p = Min(
1, Demand scoreDRt,m,p+ w1×Demand scoreTR
t,m,p+ w2×Demand scoreROt,m,p
+ w3 ×Demand scoreOPt,m,p + w4 ×Demand scoreSS
t,m
)∀ m, p; t > 1|Influxt,m,p > 0 (8)
6.2.2 Engineering score
Direct effect
The direct effect of ECs involves overplanned inventory creation on the same hierarchical level as
the modification, such as version upgrades for components. If the company keeps a list of these
ECs on each material, they can be linked to the overplanned influx of each material. Overplanned
influx is in the standard situation created at the first MRP run after the moment the EC is entered
in SAP and the BOM is changed. Let te be the week the engineering change was entered in SAP
and marked as completed. However, in some cases the effect is observed later, for instance when
planners aim to find a different demand for the material. This probability is assumed to decrease
with a constant rate. Therefore, an exponential distribution is applied with a parameter of λ = 1 to
calculate the probability that the effect is seen in week t, which is t-te weeks after implementation
of the EC. Overplanned inventory influx can be driven by the EC when the impact of the EC
shows during or after week t. The probability that an influx is driven by an EC can thus be
calculated as one minus the cumulative probability that the effect is observed before week t. This
27
cumulative probability is calculated as 1−e−λ(t−te) for t > te and 0 otherwise (Balakrishnan, 1996).
InfluxECt,m,p is the probability based on these EC lists that an Influxt,m,p is driven by an EC on
the same hierarchical level and is thus calculated as in Equation 9.
InfluxECt,m,p =
{1− (1− e−λ∗(t−te)) = e−λ(t−te) if te ≤ t0 otherwise
∀ m, p; t > 1|Influxt,m,p > 0 (9)
Dependency changes
Second, as described in Section 6.1.2, ECs can alter dependencies written in the BOM structure,
propagating to lower levels potentially inducing overplanned influx for all dependent materials. If
a list is kept by the company for all ECs with materials impacted on all hierarchical levels, a score
can be assigned in a similar fashion as for the material upgrades, using Equation 9. Unfortunately,
at ASML this list is not complete and the inventory data available only describe the influx ma-
terial and its top material, which is the independent parent of the item. Therefore, only the top
level and not all hierarchical links can be made. The in-between dependencies and whether they
are affected by an EC remain largely unknown. Due to BOM complexity in high-tech companies,
this underestimates the total impact of ECs. Measurement would be improved by including more
hierarchical levels.
To cope with the missing information, some of the indicators as introduced in Section 6.2.1 are
added to the measurement in a reverse direction. The indicators of top requirement drops and
safety stock existence do not provide indicating value in the reverse direction.
• Requirement orders (RO) remaining : Define Engineering scoreROt,m,p as one minus the fraction
of requirement orders to which m at p was allocated in week t−1 and still is in week t, powered
to a factor (factor 1): a small fraction of requirement orders remaining indicates engineering-
driven influx.
• Overplanned percentage (OP): Define Engineering scoreOPt,m,p as the fraction of existing in-
ventory of m at p in week t−1 that becomes overplanned in week t, powered to a factor (factor
2): a large fraction of existing stock that becomes overplanned indicates engineering-driven
influx.
Factors are again applied to flatten or steepen how different fractions contribute to scores.
Measurement Model
The scores for the direct material upgrades and propagation effects obtained from the EC lists and
the two indicator scores can be combined to assign a total score to indicate whether an influx is
driven by an EC, Engineering scoret,m,p. Engineering scoreECt,m,p is a direct measure and therefore
can be fully added in the total score. Similar to the demand change measurement, weights have
to be applied for indicator scores to take into account potential differences in explanatory power.
Accordingly, Engineering scoret,m,p is calculated as the minimum of 1 and the sum of the individual
engineering scores defined, multiplied with weights, as in Equation 10.
Engineering scoret,m,p = Min(
1, InfluxECt,m,p + w2 × Engineering scoreRO
t,m,p
+ w3 × (Engineering scoreOPt,m,p
)∀ m, p; t > 1|Influxt,m,p > 0 (10)
28
6.3 Sensitivity analysis and model tuning
The results of the measurement model depend on four weights and two transformation factors.
Analysis of the impact of the setting of these weights and factors is needed to guide companies in
whether settings largely impact the results and require careful assessment, or that the results are
relatively insensitive to the settings. A sensitivity analysis is performed on the available data. All
the weights were varied between 0 and 1, and the transformation factors between 0.5 and 6. The
results are shown in Appendix E. Different weights and factors lead to very different ratios of value
allocated to engineering changes or demand changes: 38.4% of value allocation depends on them.
Therefore, more consideration is needed regarding the settings of the weights and transformation
factors.
6.3.1 Model tuning
The model is tuned in two stages: a training and an evaluation stage. In the training stage, the
weights are trained on a portion of the data where the required outcome is known. This leads
to a set of weights that best satisfies the training criteria. Subsequently, in the evaluation stage
the model with the set of weights acquired in the training stage is applied to a new set of data,
to predict new outcomes. These predicted outcomes by the model are compared with the known
outcomes of the data to evaluate the performance of the model (Witten, Frank, Hall & Pal, 2016).
At ASML, no classification of whether an influx is driven by demand changes or engineering changes
exists at this moment. Therefore, all influxes with a value larger than e1,000 of week t were
manually classified by an inventory expert as either demand or engineering driven. Based on these
classifications a percentage of the total value was assigned to be demand or engineering driven.
Then, all weights mentioned in the sensitivity analysis were applied to discover with what set of
weights the model performs best on two criteria:
1. Correctly classified instances
2. Proximity of value allocation to expert allocation
The best model classified 111 out of 120 influxes correctly with a deviation of 0.03% of the value
allocation. The recorded weights and values for the transformation factors are shown in Table
6.1. Remarkably, w2 is equal to 0, meaning that in contrast to what was expected, the fraction of
requirement orders remaining does not contain information on whether an influx was demand or
engineering driven. This also makes that factor 1 is not applicable.
Table 6.1: Weights trained model
w1 0.9
w2 0
w3 0.1
w4 1
factor 1 NA
factor 2 2.5
29
model evaluation
To evaluate the model, the same inventory expert manually classified the data of the next week,
week t+ 1 as either demand or engineering driven. The training and validation on these two data
sets is not optimal, but adequate when the validation set is independent of the training set. The
model with the weights as described in Table 6.1 was applied to classify the new influxes and gener-
ate a total demand and engineering driven influx value. The model correctly classified 77 out of 95
classified instances, resulting in an accuracy of 81.05%. In terms of value the deviation between the
model’s allocation and the manual allocation was 0.4%. Based on these data, the performance of
the model is good. To further improve reliability, it would be beneficial to further tune the weights
by training and validating the model on more weeks of data. However, since expert classification
is time-consuming and the results seem satisfactory, further optimization of the model for ASML
is left for future research.
tuning lambda
An additional assumption made that needs to be analyzed is the choice of λ. This parameter was
chosen as 1, since the expected value of the number of weeks until the engineering change effect is
seen is one week. However, also later influx can be regarded as driven by the engineering change,
as a lagged impact. It is a decision for the company until what time after the engineering change
is implemented the company wants to regard an influx as engineering driven, rather than driven
by demand changes. To examine the effect of different values for λ, the parameter was varied
between 1 and 1/52, representing a maximum expected lagged impact of 52 weeks. The results
are given in Appendix E. Compared to the maximum impact, 47.0% is indeed already taken into
account by incorporating the one week effect. The impact of different λ’s is only 13.0% of the total
value. Companies can make their own decision whether they want to implement a different λ to
allocate more influx towards engineering driven influx. Although this leads to reallocation of some
demand-driven influx to engineering-driven, this does not change the overall conclusions.
6.4 Results
Due to the additional data required for the analysis, such as the EC lists and requirements for
safety stocks and manual reservations, results can only be shown from week 16 to 24, see Figure
6.1. These results are generated using the parameter settings defined in Section 6.4.1 and λ equal to
one. Contrary to what was expected, demand changes rather than engineering changes seem to be
the main driver of existing raw inventory becoming overplanned, although engineering changes still
contribute a significant amount. Therefore, both processes are important to track to gain greater
control of overplanned inventory influx.
6.4.1 Driving Processes
In the above analysis, only a split was made between demand changes and engineering changes.
However, demand changes can be further divided into the types of requirements in the master plan
that induce overplanned influx, as also in Figure 3.2 and described in the beginning of this chapter.
These can be quantified by allocating the demand driven influx to the type of demand the mate-
rial was assigned to in the week before becoming overplanned. This leads to the results in Figure 6.2.
All expected drivers are found in the high-tech environment of ASML, although some more im-
30
Figure 6.1: DC vs EC driven influx Figure 6.2: Driver quantification influx
pactful than others. This research thus serves as a case study to verify the above drivers in the
high-tech industry. In addition, the category other reflects a significant part, which includes mainly
development projects and intercompany requirements. Further investigation into what this entails
can be beneficial. Because all drivers are found and not one explains the majority of influx, tracking
signals need to be employed on all types of influx to monitor effectively.
6.5 Conclusion
Measurement of drivers shows customer requirement changes as the main contributor, driving
21.5% of the influx, followed by ECs, driving 20.4%. Other large contributors are service require-
ment changes, and forecast changes, driving 14.3% and 12.8% of influx respectively. Although less
impactful, manual reservations and safety stock changes are also found as drivers of overplanned
inventory, contributing 4.1% and 8.6% of influx. Moreover, data indicated that drops in require-
ments for development projects might constitute an important driver to be further investigated.
The quantification method does not provide a completely objective distinction between demand
and engineering-driven influx. First, this division requires interpretation because ECs and DCs are
somewhat interrelated; requirement plans can be changed as an effect of ECs in order to reduce
the impact of the EC. Whether this is regarded as demand or engineering driven is a matter of
perspective. Second, not all data is available to allow perfect measurement. Nevertheless, an
allocation based on probabilities is appropriate to be able to continue the research: it provides
insight on the direction high-tech companies need to focus on in order to eliminate existing raw
overplanned inventory influx and facilitates to determine fields that are interesting to track to
monitor overplanned inventory influx. At ASML, since not one contributor exists that explains the
majority of the problem, monitoring should entail all drivers.
31
7 Design of tracking signals
This chapter describes the design of tracking signals that enable high-tech companies to monitor
overplanned inventory influx, answering question 5: How can tracking signals be designed to monitor
the chosen overplanned raw inventory influx? The aim of the solution design is to develop tracking
signals which help companies improve inventory turnover, by providing them insight into when
significant overplanned raw inventory is created. Section 7.1 describes methods to develop tracking
signals. Section 7.2 describes the fields that were chosen to track for ASML, which can be used
as guidance for other companies to develop their own tracking signals. Section 7.3 evaluates the
methods for tracking signals and Section 7.4 concludes the chapter.
7.1 Tracking signals design methods
Designing tracking signals is about setting a performance threshold above which a signal needs to
be generated. To specify this threshold, a boundary value needs to be defined which is still in the
acceptable range of the metric. When this boundary value is violated, a signal is generated. The
boundary value needs to balance excessive alerting, when too many signals are generated, with lack
of good monitoring in the case of too little signals. Two fields within supply chain management
theory provide inspiration to derive a method to establish this threshold: forecasting and statistical
process control.
7.1.1 Forecasting
Forecasting methods attempt to determine the most probable outcome of an uncertain variable
ahead in time. In logistics, forecasting is used to predict future demands to facilitate decisions that
have to be made before the real demand is known, such as production scheduling, transportation
planning and inventory management. The same forecasting methods can be applied to determine
the expected value of overplanned inventory influx. This value can be used to establish a threshold,
a multiple of the expected value, above which a signal needs to be generated to flag abnormal in-
flux. Two main groups of forecasting methods exist: causal methods and time series extrapolation
(Ghiani, Laporte & Musmanno, 2004).
causal methods
Causal methods can be used to predict inventory influx based on values of relevant variables. Their
major advantage is that they allow for anticipation of variations based on these causal variables.
Since variations in the high-tech industry are usually large, this method provides an advantage
in terms of accuracy over methods that do not take these variations into account. Regression is
the most widely used causal method by logisticians, which is a statistical method that relates a
dependent variable y to n causal variables x1, x2, . . . , xn. The values of x1, x2, . . . , xn are known
or can be predicted. Let V aluet be the influx value in week t. In terms of predicting influx, this
represents the dependent variable of the regression. The independent variables remain to be found.
The expected outcome of the influx is a function of the independent variables, as in Equation 11.
V aluet = f(x1, x2, ..., xn) (11)
A drawback of causal methods is that they are hard to implement. Identifying causal variables
is difficult, especially ones that lead the inventory influx in time. Causal methods can be used to
32
predict influx, as is step 4 in the roadmap in Figure 1.1, when these variables are found. At this
moment however this is not yet feasible, and tracking signals are aimed at instead finding these
variables.
time series extrapolation
Time series extrapolation is based on the assumption that the main features of the historic pattern
will be replicated in the future and that a forecast can be obtained by extrapolating old patterns.
These methods are most suitable when changeover probabilities are low and the time horizon is
short. If this is the case, time series extrapolation can be an appropriate method to establish a
threshold for overplanned inventory influx. Different types of methods for time series extrapolation
can be found in forecasting literature. Ghiani et al. (2004) prescribe that it is preferred to select
the simplest appropriate method, since simple techniques are easier to understand and explain, and
in practice complex procedures seldomly yield better results.
The appropriateness of a forecasting method depends on what effects are expected in the influx
pattern. Patterns can be decomposed into four types of effects: trend, cyclical variation, seasonal
variation and residual variation (Ghiani et al., 2004). A trend can be expected for overplanned in-
ventory influx in ASML’s high-tech environment. Probabilities of overplanned inventory influx from
existing raw inventory increase with inventory levels, which are positively related to a company’s
output growth. On the other hand, ASML is actively working on inventory reduction programs
which are hoped to stimulate a negative inventory trend. The direction of trend thus remains un-
clear, but a method that takes into account trends seems relevant. Although supply chain planning
at ASML follows plan cycles, data does not show seasonality effects in influx. Cyclical variation
cannot be analyzed due to the short time horizon of the research but is not expected to have a
major impact. All in all, an adequate method should allow incorporating a trend method. An
appropriate method is the Holt-method, which is a modification of exponential smoothing able to
deal with trends. If a company believes to face seasonal effects, it can instead refer to the Holt-
Winters-method (Ghiani et al., 2004).
Holt-method
The Holt method relies on two relations. Set a1 = V alue1 and b1 = 0. Then, Equations 12 and 13
can be applied recursively to find values for at and bt for all weeks t ∈ T > 1.
at = α× V aluet + (1− α)(at−1 + bt−1) (12)
bt = β(at − at−1) + (1− β)bt−1 (13)
The forecast made in week t for the influx τ weeks later, pt(τ), is calculated using Equation 14.
pt(τ) = at + τ × bt (14)
What remains to be determined are the parameter values for α and β. Higher values of α give a
larger weight to recent observations to provide for flexibility while lower values base the forecast
more heavily on older data to make the forecast more stable. The choice of β determines the impact
of the trend effect on the forecast. To estimate the best parameter values, a posteriori evaluation
of the errors that would have resulted from different values of α and β, varying from 0.01 to 1, is
performed. The values that lead to the lowest Mean Squared Error (MSE) are chosen as the model
33
parameters. Let et = pt−1(1) − valuet, then MSE is calculated using Equation 15 (Ghiani et al.,
2004).
MSEt =
∑tk=2 e
2k
t− 2for t > 2 (15)
Signaling threshold
The obtained forecast can be used as the expected value of influx for period t+τ . However, in order
to avoid excessive signaling, a signal should not be generated every time that an influx exceeds the
forecast but only when the value is above the threshold of h times the forecast, as in Equation
16. What this h should entail is a company decision to balance the amount of influx signaled with
workload, and is further elaborated in Section 7.3.
ThresholdTSFt = h× pt−1(1) (16)
7.1.2 Statistical process control
Statistical Process Control (SPC) is a collection of problem-solving tools aiming to achieve process
stability and reduction of variability. It is based on statistical concepts and mostly applied in logis-
tics to monitor quality of outputs of production processes. The objective of SPC is to differentiate
between natural variability, which is the cumulative effect of many essentially unavoidable causes,
and other kinds of variability, called assignable causes of variation. SPC helps to quickly detect
assignable causes of variation when the process is out of statistical control so that investigation of
the process and corrective action may be undertaken. The control chart is the tool that is mostly
used for process monitoring for this purpose (Montgomery, 2009).
A similar rationale can be applied to the control of overplanned inventory influx. Each week, a
part of the overplanned inventory influx is inevitable due to many unavoidable causes, similar to
natural variability. However, when the influx is out of statistical control, a signal should be given
to investigate the assignable cause of variation. Therefore, the rationales for setting up a control
chart for quality control can provide a good method to develop tracking signals for overplanned
inventory influx.
control chart
A control chart typically consists of a center line, which represents the average value of the measure,
and two horizontal lines called the Upper Control Limit (UCL) and the Lower Control Limit (LCL).
A value outside the limits generates a signal to require investigation and if possible corrective ac-
tion. In the case of controlling overplanned inventory influx, only signals need to be generated
when the influx is above what would be expected from the in control process, making only the
UCL needed while the LCL can be disregarded. Let L be the distance of the control limit from
the center line, in units of standard deviation. Then the control chart is designed based on the
following principles:
Centerline = µinfluxUCL = µinflux + Lσinflux
34
However, µinflux and σinflux are unknown and have to be estimated from data by µinflux =∑ki=1
valueik and σinflux =
√∑ki=1(valuei−µinflux)2
k−1 where k is the number of data points. In the
case of individual control charts, it is more appropriate to base the limit on the average of the mov-
ing ranges (MR) instead of the standard deviation to make the chart less sensitive to oscillations,
and scale this by 2√π
. σinflux is thus estimated by MR = 1k−1
∑ki=2 | valuei− valuei−1 | multiplied
with 2√π
(Vermaat, Ion & Does Klaassen, 2003).
The distance L can be based on the decision of the fraction α of the influxes that is wished to
fall outside of the one-sided upper limit and thus generate a signal. If the data of the process
that needs to be controlled follow a normal distribution, or can be approximated by the nor-
mal distribution according to central limit theorem, L needs to be set according to L = zα√k, where
zα is the percentage point of the N(0, 1) distribution such that P (z ≥ zα) = α (Montgomery, 2009).
When data is not normally distributed, Vermaat et al. (2003) found that other methods rather
than the average of the moving ranges (AMR) were preferred for control charts for individual
observations. The preferred method involves control charts based on Empirical quantiles (EQ),
which behave well for a broad range of distributions. Let F be the empirical distribution function
putting mass 1k at each valuei, where 1 ≤ i ≤ k: F (x) = 1
k
∑ki=1 Ivaluei≤x for −∞ < x < ∞.
Here, Ivaluei≤x is the indicator function which equals 1 if valuei ≤ x and 0 otherwise. The q-quantile
of the distribution function F can be estimated by the empirical quantile, as in Equation 17. The
UCL, which is the signaling threshold for this method ThresholdSPC, can then be determined using
Equation 18 when values are ordered so that value1 ≤ value2 ≤ ... ≤ valuek.
F−1(q) = infinum{x | F (x) ≥ q}, 0 < q < 1 (17)
ThresholdSPC = UCL = F−1(q) = F−1(1− α) = valued(1−α)ke (18)
summary of methods
Four sets of methods were described in this section. Applying causal regression to predict influx
is not possible yet since first insight needs to be improved regarding what variables the function
should depend on and in what manner. Therefore, three methods remain attainable for tracking
signals at this moment. These are summarized in Table 7.1. Since overplanned inventory influx
data for high-tech companies is rarely normally distributed, the EQ-based control is preferred over
AMR-based control. The EQ-based process control threshold involves a static norm that needs to
be reset every fixed number (> 1) of weeks, while forecasting provides a dynamic norm adjusted
on a weekly basis.
Table 7.1: Summary of signaling methods
Norm Signal threshold
Holt-method forecasting pt(1) = at + bt pt−1(1)× hAMR-based process control µinflux µinflux + zα√
k×MR× 2√
π
EQ-based process control µinflux valued(1−α)ke
35
7.2 Tracking fields
If the right influxes are signaled, minimum effort is required to achieve results. At the same time, the
aggregation level of the tracking signals needs to be low enough so that real actions can be driven,
and it is possible to identify the responsible process owner to investigate the influx. Therefore,
tracking signals are developed at different aggregation levels. Actionable aggregation levels follow
from the driver indicators identified in Chapter 6 and include the following:
• Value of influx in week t per EC number
• Value of influx in week t per top requirement
• Value of influx in week t per requirement order
• Value of influx in week t per material number
Signals should be efficient and no overlapping signals are wanted. Therefore, if an influx of a
material is signaled on more than one level, only the highest hierarchical level should show, with
hierarchy as in above: for example, signals on a material level should only flag when the material
is not yet included in an EC, TR or RO signal. This prioritization is summarized in Algorithm 1
in Appendix F.
Higher aggregation level tracking signals track total values which help to create overview about
what parts of the organization experience abnormal amounts of influx. If this abnormal amount
can be explained by the lower aggregation level signals, these should be further investigated instead.
However, if no lower aggregation level explains the abnormal amount, it could be the case that the
abnormal influx is the sum of many influxes, and further analysis is needed into what drives this.
In addition, the identified lower aggregation level drivers may not yet comprise a complete list, and
looking for drivers based on unexplained higher aggregation level signals may lead to discovery of
new relevant lower aggregation level signals. Total inventory and according overplanned inventory
levels as well as influx are very different for different business lines and plant locations and will
thus also be monitored separately. The following will be tracked:
• Total value of influx in week t
• Value of influx in week t per business line
• Value of influx in week t per plant
7.3 Evaluation of tracking methods
This section evaluates the introduced methods to develop tracking signals: the dynamic threshold
based on time series forecasting (TSF), and the static threshold based on SPC. First, the methods
are applied to ASML’s data with different settings of the parameters h and α. Theoretically,
the method is preferred which requires the least signals to flag the most value. For companies,
additional factors might be relevant.
36
7.3.1 Efficient signaling capability
For TSF, the setting of h defines the threshold which leads to a number of signals generated, de-
pending on the data. If h is set high, fewer signals are generated. Contrastingly, if h is set at a
low value, more signals are generated. More signals lead to a larger percentage of total influx value
explained. For SPC, the same holds for the setting of α. Higher α leads to a larger number of
signals and more value flagged.
Signals were set based on the data of week 1 to 17, excluding week 2, and then applied for five con-
secutive weeks of ASML’s inventory data. Thresholds are set per different type of tracking signal,
so different thresholds exist for tracking signals per EC number, top requirement, per requirement
order and per material. The Holt-forecasting parameters used to obtain the thresholds for ASML
are provided in Appendix G. Settings of h and α are also varied separately for all the levels of
aggregation. Settings of h are varied between 1 and 10. Settings of α are varied between 0 and 0.5.
The total number of signals is the sum of the signals generated for all levels. The same number of
signals can be obtained with different compositions of signal types, resulting in different amounts of
value explained. Per number of signals the maximum value explained is found, to find the optimal
settings resulting in that specific number of signals, and plotted. Figure 7.1 shows the maximum
value explained as a percentage of the total influx per number of signals for each method on average
over the five weeks.
The graph represents the efficient signaling frontier. It is to the company to decide what balance
of signaled value and workload due to signals they want to achieve, and set h or α accordingly to
reach a point on this efficient frontier. In principal, this can be viewed as a cost-benefit trade-off.
The cost-neutral point for each company would be where marginal costs equal marginal benefits.
Marginal costs is built-up of additional full-time equivalents (FTE) that need to be spent to solve
the influx. Marginal benefits can be found as the tangent to the efficient frontier and represents
the influx value of the next signaled influx.
Figure 7.1: Efficient frontier of value explained per number of signals, comparing TSF and SPC
From these averages, it is not clear that one method strongly outperforms the other in terms of
signaling capability: on average TSF outperforms SPC on explaining 0.3% additional value per
37
signal. However, plotting the averages might not be the most appropriate method to draw this
conclusion, since depending on the data, one of the methods may start to outperform the other
over time. Appendix G shows the results for the two methods over the different weeks separately.
For the case of ASML no clear outperformance of the dynamic method over SPC is found. This
can be explained by the variability of the data, where changeover probabilities between two weeks
are high.
7.3.2 Additional considerations
When data show a strong trend, SPC thresholds will become outdated quickly. TSF on the other
hand adjusts the norm, and the number of signals per week remains relatively constant. Simu-
lations were done to test these effects, where 100 values were generated for ten weeks randomly
around an increasing and decreasing average. These 100 values per week were compared against
the dynamic norms of TSF and the static norm of SPC. Indeed, already after 10 weeks 98 out of
100 values were signaled by SPC for the positive trend data and only 6 out of 100 for the nega-
tive trend data, while TSF signal counts remained stable around 30 (for α = 0.2 and h = 1.5).
When data trends are strong, SPC does not provide an efficient threshold and companies instead
need to apply a dynamic threshold. Appendix F shows the data and signal counts of the simulation.
Another relevant consideration for companies is the cost of computing the threshold. Static thresh-
olds are less costly, since they only need to be adjusted once every number of periods. In contrast,
dynamic thresholds require computations each period. Therefore, from a workload or cost perspec-
tive, static thresholds are preferred. Static thresholds also have the advantage of ease of explanation
and communication to relevant users. The number of periods between resetting thresholds of SPC
affects the extent to which thresholds become outdated as well as computation costs. Resetting
thresholds ensures that thresholds become more strict when reductions in influx are realized.
7.4 Conclusion
Tracking signals are generated when an influx exceeds a threshold. They provide a prioritization
of the most significant influxes to adress. Two fields of supply chain management theory provide
inspiration to derive a method to establish thresholds: forecasting and SPC. Which of the two
methods is preferred depends on the influx data and other criteria such as computational efforts.
For high-tech companies, with variable inventory data for which newer observations do not have
enlarged predictive value for new values over older observations, the static threshold based on SPC
is preferred. This threshold can be reset to avoid it to become outdated. For ASML, thresholds
are set according to EQ-based SPC and are recommended to be reset twice a year.
Thresholds are established at different aggregation levels. Lower aggregation signals serve the
purpose to provide additional information regarding overplanned influx drivers and should be ac-
tionable. For ASML, these include signals on influx per EC number, per independent requirement,
per direct requirement and per material number. Higher aggregation level influx signals provide
overview and may lead to discovery of new relevant lower aggregation levels, and to communicate
to management where and when influx creation occurs. Higher level signals are employed on total
influx, influx per business line and per plant.
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8 Contribution of tracking signals
This chapter describes whether and how tracking signals can be used to contribute towards improved
inventory turnover. First, the tracking signals obtained for ASML are described as well as the
insights derived from them in Section 8.1, answering question 6: What overplanned raw inventory
influx is identified by the developed tracking signals? Then, a method is described in Section 8.2 to
secure the contribution of tracking signals by a follow-up process, to answer question 7: How can
monitoring influx using tracking signals contribute to reducing overall overplanned raw inventory
levels? Section 8.3 describes how impact can be enlarged by generalizing methods to other source
flows and inefficient inventories. Section 8.4 discusses costs that need to be considered to implement
tracking signals.
8.1 ASML signals
8.1.1 Description of signals
Data of week 1 to 17, excluding week 2, were used to establish thresholds using the EQ-based SPC
method for ASML. Then, the thresholds were applied to the data of week 20 to 24 to generate
signals (results for week 18 and 19 are not available due to missing data of week 18). Parameter
setting lead to 104 signals over these five weeks, signaling 70.8% of total influx value. Signals were
generated on ECs, top requirements including systems, service requirements, manual reservations
and forecasts, in-between level drops including intercompany orders, production work orders and
planned orders and materials. The proportion of the number of signals obtained per described type
is shown in Figure 8.1.
Figure 8.1: Distribution of types of signals obtained
In addition, several overview signals were generated for weeks 20 to 24 both for locations and
business lines which were explained by the lower level actionable signals in the representative
weeks. Additional insights may be obtained by investigating why a plant or business line faces
above norm overplanned raw influx for consecutive weeks.
8.1.2 Practical evaluation of signals
The higher level signals are valuable by increasing overview of overplanned inventory creation. The
action signals generated however, require further investigation and are evaluated based on two per-
formance criteria. The first criterion involves the correctness of the signals: does the generated
signal indeed flag significant overplanned influx? The second criterion involves the added value of
39
Response received?
No response Signal correct?
Wrong signal Added value?
Correct signal Correct signal & increased awareness
NO 50.0% YES 50.0%
NO 12.5% YES 87.5%
NO 42.9% YES 57.1%
Figure 8.2: Evaluation tree signals
the signal: does the generated signal help to increase awareness with the responsible controller?
MRP controllers of the signaled influx were asked about the overplanned raw influx: whether they
recognize the overplanned inventory, were aware of it and already had actions planned to resolve the
overplanned and stimulate outflux. A response was received for 50.0% of the signals, of wich 87.5%
evaluated the signal as correct, recognizing the overplanned inventory influx. Of these, 57.1% of
the signals added value by creating increased awareness for the MRP controller approached or pro-
viding additional information helping the MRP controller action. In total, 25.0% of the generated
signals was thus evaluated as correct and value adding. This constitutes a value of e0.92∗million
worth of influx over the measuring period.
These tracking signals help material planners, responsible for material availability and overplanned
inventory of raw materials, to identify overplanned influx at the moment it occurs, facilitating
faster decision-making. In addition, the signals help the planner to identify the root cause of the
overplanned creation, which eases the decision for the right outflux creating action: sell back, re-
furbish, transfer, usable with requirements, write-off, partial scrap or complete scrap. Without a
root cause, material planners cannot take an informed action and rather leave the inventory as it
is until they have more information. Therefore, if the right actions follow from the signal, signals
help to improve inventory efficiency by reducing overplanned inventory by e0.92∗million over the
5 measuring weeks. Projecting the same results over a year gives a direct improvement potential
by stimulating increased outflux of e9.6∗ million per year.
This number even understates expected full potential because the evaluation was done during
holidays which lowers the response rate. In addition, controllers are more likely not to respond
when they have no knowledge about the influx, so that improvement potential in the non-response
group might be even higher than for the response portion.
8.1.3 Insights from signals towards proactive inventory management
Signals follow on influx per a certain driver. Starting a dialogue with owners of these driving
processes helps to identify possibilities to change them to prevent influx in the future. Proactive
management by changing root cause processes adds real value because the problem is addressed
and recurrence can be prevented. If this is not immediately possible, these processes are at least
40
identified as risk areas where creation of overplanned inventory is likely. Building a regression
model on these risk areas may identify materials likely to become overplanned in the future so that
solutions can be started before the material even becomes overplanned. For ASML, old production
orders, last digit ECs, planning for refurbishments, planning for systems still in development, phan-
tom demands, old manual reservations, effective out dates of ECs that become within production
planning time horizon and intercompany orders for defect and obsolete materials were found as
root causes and potential risk areas. Here, proactive management involves a proactive approach
towards outflux creation, which improves inventory efficiency by reducing the time the material is
on stock.
Finally, wrong signals led to identification of improvement potential in reporting, where materials
are flagged as overplanned while in reality they are necessary. This is the case for certain business
decision inventories, and processes managed with pools instead of demand rates such as repair
pools. Improved reporting, where overplanned materials are really overplanned, helps to focus
resources to the areas where improvement potential exists.
8.2 Making tracking signals contribute
Setting up a structural improvement process following the tracking signals helps to grasp the full
improvement potential of monitoring. The Eight-Disciplines (8D) method constitutes a structured
problem solving process to correctly and timely solve problems in situations where the cause of
the problem is still unknown. It is also effectively used for process improvements. The 8D method
consists of eight steps, as described in Table 8.1 (Kaplik, Pristavka, Bujna & Vidernan, 2013).
Table 8.1: Eight Disciplines problem solving process
1. Establish a team
2. Describe the problem
3. Contain the situation
4. Analyze the root causes
5. Determine a permanent corrective action
6. Implement and validate the corrective action
7. Prevent recurrence of the problem
8. Appreciate the team
The tracking signal identifies the influxes for which the process is ought to be started. The signal
represents the trigger for the 8D problem solving process. First, a team is established which
gets the responsibility to make sure all steps are followed and to resolve the signaled influx. The
team should include the controller of the level the signal occurred on, and the material planners
of high value or many materials contributing to the influx. Then, the tracking signal provides
the description of the problem to satisfy step 2, including type of signal, the week of the influx,
affected materials, value and quantity of influx and location. In some cases, the situation needs
to be contained to prevent the problem from spreading, in step 3. Step 4 consists of a root cause
analysis of the influx. The signal here facilitates by providing information on what triggered the
overplanned, such as a specific production work order, a service demand, or an EC. The controller
of this trigger is part of the influx team and will often be knowledgeable of the root cause, since
the signal is given at the moment that the influx occurs. Knowing the right root cause allows the
41
material planner to determine the right corrective action and implement it, representing steps 5
and 6. Then, prevention of recurrence of the influx can be established by addressing the root cause
in step 7. This is also facilitated by the fact that the controller of the signal is already on the team.
Finally, step 8 prescribes that the team needs to be recognized for their efforts when the influx is
successfully solved.
8.3 Generalization to other sources and inefficient inventories
This research studies the impact of monitoring existing raw inventory that becomes overplanned.
Rationales and methods of tracking signals can also be applied to influx from other sources. Influx
from existing raw inventory only stipulates on average 20.6% of the total influx. Generalizing to
the other source flows increases the potential impact by monitoring the remaining 79.4%.
Influx from suppliers, both new and as-new flows, can be monitored using the same tracking signals
and methods, although a signal on the order number instead of the material number may be more
effective. Value of these influxes is measured at 0.97 and 0.67 times the influx from existing raw
inventory, respectively. If the same results could be obtained improvement potential would increase
with another e9.3∗ million by monitoring commitment influx and e6.4∗ million by addressing repair
and requalification influx per year, although potential may be different due to additional drivers
applying for these influxes. Influx from factories and customers cannot be monitored on these lower
levels, because not all data is available in the inventory position data snapshots. For these, only
total influx per location or business line can be monitored using the data described in this research.
Similarly, non-MRP relevant influx can only be monitored on the total level per plant location or
business line since no planned requirement exists for them in the data.
In addition to applying the methods to other source flows, rationales can be extracted and applied
to other types of inefficient inventories. At ASML, raw inventory which is on stock for more than
seven days before it is consumed is called inefficient. Influx measuring and monitoring can be
helpful to understand for what requirement a material was delivered, and which did not materialize
in that week. This gives insight into what causes late consumption of the inefficient material.
For this purpose, comparison of the Inventory Dashboards of three weeks is necessary to compare
the requirement of the material before it arrived on stock, before it became inefficient and after
it became inefficient, see Figure 8.3. The total value in inefficient inventory is far larger than
solely overplanned inventory. Applying the measuring and monitoring methods to these additional
inventories even further enlarges the potential positive impact on inventory turnover.
Figure 8.3: Timeline for measuring inefficient inventory
42
8.4 Cost considerations
Benefits of monitoring have been elaborated. However, some costs are involved with implementing
tracking signals to monitor inventory influx. Only when benefits outweigh costs, measuring and
monitoring methods should be implemented. Costs manifest in the form of data retrieval and
storage costs, working hours of team members to follow the improvement process after a signal and
tool-building costs. As explained, at ASML most of the necessary data is already retrieved and
stored for other purposes so that additional costs for the first category are neglectible. Working
hours represent a considerable cost base, and should be considered. However, as long as α is set so
that influx value is larger than the costs associated with solving the influx, benefits should outweigh
costs. The third category represents a one-time investment and can be done following instructions
in this research. No specific software is required that needs to be purchased. Therefore, costs of
tool-building also only expresses in FTEs. When budget exists to improve the inventory turnover
KPI, building a tool to apply measuring and monitoring methods may be a good investment of this
budget.
8.5 Conclusion
Influxes are signaled per different types of drivers, which leads to increased awareness of the influx
in 25.0% of the cases. Setting up a structural improvement process following the signal makes sure
that a team becomes responsible of the influx and researches the root cause of the influx. The
team then determines a corrective action to facilitate outflux and if possible prevents recurrence
by addressing the root cause. If prevention is not possible, risk areas are identified based on
which a predictive model can be built to create outflux before the overplanned influx occurs. The
designed tracking signals are expected to impose a direct inventory reduction of e9.6∗ million per
year, excluding the potentially even larger indirect effects through prevention, thereby improving
inventory turnover. Enlarging the scope to other source flows or inefficient inventory leads to even
larger contributions.
43
9 Conclusion and recommendations
9.1 Conclusion
This report studies inventory inefficiencies in the high-tech industry due to overplanned raw in-
ventories. These represent a problem, impacting financial and operational performance. Although
reducing existing overplanned inventory helps to improve inventory turnover, proactive manage-
ment of overplanned inventory, where influx is prevented before it occurs, is preferred. To achieve
this, a data-driven approach was followed where influx was first measured, and then monitored to
investigate how this can help to improve inventory turnover, answering the main research question:
How can influx monitoring using tracking signals contribute to improved inventory turnover by
reducing overplanned raw inventory levels for high-tech companies?
Influx measuring was done from two angles. First, overplanned influx arrives from different supply
chain sources and a method to measure influx per source was invented. Then, one source was ana-
lyzed on a level deeper. A method to measure the influx of this source per driver was introduced.
Together, these measuring methods allow companies to gain insight into where in the supply chain
overplanned inventory is created, and what organizational process creates it. These insights can be
presented to management, informing discussions regarding where to focus prevention initiatives.
Influx monitoring was set-up using tracking signals, which identify when tracked influxes exceed a
certain threshold, based on SPC or TSF methods. Signals prioritize the most significant influxes,
and provide additional information regarding the timing, materials, location, amount and reason
of the influx, enabling the search for the root cause. This facilitates the right corrective actions
at the moment the influx occurs. In addition, information in the signals opens dialogues with
process owners to change the process that was the root cause of the influx, preventing recurrence,
or identify indicators of likely overplanned creation. At ASML, monitoring influx from existing
non-overplanned raw inventory contributed to increased awareness for 25.0% of the signals, mean-
ing the signal added value towards outflux creation. This constitutes a direct reduction potential
of e9.6∗ million of inventory a year, excluding the even larger potential from recurrence prevention
by addressing root causes. Setting up a structural improvement process following the signal assigns
responsibility of influxes to teams and helps to grasp the most of that potential. These results
were obtained by solely measuring and monitoring influx from existing raw inventory that becomes
overplanned. The rationales of influx measuring and monitoring can be applied to more source
influxes, and other types of overplanned inventory, such as raw stock that surpasses its norm of
days on hand, to further enlarge the positive contribution to inventory turnover.
The steps that follow after measuring and monitoring to improve inventory turnover are summarized
in Figure 9.1. Influx measuring and monitoring contribute to improved inventory efficiency by
stimulating outflux of overplanned inventory and providing insights to focus reduction initatives
and prevent overplanned inventory from being created in the future by improving the root cause
process. When prevention is not possible, monitoring helps to identify risk areas based on which a
predictive model can be built to start outflux initiatives before the material becomes overplanned,
leading to proactive outflux. Influx measuring and monitoring thus comprise the building blocks
towards proactive inventory management in more than one way: by directly improving driving
processes preventing recurrence and by identifying indicators to predict likely overplanned creation
44
leading to proactive outflux. The expected roadmap as introduced in Section 1.5 was adjusted
to reflect new insights regarding how influx measuring and monitoring contribute to improved
inventory turnover.
Snapshots Measuring MonitoringPredictionbased on indicators
Outfluxcreation
(proactive)
Outflux creation (reactive)
Prevention
(proactive)
Figure 9.1: New roadmap to improve inventory turnover
9.2 Recommendations
Several recommendations for ASML to improve inventory turnover by reducing overplanned raw
inventory follow from this research. Some can be implemented directly. Others represent recom-
mendations to continue the research.
1. Improve processes identified that drive overplanned inventory
The identification of sources from which influx arrives and the expert interviews about why they
occurred already provide insights ASML is recommended to act on. For instance, the inspection
process unnecessarily creates overplanned inventory: inventory is placed out of scope of MRP and
a new supply is triggered which arrives overplanned when the inspected inventory turns out usable.
If the inventory instead is sent for repair or requalification to the supplier, this arrives overplanned
when it returns because the new supply already satisfies the original demand. Another process
bound to induce overplanned creation represents refurbishments. Because ASML does not store
the exact configurations of machines, it is unknown what is needed for the refurbishment, and
therefore production asks for all most likely materials, of which the unneeded ones might return
overplanned. Similarly, in the case of field upgrades or repairs, the service engineer does not know
the exact configuration of the customer machine, and is, due to expensive customer-downtimes, in-
duced to request more materials than actually necessary, thus creating overplanned. The diagram
in Figure 3.2 already shows possible processes to address to decrease overplanned raw influx.
2. Ensure data consistency
Practical evaluation revealed some incorrect signals where materials were reported as overplanned
while these were in reality planned manually using rates not recorded. ASML is recommended to
incorporate these demand rates so that overplanned inventory in the Inventory Dashboard reflects
the true problem. In addition, some problems occurred with missing data. All analyses described
in this research depend on data, if data consistency cannot be ensured, a data-driven approach to
45
overplanned inventory is not reliable.
3. Measure influx of overplanned raw inventory per source and driver.
Measuring influx provides insight on the dynamics of overplanned inventory showing how much
is created every week, instead of just measuring the total amount of overplanned inventory as
a static fact. The measurements of influx per source help to create overview where in the sup-
ply chain overplanned influx is created. In addition, the measurements of existing inventory that
becomes overplanned per driver give insight on what organizational functions or processes drive
overplanned creation. These measurements can be presented to management. This allows for dis-
cussions regarding how to focus prevention initiatives based on data insights. They can also be
used to breakdown the inventory KPI so that reporting provides more information regarding where
overplanned inventory comes from or what causes it. The measurements in the case of ASML
provide not one major focal point, but reduction potential majorly seems to exist for influx from
existing non-overplanned inventory, commitment and factory returns which should be addressed
first. Changes in customer orders and engineering changes are the main contributors of existing
inventory becoming overplanned, followed by service requirements and changed forecasts. Different
supply chain management departments are responsible for supply chain effects of these types of
requirements, and should be addressed.
4. Track the stock age
To improve future measuring, ASML should add a stock age field that really represents the stock
age of a material rather than the time it has not been moved. This means that stock age should not
be reset with internal movements. In addition, the stock age value is now cutoff at 104 weeks, which
gives a flawed image of the real stock age. Furthermore, the stock age field is used as an efficiency
field meaning that it is counted differently for different types of materials. For this research, this
latter did not result in problems, but if the research is to be applied in a larger scope it might
become a problem.
5. Keep track of hierarchical requirement levels
Driver quantification is now based on indicators because changes cannot be cascaded through the
BOM using the existing data fields which only store the direct and top requirement. ASML is
recommended to store more requirement levels in the data. Then, more direct links could be made,
improving the reliability of driver quantification. Otherwise, if ASML wants to apply the current
measurement model, it is recommended to further tune the weights of the proposed model in this
research on more data.
6. Monitor influx by tracking signals with static thresholds established by EQ-based
SPC
Tracking signals should be designed as described in this research. Thresholds should be set individ-
ually for total influx, influx per plant, influx per business line, influx per engineering change, influx
per top requirement, influx per requirement order and influx per material number. These thresholds
should be based on empirical quantiles, and reset twice a year to have low computing costs but en-
sure that the thresholds do not become outdated. When an influx exceeds the threshold, a signal is
generated upon which action is required. Signals help to recognize influx at the moment it occurs,
and provide a prioritization regarding which influxes are most significant to gain the maximum
46
impact with the least effort required. In addition, signals provide information on the what, where,
when, how much and why of the influx enabling the search for the right persons to solve the problem.
7. Create ownership of influx and implement a structural improvement process to
follow signals.
Make a team responsible to solve each signaled influx. This makes that real ownership of a specific
influx is felt. This is a new aggregation level to focus reduction initiatives instead of total inventory
or individual materials. The additional information in the signals, and the established team aid
root cause analysis. A structural improvement process ensures that full improvement potential is
realized by describing steps that the responsible team needs to go through. The 8D process method
is known within ASML and should ensure that the right corrective actions are taken and that root
causes are addressed to prevent recurrence.
8. Build a tool to be operated by material planners with information per overplanned
material.
Although it is recommended to solve influxes in teams, at this moment overplanned outflux cre-
ation is the responsibility of material planners and happens at the material level. Until influx
investigation is really implemented, outflux creation by material planners would be facilitated if
the information that is available by measuring influxes would be available to them. Per material,
influx measurement shows when it became overplanned due to what requirement or EC, at what
location and in what quantity. Transferring this information in a tool makes that material planners
have more information to make the right corrective decision to improve inventory turnover in the
short-term. This new tool needs to be aligned and integrated with currently existing information
and tools.
9. Research the use of causal methods based on risk indicators to predict overplanned
influx
The practical evaluation of the signals provided insight on risk areas that exist in which materials
are more likely to become overplanned. These include old production orders, last digit engineering
changes, planning for refurbishments, planning for systems which are still in development, phan-
tom demands, reaching the phasing-in date of the EC (within time horizon production planning)
and intercompany orders for defect and obsolete materials. More risk areas will be identified if
monitoring is continued. Although these should be addressed to aim for prevention, this will not
directly be the case and future research could investigate whether causal methods can be applied,
as in Section 7.2.1, to predict overplanned creation based on these variables so that actions can al-
ready be driven even before the materials actually become overplanned and thus influx is prevented.
10. Apply methods and rationales of measuring and monitoring to more influx sources
and types of inefficient inventory
Methods can be directly transferred to monitor influx from commitment and from repair and requal-
ification, with an adjustment of the lowest level signal being per order number instead of material
number. For factory returns, customer returns and influx from non-MRP relevant statuses this is
not possible, and only total influx and influx per business line or location can be monitored. The
rationale can be applied to inefficient stock by tracking the requirements two weeks back in time
before the material was delivered and monitor based on these requirements. The generalizability
47
of the methods to these other buckets greatly increases the potential impact measuring and moni-
toring using tracking signals can have on improving inventory turnover.
11. Measure outflux
While this research solely focused on influx measuring, it is recommended to track influx materi-
als for a longer period and apply the same methods to measure whether the influx is followed by
outflux within a short period of time or that the overplanned materials remain. Combining influx
and outflux measurements provides an indication of what sources and drivers cause the most severe
overplanned inventory influx. That is, the ones that are not followed by outflux but instead remain
infinitely. This might give a different indication of focal areas that need to be addressed first for
prevention.
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10 Reflection
This chapter reflects on the performed research. Section 10.1 describes how the research contributes
to academics. Section 10.2 describes the limitations of the research and Section 10.3 presents ideas
for future research following the current research.
10.1 Academic contribution
The literature study performed as preparation for this research identified a research gap in describ-
ing drivers for E&O inventory specifically for the high-tech industry (Oomen, 2017). Overplanned
inventory is a subset of E&O inventory. Consequently, the drivers found in this research for over-
planned inventory also drive E&O inventory. Measurement showed that overplanned inventory
comes from different sources, where qualitative analysis made clear that different internal func-
tions, as well as coordination issues, induce overplanned inventory, like described by Crandall and
Crandall (2003). Measurement of influx per overplanned type showed that different functions are
responsible for different types of overplanned inventory: excess, obsolete or business decisions.
Customer order changes, service requirement changes, forecast changes, safety requirement drops,
manual requirement drops and engineering changes were identified as drivers of existing inventory
to become overplanned, as well as drops in requirements for development projects. The research
thus contributes by providing a case study in the high-tech industry for drivers of E&O inventory.
Moreover, the research was inspired by supply chain management fields of research including de-
mand forecasting and statistical process control and applied methods from these streams to in-
vestigate their potential to provide thresholds to monitor inventory flows. It establishes a new
overlapping domain between these three research streams. Static thresholds based on SPC were
found to be preferred for ASML, but if companies’ data show strong trends, dynamic thresholds
based on demand forecasting may be preferred.
Furthermore, as the research falls within design science and is explorative in nature, the academic
outcome represents a technological rule for companies to follow to obtain desired results. This
research introduces methods to measure inventory influx based on inventory position snapshots, as
well as methods to design tracking signals to monitor these influxes. High-tech companies can learn
from this and apply it to their own inventory data to gain insight on inventory flows and improve
inventory turnover.
Finally, companies in other industries can benefit from the research. Although results would differ,
as long as their inventory is relatively slow-moving and highly valued, methods can still be applied.
Slow-moving inventory is necessary because the methods are based on snapshot analysis, where
movements between two snapshots are not observed. If inventory moves fast, frequency of snapshots
would have to be high to still obtain accurate results. This greatly enlarges data retrieval and
storage costs and other methods might be more applicable. The problem of overplanned inventory
is also less likely to exist for fast-moving inventory. The high value requirement is necessary because
this makes that with relatively few signals, a large amount of influx can be explained. Low value
materials would flatten the efficient frontier as in Figure 7.1 which would result that investigating
an additional signal would quickly not outweigh the additional costs.
49
10.2 Limitations
The current research is subject to several limitations. The most important one comprises data
availability. When the research was started, no historical data for the project was available. There-
fore, data was only collected from week one of the research, making the total dataset add to 23
weeks of data. Although this is adequate to explore and obtain insights, it is not enough to find
significant differences, trends or cycles, especially given the relatively long supply chain inherent
to the high-tech industry. In addition, since data was needed to set-up thresholds, evaluation of
tracking signals could only be done on 5 weeks of data. Due to the variability of the sizes of the
influx from each source, more weeks of data would provide a more accurate view of the improvement
potential.
In addition, no complete overview of ECs and materials affected by them could be found. There-
fore, analysis based on indicators helped to assign value to being engineering driven. However, the
tracking signals do not incorporate this indicator score because these cannot be linked to an EC
number, and instead only signal the ECs to which a large amount of materials can be linked based
on the lists that are obtained. Therefore, the signals on ECs are likely understating the total influx
driven by each EC.
Another limitation evolves from the fact that no unique tracking ID exists for inventory but that
MRP instead runs a random allocation of materials to requirements. If only one supply and one
demand change, tracking is possible. However, if there are multiple supply changes and demand
changes, which is often the case for some of the more fast-moving materials, it is not possible to
know what previous requirement caused the overplanned. In these situations, the overplanned is
allocated proportionally to both changed requirements. As a result some flows are overstated while
others are understated.
Furthermore, as described before, the research is subject to snapshot bias: movements within the
week between the creation of two datasets are missed. Therefore, not all influx can be explained
using the measurement methods provided. Due to the slow-moving nature of overplanned inventory
the impact is limited.
Finally, implementation of the research is out of scope. However, the real benefits from tracking
signals depend on how well the responsible team uses the signals to guide corrective actions and
address the root cause. This cannot be measured currently.
10.3 Future research
Several directions for future research follow from the current research. First, although measuring
and monitoring contributes to improved inventory efficiency, it is still rather reactive in the sense
that actions are only triggered after overplanned is created. Tracking signals could be placed on
root cause indicators to predict inventory influx and start outflux creating actions before the over-
planned even occurs, representing the second direction in Figure 9.1. These indicators are found in
signal investigation. Future research can contribute by developing methods for this and applying
them to see results.
50
Second, future research can contribute by researching how the methods can be applied to more
influx sources, and other types of inefficient inventory. This further enhances the potential benefits
that can be reaped from influx measuring and monitoring.
Third, the nature of the current research is rather explorative, opting to identify methods to achieve
the desired results. Future research could reapply these methods to other case studies to validate
effectiveness for other companies and further ground them. If for these companies more data is
available, more sophisticated methods might be available and appropriate. This research can serve
as a benchmark.
Finally, more research on drivers of overplanned or E&O inventory in the high-tech industry is
called for. The current research provides some exploratory insights, but establishing a causal effect
is not possible due to lack of data. More insight on this topic would help high-tech companies to
investigate these drivers for themselves, use them as guidelines for possible root causes, and start
addressing them.
51
11 Bibliography
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tools and techniques. Morgan Kaufmann.
54
A Identification of source types
Source type is not a field existing in ASML’s inventory data currently, and instead needs to be
created based on other data properties, as decribed in Table B.1. The column source describes the
source to be identified. Data source describes what data files are necessary to identify the source.
Properties lists the filters that need to be applied for this data.
Table A.1: Characteristics to find source types influx for Chapter 4
Source Data Source Properties
Overplanned raw: TBL: Inventory Dashboard Object type EA = ’Stock’
Dashboard main category = ’Raw’
Sloc indicator = 0
Stock status 6= ’Blocked
Stock Type 6= ’CC (Consignment stock
at customer site)’
Requirement EA type = -1
Non-overplanned raw TBL: Inventory Dashboard Object type EA = ’Stock’
Dashboard main category = ’Raw’
Sloc indicator = 0
Stock status 6= ’Blocked
Stock Type 6= ’CC (Consignment stock
at customer site)’
Requirement EA type 6= -1
Intercompany shipment TBL: Inventory Dashboard Object type EA = ’Purchase document
overplanned schedule line’
Dashboard main category = ’Raw’
OR
Category = ’Stock transport orders’
Requirement EA type 6= -1
Intercompany shipment TBL: Inventory Dashboard Object type EA = ’Purchase document
non-overplanned schedule line’
Dashboard main category = ’Raw’
OR
Category = ’Stock transport orders’
Requirement EA type = 1
55
Source Data Source Properties
Non-MRP relevant TBL: Inventory Dashboard Object type EA = ’Stock’
Dashboard main category = ’Raw’
One of the following:
- Sloc indicator 6= 0
- Stock status = ’Blocked’
Commitment TBL: Inventory Dashboard Category = ’Commitment’
Repair TBL: Inventory Dashboard Stock label = ’Materials provided to vendor’
Sub/Repair = R/blank
TBL: Demand/No Demand IM 12NC = first 12 positions Order number
Requalification TBL: Inventory Dashboard Stock label = ’Materials provided to vendor’
Sub/Repair = R/blank
TBL: Demand/No Demand IM 12NC 6= first 12 positions Order number
Factory returns SAP movements Outgoing bookings: 261, z61, 221
Return bookings: 262, z62, 222
12NC does not end with USP or FSD
Customer returns TBL: Inventory Dashboard Stock type = ’CC (Consignment stock at
customer site)
SAP Movements Outgoing bookings: 261, z61, 221
Return bookings: 262, z62, 222
12NC ends with USP or FSD
56
B Overview of assumptions
Several assumptions are necessary for the research. This appendix discusses the validity of each
assumption for ASML and how results would be impacted if the assumption is not valid.
Assumptions section 4.2
1. Items with the same material number, plant and stock age are interchangeable.
This assumption is valid for ASML because for ASML these items are essentially the same.
This assumption is necessary because MRP allocations change. Interest is not in how alloca-
tions for specific materials change, but rather what changes over all. If these items are not
interchangeable for a company, a different key is needed.
2. Items with the same material number, plant and order number are interchangeable.
Meaning and validity are similar to the previous assumption.
3. Data reporting considers items to be consumed using a FIFO strategy: items that were
stocked first are reported as moved out first.
This assumption makes analysis more difficult, since it makes that allocations of inventories
to requirements can switch per week depending on the scheduled date of the requirement.
Therefore, overplanned inventory of stock age x, can be driven by a drop in requirements for
all requirements that were allocated to inventories with stock age larger than x. This FIFO
consumption is the case at ASML. If allocations are not changed based on a FIFO principle
but remain constant, requirement links can be made more directly. If the allocations do
change but without a pattern, such as FIFO, instead the total set of requirements allocated
to inventories with all stock ages can be the driver of an influx.
4. Differences per material-plant-stock age combination in overplanned raw inventory are due
to differences for the same combination elsewhere: inventory does not magically appear and
disappear.
This is necessary for matching influxes to outfluxes and refers to flow conservation. This
assumption is valid for ASML except for movements that happen between two snapshots. If
this assumption does not hold, the measuring technique as in this research cannot be applied
and instead focus should be on improving inventory reporting.
5. Inventory position changes that happen in between two snapshot measurement points do not
significantly change the overall results.
The previous assumption does not hold for these changes, and thus measurement results
are only valuable if the impact of this is small enough. Because overplanned inventory is
slow-moving, impact is not expected to be large, especially not for large value materials.
Also, as explained in section 4.4, only 13.5% of influx could not be explained using the
measuring method which is the maximum impact of the movements in between snapshots.
The assumption is thus valid for ASML. In industries where inventory is more fast-moving,
snapshots have to be taken more frequently than on a weekly basis to still show the flows of
inventory. This increases the costs of analysis.
Assumptions section 4.2 data consistency
1. MRP systems matching supply and demand work correctly.
57
2. The creation of the Inventory Dashboard and calculation of stock age work as intended.
3. Snapshots accurately reflect inventory positions.
4. Master data is well-maintained.
All analyses are based on MRP results, the Inventory Dashboard and master data. If these are
not correct, the results of the methods are unreliable. Few problems with data consistency were
incurred during the project at ASML, such as missing data in week 2 and week 18. Data sources
are validated each week by an inventory expert before being used for the methods in this project.
At ASML, data validity is okay, but room for improvement exists. If data sources do not work
correctly, all analyses based on data are invalid and instead focus should be first on correcting data
validity.
Assumptions section 6.2
1. The indicators identified have explanatory value regarding whether an influx is demand or
engineering driven.
This assumption is necessary for the driver measurements to make sense. Intuitively, all
indicators have explanatory value. Weights analysis shows that all indicators have value
except for the remaining requirement orders. The method is still valid. If indicators provide
no value, a different method needs to be applied for driver quantification.
2. Effects of ECs are mostly observed within one week after implementation.
This assumption is valid because in the standard situation the effect is calculated at the first
MRP run after implementation, which is within one week. Data also shows this effect, see
Figure B.1. If this is not the case, the method needs to be applied with a different value for
λ.
3. The probability that an influx is driven by an EC decreases with a constant rate over time
after the completion date of the EC.
This assumption is necessary to apply an exponential distribution to calculate the InfluxECt,m,p.
Figure B.1 shows the amount of influxes that can be linked to an EC per number of weeks
since completion of that EC, counted over the weeks that quantitative analysis of drivers was
done. The exponential distribution seems indeed appropriate. If this is not the case, the score
needs to be calculated over the weeks since completion with a different distribution.
4. The individual influxes found in one week are independent of influxes in other weeks.
This assumption is necessary to be able to train and validate the model in section 6.3 on two
sets of data consisting of individual influxes in two weeks. This assumption is not completely
valid for ASML, because in the case of ECs, influx of a material affected by an EC is not
independent of the influx of that same material in another week because they are driven by
the same cause. In addition, influxes are not independent because when an item becomes
overplanned in one week, it cannot become overplanned again in the next week because it
is already overplanned. However, since the pool of possible material-plant combinations to
become overplanned is so large, the majority of the influxes is still new and not depending on
influxes in other weeks. Therefore, the assumption does not lead to problems for analysis. This
assumption only affects the possibility of training and evaluating on two weeks of consecutive
58
Figure B.1: Number of influxes linked to an EC per number of weeks since completion
influx data. Otherwise, influxes should be randomly divided over two new sets: the training
and evaluation set.
59
C Measurement of influx per source based on snapshots
A more detailed description of the measuring method explained in section 4.3 is provided. Notations
are introduced in table C.1. Following the below steps allows companies to replicate the method
to measure influx per source.
Table C.1: Notations measuring source flows
t Week t ∈ T = {1, 2, . . . }m Material m ∈M = set of all materialsp Plant p ∈ P = set of all plantsx Stock Age x ∈ X = {0, 1, ..., 104}o Order number o ∈ O = set of all order numbersUSP Set of all materials with extension USPFSD Set of all materials with extensions FSDInfluxt,x,m,p Overplanned influx in t for m at p with xI OPt,x,m,p Overplanned raw inventory quantity in t for m at p with xI rawt,x,m,p Non-overplanned raw inventory quantity in t for m at p with xI nonMRPt,x,m,p Non-MRP relevant inventory quantity in t for m at p with xI CCt,x,m,p Inventory customer consignment stock in t for m at p with xIO commitmentt,o,m,p Inventory on order for commitment in t for m at p with oIO repairt,o,m,p Inventory on order for repairs in t for m at p with oIO requalificationt,o,m,p Inventory on order for upgrades in t for m at p with oReturn bookingst,m,p Quantity of return bookings between snapshot of t− 1 and t of m at pOutgoing bookingst,m,p Quantity of outgoing bookings between snapshot of t− 1 and t of m at pOutflux rawt,x,m,p Outflux from non-overplanned raw inventory in t of m at p with xOutflux nonMRPt,x,m,p Outflux from non-MRP relevant raw inventory in t of m at p with xOutflux CCt,x,m,p Outflux from customer consignment stock week in t of m at p with xOutflux commitmentt,o,m,p Outflux from commitment in t of m at p with oOutflux repairt,o,m,p Outflux from repairs in t of m at p with oOutflux requalificationt,o,m,p Outflux from upgrades in t of m at p with oOutflux returnst,m,p Outflux week in t of m at p of all returns bookedOutflux factoryt,m,p Outflux from factory inventory in t of m at p with oOutflux customert,m,p Outflux from customer inventory in t of m at p with oOutflux RRt,m,p Outflux from repair and requalification in t of m at pTotal outfluxt,m,p Outflux from all flows in t of m at pInflux nonMRPt,m,p Influx from non-MRP relevant stock in t of m at pInflux commitmentt,m,p Influx commitment in t of m at pInflux RRt,m,p Influx from repair or requalification in t of m at pInflux customert,m,p Influx from customers in t of m at pInflux nonMRPt Total influx from non-MRP relevant in tInflux commitmentt Total influx from commitment in tInflux RRt Total influx from repair or requalification in tInflux factoryt Total influx from factories in tInflux customert Total influx from customers in tV alue rawt Value of Influx rawt in euroV alue nonMRPt Value of Influx nonMRPt in euroV alue commitmentt Value of Influx commitmentt in euroV alue RRt Value of Influx RRt in euroV alue factoryt Value of Influx factoryt in euroV alue customert Value of Influx customert in euro
1. Find influx week t into overplanned raw inventory, comparing snapshots week t− 1 and week
t for each material m at a plant p in week t with a stock age of x and the same material at
the same plant in week t − 1 with a stock age of x − 1 measured in quantities. Influx exists
when the quantity in week t is larger than the quantity in week t− 1. All inventory existing
with a stock age of 0 constitutes influx.
60
Influxt,x,m,p =
{ [I OPt,x,m,p − I OPt−1,x−1,m,p
]+if x > 0
I OPt,x,m,p if x = 0∀ x,m, p; t > 1
2. Find outflux week t from non-overplanned raw inventory, non-MRP relevant inventory and
customer consignment stock, comparing snapshots week t− 1 and week t for each material m
at a plant p in week t with a stock age of x and the same material at the same plant in week
t − 1 with a stock age of x − 1 measured in quantities. Outflux exists when the quantity in
week t− 1 is larger than the quantity in week t.
Outflux rawt,x,m,p =[I rawt−1,x−1,m,p − I rawt,x,m,p
]+∀ m, p; t > 1;x > 0
Outflux nonMRPt,x,m,p =[I nonMRPt−1,x−1,m,p–I nonMRPt,x,m,p
]+∀ m, p; t > 1;x >
0
Outflux CCt,x,m,p =[I CCt−1,x−1,m,p–I CCt,x,m,p
]+∀ m, p; t > 1;x > 0
3. Group the outflux for week t per material-plant combination.
Outflux rawt,m,p =∑
x≥0Outflux rawt,x,m,p ∀ m, p, t > 1
Outflux nonMRPt,m,p =∑
x≥0Outflux nonMRPt,x,m,p ∀ m, p; t > 1
Outflux CCt,m,p =∑
x≥0Outflux CCt,x,m,p ∀ m, p; t > 1
4. Find outflux week t from commitment and repairs, comparing snapshots week t− 1 and week
t for each material m at a plant p in week t with an order number o and the same material at
the same plant in week t with the same order number measured in quantities. Outflux exists
when the quantity in week t− 1 is larger than the quantity in week t.
Outflux commitmentt,o,m,p =[IO commitmentt−1,o,m,p–IO commitmentt,o,m,p
]+∀ o,m, p; t > 1
Outflux repairt,o,m,p =[IO repairt−1,o,m,p–IO repairt,o,m,p
]+∀ o,m, p; t > 1
5. For requalifications, the returning material number m does not need to be the same as the
material number m′ in the repair/requalification order. The material number m associated
with the upgraded item has to be searched in the order.
Outflux requalificationt,o,m,p =[IO requalificationt−1,o,m′,p − IO requalificationt,o,m′,p
]+∀ o,m, p; t > 1
6. Group the outflux for week t per material-plant combination.
Outflux commitmentt,m,p =∑
o∈O Outflux commitmentt,o,m,p ∀ m, p; t > 1
Outflux RRt,m,p =∑o∈O Outflux repairt,o,m,p +
∑o∈O Outflux requalificationt,o,m,p ∀ m, p; t > 1
7. Find outflux week t from additional data for customer returns and factory returns per
material-plant combination. The returns are the quantity of bookings returned, return bookingsm,p,
per material-plant, minus the bookings outgoing,outgoing bookingsm,p, for that material-
plant during week t − 1. Factory returns include materials where the material does not
contain USP or FSD, customer returns are the materials that do contain this suffix.
61
Outflux returnst,m,p =[Return bookingst,m,p −Outgoing bookingst,m,p
]+∀ m, p; t > 1
Outflux factoryt,m,p =
{Outflux returnst,m,p if m /∈ USP/FSD0 if m ∈ USP/FSD ∀ m, p; t > 1
Outflux customert,m,p =
{Outflux CCt,m,p if m /∈ USP/FSDOutflux CCt,m,p +Outflux returnst,m,p if m ∈ USP/FSD
∀ m, p; t > 1
8. Match influx into overplanned raw inventory week t with outflux of non-overplanned raw
inventory
Influx rawt,x,m,p = min(Influxt,x,m,p, Outflux rawt,x,m,p) ∀ m, p; t > 1;x > 0
9. Match influx into overplanned raw inventory week t of stock age 0 with outflux of other flows
and allocate influxes proportionally. The influx is the minimum of the portion of the influx
allocated to the outflux, and the total outflux.
Total outfluxt,m,p = Outflux nonMRPt,m,p +Outflux commitmentt,m,p +
Outflux RRt,m,p +Outflux factoryt,m,p +Outflux customert,m,p ∀ m, p; t > 1
Influx nonMRPt,m,p =
min(Outflux nonMRPt,m,pTotal outfluxt,m,p
× Influxt,0,m,p, Outflux nonMRPt,m,p) ∀ m, p; t > 1
Influx commitmentt,m,p =
min(Outflux commitmentt,m,p
Total outfluxt,m,p× Influxt,0,m,p, Outflux commitmentt,m,p) ∀ m, p; t > 1
Influx RRt,m,p = min(Outflux RRt,m,pTotal outfluxt,m,p
× Influxt,0,m,p, Outflux RRt,m,p) ∀ m, p; t > 1
Influx factoryt,m,p =
min(Outflux factoryt,m,pTotal outfluxt,m,p
× Influxt,0,m,p, Outflux factoryt,m,p) ∀ m, p; t > 1
Influx customert,m,p =
min(Outflux customert,m,pTotal outfluxt,m,p
× Influxt,0,m,p, Outflux customert,m,p) ∀ m, p; t > 1
10. Find total influx into overplanned raw inventory week t per flow by summing over all materials,
plants and stock ages for all t > 1.
Influx rawt =∑104
x=1
∑m∈M
∑p∈P Influx rawt,x,m,p
Influx nonMRPt =∑
m∈M∑
p∈P Influx nonMRPt,m,pInflux commitmentt =
∑m∈M
∑p∈P Influx commitmentt,m,p
Influx RRt =∑
m∈M∑
p∈P Influx RRt,m,pInflux factoryt =
∑m∈M
∑p∈P Influx factoryt,m,p
Influx customert =∑
m∈M∑
p∈P Influx customert,m,p
11. Find total influx into overplanned raw inventory week t per flow in euro value for all t > 1.
V alue rawt = Influx rawt × Standard cost price in euroV alue nonMRPt = Influx nonMRPt × Standard cost price in euro
V alue commitmentt = Influx commitmentt × Standard cost price in euroV alue RRt = Influx RRt × Standard cost price in euro
V alue factoryt = Influx factoryt × Standard cost price in euroV alue customert = Influx customert × Standard cost price in euro
62
D Driver quantification
A more detailed description of how to obtain the indicators as described in section 6.2 is provided.
Notations are introduced in table D.1. Following the below steps allows companies to find scores
for the described indicators.
Table D.1: Notations driver quantification
t Week t ∈ T = {1, 2, . . . }m Material m ∈M = set of all materialsp Plant p ∈ P = set of all plantste Week engineering change is entered in SAP and marked as completeInfluxt,m,p Overplanned influx from existing raw inventory in t for m at pV aluet,m,p Value associated with Influxt,m,pInfluxDC
t,m,p Influx quantity driven by demand in t for m at p
InfluxECt,m,p Influx quantity driven by engineering in t for m at p
Rt,m,p Direct requirements listed in t for m at pDropst,m,p Count of top requirements allocated to influxt,m,p in t− 1 that
completely disappear in t with requirement date after tTRt,m,p Total count of top requirements allocated to Influxt,m,p in tRemainst,m,p Count of requirement orders allocated to Influxt,m,p in t− 1 that
remain allocated to m at p in tROt,m,p Total count of requirement orders allocated to Influxt,m,p in t− 1It,m,p Total inventory in t for m at pDemand scoreTR
t,m,p Demand score for influxt,m,p based on top requirements
Demand scoreROt,m,p Demand score for influxt,m,p based on requirememt orders
Demand scoreOPt,m,p Demand score for influxt,m,p based on overplanned percentage
Demand scoreSSt,m,p Demand score for influxt,m,p based on existence of safety stocks
Demand scoret,m,p Total demand score for influxt,m,pEngineering scoreEC lists
t,m,p Engineering score for influxt,m,p based on EC lists
Engineering scoreROt,m,p Engineering score for influxt,m,p based on requirement orders
Engineering scoreOPt,m,p Engineering score for influxt,m,p based on overplanned percentage
Engineering scoret,m,p Total engineering score for influxt,m,pV alueDC
t Influx value driven by demand changes in tV alueEC
t Influx value driven by engineering changes in t
Scores are obtained per Influxt,m,p, where Influxt,m,p =∑104
x=0 Influxt,x,m,p. ∀m, p; t > 1. Indi-
cators are obtained for all m at p in t when Influxt,m,p > 0.
Demand changes
Direct Requirements
1. Find the direct requirements in week t− 1 for material m at plant p. If the requirement has
a requirement date, only include requirements with a date after the date the snapshot of t is
taken: Rt−1,m,p
2. Find the direct requirements in week t for material m at plant p: Rt,m,p
3. The influx for material m at plant p in week t that is driven by demand changes is equal to
the minimum of this influx and the outflux of the requirements:
InfluxDCt,m,p = Min(Influxt,m,p, [Rt−1,m,p −Rt,m,p]+)
4. The score based on direct requirements for all m at p if Influxt,m,p > 0 is equal to the portion
of the influx that can be explained by the requirements:
63
Demand scoreDRt,m,p =
InfluxDCt,m,p
Influxt,m,p
Top requirement indicator
1. Add a new field called Requirement : If the item is allocated to a system, this is the WBS
element which is the name of the specific system, otherwise the Top Requirement
2. Find per m at p for which requirements there is outflux. This means the requirement existed
for m at p in t− 1 and is gone for that m at p in t.
3. For requirements that start with VS: these are forecasts for which the name changes when
the scheduled date changes, which is frequently. Therefore, the number of VS requirements
per m at p has to be count in t− 1 and in t. If the same amount remains it is assumed that
the dates have changed and no outflux occurred. These have to be removed from the outflux
list made in step 2.
4. Per outflux requirement: get the total count of materials and assigned quantity allocated to
it in t−1 and in t. If the requirement is completely gone in t it is named a blank. This means
that no materials are allocated to this requirement anymore. Due to FIFO consumption,
allocations can change over weeks thus also have to consider requirements that were allocated
to inventory of m at p with higher stock ages. Only total disappearances contain information
because otherwise influx can still be driven by either requirement change or engineering change
on in-between levels.
5. Count the total number of outflux top requirements in t−1 allocated to all m at p: TRt−1,m,p
6. Include the past requirement date of the outflux requirements. If the date was during the
past week, it is assumed to be consumed instead of dropped, and thus named consumed.
7. Count the number of blanks and subtract the number of requirements consumed to get the
number of drops per m at p between t− 1 and t: Dropst,m,p
8. Calculate the demand score for all m at p if Influxt,m,p > 0 based on top requirement
indicator by dividing the dropped requirements by the total count of requirements in the
week before:
Demand scoreTRt,m,p =
Dropst,m,pTRt−1,m,p
Requirement orders remaining indicator
1. Per m at p with Influxt,m,p > 0, count requirement orders in t− 1: ROt−1,m,p
2. Per m at p with Influxt,m,p > 0, count requirement orders that existed in t − 1 that still
exist in t: Remainst,m,p
3. Calculate the demand score based on requirement orders by dividing the remaining require-
ment orders by the total count of requirement orders in week t − 1. The explanatory power
of the fraction is not expected to be linear. Rather, high fractions provide indication while
low fractions do not. Therefore, the fraction is powered to a factor to obtain the score:
64
Demand scoreROt,m,p = (
Remainst,m,pROt−1,m,p
)factor 1
Effects of different factors to how the fraction is translated to a score are shown in Figure
D.1.
Figure D.1: Translation of fraction value to demand score per factor
Overplanned percentage indicator
1. Per m at p with Influxt,m,p > 0, find quantity of inventory existing in t− 1: It−1,m,p
2. Calculate the demand score based on overplanned influx by dividing the remaining non-
overplanned inventory (It−1,m,p − Influxt,m,p) by the total inventory on stock in week t− 1.
The explanatory power of a fraction is again not expected to be linear. Therefore, similar to
the fraction of requirement order remaining, the overplanned percentage fraction is powered
to a factor:
Demand scoreOPt,m,p = (1− Influxt,m,p
It−1,m,p)factor 2
Existence of safety stock indicator
1. For all m, check whether a safety stock requirement exists in t: RSSt,m
2. Perm at p with Influxt,m,p > 0, calculate the demand score based on safety stock requirement
existence:
Demand scoreSSt,m,p =
{1 if RSSt,m > 0
0 if RSSt,m = 0
Combine all scores to obtain one demand score per Influxt,m,p > 0:
Demand scoret,m,p = Min(
1, Demand scoreDRt,m,p + w1 ×Demand scoreTR
t,m,p + w2 ×
Demand scoreROt,m,p + +w3 ×Demand scoreOP
t,m,p + w4 ×Demand scoreSSt,m
)Engineering changes
Material upgrades
EC direct effect: components can become overplanned due to an engineering change affecting that
component
65
1. Find components c net subtracted from a BOM per EC in ZECRPO list, find date EC was
last changed
2. Find components c upgraded to a newer version per EC in ZECRKO list, find date EC was
last changed
3. Per Influxt,m,p > 0, find last time m was affected by an EC by taking the latest change date
found from step 1 and 2 where m = c. Only include ECs that have status completed: td
EC propagation effect: materials can become overplanned due to an engineering change affecting
a parent.
4. Find the top material each Influxt,m,p was allocated to the week before it became over-
planned.
5. Per top material, find last time it was affected by an EC by taking the latest change date found
from step 1 and 2 where top material = c. Only include ECs that have status completed: ti
6. Find the latest change date of both the direct effect and the propagation effect: te =
max(td, ti). It is expected that the effect is seen the first week after the completion date.
7. Per m at p with Influxt,m,p > 0, calculate the influx per EC based on EC lists using an
exponential distribution with λ equal to 1:
InfluxECt,m,p =
{ 1− (1− e−λ(t−te)) = e−λ(t−te) if te ≤ t1− (1− e−λ(t−td)) = e−λ(t−td) if td ≤ t and te > t
1− (1− e−λ(t−ti)) = e−λ(t−ti) if ti ≤ t and te > t
0 otherwise
Requirement orders remaining indicator
1. Per m at p with Influxt,m,p > 0, count requirement orders in t− 1 : ROt−1,m,p
2. Per m at p with Influxt,m,p > 0, count requirement orders that existed in t − 1 that still
exist in wk t: Remainst,m,p
3. Calculate the engineering score based on requirement orders by dividing the remaining re-
quirement orders by the total count of requirement orders in t − 1. Similar to the demand
score, the explanatory power of the fraction for the engineering score is not expected to be
linear and the same factor is applied:
Engineering scoreROt,m,p = (1− Remainst,m,p
ROt−1,m,p)factor 1
Overplanned percentage indicator
1. Per m at p with Influxt,m,p > 0, find quantity of inventory existing in t− 1: It−1,m,p
2. Calculate the demand score based on overplanned influx by dividing the influx by the total
inventory in t− 1.Similar to the demand score, the explanatory power of the fraction for the
engineering score is not expected to be linear and the same factor is applied:
66
Engineering scoreOPt,m,p = (
Influxt,m,pIt−1,m,p
)factor 2
Combine all scores to obtain one engineering score per Influxt,m,p > 0:
Engineering scoret,m,p =
Min(
1, InfluxECt,m,p + w2 × Engineering scoreRO
t,m,p + w3 × (Engineering scoreOPt,m,p
)
67
E Results sensitivity analysis
A sensitivity analysis was performed to find the effect of different weights and factors on the driver
quantification. Weights were varied between 0 and 1 with steps of 0.1. Factors were varied between
0.5 and 6 with steps of 0.5. The value of λ was held constant at one. The total percentages of influx
that were allocated to be engineering-driven or demand-driven for all weights, and the percentage
that varies depending on the weights are shown in Figure E.1.
Figure E.1: Results sensitivity analysis varying weights and factors
A second sensitivity analysis was performed to find the effect of different values for λ, while keeping
weights and factors constant. The results are shown in Figure E.2.
Figure E.2: Results sensitivity analysis varying λ
68
F Developing efficient tracking signals
Action signals should be efficient and the same materials should not be identified in multiple signals
per week. Algorithm 1 describes how to develop efficient signals.
Initialization;
E = empty list; ECt = set of ECs completed before t; TRt = set of top requirements found in
t− 1; ROt = set of requirement orders found in t− 1; Mt = set of materials found in t− 1
Step 1: Establish thresholds ThresholdEC, ThresholdTR, ThresholdRO and ThresholdM
using Equation 18.
Step 2: For i ∈ ECtif valuet,i > ThresholdEC then
generate signal;
add materials m in i to E;end
Next
Step 3: For j ∈ TRtif valuet,j > ThresholdTR and m 3 E for all m in j then
generate signal;
add materials m in j to E;end
Next
Step 4: For k ∈ ROtif valuet,k > ThresholdRO and m 3 E for all m in k then
generate signal;
add materials m in k to Eend
Next
Step 5: For l ∈Mt
if valuet,l > ThresholdM and l 3 E then
generate signal;
add material l to Eend
NextAlgorithm 1: Development of tracking signals
69
G Evaluating tracking signals
The tracking signal methods were applied to ASML’s data to evaluate the effectiveness of signals.
The Holt-method needs parameters for α and β to minimize MSE. These are established for each
threshold that needs to be set based on data of week 1 to 17, and recorded in Table G.1.
Table G.1: Parameters Holt-method
Signal Alpha Beta
Plant 1 0.11 0.02
Plant 2 0 0
Plant 3 0.16 0
Plant 4 0.07 0.16
Locals 0.02 0.83
Business Line 1 0.16 0.01
Business Line 2 0 0
Business Line 3 0.03 0.84
Business Line 4 0.01 0.45
Business Line 5 0 0
Total 0.16 0
EC 0.02 1
TR 0.06 1
RO 0.05 1
12NC 0.04 0.99
Figure G.1 shows the percentage of value explained per number of signals given for the different
evaluation weeks. Table G.2 describes the outperformance of TSF over SPC over the weeks. No
trend is seen for this timeframe that the dynamic threshold of TSF starts to outperform the static
threshold of SPC over the weeks. This can be explained by the variability of the influx data over
weeks with high changeover probabilities, as visualized in Figure G.2.
Figure G.1: Efficient signaling frontier per method per week
70
Table G.2: Evaluation TSF and SPC over weeks
Week Outperformance TSF over SPC
20 0.2%
21 1.0%
22 1.5%
23 0.3%
24 −1.6%
Average 0.3%
Figure G.2: Variability of total influx data
71
H Simulation tracking signals with fictitious data
This appendix shows the results of simulations done to test the effects of different types of data
patterns on the signaling capability of TSF and SPC signals. Thresholds for TSF were set with h
= 1.5, and thresholds for SPC using α = 0.2. The two types of signals are applied to three sets of
fictitious data: one that shows a positive trend, one that shows a negative trend, and one with vari-
able data comparable to the data found in the case study, see Figure H.1, Figure H.2 and Figure H.3.
For ten weeks, 100 random values were generated around an increasing, decreasing or random
average which following the respective data patterns. In the positive trend scenario, after 10 weeks
already 98 out of 100 values were signaled by SPC. In the negative trend scenario, after 10 weeks
only 6 out of 100 values were signaled by SPC. For both datasets, TSF remained stable signaling
around 30 values per week. In the scenario with variable data where no strong trend is observed,
the number of SPC and TSF signals per week is comparable. Concluding, whichever method is
preferred depends on the data to which the signals are applied. When data show a strong trend,
SPC thresholds become outdated while TSF adjusts the norm to allow for a relatively constant
number of signals. When strong data trends are apparent, SPC does not provide an efficient
threshold, and companies instead need to apply a dynamic threshold.
Figure H.1: Positive trend data with signals SPC and TSF
72
Figure H.2: Negative trend data with signals SPC and TSF
Figure H.3: Variable data with signals SPC and TSF
73
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