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SPC for Services: Timeliness and Correctness Monitoring
Russell Barton
Department of Supply Chain and Information Systems
The Pennsylvania State University
Acknowledgments: John McCool. Jun Shu, Earnest Foster, Jeff Tew, Lynn Truss, Smeal College Center for Supply Chain Research
National Science Foundation
2
Overview
• What do we mean by service quality?• Process Execution Monitoring: “SPC for
Services”• Optimization versus monitoring views• Process execution monitoring: supply chain
timeliness and correctness• The work to be done
3
Customers Retailers Warehouse/Dist Manufacturer Suppliers Suppliers’ Suppliers
Supply Chain: a Service Process
Source: www.dallasfed.org/research/swe/2005/swe0502b.html
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Customers Customer Reps References/Credit Title Search Loan Design Loan Execution
Another Service Process: Mortgage Application
C
C
C
CR
CRRC
TS
TS
TS
LD
LE
LE
LE
A (narrow) Service Process View
• Transactions moving through process steps:• a mortgage application moving through credit
check, title search, loan design• a business order moving through order assembly,
packing, loading, shipping, unloading, unpacking
• Two key characteristics:• how much time in each step• correctness of sequence of steps
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Service Quality
• Timeliness of Service Processes– Entity or transaction time in a particular location (state)– Entity or transaction time between locations or states
• Correctness of Service Processes– Entity processed through a correct sequence of steps
or locations (states)– There may be more than one correct sequence– The sequence often depends on the kind and/or ID of
the entity
Service Quality
• Timeliness and Correctness characterize many types of service operations:
– Processing a mortgage– Delivering a package– Cleaning an office building– Providing emergency room treatment– Providing an educational certificate or degree– Providing airline service– A supply chain operation
Process Execution Monitoring: SPC for Services
• Idea: apply SPC and process capability methods to timeliness and correctness measures from service process execution data
• For semi-automated processes this is a special kind of Workflow Monitoring
• For the remainder of this presentation, we will focus specifically on supply chain processes, but the approach can be applied to any transaction processing system
9
Control Chart
Basics
LCL
UCL
Time →
Out of Control →
= a statistic (individual value, average, range, std. dev.) for a subgroup of performance data
10
Process Capability Basics
Cpk = min (USL – avg, avg – LSL) = 2.5/3
3
USLLSL avg
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SPC for Supply Chains: the Need
• Need for SPC/Capability– Are your suppliers’ deliveries repeatable?– What is their process capability relative to delivery
time windows?– Can you detect changes (‘out of control’) in the
delivery timeliness before there is a crisis?– What stages of the delivery process cause the
greatest variation in delivery time? How much might delivery time variation be reduced?
– How do you tell on a daily or hourly basis which parts of your supplier chains or delivery chains need attention?
12
Contrasting Process Execution Monitoring with the usual Supply Chain Management
Focus:Optimization versus Monitoring
Objective Tools
Minimize delivery time, cost Optimization, Simulation
Promise a specific lead time Process Capability
Select a vendor Process Capability
Meet a specific lead time promise Statistical Process Control
Identify and address SC anomalies Statistical Process Control
Supply Chain Process Execution Data
09/29/10 SPRC
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Core of Supply Chain Execution Data: the SIT Triple
• Abstract view: SIT triple• S: state (RFID reader location)• I: ID for entity (Case ID)• T: time stamp
1 001 12:001 001 12:011 001 12:021 002 12:021 003 12:021 001 12:03
RFID
simplified structure
Enterprise structure
(distributed RFID read data) 1 001 12:001 001 12:011 001 12:021 002 12:021 003 12:021 001 12:03
1 001 12:001 001 12:011 001 12:021 002 12:021 003 12:021 001 12:03
1 001 12:001 001 12:011 001 12:021 002 12:021 003 12:021 001 12:03
2 001 12:002 001 12:012 001 12:022 002 12:022 003 12:022 001 12:03
1 001 12:001 001 12:011 001 12:021 002 12:021 003 12:021 001 12:03
3 001 12:003 001 12:013 001 12:023 002 12:023 003 12:023 001 12:03
1 001 12:001 001 12:011 001 12:021 002 12:021 003 12:021 001 12:03
4 001 12:004 001 12:014 001 12:024 002 12:024 003 12:024 001 12:03
5 001 12:005 001 12:015 001 12:025 002 12:025 003 12:025 001 12:03
15
Using SIT Data to Monitor Timeliness and Correctness
• Sets of raw (s, i, t) data can be used to characterize ‘timeliness’ and ‘correctness’
• Use ‘echoset’ and ‘neighborhood’ concepts– To aggregate multiple reads– To determine arrival to and departure from a
readable state– Infer entrance to and departure from nonreadable
states– To allow calculation and characterization at
different levels of aggregation
SIT Data
The plot shows RFID reads for 10 items at one reader location, over time.
SIT Data and Timeliness
The boxes indicate echosets of RFID reads, considered as an aggregate presence of a transaction (or item) at a particular state over a period of time
SIT Data and Timeliness
This neighborhood is a collection of four echosets (IDs from the same order in the same echoset) that have specified characteristics.
Order 4
Order 3
Order 2
Order 1
SIT Data and Timeliness
Timeliness is measured by sojourn time of an echoset or averaged over a neighborhood of echosets
Order 4
Order 3
Order 2
Order 1
SPC for Unloading Times
Sample
Sam
ple
Mean
127113998571574329151
800
600
400
200
0
__X=235.4
UCL=565.8
LCL=-95.0
Sample
Sam
ple
StD
ev
127113998571574329151
1000
750
500
250
0
_S=169
UCL=434
LCL=0
6
1
3
6666
6
6
22
2
2222222
22666266
6
66622
2
2
22
666
6
66666
1
266666
6
2
1
22222222
1111
222222
Xbar-S Chart of McDUnloading (mins)
SIT Data and Sequence Correctness
• Correctness requires a three-dimensional view of the SIT triple
• The next figure collapses multiple states onto the vertical axis, which now capture both state and id…
• For these items, the correct sequence is state S1, then state S2, then state S4.
• Four groups have their data in the plot, resulting in two correct sequences (S1, S2, S4) and two incorrect sequences (S1, S4) and (S1, S3, S2, S4) – can you see it?
SIT Data and Sequence Correctness
Recall SIT Data and Timeliness Plot
Order 4
Order 3
Order 2
Order 1
SIT Data and Sequence Correctness
SIT Data and Sequence Correctness
Monitoring Correctness
• Measuring path correctness involves comparing an actual sequence of states to one or more prescribed sequences.
• There are a number of algorithms for measuring such matches, coming from fields such as language processing and genome sequencing. One example is Edit Distance.
• These algorithms generally rely on some form of dynamic programming, and are computationally tractable for a small number of sequence steps.
26
SIT Data and Sequence Correctness
With these data we can plot the subgroup average sequence error: SPC for Sequence Correctness!
29
SPC for Supply Chains:If Straightforward, Why is there Little Use?
• Difficulties:– Availability of data– Form of data– Multivariate data (different shipment modes,
products, destinations)– Dependencies (multiple items in same truck)– Defining measures of timeliness and correctness
at multiple scales– Inherent time lags and censoring
30
SPC for Supply Chains:Difficulties
• Some Ideas:– Dependencies (multiple items in same truck)– Inherent time lags and censoring
31
Identifying Network-Based Dependencies from Group Movements and other Causes
• If traveling common links is the major source of covariance in times, efficient methods are available to estimate covariances for different items sharing all or part of their routes.
• Variances (and perhaps covariances) in individual links paired with topology are sufficient to estimate all path covariances.
32
Network-based Covariance
• Entities traveling from 1-5 and 2-6 always share 3-4
1
4
5
62
3
xi = s1-s5 time = wi + vi
yi = s2-s6 time = wi + bi
Cov(X, Y) = Var(W)
wi
xi
yi
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Network-based Covariance
• More realistic: entities traveling from 1-5 and 2-6 sometimes share 3-4
1
4
5
62
3
xi = s1-s5 time = wi + vi
yi = s2-s6 time = ai + bi
Cov(X, Y) = Cov(A, W)
wi
xi
yi
ai
• Let C1 be the usual covariance estimator based on xi and yi, and C2 be common link estimator based on ai and bi.
Then Var(C1) = Var(C2) + Var(Q+R+S)
• Where Q, R, S are the usual estimators for Cov(V,A), Cov(V,B) and Cov(W,B) respectively
Efficiency of Common Link Covariance Estimators
35
SPC for Supply Chains:Difficulties
• Some Ideas:– Inherent time lags and censoring
36
Determining Sojourn Time at S for I
Time →
Items in I
sojourn
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Determining Sojourn Time at S for I
Time →
Items in I
sojourn
38
Determining Sojourn Time at S for I
Time →
Items in I
’20%’ sojourn
39
Censored Data Issue:Determining Sojourn Time at a Particular State Subset S for Item Subset I
Time →
Items in I
sojourn
40
SPC for Supply Chains: Work to be Done
• Identification of technology gaps and roadblocks to implementation (data access, data cleaning, data structure)
• Research on modifications to SPC and capability tools to apply to supply chain data: dependence and censoring
• Develop best presentation formats (dashboards) for capability and control analyses to enable effective supply chain management
Questions?
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