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OPTIMIZATION OF INVENTORY MANAGEMENTOF WICHORUS INC. USING SIMULATION
Muhammad Jawwad ul Haque Siddiqui
OPTIMIZATION OF INVENTORY MANAGEMENTOF WICHORUS INC. USING SIMULATION
A Project Report
Presented to
The Faculty of the Department of
General Engineering
San Jose State University
In Partial Fulfillment
of the Requirements for the Degree
Master of Science in Engineering
By
Farhan Jaleel Muhammad Jawwad ul Haque Siddiqui
May 2009
OPTIMIZATION OF INVENTORY MANAGEMENTOF
© 2009
Farhan Jaleel Muhammad Jawwad ul Haque Siddiqui
ALL RIGHTS RESERVED
APPROVED FOR THE DEPARTMENT OF GENERAL ENGINEERING
____________________________________________________
Prof. Jim Dorosti
Technical Advisor,
Professor, Department of General Engineering
San Jose State University
____________________________________________________
Mr. Rehan Jalil
Industrial Sponsor, WiChorus Inc.
_____________________________________________________
Dr. Leonard Wesley
Associate Professor, Department of Computer Engineering,
San Jose State University
ABSTRACT
OPTIMIZATION OF INVENTORY MANAGEMENT OF WICHORUS INC.
USING SIMULATION
by
Farhan Jaleel
Muhammad Jawwad Ul Haque Siddiqui
Simulation is a modeling and analysis technique that is used to evaluate and improve any
dynamic system. The aim of this project was to do the optimization of inventory
management of WiChorus Inc., through simulation modeling by using Promodel software.
Optimization of inventory management is a useful technique that can be used to decrease the
lead time and cost incurred in any manufacturing company, while increasing the customer
service level. The overall impact is a significant increase in the net profit of a company.
Thorough research was carried with the help of various case studies and research papers from
recognized journals. Our results are similar to the findings of an earlier study (Terry Harris,
1997), except that we used simulation modeling to carry out the optimization of inventory
management, which is a very cost effective technique.
ACKNOWLEDGEMENT
We would like to express our gratitude, respect and appreciation to Professor. Jim
Dorosti, Professor, Department of General Engineering, San Jose State University for
his continuous support, guidance and encouragement in completion of the project.
We would like to thank Mr. Rehan Jalil, Chief Executive Officer and Mr. Lee
Khan, Vice President Manufacturing Operations, WiChorus Inc.for their kind support
and guidance in achieving our goal.
We would like to thank Dr. Leonard Wesley, Associate Professor, Department of
Computer Engineering, San Jose State University for his suggestions and guidance
through the ENGR 298/295B course in Spring 2009 Semester.
We would also like to thank our family membersand friendswho continuously
encouraged and supported us through out the semester till the completion of our project.
Farhan Jaleel Muhammad Jawwad ul Haque Siddiqui
TABLE OF CONTENTS
S No. Description Page No.
1
1. Introduction 1
1.1 Scope of the Project 1
1.2 Problem 2
1.2.1 Raw Material Inventory 3
1.2.2 Work-in-process Inventory 3
1.2.3 Finished Goods Inventory 4
1.2.4 Lead Time 4
1.2.5 Cost of Understocking and Overstocking 5
1.3 Solution Approach 6
1.4 Hypothesis 6
1.5 Summary of Phenomenon 7
2 2. Company Background 7
2.1 Economical Fact Related to Wireless Industry, USA 11
3
3. Supply Chain Management 14
3.1 Inventory’s Role in Supply Chain 23
3.1.1 Inventory Carrying Cost 27
3.1.2 Understanding Company’s Stock 27
3.1.3 Replenishment Quantity 28
3.1.4 Reorder Point 28
3.1.5 Inventory Cost Reduction Opportunities 29
3.2 Optimized Inventory Management 29
3.3 Time as a Key to Inventory Management 34
4
4. Literature Survey 35
4.1 Lean Supply: The Design and Cost Reduction Dimension 35
4.2 Designing Supply Chain: Towards Theory Development 36
4.3 Modeling the metrics of lean, agile and leagile supply chain: An ANP Based Approach
36
4.4 Dynamic modeling and control of supply chain systems: A review
37
4.5 Supply chain simulation- a tool for education, enhancement andendeavor
37
4.6 Supply Chain Management: Strategy, Planning and Operation
38
4.7 Simulation using Promodel 39
4.8 Time as a Key to Inventory Management 40
4.9 Optimized Inventory Management 41
4.10 Five Keys to Effective Inventory Management 41
5 Manufacturing Process at WiChorus 42
6 Simulation 44
S No. Description Page No.
7
7. Promodel 48
7.1 Uncertainty measurement in ProModel 50
7.2 Modeling Elements 52
7.3 Locations 53
7.4 Entities 56
7.5 Path Networks 57
7.6 Resources 57
7.7 Processing or Routing 58
7.8 Arrivals 59
7.9 Shifts or Work Schedules 60
7.10 Scenarios and Runtime Interface 60
7.11 Additional Modeling Elements 60
8
8. Method of Investigation 62
8.1 Simulation Model 62
8.1.1 Capacity Inputs 62
8.1.2 Product Specific Data 63
8.1.3 User Specific Data 63
8.1.4 Scheduling Production Plan Data 63
8.1.5 Forecasting 64
8.1.6 Inventory Process 66
8.1.7 Issue of Considering Network Board in Lead Time 67
8.1.8 Standard Deviation 68
8.1.9 Verification 69
8.2 Investigation of Results 69
8.2.1 Credibility of the Model 70
8.2.2 Lead time Optimization 71
8.2.3 Cost Optimization 72
8.2.4 Maximum Capacity 73
9
9. Economical Justification 74
9.1 Executive Summary 75
9.2 Problem Statement 75
9.3 Solution and Value Proposition 76
9.4 Market Size 79
9.5 Competitors 81
9.5.1HENRY SCHEIN 82
9.5.2 TC Logic 82
9.5.3 Terra Technology 83
9.5.4 MCA Solution 83
9.5.5 Optiant 83
9.6 Potential Customers 84
9.7Personnel 85
9.7.1 Chief Executive Officer 86
S No. Description Page No.
9
9.7.2 Vice President of Sales and Marketing 86
9.7.3 Vice President of Business & Development 87
9.7.4 System Engineer 87
9.7.5 Industrial Engineer 87
9.7.6 Quality Engineer 88
9.7.7 Marketing Engineer 88
9.7.8 Sales Engineer 88
9.8 Cost Analysis 88
9.8.1 Investment Capital Requirement 89
9.8.2 Fixed Cost 89
9.8.3Variable Cost 90
9.9 Price Point 91
9.10 Total Income 92
9.11 Profit and Loss 93
9.11.1 Selling Price per Design 93
9.11.2 Customer Service Charges 94
9.11.3 Fixed Cost 94
9.11.4 Total Variable Cost 94
9.11.5 Total Cost 94
9.11.5 Total Revenue 94
9.11.6 Profit & Loss 94
9.12 Break Even Point 96
9.13 Norden-Rayleigh Graph 97
9.14 SWOT Assessment 100
9.15 Exit Strategy 100
10 10. Project Schedule 101
11
11. Team and Committee Members 103
11.1 Team Members 103
11.1 Committee Members 104
12 12. Conclusion 104
13 13. References 106
14
14. Appendices 107
14.1 Appendix A1 (Generalized Model Output) 111
14.2 Appendix A2 (Optimized Time Model Output) 139
14.3 Appendix A3 (Maximum Capacity Model Output) 178
LIST OF FIGURES
Figure No.
Figure Description Page No.
1 WiChorus intelligent ASN Gateway 8
2 WiMAX Mobile Internet Gateways 9
3 PDN Gateways, manufactured at WiChorus 10
4 Average Minutes of per month 12
5 US cellular Revenue Growth 12
6 Average Revenue per minute 13
7 US cellular subscriber Growth 13
8 US wireless CAPEX 14
9 Generalized Supply Chain network for an individual firm 16
10 Supply Chain relationship model 21
11 Maximizing availability per dollar 30
12 Improvements over some other approaches 31
13 Target vs on-hand 32
14 Optimized approach fits inside current systems 33
15 Manufacturing process and inventory locations at WiChorus Inc 43
16 Simulation Modeling Flow 46
17 A plot of Normal Distribution 47
18 ProModel’s Modeling Element 53
19 ProModel picture showing the different location in design model 54
20 More Elements Menu 61
21 Simulation Model Data Flow 62
22 Forecast of demand of SmartCore 64
23 Lead time for the Part Arrival at WiChorus 66
24 Lead time reduction 72
25 Cost of making changes at subsequent stage 77
26 Comparison of the cumulative system costs with and without simulation
78
27 Total GDP for top 15 States (in Millions) 80
28 Contribution of GDP by manufacturing sector (in Millions) 81
29 GDP based on industrial sector (top 10) 84
30 Organizational Structure 86
31 Graphical representation of Total Revenue from Q3 (2009) to Q4 (2010)
93
32 Profit and loss Diagram 96
33 Break-Even analysis graph 97
34 Expenditure w.r.t time 99
35 Cumulative Expenditure vs Time 99
36 Project Schedule 102
LIST OF TABLES
Figure No.
Table Description Page No.
1 Different levels of decision making in supply chain 18
2 Difference b/w conventional management and supply chain management
19
3 Four types of inventory management approaches 26
4 Process time and lead-time of expensive inventory items 51
5 Name of the Locations in design model 55
6 Name of Entities for WiChorus Simulation model 56
7 List of Arrivals at different Locations 59
8 Number of parts being ordered 65
9 Process time Verification 70
10 Number of parts left in the system and corresponding savings 73
11 List of competitors and solutions provided by them 82
12 List of the prospective customers in USA 85
13 Fixed cost for 2009 and 2010 90
14 Variable Cost per Quarter 91
15 Total revenue statement for Q3 (2009) to Q4 (2010 92
16 Profit and Loss value for Q3(2009) to Q4(2010) 95
17 Norden-Rayleigh Cost Analysis 98
18 Strength, Weakness, Opportunities and Threat Analysis 100
1
1. Introduction
The purpose of this project is to do the optimization of inventory management of WiChorus
Inc. by simulation modeling. The simulation modeling is to be carried with the help of
ProModel software. The goal is to reduce the lead-time and inventory carrying cost, which
subsequently increases the net profit (without changing the revenue) and customer service
level. The deliverable is a report containing optimized inventory management model on
ProModel software with reduced lead-time and inventory holding cost.
1.1 Scope of The Project
The purpose of this project is to optimize the inventory management of the company named
WiChorus Inc. The optimization of inventory management is to be carried out at various
stages of development and includes raw material inventory, work-in-process inventory and
finished goods inventory by using simulation-modeling tool. Supply chain of the company
involves material and information flows from raw material suppliers to all the way to the end
customers and back (some times). Optimization is to be carried out in terms of total cost and
lead time, considering all the economical and operational issues, which can contribute to the
success of WiChorus Inc. in future. This project includes optimization of the inventory
management process with the help of Promodel software. This software is a simulation tool.
Major part of the total cost of the product development also depends on the overall activities
of supply chain. These activities include transportation from point of origin to the point of
consumption and the storage of different forms of inventory. Techniques like standardization,
minimizing cost by design and cost of quality are not in scope of our project. The key to the
2
success of company is to keep the total cost of the product development as low as possible.
Due to efficient inventory management, the lead-time and inventory carrying cost can reduce
the cost of the product development. Reduction in lead-time can increase responsiveness and
thus improving customer service level. Reduction in the amount of carried inventories
directly influence the total cost, as inventory-holding cost is reduced.
1.2 Problem
WiChorus holds or carries the inventory in three forms, namely raw materials (like integrated
circuits, memory modules, cables, switches etc.), semi-finished products (Fully assembled
and tested printed circuit boards, Field replaceable modules) and finished
products(equipment itself). When the orders of the finished productsstart arriving in an
organization, it has to manage the flow of an inventory (specially raw materials and semi-
finished products) throughout its manufacturing processes, till customer receives the finished
product. For the entity that has a weak management of inventory, there will be no economy
of scale for the manufacturing operations and it will subsequently impact the net profit.
WiChorus Inc. has its flow of inventory like any manufacturing organization of specialized
equipment. There are many stages for the inventory that is involved in the manufacturing
operations of WiChorus Inc. The manufacturing operations include ordering of the parts,
transportation of the parts (between PCB manufacturer and WiChorus, PCB assembler and
WiChorus), storage of WIP inventory(semi finished products), storage of finished goods
inventory and delivery of products.
3
The serial production of equipment at WiChorus has recently started. If the inventory is not
optimally managed, than there would be many problems that could be faced by WiChorus or
any other manufacturing company. Since the cost of products being produced at WiChorus
facility is very high, the impact of mismanaged inventory is tremendous in terms of cost and
customer service (lead time). The problems that can be faced in the case of mismanaged
inventory could be:
• High carrying cost
• High transportation cost
• Longer response time
• High setup cost
• Low Customer Service Level
1.2.1 Raw Material Inventory
In case of WiChorus raw material inventory includes memory modules, integrated circuits,
capacitors, chassis, bezels, labels, lite pipes, holders etc. For the purpose of this project, we
have taken into account the raw material that contributes to 80 percent of inventory carrying
cost of WiChorus Inc.
1.2.2 Work-in-Process (or Pipeline inventories)
According to E. A. Silver, D. F. Pyke & R. Peterson (1998), work in process inventories
includes goods in transit (e.g. on trucks, on railway cars or on airplanes) between levels of
multi-echelon distribution system, or between adjacent workstation in the factory. In the case
of WiChorus, work in process inventory exists in the form of assembled PCBs. Once the
4
PCBs are manufactured they are sent to another third party manufacturers along with other
raw materials for assembling. There is a certain amount of cost, which remains tied up with
these assembled PCBs, until they are installed in the chassis with other parts at WiChorus’s
facility, tested and shipped to customers. So if quantity is high for these semi assembled or
assembled PCBs, it incurs as inventory carrying cost, till the time of its shipment.
1.2.3 Finished Goods Inventory
Once the manufactured equipment at WiChorus is ready for shipment to customers, it is held
at WiChorus facility and it is called finished goods inventory. There is also a cost associated
for holding finished goods. Usually semi finished products (assembled PCBs) are held at
WiChorus for the ease of responsiveness to unplanned customers orders, along with raw
materials inventory such as chassis, bezels, memory modules, shelf managers, power
management modules etc. This can be taken as safety stock that is always there to respond to
unforeseen orders from customers.
1.2.4 Lead Time
As per Sunil Chopra, P. Meindl & D. V. Kalra (2007), lead-time is the gap between when an
order is placed and when it is received. According to Silver et al (1998), the replenishment
lead-time is composed of five distinct components and they are given as:
1. Administrative time at the stocking point (order preparation time).
2. Transit time to the supplier. This is negligible if the order is placed electronically.
3. Time at the supplier. Its duration is materially influenced by the availability of stock
at the supplier when the order arrives.
5
4. Transit time back to the stocking point.
5. Time from order receipt until it is available on the shelf.
The problem, which is being faced by WiChorus is that, specialized raw material (like
programmable chips, memory modules etc.) is required for equipment production. Most of
these raw materials are not available off the shelf; neither off the shelf materials can be used
as raw materials for the specialized equipment. The lead times are longer for these types of
items and it ranges from 2 to 12 weeks. Once these items reach manufacturing facility, only
then manufacturing operations could start. WiChorus also uses third party manufacturers for
the PCB manufacturing and assembling. So these items needs to be ordered and stored at
WiChorus facility for the time being as raw material, and further sent to third party
manufacturers for PCB assembling. There is a cost incurred to purchase and store these raw
materials.
1.2.5 Cost of Understocking and Overstocking
According to Chopra et al (2007), overstocking cost is the loss incurred by a firm for each
unsold unit at the end of selling season. For example if finished or semi-finished goods
inventory is present at the end of selling season than that cost will be called as overstocking
cost for WiChorus Inc. Copra et al (2007) elaborates the understocking cost as the margin
lost by a firm for each lost sales because there is no inventory on hand. These costs also
depend on the forecast carried out in anticipation of orders from customers.
In an inventory management process, every company keeps a safety inventory because of
lack of future uncertainty. Since WiChorus do not have a simulation process so they cannot
track its inventory process when it desires. They have to put an input to the inventory system
6
based on the previous data and information. It is always undesirable to have an unwanted
inventory in the process. It may be a safety inventory, raw material, WIP or finished goods.
Inventory present in the process is an invested cost and increase the total cost.
Also another problem is that if the inventory design has to be change, it will give the great
risk of loss to the company in case of failure or wrong decision. Presently there is no
particular method available to implement the change in design without having a chance of
losing profit.
1.3 Solution Approach
A very cost effective solution, which can be applicable to the above-mentioned problems, is
the use of simulation modeling to achieve the inventory optimization. Simulation is used to
foresee the system’s behavior by changing system inputs; with this advantage we can give
minimum input (input stands for raw material and work-in-process inventory) to the system
to get the desired output. This reduction of the inventory saves the input cost and thus
reduces the final manufacturing cost. Cost is also reduced by altering the overdesign and
unwanted safety factors that were designed when system projection was uncertain or unclear.
1.4 Hypothesis
“If the inventory management involved in the process of product development is optimized,
the total product development cost can be reduced. Optimal utilization of resources and time
management involved in terms of inventory management is directly proportional to total
product development cost. Reduction in lead-time and carrying cost can be achieved by the
optimization of inventory management policies by using simulation techniques.”
7
1.5 Summary of the phenomenon
There are different kinds of inventories involved in the production process at WiChorus Inc.
1. Raw material inventory,
2. Work-in-process inventory and
3. Finished goods inventory.
Raw materials are imported from various countries including Japan, Israel, Taiwan and
China. Expensive raw material inventory is kept at head office warehouse. Product
designing, testing and assembly are also conducted at head office. Manufacturing and
assembling of printed circuit boards is outsourced to another company in Silicon Valley.
Safety inventory is also maintained for any unforeseen circumstances, which can affect the
smooth and timely supply of raw material and work in process items from its suppliers.
2. Company Background
Increasingly, users are looking for a similar broadband Internet experience in the wireless
world as they have in the wired world. Service providers like Sprint PCS, Verizon Inc. etc.
are eager to deliver this experience but need a cost-effective, scalable solution. Combining
the advances of OFDMA technology with the power of IP networking. Mobile WiMAX has
emerged as an ideal architecture to deliver these services. Arguably the first 4G networking
solution, mobile WiMAX offers greater bandwidth than previous technologies. WiChorus
Inc. manufactures WiMAX mobile Internet gateway products and was established in 2005.
Its head office is located in San Jose, California and branch offices in India and Pakistan.
According to WiChorus website (Retrieved Ja
4G Gateways and is a specialist that provides a new class of intelligent wireless core
platforms that enables service providers to deploy smarter, profitable, large
networks. Leveraging this platform,
provide content management, advanced services, and subscriber management and network
optimization, enabling service providers to capitalize on service differentiation opportunities
and streamline operational costs. With massive scalability and packet processing
performance, WiChorus solutions are designed to accommodate the future bandwidth and
multimedia requirements of WiMAX and LTE networks.
Figure 1: WiChorus intelligent ASN Gateway usage
WiChorus executed on the strategy of building a family of very advanced high
platforms, keeping in view the data & control capacity and content management needs of
8
According to WiChorus website (Retrieved January, 2009), WiChorus is a leader in Smart
4G Gateways and is a specialist that provides a new class of intelligent wireless core
platforms that enables service providers to deploy smarter, profitable, large-scale WiMAX
networks. Leveraging this platform, the company's Gateway and Home Agent products
provide content management, advanced services, and subscriber management and network
optimization, enabling service providers to capitalize on service differentiation opportunities
osts. With massive scalability and packet processing
performance, WiChorus solutions are designed to accommodate the future bandwidth and
multimedia requirements of WiMAX and LTE networks.
Figure 1: WiChorus intelligent ASN Gateway usage
Source: (WiChorus Inc., 2009)
WiChorus executed on the strategy of building a family of very advanced high
platforms, keeping in view the data & control capacity and content management needs of
nuary, 2009), WiChorus is a leader in Smart
4G Gateways and is a specialist that provides a new class of intelligent wireless core
scale WiMAX
the company's Gateway and Home Agent products
provide content management, advanced services, and subscriber management and network
optimization, enabling service providers to capitalize on service differentiation opportunities
osts. With massive scalability and packet processing
performance, WiChorus solutions are designed to accommodate the future bandwidth and
WiChorus executed on the strategy of building a family of very advanced high-end IP
platforms, keeping in view the data & control capacity and content management needs of
future 802.16e, 802.16m and LTE networks. The result was adv
SC 600, SC 100, and SC 20 platforms. WiChorus SmartCore platforms are designed to
accommodate a rich set of content handling and control
performance, minimum latency and massive scalability. To achi
SmartCore platforms employ a combination of processing elements and hardware engines
carefully designed for the specific tasks and intelligence needed at all levels of functionality.
Hundreds of these processing elements and hardware eng
software design enable the massive processing and learning capabilities of the SmartCore
platforms. SmartCore future proofs the mobile core for advances in WiMAX and 4G
technologies.
Figure 2: WiMAX Mobile Intern
9
future 802.16e, 802.16m and LTE networks. The result was advanced SmartCore™ SC 1400,
SC 600, SC 100, and SC 20 platforms. WiChorus SmartCore platforms are designed to
accommodate a rich set of content handling and control-path functionality with maximum
performance, minimum latency and massive scalability. To achieve this, WiChorus
SmartCore platforms employ a combination of processing elements and hardware engines
carefully designed for the specific tasks and intelligence needed at all levels of functionality.
Hundreds of these processing elements and hardware engines with a highly distributed
software design enable the massive processing and learning capabilities of the SmartCore
platforms. SmartCore future proofs the mobile core for advances in WiMAX and 4G
Figure 2: WiMAX Mobile Internet Gateways, manufactured at WiChorus.
Source: (WiChorus Inc., 2009)
anced SmartCore™ SC 1400,
SC 600, SC 100, and SC 20 platforms. WiChorus SmartCore platforms are designed to
path functionality with maximum
eve this, WiChorus
SmartCore platforms employ a combination of processing elements and hardware engines
carefully designed for the specific tasks and intelligence needed at all levels of functionality.
ines with a highly distributed
software design enable the massive processing and learning capabilities of the SmartCore
platforms. SmartCore future proofs the mobile core for advances in WiMAX and 4G
et Gateways, manufactured at WiChorus.
SmartCore platforms have been effectively leveraged to provi
networks. These products are
• ASN Gateway for WiMAX
• Home Agent for WiMAX
• Mobile Internet Gateway for
• Femto Gateway for WiMAX
SmartCore platforms are also being leveraged to develop 3GPP/LTE products, as follows:
• Service Gateway for LTE
• PDN Gateway for LTE
• MME functions for LTE
Figure 3: PDN Gateways, manufactured at WiChorus Inc.
10
SmartCore platforms have been effectively leveraged to provide products for WiMAX
networks. These products are:
ASN Gateway for WiMAX
Home Agent for WiMAX
Mobile Internet Gateway for WiMAX
Femto Gateway for WiMAX
rtCore platforms are also being leveraged to develop 3GPP/LTE products, as follows:
Service Gateway for LTE
PDN Gateway for LTE
MME functions for LTE
Figure 3: PDN Gateways, manufactured at WiChorus Inc.
Source: (WiChorus Inc., 2009)
de products for WiMAX
rtCore platforms are also being leveraged to develop 3GPP/LTE products, as follows:
Figure 3: PDN Gateways, manufactured at WiChorus Inc.
11
In addition, WiChorus also develops NWG compliant Open R6 Base Station software for
WiMAX Base Stations. The Open R6 software is being commercially used by more than five
WiMAX equipment companies, in their Base Stations, for Profile C compliance. WiChorus
is funded by top-tier venture capitalists including Accel Partners, Mayfield Fund, Pinnacle
Ventures and Redpoint Ventures. These investors have previously funded IP and telecom
infrastructure companies like Redback, Juniper and TiMetra.
2.1 Economical Facts Related to Wireless Service Provider Industry of
USA
US wireless market accounts for almost one-tenth of the global cellular subscribership. As of
2004, US customers alone counted for 163 million of the 1.7 billion subscribers in the
globe(Telecom Industry Association, 2004). This was published in Telecom Industry
Association’s recently released market review and forecast. According to the review USA
alone will have 200 million wireless subscribers and account for $150 billion in spending in
2008. This study reveals that average revenue per voice minute stopped its decline at $.10 in
2006 whereas usage of total minutes keeps on increasing to 485 (average minutes of use per
month). The voice prices keeps on falling so it means that all of the efforts that carriers have
been putting into data services might actually be paying some dividend. The different graphs
showing historical data related to US wireless industry are shown below (refer figure 4 to 8).
These facts clearly defines that we have a big market where optimization of inventory
management can be applied. As all these wireless internet or cell services provider are going
to use specialized equipments to deliver 4G wireless services in future.
0
100
200
300
400
500
600
1995 1996 1997
119 125 117
Average Minutes of Use per Month
0
20
40
60
80
100
120
140
2001 2002
65.3
76.5
US Wireless Revenue Growth in Billion $
Figure 4: Average
Source: (Telecom Industry Association, 2004)
Figure 5: US cellular Revenue Growth
Source: (Telecom Industry Association, 2004)
12
1997 1998 1999 2000 2001 2002 2003
117136
185
255
380
427
485
Average Minutes of Use per Month
2003 2004 2005 2006 2007
87.6
100.6
111.8
122.9
135.8
US Wireless Revenue Growth in Billion $
Figure 4: Average Minutes of per month
Source: (Telecom Industry Association, 2004)
Figure 5: US cellular Revenue Growth
Source: (Telecom Industry Association, 2004)
2004
540
US Wireless Revenue Growth in Billion $
2001
2002
2003
2004
2005
2006
2007
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
1995 1996 1997
0.47
0.41 0.4
Average Revenue per Minute in $
145
150
155
160
165
170
175
180
185
190
195
2004
163.1
US wireless subscriber growth in million
Figure 6: Average Revenue per minute
Source: (Telecom Industry Association, 2004)
Figure 7: US cellular subscriber Growth
Source: (Telecom Industry Association, 2004)
13
1998 1999 2000 2001 2002 2003 2004
0.33
0.24
0.18
0.12 0.11 0.1 0.1
Average Revenue per Minute in $
2005 2006 2007
173.8
182.3
191.1
US wireless subscriber growth in million
Figure 6: Average Revenue per minute
Source: (Telecom Industry Association, 2004)
Figure 7: US cellular subscriber Growth
Source: (Telecom Industry Association, 2004)
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
US wireless subscriber growth in million
2004
2005
2006
2007
All of the wireless internet or cellular service provide use specialized and expensive
equipment. Most of this kind of equipment is manufactured in USA. There is a significant
market for us to specially do optimization of inventory management of these kinds of
companies.
3. Supply Chain Management
A supply chain consists of all parties involved directly or indirectly in fulfilling a customer’s
request. The supply chain includes not only the manufacturer and supplier, but also
transporter, warehouses, retailers and even customers themselves
supply chain management may be viewed as a growing field of practice and an emerging
domain in academics. Both of these perspectives are not totally mature, but each one has a
considerable significance. Supply chain management is all about
0
5
10
15
20
25
2001 2002
15.4
21.9
US Wireless Capex in Billion $
Source: (Telecom Industry Association, 2004)
14
All of the wireless internet or cellular service provide use specialized and expensive
equipment. Most of this kind of equipment is manufactured in USA. There is a significant
do optimization of inventory management of these kinds of
3. Supply Chain Management
A supply chain consists of all parties involved directly or indirectly in fulfilling a customer’s
request. The supply chain includes not only the manufacturer and supplier, but also
transporter, warehouses, retailers and even customers themselves (Chopra et al, 2007). The
supply chain management may be viewed as a growing field of practice and an emerging
domain in academics. Both of these perspectives are not totally mature, but each one has a
considerable significance. Supply chain management is all about influencing behavior in
2003 2004 2005 2006 2007
18.917.9
17
19
21
US Wireless Capex in Billion $
Figure 8: US wireless Capex
Source: (Telecom Industry Association, 2004)
All of the wireless internet or cellular service provide use specialized and expensive
equipment. Most of this kind of equipment is manufactured in USA. There is a significant
do optimization of inventory management of these kinds of
A supply chain consists of all parties involved directly or indirectly in fulfilling a customer’s
request. The supply chain includes not only the manufacturer and supplier, but also
t al, 2007). The
supply chain management may be viewed as a growing field of practice and an emerging
domain in academics. Both of these perspectives are not totally mature, but each one has a
influencing behavior in
2001
2002
2003
2004
2005
2006
2007
15
particular directions and in particular ways (John Storey et al, 2006). It has been long
overlooked as a promising area to achieve sustainable competitive advantage.
Douglas M. Lambert, Sebastian J. Garcia-Dastugue & Keely L. Croxton (2008), in
conjunction with Global Supply Chain Forum offer following definition of supply chain
management:
“Supply Chain Management is the integration of key business processes from
end users through original suppliers that provides products, services, and the
information that add value for customers and other stake holders”
Recently, supply chain management has seen its role shifting from the theory of passive cost
control, to a proactive role in shaping long-term competitive advantage and firm’s
profitability. The top-level management has recognized this by building efficient and
effective supply chains that sustainable competitive opportunities can be achieved and
materialized. The significant advantages are timely product availability, reduced costs,
shorter lead times and improved customer service. There has been an increased development
of new manufacturing methodologies such as agile, mass customization and lean. These have
reduced manufacturing costs significantly, but at the same time they have brought new
challenges for supply chain managers. These paradigms have led manufacturing firms to
emphasize their supply chain relationships toward fewer suppliers. Moreover, there has been
an increasing movement towards adopting the concept of supply chain integration, both
forward and backward. (Paul D. Cousins & Bulent Meguc, 2005).
Figure 9: Generalized Supply Chain network for an individual firm
Figure 9 shows the generalized supply chain network for an individual
processing facility, transportation should be carried out in order to deliver the raw material or
semi finished/finished products to next manufacturing facility, until it reaches the final
customer. In the case of WiChorus some raw materia
is stored at third party manufacturer’s facility. The complete supply chain process of
WiChorus will be explained in the later part of this report.
The key enabler of supply chain is information sharing. It can b
and customer externally, and internally within the organization between different
departments. The Information that is required depends upon the nature of problem to be
solved. Supply chain is also referred as an alignment of org
or products to the market. This alignment is present in the form of a comprehensive
16
Figure 9: Generalized Supply Chain network for an individual firm
Source: (Sunil Chopra, 2007)
Figure 9 shows the generalized supply chain network for an individual firm. After each
processing facility, transportation should be carried out in order to deliver the raw material or
semi finished/finished products to next manufacturing facility, until it reaches the final
customer. In the case of WiChorus some raw materials are stored at its facility whereas some
is stored at third party manufacturer’s facility. The complete supply chain process of
WiChorus will be explained in the later part of this report.
The key enabler of supply chain is information sharing. It can be between vendor, supplier
and customer externally, and internally within the organization between different
departments. The Information that is required depends upon the nature of problem to be
solved. Supply chain is also referred as an alignment of organizations, which brings services
or products to the market. This alignment is present in the form of a comprehensive
Figure 9: Generalized Supply Chain network for an individual firm
firm. After each
processing facility, transportation should be carried out in order to deliver the raw material or
semi finished/finished products to next manufacturing facility, until it reaches the final
ls are stored at its facility whereas some
is stored at third party manufacturer’s facility. The complete supply chain process of
e between vendor, supplier
and customer externally, and internally within the organization between different
departments. The Information that is required depends upon the nature of problem to be
anizations, which brings services
or products to the market. This alignment is present in the form of a comprehensive
17
organization in which different entities cooperatively organize the networks of production,
distribution and supply for products and services. There is a well-accepted technique for this
purpose, which is described as information sharing. According to Charu Chandra, Janis
Grabis & Armen Tumanyan (2006) who has mentioned Li et al (2006) in their research
paper, four types of data are to be shared across the supply chain. These are order, demand,
inventory and shipment data. Moreover, customer service management and order fulfillment
are the issues that need to be managed by supply chain. The information related to these two
issues needs to be shared in order to accomplish the effectiveness and efficiency in supply
chain. Another thing that is of importance is how we share the information. The common
methods to share information are ERP (Enterprise Resource Planning), Web-based portals,
EDE (Electronic Data Exchange) etc. Handsome amount of cost is related to acquire these
resources.
Information sharing in complex organizations as supply chain becomes essential.
Understanding this complexity helps to realize the business requirements which are further
used to offer adequate solutions. Once supply chain’s operational and organizational details
are studied, its uncertain and dynamic nature is exposed. Most of the time ad-hoc supply
chain arrangements are used, due to which the life span of supply chain’s member is too
short. Due to this reason a collaborative information sharing system must be in place in any
organization’s supply chain. Chandra et al(2006) identifies different levels of decision-
making in supply chain, which are given in table 1.
18
Table 1: Different levels of decision making in supply chain
DECISION MAKING LEVEL
TIMELINE TYPE OF DECISION MAKING
Strategic 5-10 years Investment on plants and capacities. Creation of a logistic network. Introduction of a new product.
Tactical 3 months-2 years
Inventory policies to use. Procurement policies to be implemented. Transportation strategies to be adopted.
Operational Day-to-day
Scheduling of resources. Routing of raw materials and finished products. Solicitation of bids and quotations.
Source: (Chandra et al, 2007)
For the purpose of this project we will be focusing on Tactical and Operational decisions.
Most of the products of WiChorus are special in nature with an expected life cycle of 3 years.
As telecom technology changes rapidly, so do the types of products. Different product is
manufactured by going through different manufacturing steps. Accordingly different supply
chain procedures are adopted, although lean manufacturing techniques are followed.
According to Storey et al (2006), supply chain management is ultimately about influencing
behavior in particular directions and in particular ways. Moreover, he suggests that there are
a number of interconnected ideas and suggestions, which constitutes the assumption and
recommendations of supply chain management as described in table 2. These are compared
with various aspects of conventional management.
19
Table 2: Difference between conventional management and supply chain management
DIMENSION CONVENTIONAL MANAGEMENT
CONVENTIONAL MANAGEMENT
Unit of analysis, focal point of allegiance
Functional, department, or firm as main unit of analysis
Functional, department, or firm as main unit of analysis
Use of information and knowledge
Information denial; lack of transparency
Information denial; lack of transparency
Beneficiaries One sided benefit; win-lose
One sided benefit; win-lose
Targets Optimization; cost reduction; price control
Optimization; cost reduction; price control
Time horizons Short-term wins; periodic negotiation
Short-term wins; periodic negotiation
Relationship episode Transactional Transactional
Range of Partners Multiple competitive sourcing
Multiple competitive sourcing
Connectivity Independent logistics Independent logistics
Reactive vs proactive Reactive buyers Reactive buyers
Process of supplier selection
Competitive tendering Competitive tendering
Replenishment device Inventory Inventory
Source: (Storey et al, 2006)
The main keystone idea relates to integration and alignment is, whether services and sub-
components should be made or bought. Various researches found that a number of factors
can enable or inhibit supply chain management, which also depends on the circumstances
and the method in which the factor is utilized. These factors are transparency of information
and knowledge, supply chain behavior and performance measurements (John Storey &
Caroline Emberson, 2006).
20
As mentioned by Kimball E. Bullington & Stanley F. Bullington (2005), Deming (1986)
emphasized on the importance of long term relationships with a single source supplier in his
discussion of his fourth principle for management. He emphasized the fact that supplier
shouldn’t be selected on the factor of price alone. Customer-supplier relationship is of great
importance too. Here relationship means that instead of abstract corporate entities, people are
to be involved. Preparation for building this relationship is also important. Training is
required for the people who depend on the success of these relationships. (e.g. service
personnel, engineers, buyers at both customer and supplier companies). It is also possible that
team members are not being trained for creating manufacturer and supplier relationships.
In the same research paper (Refer Bullington et al (2005)), Schonberer (1986) described this
relationship by stating that key suppler should be treated “like family”. Keki Bhote (1989)
mentioned this relationship as marriage. McHugh et al (2003) also stated this relationship as
marriage. If the word marriage is suitable to define this relationship then distinctive qualities
found in close families should be used as a guide for forming and sustaining strong
manufacturer supplier relationship. This relationship can be further elaborated by figure 10.
21
Figure 10: Supply Chain relationship model
Source: (Bullington et al, 2005)
Here supply chain relationship model is shown graphically. If we start from the bottom,
commitment and communication are illustrated as foundation. At the top change is shown to
describe the fact that the outputs obtained during the crisis of change are the cause of
partnership. At the centre, principle acts as a compass to guide the relationship between
manufacturer and supplier. Commitment acts as a basement and it is really important
between supplier-customer relationships. The cost of keeping multiple suppliers is
considerably high. In the case of WiChorus Inc, it is mandatory to keep multiple suppliers,
due to the diversity of the components/raw material required for manufacturing different
kinds of raw materials. Commitment can be judged by actions such as lead-time, source
agreements, training, dedicated resources etc.
Change
Communicate
Commitment
Principles
Time Together
Appreciation
22
Communication acts as a backbone in customer-supplier relationship. The information like
order quantity, prices, payment schedule (terms and conditions) and design data are of
significant importance. Communication that is internal to company, like determination of
proper inventory level through forecasting, customer service level, costs, maintenance etc.
are more of technical nature if compared with external communication. Nature of internal
and external communication is considerably different from each other, and different team
members should do them. Here change is referred as crisis, which stand for a turning point.
Crisis can happen as an unplanned occurrence. Crisis or change is avoidable in a
manufacturer-supplier relationship. Any good supply chain management should be capable
of handling unplanned change. Product and process change can be termed as planned change.
A beneficial manufacturer-supplier relationship is intended to cater for the effect of
unplanned change and to take full advantage of the benefits of planned change.
Modern developments in supply chain enterprise focus on the linkage, collaboration,
integration etc. This has done in order to broaden the operations efficiency spectrum to
include the internal manufacturing operations as well as the upstream and downstream
operations. Nowadays competition is not only between companies but also between there
respective supply chain. Whoever has a proficient supply chain management wins the game.
Now it has become important for any service or manufacturing firm to confirm that the
operations at partner companies are also competent. For the last two decades just in time
(JIT) strategy has been applied in manufacturing firms. The traditional approach of JIT has
been applicable to the internal manufacturing operations of the companies. Some research
work has been done in order to investigate how the just in time strategy can be applicable to
the supply chain of an organization. The just in time approach contains principles and
23
operational techniques to improve the response time to corresponding customers. If the
delivery times of raw materials and finished products are reduced, an organization can
produce components or products efficiently. It could be said that supply chain management
capabilities significantly influence the business performance of an organization.
3.1 Inventory’s Role in Supply Chain
Before going into the details of inventory’s role in supply chain network, we should define
what inventory is and why it is necessary to carry inventory by any manufacturing or service
providing organization. Inventory is defined as a quantity of goods or material on hand
(Merriam Webster, 2009). Another definition is, “Inventory is a list of goods and material (in
semi finished or finished form) or those goods and materials themselves, held available in
stock by business” (Wikipedia, 2009). The word inventaire originated from France and it was
used for “detailed list of goods”. Inventory management is basically all about enumerating
the location and quantity of stocked items or goods. It is required at different stages within a
manufacturing facility or within different location of organization’s whole supply chain
network. It is used to protect planned course of production against any unforeseen
circumstance due to which shortage could occur. The unforeseen circumstances can be an
unplanned sales order from customer, environmental hazard (earthquake, flooding etc.),
shortage of raw materials from suppliers, seasonal trend in sales etc.
Different aspects are covered in the scope of inventory management and they include safety
inventory, replenishment lead time, inventory’s carrying cost, forecasting of inventory,
physically present inventory, available and consumed space for storing inventory, quality
management characteristics and defective goods which are returned. There are some
24
essential reasons to keep an inventory in any organization. The reasons are time, uncertainty
and economies of scale. Lead-time is a time between order placement and once the product is
received. Certain quantity of inventory should be present in stock to be used during the lead-
time. Some amount of inventory should also be kept due to uncertainty of demand. We have
already discussed the presence of uncertainty due to couple of reasons in the beginning.
Another important factor is economy of scale. In many organizations bulk buying is adapted
due to significant discount in pricing of goods.
According to Cynthia Wallin, M. Johny Rungtusanatham & Elliot Rabinovich (2006), the
typical manufacturing firm spends on average 56 cents out of every dollar of revenue (i.e. 56
percent of revenue) to cover the direct cost of purchased goods, with this percentage figure
even higher for the typical wholesaler or retailer (Monczka et al, 2002; Hansfield, 2002).
Further he refers that if we add to this figure the indirect cost of having to manage inventory
of purchase goods (which has been estimated to be 30-35 percent of the value of purchased
goods-see Chase et al, 2004), the total cost of purchased goods inventory can be quite
alarming. It means that for any time period a firm carrying $10 million in purchased items
goods will incur an additional cost of 3-3.5 million in inventory carrying and material
handling cost. Once these direct and indirect costs are reduced they can improve firms net
profit. This is the reason why different types of organizations including manufacturing,
wholesale and retail are focusing their efforts not only on direct cost but also on indirect costs
involved in inventory management. Moreover, according to Wallin et al (2006) the right
inventory management approach for any purchased item or raw material must not only
address the cash tied up in physical inventory but also the costs of planning, storing and
handling such an item or raw material.
25
Four different kinds of approaches are discussed by Wallin et al (2006), selection of which
depends on the type of an organization on which they could be applied upon:
1. Inventory Speculation
2. Inventory postponement
3. Inventory Consignment
4. Reverse inventory consignment
All these approaches are for inbound inventory of different kinds, like raw material,
components, sub-systems and retails inventory. WiChorus maintains all these types of
inventory at its own location and moreover at the location of third party manufacturers.
Relevant factors associated with these different approaches are elaborated in table 3.
26
Table 3: Four types of inventory management approaches
Inventory Management
Approach
Who owns
inventory
Where is inventory
located Benefits Risks
Inventory Speculation
Buyer Buyer
Inventory on hand to fill customers orders.
Protection against future price increases.
Volume discounts and reduced inbound transportation expense.
Inventory investment opportunity cost.
Inventory storage, handling and tracking expense.
Inventory obsolescence expense.
Inventory Postponement
Supplier Supplier
No inventory obsolescence expense
No inventory investment opportunity cost.
No inventory storage, handling and tracking expense.
Lost sales when inventory is not available in time to meet customer demand.
Higher inbound transportation expense.
Subject to future price increase.
Inventory Consignment
Supplier Buyer
Inventory on-hand to fill customer orders.
No inventory investment opportunity cost.
No inventory obsolescence expense.
Inventory storage, handling and tracking expense.
Subject to future price increases.
Reverse Inventory
Consignment Buyer Supplier
Inventory on-hand at supplier location.
Protection against future prices increases.
No inventory storage, handling and tracking cost.
Inventory investment opportunity cost.
Inventory obsolescence expense.
Source: (Wallin et al, 2006)
27
We agree with the findings of Dave Janiga (2005) that there are five most important factors
that needs to be considered while making inventory management strategies and these are:
1. Inventory Carrying Cost.
2. Understanding company’s stock
3. Replenishment quantity
4. Re-order point
5. Inventory cost reduction opportunities
3.1.1 Inventory Carrying Cost
Carrying costs in any manufacturing facility are associated with holding or carrying
inventories for a certain time period. An organization or company has to spend additional
money on the top of purchase cost in the form of storage (space), insurance, additional
equipment and personnel, clerical support, processing, forms and taxes. Most of the time
carrying costs is varying, depending upon certain factors. It is estimated that carrying costs
are 18 to 25 percent above the inventory’s value (Dave Janiga, 2005).
3.1.2 Understanding Company’s stock
The second factor that affects the strategy of company’s inventory management is the role of
the inventory itself. At WiChorus three types of inventories are maintained namely raw
material inventory, semi finished product inventory and finished product inventory.
Inventory is normally composed of two types of stock. First one is the working stock and the
second is safety stock. The first one supports the day-to-day operation of a company. And it
28
will continuously change as the component/material is consumed and replenished. The
second one, which is a safety stock, is basically kept for any unforeseen circumstance. It acts
as a barrier to shortage. It is not used under ideal operations but consumed as required, due to
unexpected events including high consumption rate. Safety stock can be taken as insurance
policy against any unexpected event and it is replenished once inventory goes below safety
limit or point.
3.1.3 Replenishment Quantity
It can be mentioned as the amount or quantity to fill the place of used inventory items.
According to Dave Janiga (2005), the most economic replenishment quantity, commonly
called the EOQ (Economic Order Quantity), represents the lowest total sum cost of total
inventory and inventory acquisition costs (order placement costs, invoice processing costs,
payable costs, freight etc.). Once replenishment order quantity is large, then less number of
replenishment cycles is required to meet the demand, and when order replenishment quantity
is small then more number of replenishment cycles is required to meet the demand. The
economic order quantity can be elucidated as the function of consumption rate, inventory
value, inventory acquisition cost and inventory carrying cost.
3.1.4 Reorder Point
The reorder point can be mentioned as a point at which components/items must be ordered to
replenish the inventory to a certain level. It can be determined by the rate of consumption and
the order lead-time given by supplier. It can be explained by following example: If a
manufacturing facility consumes 20 items per day and it takes 10 days to receive material
from supplier once order is placed, the items should be reordered when the quantity of
29
working stock reaches 200. If the company’s inventory management system is working at
100 percent (ideally speaking), the quantity of company’s working stock will get to zero as
replenishment arrives.
3.1.5 Inventory Cost Reduction Opportunities
It is not unproblematic to reduce inventory and inventory related cost without getting to the
point where company’s both kinds of stocks reaches zero. Some amount of risk is always
involved in reducing inventory related costs. Operations and supply chain people should
work with the suppliers to shorten order replenishment cycle fulfillment time in order to
minimize the need of safety stock. In order to reduce the inventory related cost another
technique would be to follow product consolidations. Here product consolidation stands for
reducing the different types of safety stock into one typically smaller safety stock. Once the
products are consolidated the consumption rates becomes higher, which in turn makes
smaller order quantities more economical.
3.2 Optimized Inventory Management
According to Terry Harris (1997), optimized inventory management eliminates the arbitrary
A, B, C classes (these classes are with respect with sales, quantities, cost or combination of
these) prevalent in finished goods reorder point systems by treating every item individually
(every time becomes its own class). This approach also optimizes availability of inventory (in
terms of per dollar invested in inventory). According to him this approach is better than any
other approach with respect to the availability the inventory provides and the investment
required by it. In the same case study it is mentioned that a distributor Cummins Engine
30
Company, service parts inventory was reduced by 32% and availability was increased from
81% to 90% within the time period of three months. This study was carried out for finished
goods inventory, but at WiChorus we are dealing with raw material inventory and semi
finished goods inventory in addition to finished goods inventory.
The time taken to convert semi-finished goods to finished goods, ready for delivery to
customers is not too long. Due to the reason that WiChorus produces specialized products, its
customers sometimes are willing to wait for a day or two to obtain the equipment, but not
later than that. The basic reason of having finished goods inventory is to provide required
items to customer, when they require and how much they require. If the items are not
available, than the customers go elsewhere and sale is lost. This not only impacts the
immediate sale but it also hurts future business with the same customer. Since the lifecycle of
products that WiChorus produces is short, so the lost sales could impact more.
Figure 11: Maximizing availability per dollar
Source: (Terry Harris, 1997)
31
According to Terry Harris (1997), as can be seen in figure 11, availability is the percent of all
items that can be supplied from inventory over a specific time period. The mentioned time
period can be an arbitrary time, lead-time or review time. The value of inventory mentioned
is for the average inventory, over the same time period at a certain cost. In figure 11 optimal
curve is generated from a series of points. Specific stocking strategy is represented item-by-
item, by each point. Each strategy represented here is optimal, at the dollar level of that point.
No other stocking strategy can give a higher or equal availability level at the dollar level of
that point. There is another curve of some other approach, which is above the optimal curve.
As per Terry Harris (1997), an inventory manager can optimize inventory in one of two ways
and these are:
1. Specify an availability level and achieve at the lowest possible investment.
2. Specify an investment level and use it to achieve the highest possible availability of
items.
Figure 12: Improvements over some other approaches
Source: (Terry Harris, 1997)
32
Figure 12 describes the progress over some different approach. Whenever we depict curves
by using some other technique, it should be worse than the optimal curve. Every point on the
other curve is on the left side of corresponding availability point on the optimal curve.
Moreover, it is above the optimal curve. Improvements can be made if we pursue following
steps. The inventory manager or planner can move from any point on the other curve to the
that of the optimal curve and in result increase the availability at the same dollar value. And
decrease dollars at the same availability level, or do some of the both strategies. There are
some enormous improvements has been achieved by the companies who had followed the
same strategy. Terry Harris (1997) has mentioned the improvements as follows. Inventory
reductions above 40% and product availability increase by 10%.
Figure 13: Target vs on-hand
Source: (Terry Harris, 1997)
Figure 13 is taken from the same research paper of Terry Harris (1997). The positions on the
dollar (figure 11) and availability (figure 12) figures for all the approaches, whether they are
33
optimized or not, are the targets. These are the targets at which inventory manager or planner
can aim. Although the aim is to achieve the target but it cannot be achieved fully. This is due
to the reason that actual forecast and lead-times are never fixed (as in ideal condition), but
they are variable. Optimized approach has confirmed it that lead-time variability and forecast
changes are intuitive.
Figure 14: Optimized approach fits inside current systems
Source: (Terry Harris, 1997)
According to Terry Harris (1997), optimized inventory management fits inside normal re-
order point systems. The optimized approach uses item level data, like cost and lead-time
(refer figure 14). Very simple interface files can be used to make data available for an
optimizing module. That in turn produces optimized re-order points as per different items.
34
3.3 Time As A Key To Inventory Management
The pattern of high level of inventory precision and reduced inventory cost that were once
standards for material management have had another crucial element added. This is the
element of time. According to Patrick C. Scanlon (1995), in today’s globally competitive
marketplace, time has become the vital factor to effectively manage inventory. This has also
been witnessed by the development of just in time(JIT) approach and Kanban systems. These
methodologies were developed to cater for world-class costumer services. For certain
markets the standards for time measurements have become hours, days and weeks instead of
months or years as a quarter. In the case of WiChorus, question is asked that how much
inventory do we need to satisfy customer’s demand with avoiding the pitfalls of creating
excess inventory, while reducing the cycle time? As per Patrick C. Scanlon (1995), an
additional tool that is called week-on-hand reporting system were developed at Rosemount
Analytical Inc. The design of this system was helpful to project forward inventory usage I
dollars. Moreover this system was helpful to identify potentially excess or obsolete inventory
before it becomes liability for the Rosemount Analytical. The results for the time span of four
years were 50% inventory reduction and increase of on-time performance from 70% to 92%.
In addition to this, stopping shortages were also eliminated.
Now a days trend is shifted towards shorter life cycle products in several industries.
According to Abbas A. Kurawarala& Hirofumi Matsuo (1996), the shorted life cycles are
caused by fast changing consumer preferences that lead to fashion and fad effect. These
trends are also due to rapid rate of innovation, especially in the computers industry, consumer
electronics and high technology products as in the case of WiChorus Inc.
35
Due to the mismatch of supply and demand, inventory exists in a supply chain. The
important role of inventory in the supply chain is that, it can be used to increase the amount
of demand that can be satisfied by having the product ready and available when the customer
needs it. Throughout in the process of supply chain, inventory is held in the form of raw
materials, work-in-process and finished goods. It is a major source of cost in the supply chain
and has a huge impact on responsiveness.
Inventory is considered to be as a cost to the company. Inventory is the static money that
should be as least as possible. Therefore, the target for any manufacturing company is to
keep the inventory as minimum as possible. Reducing the inventory ultimately increase the
revenue generated and ultimately increase the net profit.
4. Literature Survey
Various credible sources were used for the research purpose; these sources are used to
optimize the inventory management process. Different research papers were reviewed in
detail in order to retrieve the information that is relevant with the company’s infrastructure,
products and manufacturing methods. Two books related to Supply Chain Management were
short-listed which consists the different techniques that can be applied for calculation of
various aspects like quantity; cost and lead-time related time frames. Details of the case
studies and research papers that are short-listed are given as follows:
4.1 Lean Supply: The Design and Cost Reduction Dimension
This research paper is taken from European Journal of Purchasing and Supply Chain
Management, and it is written by Ronan McIvor in July 1999. The publisher of this journal is
36
Elsevier. The objective if this research paper is to analyze whether the principles of lean
supply model are currently present in the electronics industry. This research basically focuses
on the two key dimensions of lean supply chain management. The first one is the supplier
involvement in customer design activities. And the second one is joint buyer-supplier cost
reduction. The products being produced by WiChorus are customized according to different
requirements of wireless data service providers. Moreover this research has revealed that
considerable barriers currently exist to meeting the requirements of equality between partners
and the mutual sharing of benefits.
4.2 Designing Supply Chain: Towards Theory Development
This paper is taken from International Journal of Production Economics. The authors of this
research paper are Mark A. Vonderembse, Mohit Uppal, Samuel H. Huang and John P.
Dismukes and it was completed in March 2002. The publisher of the journal is Elsevier. This
research paper describes a typology for designing supply chains that work in harmony to
design, produce and deliver products with different characteristics and customer
expectations. This study blends literature and theory development with case study research to
create a typology. Moreover this paper discusses supply chain types that are compulsory for
success transversely three types of products. It also develops a framework for categorizing
the supply chain types according to the characteristics of the products.
4.3. Modeling the metrics of lean, agile and leagile supply chain: An
ANP-based approach
Asish Agarwal, Ravi Shankar and M.K. Tiwari carried out this research. It was published in
37
December 2004 and it is taken from European Journal of Operational Research. The
publisher of this journal is Elsevier. The emphasis of this paper is on adaptability to changes
in the business environment and on addressing market and customer need proactively.
Flexibility is fundamentally needed in the supply chain in order to counter the uncertainty in
decision parameters. Moreover this paper explores the relationship among service level,
quality, cost and lead-time and agility and leanness of supply chain.
4.4. Dynamic modeling and control of supply chain systems: A review
This research paper is taken from the journal of Computers and Operational Research and it
was published in February 2007. The authors of this research paper are Haralambos
Sarimveis, Panagiotis Patrinos, Chris D. Tarantilis, Chris T. Kiranoudis. All the authors are
professors from Department of Management Science and Technology, Athens University of
Economics and Business and School of Chemical Engineering National Technical University
of Athens. The publisher of this journal is Elsevier. This paper emphasizes the fact that
supply chains are complicated dynamical systems, which are triggered by the demands of the
customers. Proper selection of equipment, machinery, buildings and transportation fleets is a
key component of supply chain systems. Efficiency of supply chains most of the times
depends upon management decisions, which are based on experience and intuition. The main
goal of this research paper is that a joint co-operation between control experts and supply
chain managers has the potential to introduce more realism to the dynamical models and to
develop improved supply chain management policies.
4.5. Supply chain simulation- a tool for education, enhancement and
endeavor
The authors of this paper are Matthias Holweg and John Bicheno. It is taken from
38
International Journal of Production Economics. The authors are the professors in Lean
Enterprise Research Centre, Cardiff Business School, Wales. This paper was published in
November 2000 and the publisher of this journal is Elsevier. This research paper describes
how a participative simulation model can be used to demonstrate supply chain dynamics and
to model possible improvements to an existing supply chain. If experiences are presented
using supply chain simulations, then it is a tool to demonstrate and discuss supply chain
improvements by simulating individual characteristics in order to deploy holistic
improvements. A three-year research project in the steel supply chain for automotive industry
revealed that lack of understanding of core processes throughout supply chain causes
amplification and distortion of both supply and demand patterns.
4.6. Supply Chain Management: Strategy, Planning and Operation
This book is written by Sunil Chopra, Dharam Vir Kalra and Peter Meindl. It is used as a
course textbook for ISE-245 (Advanced Supply Chain Engineering) at San Jose State
University. The publisher of this book is Pearson Prentice Hall and it is third edition, which
was published in 2007. It is a credible book with the details of various techniques for the
formulation of supply chain strategies and their optimization supported by various case
studies. Sunil Chopra is the IBM Professor of Operations Management and Information
Systems ate Kellog School of Management. He is a PhD in Operations Research from SUNY
at Stony Brook. Peter Meindl is Finance and Economics PhD candidate in Stanford
University’s Management Science and Engineering Department. His research focuses on
portfolio optimization and dynamic hedging using stochastic programming, Monte Carlo
simulation and receding horizon control. Dharam Vir Kalra is retired army officer and holds
39
a MSc in defense studies from Madras University. He is a guest faculty at the Indian Institute
of Foreign Trade, New Delhi, and Institute of Management Technology where he teaches
supply chain management.
The goal of this book is to cover not only high-level supply chain strategy and concepts, but
also to give its readers a solid understanding of analytical tools necessary to solve supply
chain problems. In the beginning of the book strategic importance of good supply chain
design, operations and planning is discussed and highlighted. It is also emphasized that how a
good supply chain management can be a competitive advantage in a firm, and weaknesses in
the supply chain can hurt the performance of a firm. The Strategic frameworks and concepts
discussed in the book are supported with a variety of actual examples that shows how a
combination of concepts is needed to achieve significant increases in performance. The
techniques elaborated in this book will be highly beneficial for the optimization of WiChorus
supply chain.
4.7 Simulation using Promodel
This book is written by Charles R. Harrell, Biman K. Ghosh and Royce O. Bowden and it
was published in July 2003. It is a second edition and publisher of this book is McGraw-Hill.
Charles R Harell is professor of Simulation Modeling at Brigham Young University, UT.
Royce O. Bowden is an associate professor of Industrial Engineering at Mississippi State
University and Director of the Simulation and Advanced Computation Laboratory. This book
covers the art and science of simulation with the help of promodel software version 6.0. It
provides a deeper coverage how random behavior is simulated and how output results are
generated and evaluated. Case study assignments are included in this book to have deeper
40
learning of promodeling by presenting actual application in business, services and
manufacturing. It starts from simulation basics and further covers planning, data collection
and analysis, model building, model verification and validation, experimental analysis and
output analysis. It covers simulation optimization using modern techniques. This book will
be used as a learning tool for doing simulation modeling of WiChorus Inc. as some chapters
of this book are devoted to typical modeling issues encountered in manufacturing, material
handling and service system.
4.8 Time as a Key to Inventory Management
This research paper is taken from Production and Inventory Management Journal and it was
published in second quarter of 1995. The author of this research paper is Patrick C. Scanlon.
At the time when this article was written he was materials manager with Rosemount
Analytical Inc. This company was located in La Habra, California and he already had an
experience of over 20 years in inventory management, at the time ofpublish of the article.
This research paper emphasizes the addition of the element of time, in inventory
management. He had introduced a new tool for inventory management for critical review of
inventory being held at Rosemount Analytical Inc. The name of the tool was weeks-on-hand-
reporting system. This tool was designed to project forward inventory usage in dollars, and
identify potential excess or obsolete inventory before it becomes a liability (Patrick C.
Scanlon, 1995). This research paper has given an in-depth knowledge regarding the usage of
critical resource like time in the process of optimization of inventory management.
41
4.9 Optimized Inventory Management
The author of this research paper is Terry Harris and he was the member of Chicago
Consulting at the publishing time of this article. This research paper is taken from
Production and Inventory Management journal. The volume of the journal is second quarter,
1997. This research paper has helped us a lot in the formulation of hypothesis, recognizing
the problem sand recommending its solution by doing optimization of inventory
management. Optimized inventory management eliminates the arbitrary A, B, C classes
prevalent in finished goods reorder point system by treating every item individually-every
item becomes its own class (Terry Harris, 1997). Different techniques were applied to reduce
the lead-time and inventory quantity at various stages of production. These techniques have
supported our goal to increase the net profit of WiChorus Inc. by doing its inventory’s
optimization. Author had lead a project related to optimization at Cummins Engine Company
and they were able to reduce the inventory investment by 32% and had increased product
availability from 81% to 90%.
4.10 Five Keys to Effective Inventory Management
This article was published in the magazine, Plant Engineering, in June 2005. Plant
Engineering is published by Reed Business Information, which is a subsidy of Reed Elsevier
Inc. The author of this article is Dave Janiga who works at ExxonMobil Lubricants and
Specialties. Maintaining an efficient inventory management strategy can help industrial plant
managers to improve and ensure the success of their operations (Dave Janiga, 2005). When
inventory management is considered than there are some important factors that also needs to
be taken into consideration. These factors include understanding your stock, replenishment
42
quantity, lead-time, re-order point and most significant is lead time. These factors were
considered while carrying out the optimization of inventory management of WiChorus as
they are described in this article. Although the article was written while taking into
consideration oil industry(as the author works in ExxonMobil Lubricants), but the provided
techniques are applicable to any manufacturing company.
5. Manufacturing Process at WiChorus
Inventory is managed at WiChorus at different stages, which is enumerated in figure 15.
Designing of PCB is carried out at WiChorus facility. Once designing is completed, a Gerber
file is sent to 3rd party PCB manufacturer(A). PCB manufacturing(A) is carried out there and
after its completion, they carry out quality assurance. In the mean time raw material
(expensive parts) is procured according to forecast by sales department of WiChorus. These
expensive parts are held at WiChorus facility, till the time they are required for PCB
assembly manufacturer(B). In addition to these parts there are many other parts, which are
held at WiChorus till the time they are required for final product manufacturing and
assembling.
43
Figure 15: Manufacturing process and inventory locations at WiChorus Inc.
44
Additional raw material (low cost items) is held at 3rd party manufacturer(B), and they charge
some amount of inventory carrying cost to WiChorus. These are the parts that are used for
PCB assembly manufacturing(B) by third party manufacturer. After PCB assembling quality
assurance is carried out in conjunction with WiChorus experts at thirds party
manufacturer’s(B) facility. These are called as semi-finished product or work-in-process
inventory. Certain amount of this inventory is carried at WiChorus for the ease of quick
assembling/fabrication. Final assembly and testing is carried out at WiChorus facility. After
that equipment is packaged and shipped to customers.
6. Simulation
Our project is based on the simulation modeling, which is used to optimize the inventory
management process. One of the significant methods to optimize any inventory system is by
doing simulation modeling. A simulation comprises of hardware and software systems,
which are used to mimic the activities of some manufacturing phenomenon. Typically, the
phenomenon or entity being simulated is from the field of the tangible, ranging from the
behavior of a light aircraft during wind shear to the operation of integrated circuits.
Simulation may also be used to investigate and verify theoretical models, which is very
difficult to grasp conceptually.
One of the key advantages of simulation is that it is able to provide us with practical
feedback, which helps in designing real world systems. This allows the design engineers to
understand the correctness and competence of a design, before the system is actually
constructed in real time. Consequently, the user can explore the merits of alternative plan
without physically constructing the systems. By examining the effects of specific design
45
decisions during the design phase rather than constructing the system in real time, the overall
cost of building the system in actual, reduces significantly.
Another advantage of simulation is that they permit system design engineers to understand
the problem at several different levels of construction. By approaching a system at a higher
level, the designer is in better position to understand the behaviors and relations of all the
high level components within the structure, and is therefore well equipped to work against
the complication of the whole system. The system designer may easily overcome this
complexity if the problem had been approached from a lowest level. It is better to understand
the system of the higher-level components by the use of simulator, and then lower level
components may then be designed and after that system will be simulated for verification and
for evaluation of performance.
Simulation is very frequently used system in designing and analysis of system. There are
different reasons of using a simulation in our project.
1. Simulation can be used to analyze the system of different level of complexity.
2. It permits change to operating system at any time interval, in designing process without
any practical implementation.
3. It predicts the future change by having the change in present value.
In addition, the visual representation is often an important point that can sell an idea to
management or other key decision (Rohrer, 1996).
.
46
Figure 16: Simulation Modeling Flow
Source: Retrieved from Simulation Model Building for Intersection (Nov,
2008)
“The entire system may be built based upon this ``top
is often referred to as hierarchical decomposition
simulator which deals with the construction of complex systems.” (Craig, 2006)
Simulation reduces the uncertainty in the pro
the future of the present input. In this manner company can easily target their goal and
can also achieve them successfully. Considering current trend in industries,
is also looking for the Six Sigma implementation. “Six Sigma at many organizations
simply means a measure of quality that strives for near perfection. Six Sigma is a
disciplined, data-driven approach and methodology for eliminating defects (driving
towards six standard deviations between the mean and the nearest speci
any process - from manufacturing to transactional and from product to service.”
(www.isixsigma.com, February
Figure 17: A plot of Normal Distribution
Source: Retrieve from www.wikipedia.com/stardard deviation (Feb,
47
ntire system may be built based upon this ``top-down'' technique. This approach
hierarchical decomposition and is essential in any design tool and
simulator which deals with the construction of complex systems.” (Craig, 2006)
Simulation reduces the uncertainty in the process because with its help
the future of the present input. In this manner company can easily target their goal and
achieve them successfully. Considering current trend in industries,
is also looking for the Six Sigma implementation. “Six Sigma at many organizations
simply means a measure of quality that strives for near perfection. Six Sigma is a
driven approach and methodology for eliminating defects (driving
owards six standard deviations between the mean and the nearest speci
from manufacturing to transactional and from product to service.”
February 2009)
Figure 17: A plot of Normal Distribution
Retrieve from www.wikipedia.com/stardard deviation (Feb,
2009)
down'' technique. This approach
and is essential in any design tool and
simulator which deals with the construction of complex systems.” (Craig, 2006)
we can foresee
the future of the present input. In this manner company can easily target their goal and
achieve them successfully. Considering current trend in industries, WiChorus
is also looking for the Six Sigma implementation. “Six Sigma at many organizations
simply means a measure of quality that strives for near perfection. Six Sigma is a
driven approach and methodology for eliminating defects (driving
owards six standard deviations between the mean and the nearest specification limit) in
from manufacturing to transactional and from product to service.”
Retrieve from www.wikipedia.com/stardard deviation (Feb,
48
The production process of WiChorus is based on Build to Order (BTO) phenomenon. BTO
organizations can be explained as the manufacturing organizations where products are made
after the arrival of the order. WiChorus keep a safety inventory and buys many of the parts as
per order and do the process. WiChorus requires simulation because it keeps an inventory in
advance and also itrequiresit to know the process time in each and every step and further to
track it. In our project we are using software “ProModel” for the purpose of simulation. “The
advantages of special purpose simulation tools over general purpose languages are that, they
are easier to learn, generally very little programming knowledge of simulation is needed to
operate the system. In addition, the graphical component in these programs gives another
presentation medium.” (Schniederjans, Olson, 1999)
7. ProModel
The ProModel is a powerful simulation tool for modeling all the different types of processes
and it is mostly used for manufacturing process. Process or a system, ranging from very
small to large and complex mass production, can be modeled by the ProModel software. In
WiChorus, the production is not the mass production type, but they have large inventory
process and flexible manufacturing process. ProModel is Windows based software with a
intuitively available graphics. It has an outstanding graphical interface and object-oriented
modeling structure, which constructs the model and eliminate the need for programming. It
has combination of the flexible general-purpose simulation language with the expediency of
a data-driven simulator. ProModel is considered to be the best optimization tool for the
process like WiChorus have for its inventory.
49
In WiChorus, we targeted to reduce the cost and time as minimum as possible with the
available resources.
Following advantages can be achieved by reducing the total cost:
• Reduce the product cost
• Increase the profit
• Use the saved amount and resources for research and development purpose
And by reducing the time, advantages that we are providing areas follows:
• Reduces the time to market for the WiChorus products
• Quick response to orders.
• Better customer service
• Reducing time gives an edge over competitors
The developed simulation model is completely object oriented and graphical. To the highest
degree possible, all inputs are given graphically. Moreover, all the given information is
grouped by presenting an object and its type, and in a spreadsheet-like format. Its purpose is
for quick and intuitive access. ProModel works with Graphical User Interface (GUI)
standards, it means that modeler who is familiar with other standard of Windows software
like word processors or spreadsheets will have no problem in learning and using ProModel.
This way of data input approach, helps in reducing the learning curve for us to make a model
for WiChorus and maximizes the efficiency and accuracy for modifying large and complex
50
models as for WiChorus Inventory process. Development of animation is integrated with the
definition of model.
“A major drawback of many simulation software products is that their animation
development is independent from simulation model development. This makes it time
consuming and inconvenient for engineers to use animation as a
validation/verification tool. ProModel integrates system definition and animation
development into one function.” (Harrell, Field, 2001)
7.1 Uncertainty measurement in ProModel
Considering the unpredictable economy in current market in USA, uncertainty factor is also
very important factor in designing the model. Considering that WiChorus also has to consider
the uncertainty of the market and unpredictability of the time and money, ProModel is
considered to be the best solution for the inventory supply chain modeling. Variability is
related to many factors, it can be internal or external, may be controllable to some degree or
uncontrollable. Risk is defined as “A state of uncertainty where some of the possibilities
involve a loss, catastrophe, or other undesirable outcome” (Wikipedia, 2009) Uncertainty is
defined as “The lack of complete certainty, that is, the existence of more than one possibility.
The "true" outcome/state/result/value is not known.” (Wikipedia, 2009)
In his seminal work Risk, Uncertainty and Profit, Frank Knight (1921) established the
distinction between risk and uncertainty.
“... Uncertainty must be taken in a sense radically distinct from the familiar notion of
risk, from which it has never been properly separated. The term "risk," as loosely
51
used in everyday speech and in economic discussion, really covers two things which,
functionally at least, in their causal relations to the phenomena of economic
organization, are categorically different. ... The essential fact is that "risk,” means in
some cases a quantity susceptible of measurement, while at other times it is
something distinctly not of this character; and there are far-reaching and crucial
differences in the bearings of the phenomenon depending on which of the two is
really present and operating. ... It will appear that a measurable uncertainty, or "risk"
proper, as we shall use the term, is so far different from an immeasurable one that it is
not in effect an uncertainty at all. We ... accordingly restrict the term "uncertainty" to
cases of the non-quantitative type.”
ProModel consider the standard deviation of the process involved in the entities. Every single
process has their standard deviation in their time delay.
Table 4: Process time of expensive inventory items
S No. Name Process Time
(min)
Std Deviation
(min)
1 Chips 60 5
2 PCB 40 4
3 Network Board 5 1
4 Chassis 20 3
5 Bezels 5 1
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6 Filler Assembly 2 1
7 Face Plate 2 1
Table 4 shows the standard deviation of the entities process time. These deviation are taken
as normally distributed to the process time represented as N(x,y).
where x; process time which is normal
y; deviation from the normal.
ProModel has a flexibility feature that it can automatically put a given deviation randomly to
the process time to consider the uncertainty from the normal values.
7.2 Modeling Elements
ProModel has structure built on the modeling element and the model to build is represented
physically and logically by the building block. The inventory supply chain of WiChorus is
built on the building block provided by the ProModel. Different physical elements involve in
the process as in our case like Chases, PCB, ICs etc are represented graphically in the model.
53
Figure 18: ProModel’s Modeling Element
Source: Simulation modeling and optimization using ProModel (Benson, 1997)
Table 4 shows the list of the entities we used in our simulation models. Gerber file is the
design file that goes to Network Board Assembler and other entities are the raw materials
that arrive at WiChorus and become the integrated part of SmartCore products. Figure 18 is
the picture of the ProModel showing the options for building the entities.
7.3 Locations
These are a fixed places in the model’s design e.g. machines, warehouse, workstations,
shelves, etc. These are the places where above-mentioned entities are routed in their
processing. These locations are either
machines like multiple locations. These are the places wh
and to make some decision for further rout
These routing locations may have a capability greater than one entity to process and may
have periodic downtimes as a function of usage time (e.g. tool wear), clock time (e.g. shift
changes), usage frequency (e.g. change a dispen
(e.g. machine setup) or it can be dependent on some user defined situation
Figure 19: ProModel picture showing the different location in design Model
are also used to assign the input and o
selecting which entity toprocess next while output rules are of purpose for ranking entities
(i.e. LIFO, FIFIO, user-defined) in a location where they have multiple capacities. Queues
54
are either physical unit, like a single machine or a group of
like multiple locations. These are the places where these entities are routed
to make some decision for further routing as shown in figure 21.
These routing locations may have a capability greater than one entity to process and may
have periodic downtimes as a function of usage time (e.g. tool wear), clock time (e.g. shift
changes), usage frequency (e.g. change a dispenser after every n cycles), change of material
(e.g. machine setup) or it can be dependent on some user defined situation. Routing locations
: ProModel picture showing the different location in design Model
the input and output rules. These input rules are of purpose for
selecting which entity toprocess next while output rules are of purpose for ranking entities
defined) in a location where they have multiple capacities. Queues
like a single machine or a group of
these entities are routed simply
These routing locations may have a capability greater than one entity to process and may
have periodic downtimes as a function of usage time (e.g. tool wear), clock time (e.g. shift
ser after every n cycles), change of material
. Routing locations
: ProModel picture showing the different location in design Model
utput rules. These input rules are of purpose for
selecting which entity toprocess next while output rules are of purpose for ranking entities
defined) in a location where they have multiple capacities. Queues
55
and Conveyors are two special types of locations that offer movements as well as performing
holding and operation functions. Conveyors have a load spacing and particular to take the
entities from one location to the others. On the other hand queue work as of waiting lines,
which includes the movement of those entities, which has to be shifted through line.
Conveyors are the networks, which are provided to provide interconnection.There are
different locations in WiChorus where the entities are being routed as per requirements.
Following is the list of locations involved in the inventory supply chain of WiChorus.
Table 5: Name of the Locations in design model
S No. Name of Locations
1 Design Department (WiChorus)
2 Warehouse (WiChorus)
3 PCB manufacturer
4 PCB Assembler
5 PCB testing (WiChorus)
6 Configuration (WiChorus)
7 Production Department
(WiChorus)
8 QA Department (WiChorus)
56
7.4 Entities (or parts)
The items being processed in the system are called entities. These can be consists of piece
parts, WIP, raw materials, finished products, assemblies, loads etc. The entities being
processed may be of same types. These same or different type entities may merge into a
single entity, or single entity can be separated into two or more same types of entities or
change into different types of new entities. “Entities may be assigned attributes that can be
tested in making decisions or for gathering specialized statistics.” (Benson, 1997)
Table 6: Name of Entities for WiChorus Simulation model
S No. Name
1 Chips
2 PCB
3 Network Board
4 Chassis
5 Bezels
6 Filler Assembly
7 Face Plate
8 Gerber File
57
7.5 Path Networks
Path networks define the possible paths on which the entities and resources, which are in
designing process, may travel when moving in between locations or through the system. Path
networks are made of nodes connected by its segments and defined by simple mouse click
graphically. These networks may be defined as multiple path networks or multiple entities
share the same network. Such movement along a path network may be describes in terms of
speed or distance. On defining the layout scale, path distances can be automatically
computed.
ProModel have three types of networks:
• passing
• non-passing
• crane
For open path movement mostly a passing network is used where resources and entities are
free to surpass one another. Non-passing networks are those networks that consist of guide
paths of single file tracks. Crane networks describe the operating interface and envelop points
for bridge cranes.
7.6 Resources
A resource may be a tool, person, vehicle or other things that may be used to:
• Transport entities or parts between routing locations.
• Perform an operation on the entities at a location.
58
• Perform repairing or maintenance on a location or other resource to avoid or reduce
down time.
Resources may be either dynamic assigned to a path network or may be manually. Crane
orforklift is the special kind of dynamic resources. Since in WiChorus the entities are of
small size, mostly the human resources are used. It has an option of built-in decision rules
program that can be used for resource allocation and part prioritization of the entities pickup
and their delivery.
7.7 Processing or Routing
Processing or routing defines the processing sequence of the entities and their flow logic in
between the allocated routing location.
“The operation or service times at locations, resource requirements, processing logic,
input/output relationship, routing conditions, and move times or requirements can be
described using the processing element.” (Benson, 1997)
Operation times can be defined by distributions, functions constants, attributes, subroutines,
etc. or a combination of these having an expression. There are different logics available for
routing in ProModel
• IF-THEN-ELSE statements
• Loops
• Nested statement blocks
• Subroutine calls.
Some statements are related to resources that are used with Boolean expression
• GET
59
• USE
• JOINTLY GET
Some statements are built-in operation which use
• ACCUM
• JOIN
• GROUP
These statements greatly simplify the logics otherwise logics will be complex and required
processing might be time consuming.
7.8 Arrivals (or production schedule)
This element is used to model Conditional, Stochastic or Deterministic arrivals. ProModel
has feature that the external files, which may be arrival data or production schedules, can be
read into the arrivals element. Table 7 shows the list of the arrivals at different locations in
design model for WiChorus inventory simulation. ProModel can also use user defined or
built-in spread sheet or distributions to define the internal arrival frequency, quantities and
times.
Table 7: List of Arrivals at different Locations
S No. Name
1 Chips
2 PCB
3 Network Board
4 Chassis
5 Bezels
6 Service Card
60
7.9 Shifts (or work schedules)
ProModel has a powerful feature to define the work by own used and schedules can be
broken through ProModel's shifts module. Break and work schedules are graphically defined
as time and day of the week. After that locations or resources are allocated to a particular
shift schedule. In addition, shift and break logic can be defined as, that controls resources and
location actions when they become off-line and the behavior once they become off-line
7.10 Scenarios And Runtime Interface
“The runtime interface (RTI) is a convenient and controlled environment for modifying
selected model parameters (capacities, operation times, etc.) without having to change the
model data directly.” (Benson, 1997)
It provides an investigational environment, which allows the users to permits numerous
scenarios to be designed, defined and simulated. The Runtime Interface can be approached at
the start of a simulation run for the purpose of making modifications for a single simulation
run or for the modification of alternatives for doing the analysis of multiple scenarios.
7.11 Additional Modeling Elements
There are additional modeling elements available to make an extra options and functions to
the model.
• Variables
• Attributes
• Arrays
61
• Macros
• Subroutines
• External Files
Figure 20: More Elements Menu
Source: Promodel using Simulation (Benson, 1997)
8. Method of Investigation
8.1 Simulation Model
The simulation model locates the machine location in accordance to the layout and design the
process flows as follows the flow chart shown in Figure 21.
62
8.1.1 Capacity Inputs
The information provided in this module indicates the number of machines in each process
and the schedule cycles of workers to operate. These aids in determining the amount of
“capacity” should be in each time horizon.” (Tearwattanarattikal et al, 2008)
Figure 21: Simulation Model Data Flow
8.1.2 Product Specific Data
Processingdata required each product type such as setup, load-unload time, production rates,
flow line and processing batch size.
63
8.1.3 User Specific Data
In ProModel user can be able to customize the simulation design by altering certain
circumstances in the model.
8.1.4 Scheduling Production Plan Data
The sequencing time table of product items for production to follow, this aims to find the
ability of the plant to complete the demand within the limitation period, one month.”
(Tearwattanarattikal et al, 2008).
Since WiChorus is not a mass production organization, they produce an average of 15 units
of SmartCore every month. They do the forecasting by using a survey method from
marketing and sales persons. We started our investigation by analyzing the current inventory
process in WiChorus. As per our scope of project we are only focusing on the production on
SmartCore but there are few other products that are being manufactured at WiChorus.
SmartCore is the prominent and revenue driven product among all. Research has been done
on the current and past data to find the values and numbers to design our model. There are
always two major factors in any inventory process.
• Cost (This is cost of every entities)
• Time (Time to do the particular process or process time)
To optimize the inventory process these two factors should be the major part in investigation
as in our project. We did not include cost as a direct input but we consider the entities that are
the cost driven in the production of SmartCore. But the time is the direct input in designing
the simulation model.
64
8.1.5 Forecasting
Forecasting has been done in WiChorus through surveying from marketing and sale
personnel. Forecasting is the first input data to the simulation model. This forecasting data is
obtained as month-by-month basis. Forecast data is adjusted in the arrivals at locations where
they arrive. In our research we have found the following forecasting data from year 2009 for
the SmartCore.
Figure 22: Forecast of demand of SmartCore for 2009(1st Quarter)
We used the forecasted data because when the simulation will be used, the input will be the
forecasted data. Purpose of the simulation is to foresee the future and to analyze and alter the
currents inputs to the system considering the future situation. Above table shows that demand
for January 2009 and February 2009 is15 units and for March 2009 is 16 units. Taking an
average we use 15 units demand for our model.
14.4
14.6
14.8
15
15.2
15.4
15.6
15.8
16
16.2
Jan (2009) Feb (2009) March (2009)
Demand Forecast (2009)
No. of SmartCore
65
Following data shows the demand for individual parts for the single and the number of unit
being ordered for the parts. This data is given by the WiChorus, it shows the number of parts
they ordered in a single order.
Table 8: Number of parts being ordered
S No. Name
Required Qty for 1
unit Order Qty
1 Chips 1 20
2 Service Card 2 30
3 Network Board 1 20
4 Chassis 1 15
5 Bezels 1 30
Above table shows that Chassis are ordered in exact quantity, which is 15 numbers for 15
forecasted units. Whereas the other order quantities are ordered greater than the required
quantity. It happened because WiChorus is always ordered in greater due to safety
considering an uncertainty in demand. This problem can be overcome by using of simulation.
They always keep a safety inventory in order to be safe from shortage of resource available
in case of unforeseen situation.
66
8.1.6 Inventory Process
Inventory process starts with the arrival of the entities or parts at WiChorus. Each part has
their lead-time before arrival to WiChorus.
Parts that arrives WiChorus have the lead-time depending upon the origin locations of the
parts. Following is the lead-time of the different parts that arrive at WiChorus.
Figure 23: Lead-time for the Part Arrival at WiChorus
Above table shows that PCB has very high lead-time of 6 weeks. WiChorus send the gerber
file (PCB design file) to the printed circuit board-manufacturer; they take 4 weeks to return
the network board.
0
1
2
3
4
5
6
7
Chips PCB Network Board
Chassis Bezels Service Card Face Plate
Nu
nb
er o
f w
eek
s
Parts
Lead Time
Chips PCB Network Board Chassis Bezels Service Card Face Plate
67
8.1.7 Issue of considering Network Board in Lead Time:
Issue: All of the lead time involve in the inventory process are before the arrival of the
system but the process time(manufacturing time) for the network board should be considered
as a lead time with the other lead times.
Reason: Reason for the this consideration is that if we wait 6 weeks for the single process we
will not be able to reach the target of making 15 units in one month
Action: Because of having this problem, instead of considering this as a process time for
manufacturing of network board, it is suggested that network board should be made in
advance because of having large process time and considered as a lead time. In our model we
put a process time at the location of PCB manufacturing of 120 min to avoid the lead-time of
6 weeks.
Arrival Collected input data are being put as input to the location in terms of arrival. These
arrivals have different frequencies and occurrences. Since we are modeling for 1 month, the
occurrence for every arrival is put to be 1. It is because the raw material or part required to
make a SmartCore is ordered as a single time in a month.
Queue: Queue is present between every location. Purpose of having this queue is when the
entities are entering the locations and being process, rest of the entities wait in the queue
before its turn. If we have to find how many entities are in process after the completion of the
target output we can find in the locations and in the queue that how much is the remaining
entities and they can remove by optimizing the process.
68
All entities arrive at the WiChorus warehouse. In warehouse these entities kept in there, until
required to process further. There is a certain process time at every location to process the
entities.
First the Gerber file that is designed at the design departmentis being transferred to the
Printed Circuit Board Manufacturer (locations). They have a process time of about 4 weeks
but as suggested before this process time considered as a lead-time. Giving the design to
board manufacturer lead-time ahead of the month arrival, can do it.
8.1.8 Standard deviation
Considering an uncertainty in the process due to presence of any unwanted circumstances,
we applied the standard deviation to the process. ProModel has remarkable feature of
considering the standard deviation of the process time. It is given in range value that is
considered to increase or decrease in the exact process time.
After the Gerber file being processed, this entity is converted into the printed circuit board.
PCB goes to the PCB assembler and in the mean time chips from the WiChorus also
transferred to the PCB manufacturer or assembler. After the assembling of PCB is tested, it is
then shifted to the assembly department of WiChorus. In this department other entities that
are kept at warehouse along with assembled PCB are started to assemble in the chassis.
Chassis is the main structure on which the other entities being assembled.
As can be seen from the ordering data, WiChorus ordered 15 numbers of chassis. WiChorus
always ordered the exact number of chassis considering the forecast for the month. Reason is
that the cost of chassis is very high and it is not a good idea to keep chassis as safety
69
inventory. After assembling stage assembled unit transfer to configuration department after
that unit will be tested at QA department.
8.1.9 Verification
After the modeling is done, design is to be verified for its feasibility. When the simulation is
run it is verified that the desired number of units i.e. 15 units in our case is exit at the end of
the defined period, that is one month in our case.
8.2 Investigation of Results
After modeling a simulation design of WiChorus Inventory process and following the
simulation process flow, we have carried out the analysis and optimization of the model.
From our modeling of inventory process we get the output results. Result investigation is
done in two parts, which has first shown the validity and then the significance of the
simulation modeling.
In our investigation we targeted time and cost. In cost optimization, the process cost has been
reduced. This cost is related to inventory, which comprises of raw material inventory, work
in process inventory, and finished goods inventory. Reduction in cost results increase in
profit as described earlier. Secondary target is time. Reducing the total time for the
production gives a benefit of good customer service, better time to market and increase in
ability to work on the parallel projects.
Firstly, investigation shows the validity of the model and after that investigation of the
optimized model is carried out.
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8.2.1 Credibility of the Model
Credibility of the model describes the validity of the input and output data. According to our
research data of the WiChorus, it has been found that they are making 15 units of SmartCore
per month as described earlier. We used the process time as found for each entity at every
location similar as found in WiChorus inventory process. The number of output units as per
requirement can find out validation. Run time of the simulation is one month. Following is
the output result from our design model in ProModel for inventory process at WiChorus.
Table 9: Process time verification
Variable Name Current Value Comments
Chips Assembly time
118.93 (Average)
Chips Assembly time
5.43 (Std. Dev.)
Chips Assembly time
115.04 (95% C.I. Low)
Chips Assembly time
122.82 (95% C.I. High)
Chassis assembly time 14.54 (Average)
Chassis assembly time 2.17 (Std. Dev.)
Chassis assembly time 12.99 (95% C.I. Low)
Chassis assembly time 16.1 (95% C.I. High)
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Above result are shown in detail in Appendix A1. This is an extract part from the simulation
result. This result shows the validity of the optimized result. The process time of the chips
assembling and chassis assembling is N(120,5) and N(15,2) respectively. It means that for
chips assembling time, time has to be between115 and 125, which is visible from the above
table that C.I.(control limit) low and C.I.(control limit) high is in range. Similarly the range
for the Chassis assembly time is 13 and 17 and current value of the output is in range of the
given deviation.
8.2.2 Lead Time Optimization
Lead-time optimization is the optimization to reduce the total time to make a single unit. In
WiChorus inventory simulation model, total time to make 15 units is the runtime of the
model. Entities, which are the part of the SmartCore unit, arrived at the start of the simulation
time, and ends with the exit of the 15 units. Runtime of the simulation model is 1 month. In
regular model, which is designed with current scenario with the exit of 15 units per month,
the schedule hours is 49 hours to get 15 unit ready at the end of the month.
We made a separate model for the time optimization model. By applying the optimization
techniques, schedule hours have been reduced. It has been done, by changing the available
resources. It has been found from the analysis that the resources can make the most
significant change to reduce the runtime. It has been found by the utilization of the resources
and also depends upon which location or resources have high dependency in the process. In
our case we have found that PCB Testing Department can affect the lead-time for the
production. If the capacity of the testing department is increased or by increasing the staff of
the testing department, lead-time or run time can be reduced significantly to 36 hours as
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shown in figure 24. Detailed results are shown in Appendix A2. Therefore, by the
optimization analysis, there is a possibility of 13 hours reduction in time to make the same
number of units (15 units). It helps the company to have good customer service and better
time to market, which is the most important feature of any company.
Figure 24: Lead-time reduction
8.2.3 Cost Optimization
Cost optimization has been done to reduce the total cost. In our scope we focused on the
material related cost. After simulation of the regular model with the real values, weakness
has been found out in the process. With the number of orders been made for raw materials
and put into the process to make SmartCore, with the simulation it is found that there are still
some parts or entities which remains in the system after desired output of 15 units. These
extra parts or inventory are considered to be the waste and it just only increases the material
0 10 20 30 40 50 60
Before Optimization
Before Optimization
# of Replication
Lead Time/Schedule Hours
After Optimization
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cost of the production. Eventually it increases the total cost of the unit. From Appendix A1,
number of entities remain in the system is shown in table 10.
Table 10: Number of parts left in the system and corresponding savings
COST SAVINGS
PARTS MATERIAL COST HOLDING COST TOTAL
BEZELS $16,200 $270 $16,470
NETWORK BOARD $40,800 $2,040 $42,840
CHIPS $43,200 $2160 $45,360
TOTAL $104,670
From the simulation investigation it has been found that there is a possibility of cost
optimization and total of $104,670. Reduction in cost allows WiChorus to reduce the
production cost and can increase their profit margin. (Note: Annual Savings=Material cost +
Holding cost=QC+(Q/2)hC, Q=number of inventories, h=holding cost per year as a fraction
of product cost, C=cost per unit, H=holding cost where H=hC)
8.2.4 Maximum Capacity
In addition to the time and cost investigation, we did the analysis to find out the maximum
capacity of WiChorus to build SmartCore with present resources. Previous two models are
designed to build 15 units per month. To investigate the analysis of the maximum capacity of
WiChorus to produce SmartCore with the current scenario, new model is generated with
maximum runtime. Maximum runtime is for the 1 month and comprises of 168 hours per
month, so the model has run time of 168 hours. After the simulation has been done for
maximum output analysis, we came to know that WiChorus have a capacity of making 36
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units per month, if all the resources has been utilized to their maximum available usage.
Results are shown in Appendix A3. Right now WiChorus is making an average of 15 units
per month, because of the arrival of low number of orders, since WiChorus is a startup
company. That is why they are not using their full capacity for the production but when their
business will grow they can produce up to 36 units with the same available resources.
9. Economic Justification
The economic justification gives the advantages and justifications of the project in terms of
financial investment. ProModel gives an optimized simulation that helps to reduce the
inventory level and consequently the cost of the production.
“Cost is always an important issue when considering the use of any software tool, and
simulation is no exception. Simulation should not be used if cost exceeds the expected
benefits” (Charles et al, 2004).
This statement shows that both the benefits and costs should be carefully estimated before
making a design simulation decision. It is often observed that the simulation is too soon
dismissed because of failure in recognizing the savings and potential benefits, which
simulation can produce. Most people are reluctant to use simulation because of the notion
that simulation is difficult to execute, time consuming and very expensive. But this concept is
not true because in long run observation simulation saves much more in terms of cost.
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9.1 Executive Summary
Simulation identifies and eliminates the problems and inefficiencies present in the system.
These inefficiencies remain unnoticed until the model design is formulated and simulated. In
the absence of simulation, companies usually keep much more extra safety factors because of
failure to know the future outcome at the present stage. This extra safety inventory introduces
an extra cost to the system, which consequently increases the production cost. In our project
we are targeting to reduce the manufacturing cost and consequently increasing the profit. A
WiChorus keeps safety inventory at their warehouse in case of uncertainty of demand and
supply. If simulation helps in reducing the safety inventory, it can save WiChorus or any
other firm thousands of dollars every year. We plan to provide a low cost inventory
optimization solution to manufacturing industry of USA. The main advantage in getting the
inventory optimized by using low cost inventory optimization solution is that total
manufacturing cost is reduced significantly without the change in revenue. Our potential
customers are going to be from manufacturing industry, because in-depth research shows that
California, USA has the highest market share among the manufacturing industries of USA.
We require $235,000 as initial investment and we will achieve break-even point in the end of
third quarter since the company startup. We can generate revenue of up to $414,000 by the
end of 4th quarter of 2010.
9.2 Problem Statement
When the purchase orders of the productsstart arriving in an organization, it has to manage
the flow of an inventory (specially raw materials and semi-finished products) throughout its
manufacturing processes, till customer receives the finished product. If the operational
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management has not made the methodical decision proactively about the inventory
management, then there can be many problems that can arise like increased manufacturing
cost and lower customer service level. For the manufacturing company that has a weak or
inefficient management of inventory, there will be no economy of scale for the
manufacturing operations and it will subsequently impact its net profit.
9.3 Solution and value proposition
There exists an only solution to the problem of having an extra inventory, and that is the
simulation modeling. Simulation can foresee the system; with this advantage we can give
minimum input to the system to get the desired output. This reduction of the inventory save
the input cost and thus reduces the manufacturing cost. Cost is also reduced by altering the
overdesign and unwanted safety factors that were designed when system demand and supply
projection was uncertain.
Very significant savings in terms of cost and time comes from the fact that simulation using
promodel allows the design engineer to make errors or mistakes on the design model rather
than in actual world. In case of WiChorus it is $104,670 per annum and lead time can be
reduced by 26%. Its importance is best represented by the rule of tens.
“This principle states that the cost to correct a problem increase by a factor 10 for every
design stage through which it passes without being detected.” (Herrel et al, 2004).
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Figure 25: Cost of making changes at subsequent stage
Source: Cost of change in traditional processes, by Scott W. Ambler (Retrieved March14,
2009)
Figure 25 shows the increase in the cost if change occurs in subsequent stages of system.
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Figure 26: Comparison of the cumulative system costs with and without simulation
Source: Simulation using ProModel (Retrieved March14, 2009)
Simulation definitely helps in many outstanding costs that can be incurred by poor decision
made in designing phase. Figure 26 show that how the simulation reduces the cumulativecost
for system design with simulation as compared to without simulation. It is possible the initial
cost with simulation is little bit higher because of initial investment (included training and
software cost) but long term cost of the capital investment is higher without simulation.
79
9.4 Market Size
ProModel is suitable for any kind of industry where the process is involved (it can be a
manufacturing or service process). But the main industry that can be beneficial by the
simulation modeling is the Manufacturing Industry. Simulation in time is proved to be
effective in helping to sort the complex manufacturing issues and decisions. Kochan (1986)
notes that in manufacturing system;
“The possible permutation and combination of work pieces, tools, pallets, transport
vehicles, transport routes, operations etc, etc., and their resulting performance, are
almost endless. Computer simulation has become an absolute necessity in the
design of practical systems, and the trend toward broadening its capabilities is
continuing as systems move to encompass more and more of the factory.” (R.
Harrel, 2004)
Considering all these factor and importance of simulation in manufacturing Industries,
we target our market as manufacturing industries.
Figure 27
Source: Manufacturing Matters (Retrieved 12
California is biggest contribution to GDP of the US as shown in figure
figure 28, considering manufacturing s
contributor. California is ranked
millions amongst all states, when considering contribution to GDP by manufacturing sector
$0 $200
Maryland
Massachusetts
Virginia
North Carolina
Ohio
Illinois
New York
California
US Total GDP for Top 15 States, 2007 (in
80
7: Total GDP for top 15 States (in Millions)
: Manufacturing Matters (Retrieved 12 February, 2009)
California is biggest contribution to GDP of the US as shown in figure 27. Moreover,
anufacturing sector, California is amongst the highest GDP
contributor. California is ranked number 1 among states in USA as contributing $179
, when considering contribution to GDP by manufacturing sector
$200 $400 $600 $800 $1,000 $1,200 $1,400 $1,600
$269
$311
$352
$382
$383
$397
$399
$465
$466
$531
$610
$735
$1,103
$1,142
US Total GDP for Top 15 States, 2007 (in million $)
, 2009)
Moreover, from
the highest GDP
as contributing $179
, when considering contribution to GDP by manufacturing sector.
$1,600 $1,800 $2,000
$1,813
US Total GDP for Top 15 States, 2007 (in
Figure 28: Contribution of GDP by manufacturing sector
Source: Manufacturing Matters (Retrieved 12
9.5 Competitors
Since we are providing the Inventory Optimization Solution (IOS), our competitors are the
supply chain solution providers. There are many companies
provide the inventory optimization
not feasible for the low scale
web based solution which requires extra resources like data bac
internet facilities etc. that is a
provides a low cost solution with minimum
$0.00 $20.00
Florida
Tenessee
Wisconsin
Indiana
New York
Pennsylvania
Ohio
California
USA-Contribution to GDP (in million $) by Manufacturing Sector
81
Contribution of GDP by manufacturing sector (in Millions)
: Manufacturing Matters (Retrieved 12 February, 2009)
Since we are providing the Inventory Optimization Solution (IOS), our competitors are the
supply chain solution providers. There are many companies (as shown in tab
optimization solutions, but they provide expensive services
(in terms of volume) companies. Also most of them provide
web based solution which requires extra resources like data backup, large memories
. that is a limitation for many of the consumers. But our solution
provides a low cost solution with minimum required resources and it works on real time data.
$20.00 $40.00 $60.00 $80.00 $100.00 $120.00 $140.00
$36.60 $40.80 $39.40
$43.60 $47.70 $50
$62.70 $64.50 $66
$74 $75.20 $76.60
$85.10
Contribution to GDP (in million $) by Manufacturing Sector-2007
(in Millions)
, 2009)
Since we are providing the Inventory Optimization Solution (IOS), our competitors are the
table 11), who
expensive services, which is
companies. Also most of them provide
kup, large memories or RAM,
limitation for many of the consumers. But our solution
works on real time data.
$140.00 $160.00 $180.00
$153.30 $179
Contribution to GDP (in million $) by
82
Table 11: List of competitors and solutions provided by them.
Company Name Description
HENRY SCHEIN Provides web based inventory management System
TC Logic Web-based inventory solution provider
Terra Technology Provide demand sensing and inventory optimization
MCA Solution Provides service parts inventory optimization solution
planning
Optiant Provides resilient supply chain and customer service
9.5.1 HENRY SCHEIN
HENRY SCHEIN’S Inventory Optimization Solutions (IOS) offer web-based inventory-
management systems, which are designed to centralize both the purchasing and distribution
processes, and are perfect for the multi-physician practice that keeps stock on location
(Henry Schein, 2009)
9.5.2 TC Logic
Since 1997, TCLogic has provided an inventory optimization solution that provides the
highest level of service at the least cost. This is accomplished through our web-based
application called, ROI+. It is through this application that our clients have achieved
inventory reductions as high as 30% while maintaining or increasing service levels above
98%. (TC Logic, 2009)
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9.5.3 Terra Technology
Terra Technology is the leading provider of demand sensing and inventory optimization
software for consumer goods companies. Terra's solutions incorporate demand signals from
throughout the supply chain including retailer data, reducing forecast error by up to 50
percent and inventory up to 20 percent. More accurate forecasts and inventory targets
improve customer service, lower inventory, decrease unplanned changeovers and reduce
costs. (TerraTechnology, 2009)
9.5.4 MCA Solutions
MCA’s offerings encompass the complete range of a company’s service needs, from strategic
consulting that helps organizations evaluate their current service offerings and create a
roadmap for the future, to our industry-leading Service Planning and Optimization (SPO™)
product suite, which provides every tool necessary. (mcasolutions, 2009)
9.5.5 Optiant
Optiant helps companies optimize their bottom-line performance by creating responsive and
resilient supply chains. As the leading provider of inventory planning and optimization
solutions, Optiant’s solutions optimally balance resources, total costs, and customer service
across the supply chain to deliver greater profitability, increase satisfaction, more efficient
use of capital and a resilient, and a resilient supply chain that fully handles uncertainty in
supply and demand. (Optiant, 2009).
9.6 Potential Customers
Manufacturing industries are
industries where manufacturin
Other than manufacturing industry, in every industry there can be our customers that require
process optimization by simulation
potential customers.
Since we provide a low cost solution
which cannot afford expensive inventory solutio
has a capacity of inventory optimization of mass production companies, but it also has
acapacity for inventory optimization
wireless equipment manufacturers
Figure 29
Source: Manufact
Administrative & Waste Services
Information
Construction
Professional Scientific & Tech.
Wholesale Trade
Healthcare & Social Assistance
Retail Trade
Real Estate, Rental & Leasing
Finance & Insurance
Manufacturing
84
9.6 Potential Customers
considered to be our top targeted potential customers. Al
manufacturing is being carrying out, there is a need of simulation
Other than manufacturing industry, in every industry there can be our customers that require
process optimization by simulation, but manufacturing industries is on the top of list of
Since we provide a low cost solution so we can target the low scale or low budget companies,
cannot afford expensive inventory solutions. It can happen because ProModel not only
entory optimization of mass production companies, but it also has
inventory optimization of low production companies, as in the case of 4G
less equipment manufacturers.
Figure 29: GDP based on industrial sector (top 10)
Source: Manufacturing Matters, 2009
0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00%
Administrative & Waste Services
Information
Construction
Professional Scientific & Tech.
Wholesale Trade
Healthcare & Social Assistance
Retail Trade
Real Estate, Rental & Leasing
Finance & Insurance
Manufacturing
2.60%
3.17%
4.19%
4.93%
5.32%
6.18%
6.27%
9.43%
12.71%
COMPARISON OF GDP
considered to be our top targeted potential customers. All the
imulation modeling.
Other than manufacturing industry, in every industry there can be our customers that require
s is on the top of list of
budget companies,
ProModel not only
entory optimization of mass production companies, but it also has
of low production companies, as in the case of 4G
16.00% 18.00% 20.00%
12.71%
18.60%
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One of the reasons that we are targeting the manufacturing industry is that because it is the
biggest industry in USA. In USA manufacturing contributes the 18.6% of the industries
market as shown in figure 29.
Since we are located in the Silicon Valley, our targeted customers are specialized and mass
production companies, which are situated in, bay area. WiChorus is the present customer.
List of the some of the prospective customers is as follows.
Table 12: List of the prospective customers in USA
Company No of Employee Company URL Headquarters
IBM 386,558(worldwide) www.ibm.com Armonk, NY
Cisco Systems 66,000 www.cisco.com San Jose, CA
Applied Materials 14,500 www.appliedmaterials.com Santa Clara, CA
Juniper 7,000 www.juniper.net Sunnyvale, CA
Dell 80, 800 www.dell.com Round Rock, TX
HP 321,000(worldwide) www.hp.com PaloAlto, CA
9.7 Personnel
As a start company we need to hire some personnel to carry out the operations of the
company. Initially CEO will be hired who will be responsible for the formulation of whole
setup including hiring senior management of his or her choice. Senior management will
further hire the managerial staff, while keeping CEO in the loop. The organizational structure
is shown in figure 30.
Figure 30
9.7.1 Chief Executive Officer
We need to hire a chief executive officer to start and run our company. CEO should have
over 15 years of technical management and sales experience in manufacturing and services
industry. System integration is a plus. Preferably he or she should be from Silicon Valley. It
is going to give added advantage due to familiarity with the companies of San Jose.
9.7.2 Vice President of Sales and Marketing
Vice president of sales and marketing will be responsible for over all sales and marketing of
our company. Multicultural experience is required for this post, as sales people should be
able to take care of cultural difference
VP
Business Delopment
System Engineer
Industrial Engineer
86
Figure 30: Organizational Structure
Chief Executive Officer
We need to hire a chief executive officer to start and run our company. CEO should have
management and sales experience in manufacturing and services
industry. System integration is a plus. Preferably he or she should be from Silicon Valley. It
is going to give added advantage due to familiarity with the companies of San Jose.
sident of Sales and Marketing
Vice president of sales and marketing will be responsible for over all sales and marketing of
our company. Multicultural experience is required for this post, as sales people should be
able to take care of cultural differences due to different locations of customers. Initially we
CEO
VP
Business Delopment
Industrial Engineer
Quality Engineer
VP
Sales and Marketing
Marketing Engineer
We need to hire a chief executive officer to start and run our company. CEO should have
management and sales experience in manufacturing and services
industry. System integration is a plus. Preferably he or she should be from Silicon Valley. It
is going to give added advantage due to familiarity with the companies of San Jose.
Vice president of sales and marketing will be responsible for over all sales and marketing of
our company. Multicultural experience is required for this post, as sales people should be
s due to different locations of customers. Initially we
Sales and Marketing
Sales Engineer
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will cater for customers located in California, but as we are going to grow, we will start
covering customers through out the United States. Sales and marketing team will work under
his guidance.
9.7.3 Vice President of Business Development
As we plan to expand our company to cater for design for manufacturability services to
different companies in addition to optimization of inventory management, therefore we need
to hire a team member with over 15 years experience at this post. He or she should have very
good relationship with the present manufacturing companies in Silicon Valley, as it will also
help in bringing and expanding business further.
9.7.4 System Engineer
Company will need one system engineer who should know the complexities of
manufacturing operations and moreover system integration. VP of business development will
be responsible for his or her supervision. Required education will be at least Masters in
system engineering, with over 7 years of experience.
9.7.5 Industrial Engineer
An industrial engineer will be needed, as we will be mostly working with manufacturing
industry. At least 8 years of experienced will be required in manufacturing operations.
Masters degree in operations will be an added advantage. His knowledge of manufacturing
operations will be helpful in the identification of slack times in the manufacturing industry.
88
9.7.6 Quality Engineer
Black belt in quality will be required to carry out the quality assurance for our inventory
optimization procedures. Required experience will be 8 years in manufacturing operations.
9.7.7 Marketing Engineer
Company will need a MBA in marketing for doing the publicity and moreover to work with
VP of marketing and sales. He or she will be responsible for carrying out the marketing
campaign. Identification of prospective customers will also be his responsibility. He will be
closely working with VP of sales and marketing to increase the customer’s strength.
9.7.8 Sales Engineer
Company will also need a sales engineer. Preferably MBA in sales will be required.
Engineering bachelor degree will be an added advantage. Experience of 8 years will be
required in consultancy sales.
9.8 Cost Analysis
Simulation design models are designed internally for the customers. These models are going
to be designed by our expert design engineers. In our company there is no manufacturing cost
occurred. We are using the simulation software ProModel that use to simulate the process to
be visualize and optimize. We charged the customers out consultancy charges and customer
service, which incur after the model is sold. We use the software ProModel that we bought
and get the license that we have to pay the yearly fees for using it. Simulation model design
cost is estimated with fix and variable cost.
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9.8.1 Investment Capital Requirement
Capital is required in the initial stage to invest in the startup company. To keep the initial
investment to low amount, we prefer lease the building, equipments and other facilities. It is
trade-off between reducing the net profit quarterly and targeting the breakeven point much
earlier. This lease will be paid off and assets will owned when the company will financially
established.
9.8.2 Fixed cost
This cost incurred periodically and remain fixed for design &development in a company.
This cost in minimumis supposed to be covered by the selling price. This cost does not vary
with the increase number of the customers or number of model design sale. Since some assets
are leased in start therefore these lease amount is also considered as fixed cost incurred to the
company. Fixed cost also includes the license fees of ProModel, which has to be paid
annually. Fixed cost from Q3 of 2009 to Q2 of 2010 is given in table 13.
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Table 13: Fixed cost for 2009 and 2010
2009 2010 Expenses Q3 Q4 Q1 Q2
Simulation tool (ProModel) $15,000 $0 $0 $0
License $8,000 $0 $0 $0
Other software tools $1,000 $1,000 $1,000 $1,000
Building Lease $25,000 $25,000 $25,000 $25,000
Equipment Lease $10,000 $10,000 $10,000 $10,000
Furniture and Fixtures $5,500 $5,500 $5,500 $5,500
Internet $500 $500 $500 $500
Telephone $1,000 $1,000 $1,000 $1,000
Maintenance $5,000 $5,000 $5,000 $5,000
Utilities $2,000 $2,000 $2,000 $2,000
Salaries
CEO $30,000 $30,000 $30,000 $30,000
VP Sales and Marketing $25,000 $25,000 $25,000 $25,000
VP Business Development $25,000 $25,000 $25,000 $25,000
System Engineer $20,000 $20,000 $20,000 $20,000
Industrial Engineer $18,000 $18,000 $18,000 $18,000
Quality Engineer $18,000 $18,000 $18,000 $18,000
Marketing Engineer $15,000 $15,000 $15,000 $15,000
Sales Engineer $15,000 $15,000 $15,000 $15,000
Total $239,000 $216,000 $216,000 $216,000
9.8.3 Variable Cost
Variable cost depends on the market and sales. One of the variable cost the customer service
cost. When the model is sold to the customer it required maintenance and revision of the
model as per customer requirement. Other variable cost is the part time employee salaries,
which will require if the demand is higher than the current capacity of the company or there
is a need of specialist engineer. Table 14 shows the list of variable cost per quarter.
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Table 14: Variable Cost per Quarter
Expenses Cost
Part-time Wages $1,500
Marketing Cost $1,000
Customer Service $4,000
Consultancy $3,000
Total $9,500
9.9 Price Point
We are offering an inventory optimization solution to the customers. For the optimization
existing process has to be analyzed, visualize and optimize to get the desired cost efficient
results. For optimization purpose simulation model have to be design with current scenario
and optimize. We sell this model and solution to the customers. The price of this service
depends upon the total cost incurred to the company and also influenced by the market
values. Using the total cost analysis and market analysis we plan to charge our simulation
model price as $30,000, which is a one-time cost, and also the customer service as $8,000 per
quarter. Targeted customers will be willing to this investment since these optimization
solutions can save them up to $100,000 per annum and improve relationships with their
customers. Customer service charges include the maintenance and upgrade of the model as
per customer requirements and also providing the detailed result analysis of the simulation
output. In company policy, minimum duration of the customer service is 2 quarter, which can
be increase on customer demand.
92
Initially the target companies are small size and size companies but with the time when the
business gets in better shape the target will be the large size company. This will also affect
the service charges because big scale companies have large inventory process and they can
be charge more than the fixed service charges, as it requires more work and analyzing.
9.10 Total Income
Total income will be depends on the number of target achieved, which is expected to be low
in initial stages. By using the marketing strategies business will increase eventually and more
number of customers expected.
Once the services and model has been sold it requires maintenance and updating of the model
when the customer alter it process. This customer service charge will be applied from the
same quarter. The duration of the service charges depends on the customers. In cost analysis
we fixed the duration to the 2 quarters as per out policy, which is a minimum duration. Total
revenue analysis is shown in table 15 for Q3 of 2009 to Q4 of 2010.
Table 15: Total revenue statement for Q3 (2009) to Q4 (2010)
Quarter Q3 '09 Q4 '09 Q1 '10 Q2 '10 Q3 '10 Q4 '10
Expected Customers 3 4 6 7 9 9
Simulation Model Price per Customer
30000 30000 30000 30000 30000 30000
Customer Service charges per customer
8000 8000 8000 8000 8000 8000
Total Customer charges 24000 56000 80000 104000 128000 144000
Total Revenue 114000 183000 260000 314000 398000 414000
Customers are expected to increase from 2009 to
revenue. Total customers service charges vary depend on the number of customers.
Following figure represent the total revenue in graphical form.
Figure 31: Graphical representation of Total Revenue from Q3 (2009) to Q4 (2010
9.11 Profit and Loss
Profit and loss analysis shows the comparison of the revenue and profit for different quarters.
Forecast has been done to find out the expected number of customers in the next six quarters.
Profit and loss diagram also determine the
variable is used to find the profit and loss values.
9.11.1 Selling price per design
It consists of design price for the customers.
0
100
200
300
400
500
Q3 '09
$114
Rev
enu
e (x
10
00
$)
93
Customers are expected to increase from 2009 to 2010, which will eventually increase the
revenue. Total customers service charges vary depend on the number of customers.
Following figure represent the total revenue in graphical form.
: Graphical representation of Total Revenue from Q3 (2009) to Q4 (2010
Profit and loss analysis shows the comparison of the revenue and profit for different quarters.
Forecast has been done to find out the expected number of customers in the next six quarters.
Profit and loss diagram also determine the breakeven point of the company. Following
variable is used to find the profit and loss values.
9.11.1 Selling price per design
It consists of design price for the customers.
Q4 '09 Q1 '10 Q2 '10 Q3 '10 Q4 '10
$114$183
$260$314
$398
Time
Total Revenue
will eventually increase the
revenue. Total customers service charges vary depend on the number of customers.
: Graphical representation of Total Revenue from Q3 (2009) to Q4 (2010)
Profit and loss analysis shows the comparison of the revenue and profit for different quarters.
Forecast has been done to find out the expected number of customers in the next six quarters.
breakeven point of the company. Following
Q4 '10
$414
94
9.11.2 Customer service Charges
It consists of service charges for maintenance and alteration in design.
9.11.3 Fixed Cost
This cost remains fixed and does not vary with the number of customer.
9.11.4 Total Variable Cost
This cost related to the customer and varies with number of customers. It is equal to multiple
of number of customer and variable cost.
9.11.5 Total cost
It is an addition of the Fixed and Variable Cost
9.11.6 Total Revenue
It is an addition of the selling price and customer service charges.
9.11.7 Profit/Loss
Profit/loss = Total Cost – Total Revenue
Following table shows the profit and loss analysis from the total cost and revenue.
95
Table 16: Profit and Loss value for Q3(2009) to Q4(2010)
Quarter Number of Customers
Fixed Cost
($)
Variable Cost
($)
Total Variable Cost ($)
Total Cost
($)
Total Revenue
($)
Profit/Loss
($)
( c ) (a) (v) (b= a*c) (x=a+b) (y) (x+y)
Q3(2009) 3 230000 9500 28500 258500 114000 -144500
Q4(2009) 4 216000 9500 38000 254000 183000 -71000
Q1(2010) 6 216000 9500 57000 273000 260000 -13000
Q2(2010) 7 216000 9500 66500 282500 314000 31500
Q3(2010) 9 216000 9500 85500 301500 398000 96500
Q4(2010) 9 216000 9500 85500 301500 414000 112500
Figure 32 shows the P&L diagram showing the profit and loss as depending on the forecasted
number of customers. From profit and loss analysis it has been found that we required
$235,000 as an initial investment. This investment will be used in covering the fixed costs
and some part of it will be in vested in customer service cost, depending on the numbers of
the actual customers.
Figure 32
9.12 Break-Even Point
Breakeven point is the point at which company starts to recover the invested cost and
generates the profit. Breakeven point is an important factor for the investors to attract
towards the project. It provides the information that after at quarter certain company starts to
generate profit. Figure 33 shows that in the end of 3rd quarter company achieve its breakeven
point, which shows good prospects
-$200,000
-$100,000
$0
$100,000
$200,000
$300,000
$400,000
$500,000
Q3(2009) Q4(2009)
96
Figure 32: Profit and loss Diagram
Breakeven point is the point at which company starts to recover the invested cost and
generates the profit. Breakeven point is an important factor for the investors to attract
rds the project. It provides the information that after at quarter certain company starts to
shows that in the end of 3rd quarter company achieve its breakeven
prospects for the company.
Q4(2009) Q1(2010) Q2(2010) Q3(2010) Q4(2010)
Profit and Loss
Breakeven point is the point at which company starts to recover the invested cost and
generates the profit. Breakeven point is an important factor for the investors to attract
rds the project. It provides the information that after at quarter certain company starts to
shows that in the end of 3rd quarter company achieve its breakeven
Fixed Cost
Total Variable Cost
Total Cost
Total Rvenue
Profit/Loss
Figure 33
9.13 Norden-Rayleigh Graph
Table 17 shows the Norden-Rayleigh graph analysis for our project. It shows the expenditure
of the project with respect to time. It is calculated by the following probability density
function formula:
where, a ; financial cost drivers
d ; estimated total budget
t ; time
0
50000
100000
150000
200000
250000
300000
350000
400000
450000
Q3(2009) Q4(2009)
Do
lla
rs
97
Figure 33: Break-Even Analysis Graph
Rayleigh Graph
Rayleigh graph analysis for our project. It shows the expenditure
of the project with respect to time. It is calculated by the following probability density
V(t) = 2adtexp(-at2)
a ; financial cost drivers
d ; estimated total budget
Q4(2009) Q1(2010) Q2(2010) Q3(2010) Q4(2010)
Break-Even
Rayleigh graph analysis for our project. It shows the expenditure
of the project with respect to time. It is calculated by the following probability density
Total Cost
Total Rvenue
98
Table 17: Norden-Rayleigh Cost Analysis
t d a e(-at2) v(t)=2adtexp(-at2)
0 258000 0.1 1 0.0000
1 258500 0.1 0.904837 46780.0945
2 256200 0.1 0.67032 68694.3983
3 254000 0.2 0.165299 50383.1011
4 265000 0.2 0.040762 17283.1745
5 273000 0.2 0.006738 3678.9191
6 276000 0.3 2.04E-05 20.2689
7 280000 0.4 3.07E-09 0.0048
8 282500 0.4 7.62E-12 0.0000
9 291000 0.5 2.58E-18 0.0000
10 301500 0.5 1.93E-22 0.0000
11 301500 0.5 5.31E-27 0.0000
12 301500 0.5 5.38E-32 0.0000
Keeping value of “a” to low level because of having consideration of startup company. It is
designed to have a low investment cost and low risk. Figure shows the Norden-Rayleigh
graph.
99
Figure 34: Expenditure w.r.t. time
Figure 35: Cumulative Expenditure vs Time
0
10000
20000
30000
40000
50000
60000
70000
80000
1 2 3 4 5 6 7 8 9 10 11 12 13
Do
lla
rs ($
)
Time (t)
Norden-Rayleigh Graph
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
200000
1 2 3 4 5 6 7 8 9 10 11 12 13
Do
lla
rs (
$)
Time (t)
Cumulative Expenditure
100
9.14 SWOT Assessment
Table 17: Strength, Weakness, Opportunities and Threat Analysis
STRENGTH WEAKNESS
1. It enables the clients to VISUALISE, ANALYSE and OPTIMISE to make better decision regardingoptimization of inventory.
2. Clients can implement the changes in inventory process with NoRisk.
3. Low Cost inventory optimization solution. 4. Helps increase productivity by better use of
existing assets/resources.
1. Uncertainty of the market cannot be taken into account.
2. Provides less increase in profit for small-scale company.
OPPORTUNITIES THREATS
1. Since manufacturing industryhas largest share of US GDP within various industries, this project has a large market.
2. Larger the productivity, greater the profit opportunity.
3. Optimization can be applied to any industry (e.g. service industry).
1. Other inventory solution providers offer less expensive solution.
2. Recession force companies to avoid getting consultancy.
3. Increase in the price of simulation software.
9.15 Exit Strategy
There is always a chance of recession, whichcan force the consumers to not to go towards
our opportunity. We have few exit strategies in case of some unfortunate’s scenario:
1. Merge with the big inventory solution provider.
2. Selling the models to the competitors that can recover the asset values.
3. We can also switch to service provider companies other than manufacturing industries.
101
In case if the software cost is increases, we have the following strategies like,
1. We can increase our service charges
2. We can switch to the more affordable optimization software. There are many others
software available in market. Some of them are following
a. Arena Simulation
b. Montecarlo Simulation Software
c. Flexsim Simulation Software etc.
10. Project Schedule
Project schedule describes the sequences and duration of the task that we assigned in the start
of the project. All the tasks have been completed in accordance to the schedule. Figure 30
shows the Gantt chart for our project, which is the graphical presentation of the project
schedule.
102
Figure 36: Project Schedule
103
11. Team and Committee Members
11.1 Team Members
Farhan Jaleel (MSE-Engineering Management)
Farhan holds a BE degree in Aerospace. He has about seven and half years of experience in
fighter aircraft maintenance and operations. These aircraft include MFI-17, T-37, K-8, F-7,
and Mirage. He also possesses an experience of various structural and avionics modifications
on these aircraft. He has also worked for about two years in an IT company as general
manager operations. He has accomplished various wireless networking projects during this
capacity. He is currently working as operations intern at WiChorus Inc.
He was responsible for the optimization of structural framework for the inventory
management process of WiChorus Inc. Moreover he also looked after the WiChorus
transportations strategy, and pricing structure of the process. He has also worked with
Jawwad for simulation modeling.
Muhammad Jawwad ul Haque Siddiqui (MSE-Engineering Management)
Muhammad Jawwad Siddiqui has about 1 year of industrial work experience. He has worked
on different industrial projects in the field of Industrial Electronics. He worked as an
Automation engineer on several project and also has an experience as a project engineer in
PARCO (Oil and Gas Company). He has done his undergraduate in Electronics Engineering.
104
He was responsible for analyzing the past and current data for the supply chain, formulation
of the data; analyze the inputs to simulation to optimize the inventory management procedure
and moreover application of management tools to reduce the response time.
11.2 Committee Members
Rehan Jalil (President and CEO, WiChorus Inc.)
Rehan has over 15 years of technical management and sales experience in
telecommunications, networking, and multi-core processors. Prior to WiChorus, he was the
chief architect of WiMAX for Aperto Networks and played diverse leadership roles in
technology and sales.
Jim Dorosti (Professor, MSE program, SJSU)
Dr. Dorosti was the Director of MSE Programs, College of Engineering at San Jose State
University where he also teaches engineering courses in the MSE program. Jim has extensive
experience with Fortune 500 and start-up businesses in the semiconductor industry, and a
unique record in managing both technical organizations as well as Corporate Total Quality
Management Systems (TQMS).
12. Conclusion
Optimization of the inventory management of WiChorus Inc. was successfully carried out
with the help of simulation modeling. Lead-time and production cost (inventory carrying and
buying cost based on quantity) was reduced significantly with the proposed model. Total
capacity to manufacture products was also calculated which will be helpful in the future
expansion program of the company. The saved cost can be effectively used in the research
105
and development programs of the company. Reduced lead-time will be helpful in competing
the competitors of WiChorus, because time to satisfy customers demand and time to market
is reduced. Moreover, simulation helps to foresee the future and let the designer to analyze
and design the process. Simulation nullifies the risk of change in process design, which
incurs a high cost in real system if the design is changed later.
WiChorus can save up to $104,670 annually. Lead-time of the inventory process of
WiChorus can be reduced by 26% for higher service level and quick product availability in
market.We will reach break-even point for our proposed company quickly by the end of 3rd
quarter 2009 from the start with low investment.
106
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111
14. Appendices
14.1 Appendix ‘A1’ Generalized Model Output
--------------------------------------------------------------------------------
General Report
Output from H:\Jawwad's Project\Final Project.mod
Date: Apr/21/2009 Time: 09:09:57 PM
--------------------------------------------------------------------------------
Scenario : Normal Run
Replication : Average
Period : Final Report (0 sec to 49 hr Elapsed: 49 hr)
Simulation Time : 49 hr
--------------------------------------------------------------------------------
LOCATIONS
Average
112
Location Scheduled Total Minutes Average Maximum Current
Name Hours Capacity Entries Per Entry Contents Contents Contents % Util
----------------- --------- -------- ------- --------- -------- -------- -------- ------
Warehouse Wi 49 1 0 0.0 0 0 0 0.0 (Average)
Warehouse Wi 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
Warehouse Wi 49 1 0 0.0 0 0 0 0.0 (95% C.I. Low)
Warehouse Wi 49 1 0 0.0 0 0 0 0.0 (95% C.I. High)
PCB Manf 49 1 0 0.0 0 0 0 0.0 (Average)
PCB Manf 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
PCB Manf 49 1 0 0.0 0 0 0 0.0 (95% C.I. Low)
PCB Manf 49 1 0 0.0 0 0 0 0.0 (95% C.I. High)
PCB Assmb 49 1 20 120.99 0.82 1 0 82.31 (Average)
PCB Assmb 0 0 0 0.77 0.0 0 0 0.53 (Std. Dev.)
113
PCB Assmb 49 1 20 120.44 0.81 1 0 81.93 (95% C.I.
Low)
PCB Assmb 49 1 20 121.55 0.82 1 0 82.69 (95% C.I.
High)
PCB Testing 49 1 16.1 175.22 0.95 1 1 95.92 (Average)
PCB Testing 0 0 0.31 3.35 0.0 0 0 0.14 (Std. Dev.)
PCB Testing 49 1 15.87 172.82 0.95 1 1 95.82 (95% C.I.
Low)
PCB Testing 49 1 16.32 177.61 0.96 1 1 96.02 (95% C.I.
High)
Configuration Wi 49 1 0 0.0 0 0 0 0.0 (Average)
Configuration Wi 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
Configuration Wi 49 1 0 0.0 0 0 0 0.0 (95% C.I. Low)
Configuration Wi 49 1 0 0.0 0 0 0 0.0 (95% C.I. High)
QA Dept 49 1 0 0.0 0 0 0 0.0 (Average)
114
QA Dept 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
QA Dept 49 1 0 0.0 0 0 0 0.0 (95% C.I. Low)
QA Dept 49 1 0 0.0 0 0 0 0.0 (95% C.I. High)
Prod Dept Wi 49 1 15.1 61.01 0.31 1 0.1 31.33 (Average)
Prod Dept Wi 0 0 0.31 1.57 0.0 0 0.31 0.83 (Std. Dev.)
Prod Dept Wi 49 1 14.87 59.88 0.30 1 -0.12 30.74 (95% C.I.
Low)
Prod Dept Wi 49 1 15.32 62.14 0.31 1 0.32 31.93 (95% C.I.
High)
Des Manf Q 49 999999 0 0.0 0 0 0 0.0 (Average)
Des Manf Q 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
Des Manf Q 49 999999 0 0.0 0 0 0 0.0 (95% C.I. Low)
Des Manf Q 49 999999 0 0.0 0 0 0 0.0 (95% C.I. High)
115
Manf Assmb Q 49 999999 20 1150.15 7.82 20 0 0.0
(Average)
Manf Assmb Q 0 0 0 12.56 0.08 0 0 0.0 (Std. Dev.)
Manf Assmb Q 49 999999 20 1141.16 7.76 20 0 0.0 (95%
C.I. Low)
Manf Assmb Q 49 999999 20 1159.13 7.88 20 0 0.0 (95%
C.I. High)
Assmb Testing Q 49 999999 20 494.76 3.36 6.8 3.9 0.0
(Average)
Assmb Testing Q 0 0 0 21.43 0.14 0.42 0.31 0.0 (Std. Dev.)
Assmb Testing Q 49 999999 20 479.43 3.26 6.49 3.67 0.0 (95%
C.I. Low)
Assmb Testing Q 49 999999 20 510.10 3.47 7.10 4.12 0.0 (95%
C.I. High)
Testing Prod Q 49 999999 15.1 0.0 0 1 0 0.0 (Average)
Testing Prod Q 0 0 0.31 0.0 0 0 0 0.0 (Std. Dev.)
116
Testing Prod Q 49 999999 14.87 0.0 0 1 0 0.0 (95% C.I.
Low)
Testing Prod Q 49 999999 15.32 0.0 0 1 0 0.0 (95% C.I.
High)
Warehouse Assmb Q 49 999999 0 0.0 0 0 0 0.0 (Average)
Warehouse Assmb Q 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
Warehouse Assmb Q 49 999999 0 0.0 0 0 0 0.0 (95% C.I.
Low)
Warehouse Assmb Q 49 999999 0 0.0 0 0 0 0.0 (95% C.I.
High)
Warehouse Prod Q 49 999999 15 1624.38 8.28 15 0 0.0
(Average)
Warehouse Prod Q 0 0 0 24.37 0.12 0 0 0.0 (Std. Dev.)
Warehouse Prod Q 49 999999 15 1606.94 8.19 15 0 0.0 (95%
C.I. Low)
Warehouse Prod Q 49 999999 15 1641.81 8.37 15 0 0.0 (95%
C.I. High)
117
Prod Confg Q 49 999999 0 0.0 0 0 0 0.0 (Average)
Prod Confg Q 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
Prod Confg Q 49 999999 0 0.0 0 0 0 0.0 (95% C.I. Low)
Prod Confg Q 49 999999 0 0.0 0 0 0 0.0 (95% C.I.
High)
Confg QA Q 49 999999 0 0.0 0 0 0 0.0 (Average)
Confg QA Q 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
Confg QA Q 49 999999 0 0.0 0 0 0 0.0 (95% C.I. Low)
Confg QA Q 49 999999 0 0.0 0 0 0 0.0 (95% C.I.
High)
Warehouse Q 49 999999 15 1623.80 8.28 15 0 0.0 (Average)
Warehouse Q 0 0 0 23.75 0.12 0 0 0.0 (Std. Dev.)
Warehouse Q 49 999999 15 1606.81 8.19 15 0 0.0 (95% C.I.
Low)
118
Warehouse Q 49 999999 15 1640.79 8.37 15 0 0.0 (95% C.I.
High)
Chips Q 49 999999 20 1952.71 13.28 20 5 0.0 (Average)
Chips Q 0 0 0 18.17 0.12 0 0 0.0 (Std. Dev.)
Chips Q 49 999999 20 1939.71 13.19 20 5 0.0 (95% C.I.
Low)
Chips Q 49 999999 20 1965.71 13.37 20 5 0.0 (95% C.I.
High)
Filler Q 49 999999 40 2445.94 33.27 40 25 0.0 (Average)
Filler Q 0 0 0 9.10 0.12 0 0 0.0 (Std. Dev.)
Filler Q 49 999999 40 2439.42 33.18 40 25 0.0 (95% C.I.
Low)
Filler Q 49 999999 40 2452.45 33.36 40 25 0.0 (95% C.I.
High)
Bezel Q 49 999999 30 2281.57 23.28 30 15 0.0 (Average)
119
Bezel Q 0 0 0 12.41 0.12 0 0 0.0 (Std. Dev.)
Bezel Q 49 999999 30 2272.69 23.19 30 15 0.0 (95% C.I.
Low)
Bezel Q 49 999999 30 2290.46 23.37 30 15 0.0 (95% C.I.
High)
LOCATION STATES BY PERCENTAGE (Multiple Capacity)
% |
Location Scheduled % Partially % | %
Name Hours Empty Occupied Full | Down
----------------- --------- ------ --------- ---- | ----
Des Manf Q 49 100.00 0.0 0.0 | 0.0 (Average)
Des Manf Q 0 0.0 0.0 0.0 | 0.0 (Std. Dev.)
Des Manf Q 49 100.00 0.0 0.0 | 0.0 (95% C.I. Low)
Des Manf Q 49 100.00 0.0 0.0 | 0.0 (95% C.I. High)
120
Manf Assmb Q 49 21.73 78.27 0.0 | 0.0 (Average)
Manf Assmb Q 0 0.68 0.68 0.0 | 0.0 (Std. Dev.)
Manf Assmb Q 49 21.25 77.78 0.0 | 0.0 (95% C.I. Low)
Manf Assmb Q 49 22.22 78.75 0.0 | 0.0 (95% C.I. High)
Assmb Testing Q 49 10.72 89.28 0.0 | 0.0 (Average)
Assmb Testing Q 0 0.97 0.97 0.0 | 0.0 (Std. Dev.)
Assmb Testing Q 49 10.03 88.59 0.0 | 0.0 (95% C.I. Low)
Assmb Testing Q 49 11.41 89.97 0.0 | 0.0 (95% C.I. High)
Testing Prod Q 49 100.00 0.0 0.0 | 0.0 (Average)
Testing Prod Q 0 0.0 0.0 0.0 | 0.0 (Std. Dev.)
Testing Prod Q 49 100.00 0.0 0.0 | 0.0 (95% C.I. Low)
Testing Prod Q 49 100.00 0.0 0.0 | 0.0 (95% C.I. High)
121
Warehouse Assmb Q 49 100.00 0.0 0.0 | 0.0 (Average)
Warehouse Assmb Q 0 0.0 0.0 0.0 | 0.0 (Std. Dev.)
Warehouse Assmb Q 49 100.00 0.0 0.0 | 0.0 (95% C.I. Low)
Warehouse Assmb Q 49 100.00 0.0 0.0 | 0.0 (95% C.I. High)
Warehouse Prod Q 49 2.07 97.93 0.0 | 0.0 (Average)
Warehouse Prod Q 0 1.09 1.09 0.0 | 0.0 (Std. Dev.)
Warehouse Prod Q 49 1.30 97.15 0.0 | 0.0 (95% C.I. Low)
Warehouse Prod Q 49 2.85 98.70 0.0 | 0.0 (95% C.I. High)
Prod Confg Q 49 100.00 0.0 0.0 | 0.0 (Average)
Prod Confg Q 0 0.0 0.0 0.0 | 0.0 (Std. Dev.)
Prod Confg Q 49 100.00 0.0 0.0 | 0.0 (95% C.I. Low)
Prod Confg Q 49 100.00 0.0 0.0 | 0.0 (95% C.I. High)
Confg QA Q 49 100.00 0.0 0.0 | 0.0 (Average)
122
Confg QA Q 0 0.0 0.0 0.0 | 0.0 (Std. Dev.)
Confg QA Q 49 100.00 0.0 0.0 | 0.0 (95% C.I. Low)
Confg QA Q 49 100.00 0.0 0.0 | 0.0 (95% C.I. High)
Warehouse Q 49 2.09 97.91 0.0 | 0.0 (Average)
Warehouse Q 0 1.06 1.06 0.0 | 0.0 (Std. Dev.)
Warehouse Q 49 1.34 97.15 0.0 | 0.0 (95% C.I. Low)
Warehouse Q 49 2.85 98.66 0.0 | 0.0 (95% C.I. High)
Chips Q 49 0.03 99.97 0.0 | 0.0 (Average)
Chips Q 0 0.04 0.04 0.0 | 0.0 (Std. Dev.)
Chips Q 49 0.0 99.94 0.0 | 0.0 (95% C.I. Low)
Chips Q 49 0.06 100.00 0.0 | 0.0 (95% C.I. High)
Filler Q 49 0.03 99.97 0.0 | 0.0 (Average)
123
Filler Q 0 0.02 0.02 0.0 | 0.0 (Std. Dev.)
Filler Q 49 0.02 99.96 0.0 | 0.0 (95% C.I. Low)
Filler Q 49 0.04 99.98 0.0 | 0.0 (95% C.I. High)
Bezel Q 49 0.03 99.97 0.0 | 0.0 (Average)
Bezel Q 0 0.03 0.03 0.0 | 0.0 (Std. Dev.)
Bezel Q 49 0.01 99.95 0.0 | 0.0 (95% C.I. Low)
Bezel Q 49 0.05 99.99 0.0 | 0.0 (95% C.I. High)
LOCATION STATES BY PERCENTAGE (Single Capacity/Tanks)
Location Scheduled % % % % % %
Name Hours Operation Setup Idle Waiting Blocked Down
---------------- --------- --------- ----- ------ ------- ------- ----
Warehouse Wi 49 0.0 0.0 100.00 0.0 0.0 0.0 (Average)
124
Warehouse Wi 0 0.0 0.0 0.0 0.0 0.0 0.0 (Std. Dev.)
Warehouse Wi 49 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. Low)
Warehouse Wi 49 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. High)
PCB Manf 49 0.0 0.0 100.00 0.0 0.0 0.0 (Average)
PCB Manf 0 0.0 0.0 0.0 0.0 0.0 0.0 (Std. Dev.)
PCB Manf 49 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. Low)
PCB Manf 49 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. High)
PCB Assmb 49 82.31 0.0 17.69 0.0 0.0 0.0 (Average)
PCB Assmb 0 0.53 0.0 0.53 0.0 0.0 0.0 (Std. Dev.)
PCB Assmb 49 81.93 0.0 17.31 0.0 0.0 0.0 (95% C.I. Low)
PCB Assmb 49 82.69 0.0 18.07 0.0 0.0 0.0 (95% C.I. High)
PCB Testing 49 95.92 0.0 4.08 0.0 0.0 0.0 (Average)
PCB Testing 0 0.14 0.0 0.14 0.0 0.0 0.0 (Std. Dev.)
125
PCB Testing 49 95.82 0.0 3.98 0.0 0.0 0.0 (95% C.I. Low)
PCB Testing 49 96.02 0.0 4.18 0.0 0.0 0.0 (95% C.I. High)
Configuration Wi 49 0.0 0.0 100.00 0.0 0.0 0.0 (Average)
Configuration Wi 0 0.0 0.0 0.0 0.0 0.0 0.0 (Std. Dev.)
Configuration Wi 49 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. Low)
Configuration Wi 49 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. High)
QA Dept 49 0.0 0.0 100.00 0.0 0.0 0.0 (Average)
QA Dept 0 0.0 0.0 0.0 0.0 0.0 0.0 (Std. Dev.)
QA Dept 49 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. Low)
QA Dept 49 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. High)
Prod Dept Wi 49 31.33 0.0 68.67 0.0 0.0 0.0 (Average)
Prod Dept Wi 0 0.83 0.0 0.83 0.0 0.0 0.0 (Std. Dev.)
126
Prod Dept Wi 49 30.74 0.0 68.07 0.0 0.0 0.0 (95% C.I. Low)
Prod Dept Wi 49 31.93 0.0 69.26 0.0 0.0 0.0 (95% C.I. High)
FAILED ARRIVALS
Entity Location Total
Name Name Failed
------------- ---------------- ------
Chips Chips Q 0 (Average)
Chips Chips Q 0 (Std. Dev.)
Chips Chips Q 0 (95% C.I. Low)
Chips Chips Q 0 (95% C.I. High)
127
Network Board Manf Assmb Q 0 (Average)
Network Board Manf Assmb Q 0 (Std. Dev.)
Network Board Manf Assmb Q 0 (95% C.I. Low)
Network Board Manf Assmb Q 0 (95% C.I. High)
Chasis Warehouse Q 0 (Average)
Chasis Warehouse Q 0 (Std. Dev.)
Chasis Warehouse Q 0 (95% C.I. Low)
Chasis Warehouse Q 0 (95% C.I. High)
Bezels Bezel Q 0 (Average)
Bezels Bezel Q 0 (Std. Dev.)
Bezels Bezel Q 0 (95% C.I. Low)
Bezels Bezel Q 0 (95% C.I. High)
128
Service Card Filler Q 0 (Average)
Service Card Filler Q 0 (Std. Dev.)
Service Card Filler Q 0 (95% C.I. Low)
Service Card Filler Q 0 (95% C.I. High)
Final Product Warehouse Prod Q 0 (Average)
Final Product Warehouse Prod Q 0 (Std. Dev.)
Final Product Warehouse Prod Q 0 (95% C.I. Low)
Final Product Warehouse Prod Q 0 (95% C.I. High)
ENTITY ACTIVITY
Average Average Average Average Average
Current Minutes Minutes Minutes Minutes Minutes
Entity Total Quantity In In Move Wait For In
129
Name Exits In System System Logic Res, etc. Operation Blocked
------------- ----- --------- ------- ------- --------- --------- -------
Final Board 15 0 1624.21 0.0 993.70 362.35 268.14 (Average)
Final Board 0 0 24.24 0.0 20.03 2.90 3.99 (Std. Dev.)
Final Board 15 0 1606.86 0.0 979.37 360.27 265.29 (95% C.I. Low)
Final Board 15 0 1641.55 0.0 1008.03 364.43 271.00 (95% C.I. High)
Chips 15 5 1623.94 0.0 1432.03 0.0 191.90 (Average)
Chips 0 0 24.19 0.0 22.37 0.0 2.10 (Std. Dev.)
Chips 15 5 1606.63 0.0 1416.02 0.0 190.40 (95% C.I. Low)
Chips 15 5 1641.25 0.0 1448.04 0.0 193.41 (95% C.I. High)
PCB 0 0 - - - - - (Average)
PCB 0 0 - - - - - (Std. Dev.)
PCB 0 0 - - - - - (95% C.I. Low)
PCB 0 0 - - - - - (95% C.I. High)
130
Network Board 0 5 - - - - - (Average)
Network Board 0 0 - - - - - (Std. Dev.)
Network Board 0 5 - - - - - (95% C.I. Low)
Network Board 0 5 - - - - - (95% C.I. High)
Chasis 15 0 1623.80 0.0 1431.90 0.0 191.89 (Average)
Chasis 0 0 23.75 0.0 21.96 0.0 2.07 (Std. Dev.)
Chasis 15 0 1606.81 0.0 1416.18 0.0 190.41 (95% C.I. Low)
Chasis 15 0 1640.79 0.0 1447.61 0.0 193.38 (95% C.I. High)
Bezels 15 15 1624.03 0.0 1432.11 0.0 191.91 (Average)
Bezels 0 0 24.47 0.0 22.62 0.0 2.13 (Std. Dev.)
Bezels 15 15 1606.52 0.0 1415.93 0.0 190.38 (95% C.I. Low)
Bezels 15 15 1641.54 0.0 1448.30 0.0 193.43 (95% C.I. High)
131
Service Card 15 25 1624.01 0.0 1432.09 0.0 191.91 (Average)
Service Card 0 0 24.19 0.0 22.35 0.0 2.11 (Std. Dev.)
Service Card 15 25 1606.70 0.0 1416.10 0.0 190.40 (95% C.I. Low)
Service Card 15 25 1641.31 0.0 1448.09 0.0 193.42 (95% C.I. High)
Final Product 15 0 1624.38 0.0 1624.38 0.0 0.0 (Average)
Final Product 0 0 24.37 0.0 24.37 0.0 0.0 (Std. Dev.)
Final Product 15 0 1606.94 0.0 1606.94 0.0 0.0 (95% C.I. Low)
Final Product 15 0 1641.81 0.0 1641.81 0.0 0.0 (95% C.I. High)
132
ENTITY STATES BY PERCENTAGE
% %
Entity In Move Wait For % %
Name Logic Res, etc. In Operation Blocked
------------- ------- --------- ------------ -------
Final Board 0.0 61.18 22.31 16.51 (Average)
Final Board 0.0 0.38 0.31 0.12 (Std. Dev.)
Final Board 0.0 60.90 22.09 16.43 (95% C.I. Low)
Final Board 0.0 61.45 22.54 16.59 (95% C.I. High)
Chips 0.0 88.18 0.0 11.82 (Average)
Chips 0.0 0.09 0.0 0.09 (Std. Dev.)
Chips 0.0 88.12 0.0 11.76 (95% C.I. Low)
Chips 0.0 88.24 0.0 11.88 (95% C.I. High)
133
PCB - - - - (Average)
PCB - - - - (Std. Dev.)
PCB - - - - (95% C.I. Low)
PCB - - - - (95% C.I. High)
Network Board - - - - (Average)
Network Board - - - - (Std. Dev.)
Network Board - - - - (95% C.I. Low)
Network Board - - - - (95% C.I. High)
Chasis 0.0 88.18 0.0 11.82 (Average)
Chasis 0.0 0.09 0.0 0.09 (Std. Dev.)
Chasis 0.0 88.12 0.0 11.76 (95% C.I. Low)
Chasis 0.0 88.24 0.0 11.88 (95% C.I. High)
134
Bezels 0.0 88.18 0.0 11.82 (Average)
Bezels 0.0 0.09 0.0 0.09 (Std. Dev.)
Bezels 0.0 88.12 0.0 11.76 (95% C.I. Low)
Bezels 0.0 88.24 0.0 11.88 (95% C.I. High)
Service Card 0.0 88.18 0.0 11.82 (Average)
Service Card 0.0 0.09 0.0 0.09 (Std. Dev.)
Service Card 0.0 88.12 0.0 11.76 (95% C.I. Low)
Service Card 0.0 88.24 0.0 11.88 (95% C.I. High)
Final Product 0.0 100.00 0.0 0.0 (Average)
Final Product 0.0 0.0 0.0 0.0 (Std. Dev.)
Final Product 0.0 100.00 0.0 0.0 (95% C.I. Low)
Final Product 0.0 100.00 0.0 0.0 (95% C.I. High)
135
VARIABLES
Average
Variable Total Minutes Minimum Maximum Current Average
Name Changes Per Change Value Value Value Value
------------------------ ------- ---------- ------- ------- ------- -------
PCB Manf Time 0 0.0 0 0 0 0 (Average)
PCB Manf Time 0 0.0 0 0 0 0 (Std. Dev.)
PCB Manf Time 0 0.0 0 0 0 0 (95% C.I. Low)
PCB Manf Time 0 0.0 0 0 0 0 (95% C.I. High)
Chips Assmb time 20 115.08 0 130.28 118.93 120.79 (Average)
Chips Assmb time 0 1.00 0 2.82 5.43 0.69 (Std. Dev.)
Chips Assmb time 20 114.36 0 128.26 115.04 120.29 (95% C.I. Low)
Chips Assmb time 20 115.80 0 132.30 122.82 121.28 (95% C.I. High)
136
PCB Testing time 16.1 176.16 0 133.27 119.34 115.02 (Average)
PCB Testing time 0.31 2.04 0 2.83 10.37 1.89 (Std. Dev.)
PCB Testing time 15.87 174.70 0 131.25 111.92 113.66 (95% C.I. Low)
PCB Testing time 16.32 177.62 0 135.30 126.76 116.38 (95% C.I. High)
Chasis assmb time 15.1 187.83 0 18.79 14.54 13.69 (Average)
Chasis assmb time 0.31 2.33 0 0.85 2.17 0.43 (Std. Dev.)
Chasis assmb time 14.87 186.17 0 18.17 12.99 13.38 (95% C.I. Low)
Chasis assmb time 15.32 189.50 0 19.40 16.10 14.01 (95% C.I. High)
Filler Assmb time 15 188.83 0 6.64 1.97 1.85 (Average)
Filler Assmb time 0 2.17 0 3.83 1.83 0.65 (Std. Dev.)
Filler Assmb time 15 187.28 0 3.90 0.65 1.38 (95% C.I. Low)
Filler Assmb time 15 190.38 0 9.39 3.28 2.32 (95% C.I. High)
137
Pcb Faceplate time 15 188.96 0 6.11 2.49 1.78 (Average)
Pcb Faceplate time 0 2.15 0 2.50 1.87 0.45 (Std. Dev.)
Pcb Faceplate time 15 187.42 0 4.32 1.15 1.46 (95% C.I. Low)
Pcb Faceplate time 15 190.51 0 7.90 3.83 2.11 (95% C.I. High)
Final Assmb Testing Time 15 189.13 0 7.92 1.95 1.99 (Average)
Final Assmb Testing Time 0 2.15 0 2.42 1.20 0.44 (Std. Dev.)
Final Assmb Testing Time 15 187.58 0 6.18 1.08 1.67 (95% C.I. Low)
Final Assmb Testing Time 15 190.67 0 9.65 2.81 2.30 (95% C.I. High)
configuration time 0 0.0 0 0 0 0 (Average)
configuration time 0 0.0 0 0 0 0 (Std. Dev.)
configuration time 0 0.0 0 0 0 0 (95% C.I. Low)
configuration time 0 0.0 0 0 0 0 (95% C.I. High)
QA time 0 0.0 0 0 0 0 (Average)
138
QA time 0 0.0 0 0 0 0 (Std. Dev.)
QA time 0 0.0 0 0 0 0 (95% C.I. Low)
QA time 0 0.0 0 0 0 0 (95% C.I. High)
Installing Network Board 15 189.26 0 45.08 40.65 35.39 (Average)
Installing Network Board 0 2.10 0 2.07 3.21 0.73 (Std. Dev.)
Installing Network Board 15 187.75 0 43.59 38.35 34.87 (95% C.I. Low)
Installing Network Board 15 190.77 0 46.56 42.95 35.91 (95% C.I. High)
PCB Configuration 15.5 183.67 0 69.20 59.40 55.29 (Average)
PCB Configuration 0.52 2.42 0 3.25 5.24 1.07 (Std. Dev.)
PCB Configuration 15.12 181.93 0 66.87 55.65 54.52 (95% C.I. Low)
PCB Configuration 15.87 185.40 0 71.53 63.14 56.06 (95% C.I. High)
139
14.2 Appendix ‘A2’ Optimized Time Model Output
--------------------------------------------------------------------------------
General Report
Output from H:\Jawwad's Project\Final Project.mod
Date: Apr/21/2009 Time: 09:06:59 PM
--------------------------------------------------------------------------------
Scenario : Normal Run
Replication : Average
Period : Final Report (0 sec to 36 hr Elapsed: 36 hr)
Simulation Time : 36 hr
--------------------------------------------------------------------------------
140
LOCATIONS
Average
Location Scheduled Total Minutes Average Maximum Current
Name Hours Capacity Entries Per Entry Contents Contents Contents % Util
----------------- --------- -------- ------- --------- -------- -------- -------- ------
Warehouse Wi 36 1 0 0.0 0 0 0 0.0 (Average)
Warehouse Wi 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
Warehouse Wi 36 1 0 0.0 0 0 0 0.0 (95% C.I. Low)
Warehouse Wi 36 1 0 0.0 0 0 0 0.0 (95% C.I. High)
PCB Manf 36 1 0 0.0 0 0 0 0.0 (Average)
PCB Manf 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
141
PCB Manf 36 1 0 0.0 0 0 0 0.0 (95% C.I. Low)
PCB Manf 36 1 0 0.0 0 0 0 0.0 (95% C.I. High)
PCB Assmb 36 1 18.6 116.16 0.99 1 1 99.96 (Average)
PCB Assmb 0 0 0.51 3.25 0.0 0 0 0.03 (Std. Dev.)
PCB Assmb 36 1 18.23 113.83 0.99 1 1 99.94 (95% C.I.
Low)
PCB Assmb 36 1 18.96 118.49 0.99 1 1 99.99 (95% C.I.
High)
PCB Testing 36 2 17.6 173.06 1.40 2 1.6 70.46 (Average)
142
PCB Testing 0 0 0.51 4.90 0.01 0 0.51 0.98 (Std. Dev.)
PCB Testing 36 2 17.23 169.55 1.39 2 1.23 69.76 (95% C.I.
Low)
PCB Testing 36 2 17.96 176.57 1.42 2 1.96 71.16 (95% C.I.
High)
Configuration Wi 36 1 0 0.0 0 0 0 0.0 (Average)
Configuration Wi 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
Configuration Wi 36 1 0 0.0 0 0 0 0.0 (95% C.I. Low)
Configuration Wi 36 1 0 0.0 0 0 0 0.0 (95% C.I. High)
QA Dept 36 1 0 0.0 0 0 0 0.0 (Average)
143
QA Dept 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
QA Dept 36 1 0 0.0 0 0 0 0.0 (95% C.I. Low)
QA Dept 36 1 0 0.0 0 0 0 0.0 (95% C.I. High)
Prod Dept Wi 36 1 16 61.32 0.45 1 1 45.43 (Average)
Prod Dept Wi 0 0 0 2.22 0.01 0 0 1.65 (Std. Dev.)
Prod Dept Wi 36 1 16 59.73 0.44 1 1 44.25 (95% C.I.
Low)
Prod Dept Wi 36 1 16 62.91 0.46 1 1 46.61 (95% C.I.
High)
Des Manf Q 36 999999 0 0.0 0 0 0 0.0 (Average)
144
Des Manf Q 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
Des Manf Q 36 999999 0 0.0 0 0 0 0.0 (95% C.I. Low)
Des Manf Q 36 999999 0 0.0 0 0 0 0.0 (95% C.I. High)
Manf Assmb Q 36 999999 20 1133.34 10.49 20 1.4 0.0
(Average)
Manf Assmb Q 0 0 0 11.19 0.10 0 0.51 0.0 (Std. Dev.)
Manf Assmb Q 36 999999 20 1125.33 10.41 20 1.03 0.0 (95%
C.I. Low)
Manf Assmb Q 36 999999 20 1141.34 10.56 20 1.76 0.0 (95%
C.I. High)
145
Assmb Testing Q 36 999999 17.6 0.0 0 1 0 0.0 (Average)
Assmb Testing Q 0 0 0.51 0.0 0 0 0 0.0 (Std. Dev.)
Assmb Testing Q 36 999999 17.23 0.0 0 1 0 0.0 (95% C.I.
Low)
Assmb Testing Q 36 999999 17.96 0.0 0 1 0 0.0 (95% C.I.
High)
Testing Prod Q 36 999999 16 0.0 0 1 0 0.0 (Average)
Testing Prod Q 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
Testing Prod Q 36 999999 16 0.0 0 1 0 0.0 (95% C.I.
Low)
Testing Prod Q 36 999999 16 0.0 0 1 0 0.0 (95% C.I.
High)
146
Warehouse Assmb Q 36 999999 0 0.0 0 0 0 0.0 (Average)
Warehouse Assmb Q 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
Warehouse Assmb Q 36 999999 0 0.0 0 0 0 0.0 (95% C.I.
Low)
Warehouse Assmb Q 36 999999 0 0.0 0 0 0 0.0 (95% C.I.
High)
Warehouse Prod Q 36 999999 15 1203.50 8.35 15 0 0.0
(Average)
Warehouse Prod Q 0 0 0 12.41 0.08 0 0 0.0 (Std. Dev.)
Warehouse Prod Q 36 999999 15 1194.62 8.29 15 0 0.0 (95%
C.I. Low)
147
Warehouse Prod Q 36 999999 15 1212.38 8.41 15 0 0.0 (95%
C.I. High)
Prod Confg Q 36 999999 0 0.0 0 0 0 0.0 (Average)
Prod Confg Q 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
Prod Confg Q 36 999999 0 0.0 0 0 0 0.0 (95% C.I. Low)
Prod Confg Q 36 999999 0 0.0 0 0 0 0.0 (95% C.I.
High)
Confg QA Q 36 999999 0 0.0 0 0 0 0.0 (Average)
Confg QA Q 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
Confg QA Q 36 999999 0 0.0 0 0 0 0.0 (95% C.I. Low)
148
Confg QA Q 36 999999 0 0.0 0 0 0 0.0 (95% C.I.
High)
Warehouse Q 36 999999 15 1203.98 8.36 15 0 0.0 (Average)
Warehouse Q 0 0 0 12.64 0.08 0 0 0.0 (Std. Dev.)
Warehouse Q 36 999999 15 1194.93 8.29 15 0 0.0 (95% C.I.
Low)
Warehouse Q 36 999999 15 1213.02 8.42 15 0 0.0 (95% C.I.
High)
Chips Q 36 999999 20 1442.21 13.35 20 5 0.0 (Average)
Chips Q 0 0 0 9.18 0.08 0 0 0.0 (Std. Dev.)
149
Chips Q 36 999999 20 1435.64 13.29 20 5 0.0 (95% C.I.
Low)
Chips Q 36 999999 20 1448.78 13.41 20 5 0.0 (95% C.I.
High)
Filler Q 36 999999 40 1800.95 33.35 40 25 0.0 (Average)
Filler Q 0 0 0 5.03 0.09 0 0 0.0 (Std. Dev.)
Filler Q 36 999999 40 1797.35 33.28 40 25 0.0 (95% C.I.
Low)
Filler Q 36 999999 40 1804.55 33.41 40 25 0.0 (95% C.I.
High)
Bezel Q 36 999999 30 1681.07 23.34 30 15 0.0 (Average)
150
Bezel Q 0 0 0 6.67 0.09 0 0 0.0 (Std. Dev.)
Bezel Q 36 999999 30 1676.29 23.28 30 15 0.0 (95% C.I.
Low)
Bezel Q 36 999999 30 1685.85 23.41 30 15 0.0 (95% C.I.
High)
LOCATION STATES BY PERCENTAGE (Multiple Capacity)
% |
Location Scheduled % Partially % | %
Name Hours Empty Occupied Full | Down
----------------- --------- ------ --------- ----- | ----
PCB Testing 36 5.68 47.73 46.60 | 0.0 (Average)
PCB Testing 0 0.16 1.99 1.97 | 0.0 (Std. Dev.)
151
PCB Testing 36 5.56 46.30 45.19 | 0.0 (95% C.I. Low)
PCB Testing 36 5.79 49.15 48.00 | 0.0 (95% C.I. High)
Des Manf Q 36 100.00 0.0 0.0 | 0.0 (Average)
Des Manf Q 0 0.0 0.0 0.0 | 0.0 (Std. Dev.)
Des Manf Q 36 100.00 0.0 0.0 | 0.0 (95% C.I. Low)
Des Manf Q 36 100.00 0.0 0.0 | 0.0 (95% C.I. High)
Manf Assmb Q 36 0.04 99.96 0.0 | 0.0 (Average)
Manf Assmb Q 0 0.03 0.03 0.0 | 0.0 (Std. Dev.)
152
Manf Assmb Q 36 0.01 99.94 0.0 | 0.0 (95% C.I. Low)
Manf Assmb Q 36 0.06 99.99 0.0 | 0.0 (95% C.I. High)
Assmb Testing Q 36 100.00 0.0 0.0 | 0.0 (Average)
Assmb Testing Q 0 0.0 0.0 0.0 | 0.0 (Std. Dev.)
Assmb Testing Q 36 100.00 0.0 0.0 | 0.0 (95% C.I. Low)
Assmb Testing Q 36 100.00 0.0 0.0 | 0.0 (95% C.I. High)
Testing Prod Q 36 100.00 0.0 0.0 | 0.0 (Average)
Testing Prod Q 0 0.0 0.0 0.0 | 0.0 (Std. Dev.)
Testing Prod Q 36 100.00 0.0 0.0 | 0.0 (95% C.I. Low)
153
Testing Prod Q 36 100.00 0.0 0.0 | 0.0 (95% C.I. High)
Warehouse Assmb Q 36 100.00 0.0 0.0 | 0.0 (Average)
Warehouse Assmb Q 0 0.0 0.0 0.0 | 0.0 (Std. Dev.)
Warehouse Assmb Q 36 100.00 0.0 0.0 | 0.0 (95% C.I. Low)
Warehouse Assmb Q 36 100.00 0.0 0.0 | 0.0 (95% C.I. High)
Warehouse Prod Q 36 5.61 94.39 0.0 | 0.0 (Average)
Warehouse Prod Q 0 1.19 1.19 0.0 | 0.0 (Std. Dev.)
Warehouse Prod Q 36 4.76 93.54 0.0 | 0.0 (95% C.I. Low)
154
Warehouse Prod Q 36 6.46 95.24 0.0 | 0.0 (95% C.I. High)
Prod Confg Q 36 100.00 0.0 0.0 | 0.0 (Average)
Prod Confg Q 0 0.0 0.0 0.0 | 0.0 (Std. Dev.)
Prod Confg Q 36 100.00 0.0 0.0 | 0.0 (95% C.I. Low)
Prod Confg Q 36 100.00 0.0 0.0 | 0.0 (95% C.I. High)
Confg QA Q 36 100.00 0.0 0.0 | 0.0 (Average)
Confg QA Q 0 0.0 0.0 0.0 | 0.0 (Std. Dev.)
Confg QA Q 36 100.00 0.0 0.0 | 0.0 (95% C.I. Low)
Confg QA Q 36 100.00 0.0 0.0 | 0.0 (95% C.I. High)
155
Warehouse Q 36 5.59 94.41 0.0 | 0.0 (Average)
Warehouse Q 0 1.18 1.18 0.0 | 0.0 (Std. Dev.)
Warehouse Q 36 4.74 93.57 0.0 | 0.0 (95% C.I. Low)
Warehouse Q 36 6.43 95.26 0.0 | 0.0 (95% C.I. High)
Chips Q 36 0.06 99.94 0.0 | 0.0 (Average)
Chips Q 0 0.05 0.05 0.0 | 0.0 (Std. Dev.)
Chips Q 36 0.02 99.91 0.0 | 0.0 (95% C.I. Low)
Chips Q 36 0.09 99.98 0.0 | 0.0 (95% C.I. High)
Filler Q 36 0.03 99.97 0.0 | 0.0 (Average)
156
Filler Q 0 0.03 0.03 0.0 | 0.0 (Std. Dev.)
Filler Q 36 0.01 99.94 0.0 | 0.0 (95% C.I. Low)
Filler Q 36 0.06 99.99 0.0 | 0.0 (95% C.I. High)
Bezel Q 36 0.06 99.94 0.0 | 0.0 (Average)
Bezel Q 0 0.07 0.07 0.0 | 0.0 (Std. Dev.)
Bezel Q 36 0.0 99.89 0.0 | 0.0 (95% C.I. Low)
Bezel Q 36 0.11 100.00 0.0 | 0.0 (95% C.I. High)
157
LOCATION STATES BY PERCENTAGE (Single Capacity/Tanks)
Location Scheduled % % % % % %
Name Hours Operation Setup Idle Waiting Blocked Down
---------------- --------- --------- ----- ------ ------- ------- ----
Warehouse Wi 36 0.0 0.0 100.00 0.0 0.0 0.0 (Average)
Warehouse Wi 0 0.0 0.0 0.0 0.0 0.0 0.0 (Std. Dev.)
Warehouse Wi 36 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. Low)
Warehouse Wi 36 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. High)
PCB Manf 36 0.0 0.0 100.00 0.0 0.0 0.0 (Average)
PCB Manf 0 0.0 0.0 0.0 0.0 0.0 0.0 (Std. Dev.)
158
PCB Manf 36 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. Low)
PCB Manf 36 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. High)
PCB Assmb 36 99.96 0.0 0.04 0.0 0.0 0.0 (Average)
PCB Assmb 0 0.03 0.0 0.03 0.0 0.0 0.0 (Std. Dev.)
PCB Assmb 36 99.94 0.0 0.02 0.0 0.0 0.0 (95% C.I. Low)
PCB Assmb 36 99.98 0.0 0.06 0.0 0.0 0.0 (95% C.I. High)
Configuration Wi 36 0.0 0.0 100.00 0.0 0.0 0.0 (Average)
Configuration Wi 0 0.0 0.0 0.0 0.0 0.0 0.0 (Std. Dev.)
159
Configuration Wi 36 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. Low)
Configuration Wi 36 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. High)
QA Dept 36 0.0 0.0 100.00 0.0 0.0 0.0 (Average)
QA Dept 0 0.0 0.0 0.0 0.0 0.0 0.0 (Std. Dev.)
QA Dept 36 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. Low)
QA Dept 36 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. High)
Prod Dept Wi 36 44.85 0.0 54.57 0.57 0.0 0.0 (Average)
Prod Dept Wi 0 1.16 0.0 1.65 0.72 0.0 0.0 (Std. Dev.)
Prod Dept Wi 36 44.03 0.0 53.39 0.06 0.0 0.0 (95% C.I. Low)
160
Prod Dept Wi 36 45.68 0.0 55.75 1.09 0.0 0.0 (95% C.I. High)
FAILED ARRIVALS
Entity Location Total
Name Name Failed
------------- ---------------- ------
Chips Chips Q 0 (Average)
Chips Chips Q 0 (Std. Dev.)
Chips Chips Q 0 (95% C.I. Low)
Chips Chips Q 0 (95% C.I. High)
161
Network Board Manf Assmb Q 0 (Average)
Network Board Manf Assmb Q 0 (Std. Dev.)
Network Board Manf Assmb Q 0 (95% C.I. Low)
Network Board Manf Assmb Q 0 (95% C.I. High)
Chasis Warehouse Q 0 (Average)
Chasis Warehouse Q 0 (Std. Dev.)
Chasis Warehouse Q 0 (95% C.I. Low)
Chasis Warehouse Q 0 (95% C.I. High)
Bezels Bezel Q 0 (Average)
162
Bezels Bezel Q 0 (Std. Dev.)
Bezels Bezel Q 0 (95% C.I. Low)
Bezels Bezel Q 0 (95% C.I. High)
Service Card Filler Q 0 (Average)
Service Card Filler Q 0 (Std. Dev.)
Service Card Filler Q 0 (95% C.I. Low)
Service Card Filler Q 0 (95% C.I. High)
Final Product Warehouse Prod Q 0 (Average)
163
Final Product Warehouse Prod Q 0 (Std. Dev.)
Final Product Warehouse Prod Q 0 (95% C.I. Low)
Final Product Warehouse Prod Q 0 (95% C.I. High)
ENTITY ACTIVITY
Average Average Average Average Average
Current Minutes Minutes Minutes Minutes Minutes
Entity Total Quantity In In Move Wait For In
Name Exits In System System Logic Res, etc. Operation Blocked
------------- ----- --------- ------- ------- --------- --------- -------
164
Final Board 15 0 1203.69 0.0 728.32 363.30 112.06 (Average)
Final Board 0 0 12.13 0.0 9.36 2.78 1.25 (Std. Dev.)
Final Board 15 0 1195.00 0.0 721.62 361.31 111.16 (95% C.I. Low)
Final Board 15 0 1212.37 0.0 735.02 365.29 112.96 (95% C.I. High)
Chips 15 5 1203.35 0.0 1067.44 0.0 135.91 (Average)
Chips 0 0 12.25 0.0 10.89 0.0 1.69 (Std. Dev.)
Chips 15 5 1194.59 0.0 1059.64 0.0 134.69 (95% C.I. Low)
Chips 15 5 1212.11 0.0 1075.23 0.0 137.12 (95% C.I. High)
PCB 0 0 - - - - - (Average)
165
PCB 0 0 - - - - - (Std. Dev.)
PCB 0 0 - - - - - (95% C.I. Low)
PCB 0 0 - - - - - (95% C.I. High)
Network Board 0 5 - - - - - (Average)
Network Board 0 0 - - - - - (Std. Dev.)
Network Board 0 5 - - - - - (95% C.I. Low)
Network Board 0 5 - - - - - (95% C.I. High)
Chasis 15 0 1203.98 0.0 1068.02 0.0 135.95 (Average)
166
Chasis 0 0 12.64 0.0 11.30 0.0 1.70 (Std. Dev.)
Chasis 15 0 1194.93 0.0 1059.94 0.0 134.73 (95% C.I. Low)
Chasis 15 0 1213.02 0.0 1076.11 0.0 137.16 (95% C.I. High)
Bezels 15 15 1203.34 0.0 1067.43 0.0 135.90 (Average)
Bezels 0 0 12.75 0.0 11.35 0.0 1.73 (Std. Dev.)
Bezels 15 15 1194.22 0.0 1059.31 0.0 134.67 (95% C.I. Low)
Bezels 15 15 1212.47 0.0 1075.55 0.0 137.14 (95% C.I. High)
Service Card 15 25 1203.79 0.0 1067.85 0.0 135.93 (Average)
Service Card 0 0 12.72 0.0 11.36 0.0 1.71 (Std. Dev.)
167
Service Card 15 25 1194.69 0.0 1059.72 0.0 134.71 (95% C.I. Low)
Service Card 15 25 1212.89 0.0 1075.98 0.0 137.16 (95% C.I. High)
Final Product 15 0 1203.50 0.0 1203.50 0.0 0.0 (Average)
Final Product 0 0 12.41 0.0 12.41 0.0 0.0 (Std. Dev.)
Final Product 15 0 1194.62 0.0 1194.62 0.0 0.0 (95% C.I. Low)
Final Product 15 0 1212.38 0.0 1212.38 0.0 0.0 (95% C.I. High)
168
ENTITY STATES BY PERCENTAGE
% %
Entity In Move Wait For % %
Name Logic Res, etc. In Operation Blocked
------------- ------- --------- ------------ -------
Final Board 0.0 60.51 30.18 9.31 (Average)
Final Board 0.0 0.23 0.22 0.05 (Std. Dev.)
Final Board 0.0 60.34 30.03 9.27 (95% C.I. Low)
Final Board 0.0 60.67 30.34 9.35 (95% C.I. High)
169
Chips 0.0 88.71 0.0 11.29 (Average)
Chips 0.0 0.08 0.0 0.08 (Std. Dev.)
Chips 0.0 88.65 0.0 11.24 (95% C.I. Low)
Chips 0.0 88.76 0.0 11.35 (95% C.I. High)
PCB - - - - (Average)
PCB - - - - (Std. Dev.)
PCB - - - - (95% C.I. Low)
PCB - - - - (95% C.I. High)
170
Network Board - - - - (Average)
Network Board - - - - (Std. Dev.)
Network Board - - - - (95% C.I. Low)
Network Board - - - - (95% C.I. High)
Chasis 0.0 88.71 0.0 11.29 (Average)
Chasis 0.0 0.08 0.0 0.08 (Std. Dev.)
Chasis 0.0 88.65 0.0 11.23 (95% C.I. Low)
Chasis 0.0 88.77 0.0 11.35 (95% C.I. High)
Bezels 0.0 88.71 0.0 11.29 (Average)
171
Bezels 0.0 0.08 0.0 0.08 (Std. Dev.)
Bezels 0.0 88.65 0.0 11.24 (95% C.I. Low)
Bezels 0.0 88.76 0.0 11.35 (95% C.I. High)
Service Card 0.0 88.71 0.0 11.29 (Average)
Service Card 0.0 0.08 0.0 0.08 (Std. Dev.)
Service Card 0.0 88.65 0.0 11.23 (95% C.I. Low)
Service Card 0.0 88.77 0.0 11.35 (95% C.I. High)
Final Product 0.0 100.00 0.0 0.0 (Average)
172
Final Product 0.0 0.0 0.0 0.0 (Std. Dev.)
Final Product 0.0 100.00 0.0 0.0 (95% C.I. Low)
Final Product 0.0 100.00 0.0 0.0 (95% C.I. High)
VARIABLES
Average
Variable Total Minutes Minimum Maximum Current Average
Name Changes Per Change Value Value Value Value
------------------------ ------- ---------- ------- ------- ------- -------
PCB Manf Time 0 0.0 0 0 0 0 (Average)
PCB Manf Time 0 0.0 0 0 0 0 (Std. Dev.)
PCB Manf Time 0 0.0 0 0 0 0 (95% C.I. Low)
173
PCB Manf Time 0 0.0 0 0 0 0 (95% C.I. High)
Chips Assmb time 18.6 113.44 0 127.95 120.53 120.02 (Average)
Chips Assmb time 0.51 1.15 0 3.04 3.79 1.40 (Std. Dev.)
Chips Assmb time 18.23 112.61 0 125.77 117.81 119.01 (95% C.I. Low)
Chips Assmb time 18.96 114.27 0 130.12 123.25 121.02 (95% C.I. High)
PCB Testing time 17.6 119.89 0 133.78 118.86 114.10 (Average)
PCB Testing time 0.51 1.38 0 3.46 7.38 1.02 (Std. Dev.)
PCB Testing time 17.23 118.90 0 131.31 113.57 113.36 (95% C.I. Low)
174
PCB Testing time 17.96 120.88 0 136.26 124.14 114.83 (95% C.I. High)
Chasis assmb time 16 131.09 0 18.80 14.59 12.86 (Average)
Chasis assmb time 0 1.62 0 0.99 1.10 0.27 (Std. Dev.)
Chasis assmb time 16 129.92 0 18.09 13.80 12.66 (95% C.I. Low)
Chasis assmb time 16 132.25 0 19.52 15.38 13.06 (95% C.I. High)
Filler Assmb time 16 132.00 0 7.09 2.03 2.06 (Average)
Filler Assmb time 0 1.65 0 1.22 1.52 0.44 (Std. Dev.)
Filler Assmb time 16 130.81 0 6.21 0.94 1.74 (95% C.I. Low)
Filler Assmb time 16 133.18 0 7.97 3.12 2.38 (95% C.I. High)
175
Pcb Faceplate time 16 132.12 0 7.78 2.53 1.81 (Average)
Pcb Faceplate time 0 1.67 0 2.80 2.12 0.42 (Std. Dev.)
Pcb Faceplate time 16 130.93 0 5.77 1.01 1.51 (95% C.I. Low)
Pcb Faceplate time 16 133.32 0 9.78 4.06 2.12 (95% C.I. High)
Final Assmb Testing Time 16 132.28 0 5.61 1.06 1.48 (Average)
Final Assmb Testing Time 0 1.64 0 2.86 0.67 0.60 (Std. Dev.)
Final Assmb Testing Time 16 131.11 0 3.56 0.58 1.05 (95% C.I. Low)
Final Assmb Testing Time 16 133.46 0 7.66 1.54 1.91 (95% C.I. High)
176
configuration time 0 0.0 0 0 0 0 (Average)
configuration time 0 0.0 0 0 0 0 (Std. Dev.)
configuration time 0 0.0 0 0 0 0 (95% C.I. Low)
configuration time 0 0.0 0 0 0 0 (95% C.I. High)
QA time 0 0.0 0 0 0 0 (Average)
QA time 0 0.0 0 0 0 0 (Std. Dev.)
QA time 0 0.0 0 0 0 0 (95% C.I. Low)
QA time 0 0.0 0 0 0 0 (95% C.I. High)
177
Installing Network Board 16 132.35 0 44.55 40.52 33.94 (Average)
Installing Network Board 0 1.65 0 1.10 2.37 0.84 (Std. Dev.)
Installing Network Board 16 131.16 0 43.76 38.81 33.34 (95% C.I. Low)
Installing Network Board 16 133.54 0 45.34 42.22 34.55 (95% C.I. High)
PCB Configuration 16.4 127.18 0 69.89 61.52 54.13 (Average)
PCB Configuration 0.51 1.65 0 3.10 5.23 1.19 (Std. Dev.)
PCB Configuration 16.03 126.00 0 67.67 57.78 53.27 (95% C.I. Low)
PCB Configuration 16.76 128.37 0 72.12 65.27 54.98 (95% C.I. High)
178
14.3 Appendix ‘A3’ Maximum Capacity Model Output
--------------------------------------------------------------------------------
General Report
Output from C:\Users\jawwad\Desktop\Project Report\Final Project-max.MOD
Date: Apr/25/2009 Time: 12:25:13 AM
--------------------------------------------------------------------------------
Scenario : Normal Run
Replication : Average
Period : Final Report (0 sec to 168 hr Elapsed: 168 hr)
Simulation Time : 168 hr
--------------------------------------------------------------------------------
179
LOCATIONS
Average
Location Scheduled Total Minutes Average Maximum Current
Name Hours Capacity Entries Per Entry Contents Contents Contents % Util
----------------- --------- -------- ------- --------- -------- -------- -------- ------
Warehouse Wi 168 1 0 0.0 0 0 0 0.0 (Average)
Warehouse Wi 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
Warehouse Wi 168 1 0 0.0 0 0 0 0.0 (95% C.I. Low)
Warehouse Wi 168 1 0 0.0 0 0 0 0.0 (95% C.I. High)
PCB Manf 168 1 0 0.0 0 0 0 0.0 (Average)
180
PCB Manf 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
PCB Manf 168 1 0 0.0 0 0 0 0.0 (95% C.I. Low)
PCB Manf 168 1 0 0.0 0 0 0 0.0 (95% C.I. High)
PCB Assmb 168 1 34 296.45 0.99 1 1 99.99 (Average)
PCB Assmb 0 0 0 0.01 0.0 0 0 0.01 (Std. Dev.)
PCB Assmb 168 1 34 296.43 0.99 1 1 99.99 (95% C.I.
Low)
PCB Assmb 168 1 34 296.46 0.99 1 1 100.00 (95% C.I.
High)
181
PCB Testing 168 2 33 179.18 0.58 1 0.6 29.33 (Average)
PCB Testing 0 0 0 1.40 0.0 0 0.51 0.23 (Std. Dev.)
PCB Testing 168 2 33 178.18 0.58 1 0.23 29.17 (95% C.I.
Low)
PCB Testing 168 2 33 180.19 0.58 1 0.96 29.50 (95% C.I.
High)
Configuration Wi 168 1 0 0.0 0 0 0 0.0 (Average)
Configuration Wi 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
Configuration Wi 168 1 0 0.0 0 0 0 0.0 (95% C.I. Low)
Configuration Wi 168 1 0 0.0 0 0 0 0.0 (95% C.I. High)
182
QA Dept 168 1 0 0.0 0 0 0 0.0 (Average)
QA Dept 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
QA Dept 168 1 0 0.0 0 0 0 0.0 (95% C.I. Low)
QA Dept 168 1 0 0.0 0 0 0 0.0 (95% C.I. High)
Prod Dept Wi 168 1 32.4 60.91 0.19 1 0.4 19.58 (Average)
Prod Dept Wi 0 0 0.51 1.22 0.0 0 0.51 0.27 (Std. Dev.)
Prod Dept Wi 168 1 32.03 60.03 0.19 1 0.03 19.38 (95% C.I.
Low)
Prod Dept Wi 168 1 32.76 61.79 0.19 1 0.76 19.77 (95% C.I.
High)
183
Des Manf Q 168 999999 0 0.0 0 0 0 0.0 (Average)
Des Manf Q 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
Des Manf Q 168 999999 0 0.0 0 0 0 0.0 (95% C.I.
Low)
Des Manf Q 168 999999 0 0.0 0 0 0 0.0 (95% C.I.
High)
Manf Assmb Q 168 999999 81 7931.66 63.73 81 47 0.01
(Average)
Manf Assmb Q 0 0 0 9.73 0.07 0 0 0.0 (Std. Dev.)
Manf Assmb Q 168 999999 81 7924.70 63.68 81 47 0.01 (95%
C.I. Low)
Manf Assmb Q 168 999999 81 7938.62 63.79 81 47 0.01 (95%
C.I. High)
184
Assmb Testing Q 168 999999 33 0.0 0 1 0 0.0 (Average)
Assmb Testing Q 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
Assmb Testing Q 168 999999 33 0.0 0 1 0 0.0 (95% C.I.
Low)
Assmb Testing Q 168 999999 33 0.0 0 1 0 0.0 (95% C.I.
High)
Testing Prod Q 168 999999 32.4 0.0 0 1 0 0.0 (Average)
Testing Prod Q 0 0 0.51 0.0 0 0 0 0.0 (Std. Dev.)
Testing Prod Q 168 999999 32.03 0.0 0 1 0 0.0 (95% C.I.
Low)
185
Testing Prod Q 168 999999 32.76 0.0 0 1 0 0.0 (95% C.I.
High)
Warehouse Assmb Q 168 999999 0 0.0 0 0 0 0.0 (Average)
Warehouse Assmb Q 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
Warehouse Assmb Q 168 999999 0 0.0 0 0 0 0.0 (95% C.I.
Low)
Warehouse Assmb Q 168 999999 0 0.0 0 0 0 0.0 (95% C.I.
High)
Warehouse Prod Q 168 999999 81 8153.31 65.51 81 49 0.01
(Average)
Warehouse Prod Q 0 0 0 10.19 0.08 0 0 0.0 (Std. Dev.)
186
Warehouse Prod Q 168 999999 81 8146.02 65.45 81 49 0.01 (95%
C.I. Low)
Warehouse Prod Q 168 999999 81 8160.60 65.57 81 49 0.01 (95%
C.I. High)
Prod Confg Q 168 999999 0 0.0 0 0 0 0.0 (Average)
Prod Confg Q 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
Prod Confg Q 168 999999 0 0.0 0 0 0 0.0 (95% C.I.
Low)
Prod Confg Q 168 999999 0 0.0 0 0 0 0.0 (95% C.I.
High)
Confg QA Q 168 999999 0 0.0 0 0 0 0.0 (Average)
187
Confg QA Q 0 0 0 0.0 0 0 0 0.0 (Std. Dev.)
Confg QA Q 168 999999 0 0.0 0 0 0 0.0 (95% C.I.
Low)
Confg QA Q 168 999999 0 0.0 0 0 0 0.0 (95% C.I.
High)
Warehouse Q 168 999999 81 8152.21 65.50 81 49 0.01
(Average)
Warehouse Q 0 0 0 9.91 0.07 0 0 0.0 (Std. Dev.)
Warehouse Q 168 999999 81 8145.12 65.45 81 49 0.01 (95%
C.I. Low)
Warehouse Q 168 999999 81 8159.30 65.56 81 49 0.01 (95%
C.I. High)
188
Chips Q 168 999999 81 8153.24 65.51 81 49 0.01 (Average)
Chips Q 0 0 0 10.25 0.08 0 0 0.0 (Std. Dev.)
Chips Q 168 999999 81 8145.91 65.45 81 49 0.01 (95% C.I.
Low)
Chips Q 168 999999 81 8160.58 65.57 81 49 0.01 (95% C.I.
High)
Filler Q 168 999999 165 8188.08 134.03 165 101 0.01
(Average)
Filler Q 0 0 0 10.12 0.16 0 0 0.0 (Std. Dev.)
Filler Q 168 999999 165 8180.84 133.91 165 101 0.01 (95%
C.I. Low)
189
Filler Q 168 999999 165 8195.32 134.15 165 101 0.01 (95%
C.I. High)
Bezel Q 168 999999 81 8153.29 65.51 81 49 0.01 (Average)
Bezel Q 0 0 0 10.21 0.08 0 0 0.0 (Std. Dev.)
Bezel Q 168 999999 81 8145.99 65.45 81 49 0.01 (95% C.I.
Low)
Bezel Q 168 999999 81 8160.60 65.57 81 49 0.01 (95% C.I.
High)
190
LOCATION STATES BY PERCENTAGE (Multiple Capacity)
% |
Location Scheduled % Partially % | %
Name Hours Empty Occupied Full | Down
----------------- --------- ------ --------- ---- | ----
PCB Testing 168 41.34 58.66 0.0 | 0.0 (Average)
PCB Testing 0 0.46 0.46 0.0 | 0.0 (Std. Dev.)
PCB Testing 168 41.01 58.33 0.0 | 0.0 (95% C.I. Low)
PCB Testing 168 41.67 58.99 0.0 | 0.0 (95% C.I. High)
Des Manf Q 168 100.00 0.0 0.0 | 0.0 (Average)
191
Des Manf Q 0 0.0 0.0 0.0 | 0.0 (Std. Dev.)
Des Manf Q 168 100.00 0.0 0.0 | 0.0 (95% C.I. Low)
Des Manf Q 168 100.00 0.0 0.0 | 0.0 (95% C.I. High)
Manf Assmb Q 168 0.01 99.99 0.0 | 0.0 (Average)
Manf Assmb Q 0 0.01 0.01 0.0 | 0.0 (Std. Dev.)
Manf Assmb Q 168 0.0 99.99 0.0 | 0.0 (95% C.I. Low)
Manf Assmb Q 168 0.01 100.00 0.0 | 0.0 (95% C.I. High)
Assmb Testing Q 168 100.00 0.0 0.0 | 0.0 (Average)
192
Assmb Testing Q 0 0.0 0.0 0.0 | 0.0 (Std. Dev.)
Assmb Testing Q 168 100.00 0.0 0.0 | 0.0 (95% C.I. Low)
Assmb Testing Q 168 100.00 0.0 0.0 | 0.0 (95% C.I. High)
Testing Prod Q 168 100.00 0.0 0.0 | 0.0 (Average)
Testing Prod Q 0 0.0 0.0 0.0 | 0.0 (Std. Dev.)
Testing Prod Q 168 100.00 0.0 0.0 | 0.0 (95% C.I. Low)
Testing Prod Q 168 100.00 0.0 0.0 | 0.0 (95% C.I. High)
Warehouse Assmb Q 168 100.00 0.0 0.0 | 0.0 (Average)
Warehouse Assmb Q 0 0.0 0.0 0.0 | 0.0 (Std. Dev.)
193
Warehouse Assmb Q 168 100.00 0.0 0.0 | 0.0 (95% C.I. Low)
Warehouse Assmb Q 168 100.00 0.0 0.0 | 0.0 (95% C.I. High)
Warehouse Prod Q 168 0.01 99.99 0.0 | 0.0 (Average)
Warehouse Prod Q 0 0.01 0.01 0.0 | 0.0 (Std. Dev.)
Warehouse Prod Q 168 0.0 99.99 0.0 | 0.0 (95% C.I. Low)
Warehouse Prod Q 168 0.01 100.00 0.0 | 0.0 (95% C.I. High)
Prod Confg Q 168 100.00 0.0 0.0 | 0.0 (Average)
Prod Confg Q 0 0.0 0.0 0.0 | 0.0 (Std. Dev.)
194
Prod Confg Q 168 100.00 0.0 0.0 | 0.0 (95% C.I. Low)
Prod Confg Q 168 100.00 0.0 0.0 | 0.0 (95% C.I. High)
Confg QA Q 168 100.00 0.0 0.0 | 0.0 (Average)
Confg QA Q 0 0.0 0.0 0.0 | 0.0 (Std. Dev.)
Confg QA Q 168 100.00 0.0 0.0 | 0.0 (95% C.I. Low)
Confg QA Q 168 100.00 0.0 0.0 | 0.0 (95% C.I. High)
Warehouse Q 168 0.02 99.98 0.0 | 0.0 (Average)
Warehouse Q 0 0.02 0.02 0.0 | 0.0 (Std. Dev.)
Warehouse Q 168 0.0 99.97 0.0 | 0.0 (95% C.I. Low)
195
Warehouse Q 168 0.03 100.00 0.0 | 0.0 (95% C.I. High)
Chips Q 168 0.01 99.99 0.0 | 0.0 (Average)
Chips Q 0 0.01 0.01 0.0 | 0.0 (Std. Dev.)
Chips Q 168 0.0 99.99 0.0 | 0.0 (95% C.I. Low)
Chips Q 168 0.01 100.00 0.0 | 0.0 (95% C.I. High)
Filler Q 168 0.01 99.99 0.0 | 0.0 (Average)
Filler Q 0 0.01 0.01 0.0 | 0.0 (Std. Dev.)
Filler Q 168 0.0 99.98 0.0 | 0.0 (95% C.I. Low)
196
Filler Q 168 0.02 100.00 0.0 | 0.0 (95% C.I. High)
Bezel Q 168 0.01 99.99 0.0 | 0.0 (Average)
Bezel Q 0 0.01 0.01 0.0 | 0.0 (Std. Dev.)
Bezel Q 168 0.0 99.99 0.0 | 0.0 (95% C.I. Low)
Bezel Q 168 0.01 100.00 0.0 | 0.0 (95% C.I. High)
LOCATION STATES BY PERCENTAGE (Single Capacity/Tanks)
Location Scheduled % % % % % %
Name Hours Operation Setup Idle Waiting Blocked Down
---------------- --------- --------- ----- ------ ------- ------- ----
Warehouse Wi 168 0.0 0.0 100.00 0.0 0.0 0.0 (Average)
197
Warehouse Wi 0 0.0 0.0 0.0 0.0 0.0 0.0 (Std. Dev.)
Warehouse Wi 168 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. Low)
Warehouse Wi 168 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. High)
PCB Manf 168 0.0 0.0 100.00 0.0 0.0 0.0 (Average)
PCB Manf 0 0.0 0.0 0.0 0.0 0.0 0.0 (Std. Dev.)
PCB Manf 168 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. Low)
PCB Manf 168 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. High)
PCB Assmb 168 100.00 0.0 0.0 0.0 0.0 0.0 (Average)
198
PCB Assmb 0 0.01 0.0 0.01 0.0 0.0 0.0 (Std. Dev.)
PCB Assmb 168 99.99 0.0 0.0 0.0 0.0 0.0 (95% C.I. Low)
PCB Assmb 168 100.00 0.0 0.01 0.0 0.0 0.0 (95% C.I. High)
Configuration Wi 168 0.0 0.0 100.00 0.0 0.0 0.0 (Average)
Configuration Wi 0 0.0 0.0 0.0 0.0 0.0 0.0 (Std. Dev.)
Configuration Wi 168 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. Low)
Configuration Wi 168 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. High)
QA Dept 168 0.0 0.0 100.00 0.0 0.0 0.0 (Average)
QA Dept 0 0.0 0.0 0.0 0.0 0.0 0.0 (Std. Dev.)
199
QA Dept 168 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. Low)
QA Dept 168 0.0 0.0 100.00 0.0 0.0 0.0 (95% C.I. High)
Prod Dept Wi 168 19.58 0.0 80.42 0.0 0.0 0.0 (Average)
Prod Dept Wi 0 0.27 0.0 0.27 0.0 0.0 0.0 (Std. Dev.)
Prod Dept Wi 168 19.38 0.0 80.23 0.0 0.0 0.0 (95% C.I. Low)
Prod Dept Wi 168 19.77 0.0 80.62 0.0 0.0 0.0 (95% C.I. High)
200
FAILED ARRIVALS
Entity Location Total
Name Name Failed
------------- ---------------- ------
Chips Chips Q 0 (Average)
Chips Chips Q 0 (Std. Dev.)
Chips Chips Q 0 (95% C.I. Low)
Chips Chips Q 0 (95% C.I. High)
Network Board Manf Assmb Q 0 (Average)
Network Board Manf Assmb Q 0 (Std. Dev.)
201
Network Board Manf Assmb Q 0 (95% C.I. Low)
Network Board Manf Assmb Q 0 (95% C.I. High)
Chassis Warehouse Q 0 (Average)
Chassis Warehouse Q 0 (Std. Dev.)
Chassis Warehouse Q 0 (95% C.I. Low)
Chassis Warehouse Q 0 (95% C.I. High)
Bezels Bezel Q 0 (Average)
Bezels Bezel Q 0 (Std. Dev.)
202
Bezels Bezel Q 0 (95% C.I. Low)
Bezels Bezel Q 0 (95% C.I. High)
Service Card Filler Q 0 (Average)
Service Card Filler Q 0 (Std. Dev.)
Service Card Filler Q 0 (95% C.I. Low)
Service Card Filler Q 0 (95% C.I. High)
Final Product Warehouse Prod Q 0 (Average)
Final Product Warehouse Prod Q 0 (Std. Dev.)
Final Product Warehouse Prod Q 0 (95% C.I. Low)
203
Final Product Warehouse Prod Q 0 (95% C.I. High)
ENTITY ACTIVITY
Average Average Average Average Average
Current Minutes Minutes Minutes Minutes Minutes
Entity Total Quantity In In Move Wait For In
Name Exits In System System Logic Res, etc. Operation Blocked
------------- ----- --------- ------- ------- --------- --------- -------
Final Board 32 0 5204.43 0.0 4370.93 542.20 291.29 (Average)
Final Board 0 0 24.85 0.0 21.53 2.89 1.41 (Std. Dev.)
Final Board 32 0 5186.65 0.0 4355.53 540.14 290.28 (95% C.I. Low)
204
Final Board 32 0 5222.21 0.0 4386.33 544.27 292.30 (95% C.I. High)
Chips 32 49 5204.22 0.0 4896.06 0.0 308.15 (Average)
Chips 0 0 25.57 0.0 24.25 0.0 1.66 (Std. Dev.)
Chips 32 49 5185.92 0.0 4878.72 0.0 306.96 (95% C.I. Low)
Chips 32 49 5222.51 0.0 4913.41 0.0 309.34 (95% C.I. High)
Network Board 0 49 - - - - - (Average)
Network Board 0 0 - - - - - (Std. Dev.)
Network Board 0 49 - - - - - (95% C.I. Low)
Network Board 0 49 - - - - - (95% C.I. High)
205
Chassis 32 49 5203.19 0.0 4895.06 0.0 308.12 (Average)
Chassis 0 0 25.11 0.0 23.83 0.0 1.63 (Std. Dev.)
Chassis 32 49 5185.22 0.0 4878.01 0.0 306.95 (95% C.I. Low)
Chassis 32 49 5221.15 0.0 4912.12 0.0 309.29 (95% C.I. High)
Bezels 32 49 5204.27 0.0 4896.11 0.0 308.15 (Average)
Bezels 0 0 25.53 0.0 24.22 0.0 1.65 (Std. Dev.)
Bezels 32 49 5186.00 0.0 4878.78 0.0 306.97 (95% C.I. Low)
Bezels 32 49 5222.54 0.0 4913.44 0.0 309.34 (95% C.I. High)
Service Card 64 101 5204.04 0.0 5049.96 0.0 154.07 (Average)
206
Service Card 0 0 25.57 0.0 24.91 0.0 0.83 (Std. Dev.)
Service Card 64 101 5185.74 0.0 5032.14 0.0 153.48 (95% C.I. Low)
Service Card 64 101 5222.33 0.0 5067.78 0.0 154.66 (95% C.I. High)
Final Product 32 49 5204.28 0.0 5204.28 0.0 0.0 (Average)
Final Product 0 0 25.51 0.0 25.51 0.0 0.0 (Std. Dev.)
Final Product 32 49 5186.03 0.0 5186.03 0.0 0.0 (95% C.I. Low)
Final Product 32 49 5222.53 0.0 5222.53 0.0 0.0 (95% C.I. High)
207
ENTITY STATES BY PERCENTAGE
% %
Entity In Move Wait For % %
Name Logic Res, etc. In Operation Blocked
------------- ------- --------- ------------ -------
Final Board 0.0 83.98 10.42 5.60 (Average)
Final Board 0.0 0.04 0.04 0.01 (Std. Dev.)
Final Board 0.0 83.95 10.39 5.59 (95% C.I. Low)
Final Board 0.0 84.02 10.44 5.61 (95% C.I. High)
Chips 0.0 94.08 0.0 5.92 (Average)
208
Chips 0.0 0.02 0.0 0.02 (Std. Dev.)
Chips 0.0 94.07 0.0 5.91 (95% C.I. Low)
Chips 0.0 94.09 0.0 5.93 (95% C.I. High)
Network Board - - - - (Average)
Network Board - - - - (Std. Dev.)
Network Board - - - - (95% C.I. Low)
Network Board - - - - (95% C.I. High)
Chassis 0.0 94.08 0.0 5.92 (Average)
209
Chassis 0.0 0.02 0.0 0.02 (Std. Dev.)
Chassis 0.0 94.06 0.0 5.91 (95% C.I. Low)
Chassis 0.0 94.09 0.0 5.94 (95% C.I. High)
Bezels 0.0 94.08 0.0 5.92 (Average)
Bezels 0.0 0.02 0.0 0.02 (Std. Dev.)
Bezels 0.0 94.07 0.0 5.91 (95% C.I. Low)
Bezels 0.0 94.09 0.0 5.93 (95% C.I. High)
Service Card 0.0 97.04 0.0 2.96 (Average)
Service Card 0.0 0.01 0.0 0.01 (Std. Dev.)
210
Service Card 0.0 97.03 0.0 2.95 (95% C.I. Low)
Service Card 0.0 97.05 0.0 2.97 (95% C.I. High)
Final Product 0.0 100.00 0.0 0.0 (Average)
Final Product 0.0 0.0 0.0 0.0 (Std. Dev.)
Final Product 0.0 100.00 0.0 0.0 (95% C.I. Low)
Final Product 0.0 100.00 0.0 0.0 (95% C.I. High)
VARIABLES
Average
Variable Total Minutes Minimum Maximum Current Average
211
Name Changes Per Change Value Value Value Value
------------------------ ------- ---------- ------- ------- ------- -------
PCB Manf Time 0 0.0 0 0 0 0 (Average)
PCB Manf Time 0 0.0 0 0 0 0 (Std. Dev.)
PCB Manf Time 0 0.0 0 0 0 0 (95% C.I. Low)
PCB Manf Time 0 0.0 0 0 0 0 (95% C.I. High)
Chips Assmb time 34 291.76 0 324.03 292.32 300.80 (Average)
Chips Assmb time 0 1.39 0 3.98 7.99 1.36 (Std. Dev.)
Chips Assmb time 34 290.76 0 321.18 286.60 299.82 (95% C.I. Low)
Chips Assmb time 34 292.76 0 326.88 298.04 301.78 (95% C.I. High)
212
PCB Testing time 33 300.60 0 133.80 118.52 117.05 (Average)
PCB Testing time 0 1.43 0 3.35 8.70 1.01 (Std. Dev.)
PCB Testing time 33 299.57 0 131.40 112.30 116.32 (95% C.I. Low)
PCB Testing time 33 301.63 0 136.20 124.75 117.77 (95% C.I. High)
Chassis assmb time 32.4 306.16 0 18.87 14.86 14.30 (Average)
Chassis assmb time 0.51 1.68 0 0.61 2.27 0.36 (Std. Dev.)
Chassis assmb time 32.03 304.95 0 18.43 13.23 14.04 (95% C.I. Low)
Chassis assmb time 32.76 307.37 0 19.31 16.49 14.56 (95% C.I. High)
213
Filler Assmb time 32.3 306.74 0 8.11 1.12 1.89 (Average)
Filler Assmb time 0.48 1.62 0 2.60 0.99 0.34 (Std. Dev.)
Filler Assmb time 31.95 305.57 0 6.25 0.41 1.65 (95% C.I. Low)
Filler Assmb time 32.64 307.90 0 9.98 1.83 2.14 (95% C.I. High)
Pcb Faceplate time 32.2 306.85 0 7.97 2.49 1.98 (Average)
Pcb Faceplate time 0.42 1.58 0 2.09 1.96 0.33 (Std. Dev.)
Pcb Faceplate time 31.89 305.72 0 6.47 1.09 1.74 (95% C.I. Low)
Pcb Faceplate time 32.50 307.99 0 9.47 3.90 2.22 (95% C.I. High)
214
Final Assmb Testing Time 32.2 306.93 0 9.00 1.99 2.01 (Average)
Final Assmb Testing Time 0.42 1.58 0 2.28 2.44 0.36 (Std. Dev.)
Final Assmb Testing Time 31.89 305.80 0 7.37 0.25 1.75 (95% C.I. Low)
Final Assmb Testing Time 32.50 308.06 0 10.63 3.74 2.27 (95% C.I. High)
configuration time 0 0.0 0 0 0 0 (Average)
configuration time 0 0.0 0 0 0 0 (Std. Dev.)
configuration time 0 0.0 0 0 0 0 (95% C.I. Low)
configuration time 0 0.0 0 0 0 0 (95% C.I. High)
215
QA time 0 0.0 0 0 0 0 (Average)
QA time 0 0.0 0 0 0 0 (Std. Dev.)
QA time 0 0.0 0 0 0 0 (95% C.I. Low)
QA time 0 0.0 0 0 0 0 (95% C.I. High)
Installing Network Board 32.2 306.99 0 46.11 39.95 38.09 (Average)
Installing Network Board 0.42 1.57 0 1.02 3.18 0.60 (Std. Dev.)
Installing Network Board 31.89 305.87 0 45.38 37.67 37.65 (95% C.I. Low)
Installing Network Board 32.50 308.12 0 46.85 42.23 38.52 (95% C.I. High)
PCB Configuration 32.8 304.31 0 69.30 58.46 56.94 (Average)
216
PCB Configuration 0.42 1.56 0 2.12 2.88 0.94 (Std. Dev.)
PCB Configuration 32.49 303.20 0 67.78 56.39 56.27 (95% C.I. Low)
PCB Configuration 33.10 305.43 0 70.82 60.52 57.62 (95% C.I. High)
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