APPLYING A MEIO APPROACH TO MANAGE INTEL'S VMI HUB SUPPLY
CHAIN
By
Min Fang Hsieh
Bachelor of Science in Astrophysics, National Central University (2001)
Master of Science in Electrical Engineering, National Chiao Tung University (2003)
Submitted to the MIT Sloan School of Management and the Department of Engineering
Systems in Partial Fulfillment of the Requirements for the Degrees of MASSACHUSETTS
INSTI
OF TECHNOLOGY
Master of Business Administration
AND
Master of Science in Engineering System Division
In conjunction with the Leaders for Global Operations Program at the
Massachusetts Institute of Technology
JUN 15 2011
LIBRARIES
June 2011
02011 Min Fang Hsieh. All rights reserved.
The author hereby grants MIT permission to reproduce and to distribute publicly
copies of this thesis document in whole or in part in any medium now known or hereafter
created.
Signature of Author
May 4, 2011
MIT Sloan School of Management and Engineering Systems Division
Certified by
Stephen Graves, Thesis Advisor
Abraham J. Siegel Professor of Management, MIT Sloan School of Management
Certified
by
___
Professoid
David Simchi-Levi, Thesis Advisor
ineering Systems and 1il & Environmental Engineering
Accepted by ___________
N~ancy Leveson
Professor of Engineering Systems and Aeronautics & Astronautics
Chairman of Enaineerine Systems Division Education Committee
Accepted by
Debbie tserechman
Executive Director of MBA Program, MIT Sloan School of Management
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APPLYING A MEIO APPROACH TO MANAGE INTEL'S VMI HUB SUPPLY
CHAIN
By
Min Fang Hsieh
Submitted to the MIT Sloan School of Management and. the Department of Engineering
Systems on May 4, 2011 in Partial Fulfillment of the Requirements for the Degrees of
Master of Business Administration
AND
Master of Science in Engineering System Division
ABSTRACT
To improve customer service levels, Intel implemented Vendor Managed Inventory
(VMI) hub process for its Central Processing Unit (CPU) Finished Good (FG) inventory,
which allows Intel's customers to pull inventory directly from the hubs. However, this
process change resulted in increased inventory in Intel's overall supply chain and thus
increased inventory costs. This work investigates reducing inventory cost by applying a
Multi Echelon Inventory Optimization (MEIO) approach to manage Intel's VMI Hub
Supply Chain. The goal is to evaluate the hypothesis that an MEIO approach for
inventory management and replenishment will result in a more efficient use of FG
inventory. To assess the hypothesis, we developed a three-step modeling framework. In
each step, we conducted several experiments, applying the MEIO model approach, to
determine the optimal CPU FG inventory stocking levels needed to meet customer
service level goals for different products and locations. The study was concluded with
quantitative and qualitative business impact of implementing an MEIO approach for Intel
CPU FG Supply Chain. The MEIO modeling result shows significant inventory
reduction opportunities and were presented to the Senior Management team of Intel
Supply Chain.
Thesis Advisor: Stephen Graves
Title: Abraham J. Siegel Professor of Management, MIT Sloan School of Management
Thesis Advisor: David Simchi-Levi
Title: Professor of Engineering Systems and Civil & Environmental Engineering
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ACKNOWLEDGMENTS
First, I wish to express my sincere appreciation to the Intel Corporation for providing me
with the resources and support for this work. The success of this internship was possible
thanks to the endless support, guidance, and enthusiasm of my wonderful project advisor,
Maria Mentzer, who allowed me to learn, grow, and enjoy myself through this amazing
experience. Brian Wieland, an amazing teacher, also had a direct impact on this project
by offering his valuable time, insights, and knowledge. Thanks also to Sean
Cunningham, Tom Sanger, Pat Mastrantonio, Asima Mishra, Carlos Mazariegos, and
Chuck Arnold, along with everyone at Intel with whom I worked during my internship.
I also must acknowledge the Leaders for Global Operations (LGO) Program for its rich
collection of resource and tools that were made available to me. I would also like to
extend a special thanks to my faculty advisors, Dr. Stephen Graves and Dr. David
Simchi-Levi, for offering invaluable guidance, advice, and feedback throughout the entire
thesis process.
Next, this thesis would not have been possible without the love and support from my
friends and family. I was very lucky and proud to be part of the LGO Class of 2011.
Thank you for your support and friendship for making these last two years truly an
unforgettable journey for me.
Finally, I would like to dedicate this thesis to my parents, sisters, Joe and Elaine. For their
love and support mean the world to me.
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TABLE OF CONTENTS
Abstract
Acknowledgements
Table of Contents
List of Figures
List of Tables
1
2
3
4
5
Introduction..............................................................................
1.1
Statement of Problem.......................................................
1.2
Thesis Organization.........................................................18
15
17
Company Overview...................................................................19
2.1
Intel Corporation.............................................................
20
2.2
Intel CPU FG Supply Chain.............................................
21
2.3
Inventory Optimization Efforts at Intel.....................25
2.4
Inventory Policy for Safety Stock Target Generation..................27
2.4.1
Flat Inventory Policy................................................27
2.4.2
SEIO implementation at Intel VMI Supply Chain..............29
2.4.3
MEIO implementation in Intel Box Channel supply chain..31
Literature Review......................................................................34
3.1
ITS Approach................................................................34
3.2
Boxed CPU Demand Characterization...................................37
Three Lens Analysis....................................................................43
4.1
Strategic Design Perspective.............................................
4.2
Political Perspective...........................................................48
4.3
Cultural Perspective...........................................................53
43
Approach and Methodology.........................................................57
5.1
Document Current State.....................................................57
5.2
Model Development...........................................................62
5.3
Study of Inventory Reduction Opportunities.............................65
6
Model Implementation and Results.................................................68
7
N etw ork Redesign...........................................................................................
79
8
Summary of Recommendation and Conclusions .................................
89
7
8.1
Summary of Results............................................................89
8.2
Summary of Recommendations............................................89
8.3
Model growth................................................................93
8.4
MEIO's Future at Intel.......................................................94
8.5
Conclusions ....................................................................
Bibliography
96
LIST OF FIGURES
Figure 1: Intel implemented VMI hub to improve its customer service level............16
Figure 2: OEM reduced its onsite inventory through VMI implementation.................16
Figure 3: Total Intel FG inventory increased after VMI implementation.................17
Figure 4: VMI Hub Inventory..................................................................17
Figure 5: Manufacturing process and lead times for microprocessor products.............21
Figure 6: Two different packaging form factors for CPU FG products.....................21
Figure 7: Intel CPU distribution channels....................................................22
Figure 8: CPU FG Supply Chain..............................................................23
Figure 9: VMI hubs network diagram.........................................................24
Figure 10: Boxed CPU supply network diagram...........................................25
Figure 11: Efficient Frontier progression at Intel...........................................26
Figure 12: VMI Hub Guidance sample sheet...................................................28
Figure 13: SEIO moves the efficient frontier...................................................29
Figure 14: Days of Inventory (DOI) for the ITS VMI pilot..................................30
Figure 15: SKUs managed by the MEIO tool have significantly higher service levels than
planner-m anaged SK U s............................................................................32
Figure 16: Modified Sigma and Kernel Smoothing Impact on CSPO Inventory
E fficiency ..........................................................................................
33
Figure 17: ITS "Order Up-To" Target Generation.............................................35
Figure 18: Example of ITS calculation...........................................................36
Figure 19: Intel Channel Forecast Error Summary..........................................39
Figure 20: Forecast Bias has significant impact on SDFE value..........................39
Figure 21: Processes in Strategic Design......................................................44
Figure 22: Functional Grouping Structure....................................................44
Figure 23: Simplified Org Chart for Supply Planning Operations...........................45
Figure 24: Functional/Product Matrix structure.............................................47
Figure 25: Stakeholder Mapping Analysis....................................................51
Figure 26: Capability Analysis....................................................................52
Figure 27: Project Commitment Chart...........................................................52
Figure 28: Hub Guidance monthly Safety Stock targets setting process..................58
Figure 29: ITS Safety Stock targets setting process............................................58
Figure 30: MEIO Safety Stock targets setting process......................................59
Figure 31: MEIO Data Flow..................................................................60
Figure 32: MEIO SS Target Setting Processes...............................................61
Figure 33: A three-step modeling framework was developed to evaluate the MEIO
approach .......................................................................................
. ... 62
Figure 34: Step One- Single Echelon Inventory Optimization............................63
Figure 35: Step Two- Multi Echelon Inventory Optimization, including VMI Hub and
Box Channel Supply Chains...................................................................64
Figure 36: Step Three- Multi Echelon Inventory Optimization, including VMI Hub, Box
Channel, and Tray Disti Customer Supply chains...........................................65
Figure 37: Baseline model and what-if model for the Inventory Reduction...............67
Figure 38: SS targets using different SEIO and demand characterization methodology..69
Figure 39: SEIO modeling results by products..................................................70
Figure 40: the SS target output distribution by ITS models...............................70
Figure 41: the SS target output distribution by the off-the-shelf MEIO software ......... 71
Figure 42: Forecast Error distribution for product 5 in location 3............................73
Figure 43: forecast error distributions across six VMI Hubs...............................74
Figure 44: Inventory Reduction for different Inventory Optimization Options............76
Figure 45: Inventory Reduction for risk-pooling at VMI hubs............................77
Figure 46: OCCND Network Re-design using MEIO approach..........................79
Figure 47: MEIO model based on different Supply Chain design............................80
Figure 48: MEIO model based on global network design..................................81
Figure 49: To- Be Scenario 1 for the OCCND project......................................82
Figure 50: To- Be Scenario 2 for the OCCND project......................................83
Figure 51: To- Be Scenario 3 for the OCCND project......................................84
Figure 52: To- Be Scenario 4 for the OCCND project......................................85
Figure 53: To- Be Scenario 5 for the OCCND project......................................86
Figure 54: Total Inventory Investment for different network design scenarios ............ 88
Figure 55: Total Stock cost for different network design scenarios.........................88
Figure 56: project implementation pyramid..................................................90
Figure 57: the Four Pillars of Intel Supply Chain Strategy.................................91
Figure 58: Intel Supply Chain Map............................................................94
Figure 59: criteria for MEIO implementation for FG CPU supply chain....................95
LIST OF TABLES
Table 1: The SS targets generated through ITS and MEIO off-the-shelf software.........71
Table 2: Step 2 modeling Safety Stock inventory reduction result...........................74
Table 3: Step 3 modeling result...................................................................74
Table 4: MEIO results for 5 different To-Be network scenarios..........................86
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1
Introduction
"Vendor ManagedInventory (VMI). In the VMI process, the vendor assumes
responsibilityfor managing the replenishmentof stock. Rather than a customer
submitting orders, the vendor will replenish stock as needed This is sometimes referred
to as supplier-managedinventory (SM) or co-managed inventory [1]."
Poor visibility across the supply chain partners, inadequate ability to respond to
demand fluctuations, inflexible manufacturing processes, short product life cycle, and
long lead time have perpetuated significant supply chain challenges for the companies in
the semiconductor manufacturing industry. With increasing competition, the firm that is
able to provide quick and effective service to its customers would enjoy a competitive
advantage. As the competitive landscape continues to shift, it has resulted in increasing
pressure on all semiconductor manufacturers to consider strategies such as Vendor
Managed Inventory (VMI) that locates inventory at the supplier hubs near customer
manufacturing sites.
Like its competitors, Intel decided to reevaluate its customer fulfillment strategies
and considered ways to make inventory reliably available to its customers with low cost
solutions, including the combined use of vendor-managed and customer-managed
inventory locations. To maintain its competitive position, Intel deployed VMI hubs for
the Central Processor Unit (CPU) Finished Good (FG) inventory in 2008 for some high
volume, high revenue customers. Several regional hubs were strategically located near
clusters of customer sites with a goal to provide better customer service level, improve
[1] Logistics Dictionary, http://www.hksunlogistics.com/Homepage2/kclogglossary.htm
forecast error, capitalize on pooling opportunity, and reduce customer owned inventory.
As shown in Figure 1, Intel shipped its CPU FG products to the Original Equipment
Manufacturer (OEM) customers from the component Central Warehouses (CW) prior to
2008; post-VMI stage, the OEM customers pulled inventory directly from the VMI hubs.
=*E
c:> customer
m
Hub
Customer
cVM
-,Customer
Figure 1: Intel implemented the VMI hubs to improve its customer service level
Over the past several years, the OEMs have asked their suppliers to implement
various programs to minimize their (OEMs) risk to excessive inventory. In 2002,
inventory across Intel was $2.3B or 8.5% of sales, including $0.7B of FG inventory. If
the risk reduction approaches of these OEMs and distributors were to be adopted, we
could expect the FG inventory to increase by >20%, resulting in higher levels of
inventory risk to Intel [2]. Figure 2 illustrates that OEM customers were able to reduce
its onsite inventory, while the total Intel pipeline inventory has increased due to VMI.
0
Pre-V1I
Post-VMI
motH oR-site Zoventory WOtI
Utatal Total PGplpenne wOI
Figure 2: OEM reduced its onsite inventory through VMI implementation
[2] Kurt L. Johnson, Inventory Modeling, Intel Technology Journal, Volume 9, Issue 3, 2005
Statement of Problem
1.1
The VMI hubs are efforts to improve customer service levels by allowing Intel's
customers to directly pull inventory from the hubs. However, supporting the VMI hubs
means increased inventory in the overall system for Intel, which increases inventory
costs. While OEM customers have enjoyed inventory reduction results, Figure 3 shows
that the total FG inventory has increased for Intel after the VMI hub implementation.
c
C
Pv-WVM
Post-VMI
* CW
In transit sVNI Hub |
Figure3: Total Intel FG inventory increased after VMI hub implementation
Figure 4 shows that the current VMI hub inventories are high compared to the
Hub Targets. The current inventory targets are static (cycle stock+ safety stock).
2
Avg Hub WO based
on CurreW promess
1
2
3
---
4
Av
5
6 7 8 9
gb WV IFCST Baed)
10 11 12 13 14 15 16 17 18 19 20 21 22 (week)
. a ub,Targe WOE
Figure 4: VMI Hub Inventory
This research will investigate improving both customer service levels and
inventory cost by applying a Multi Echelon Inventory Optimization (MEIO) approach to
manage Intel's VMI Hub Supply Chain.
1.2
Thesis Organization
This thesis is organized into eight chapters. An introduction and problem
statement are presented in chapter one, after which chapter two gives brief company
overviews of Intel, the CPU FG Supply Chain, and the inventory optimization effort at
Intel. Chapter three reviews literature that described some inventory optimization
challenges in the Intel FG Supply Chain and the adjustment procedures that were
developed to produce appropriate inventory targets in the presence of forecast error and
bias. Chapter four offers unbiased observations, analysis, and characterization of the
current Intel Inventory Optimization practices based on a "Three Lens Analysis"
framework. Chapter five details the approaches and methodologies that were employed
to modeling the Intel CPU FG Supply Chain. Chapter six describes the model
implementation and the simulation results. Chapter seven expounds the collaboration
work with the Intel Network Design Center of Excellence team to model the inventory
level and supply chain cost for five different network design scenarios. Finally, chapter
eight provides a summary of the findings and recommendations made and finalizes with a
conclusion.
2
Company Overview
2.1
Intel Corporation
Intel Corporation (Public, NASDAQ:INTC), founded in 1968, is the world's
largest semiconductor chip maker, based on revenue ($43.6 billion). Intel designs and
manufactures computing and communications components, such as microprocessors,
chipsets, motherboards, and wireless and wired connectivity products, as well as
platforms that incorporate these components. Intel employs more than 80,000 employees
around the world, producing more than 10 million chips every week.
Over the years, Intel continues to offer innovative products and platforms that
accelerate the computing and communication market expansion. With its Atom
processor, Intel is expanding from its core business of personal computers and servers
into adjacent markets of netbooks, embedded devices and consumer electronics. These
new markets have extremely variable demand and require higher service levels at lower
costs than the core business. Intel realized that it will need to reduce its current supply
chain cost structure significantly to remain its competitive advantage. In the recent years,
Intel's effort to create supply chain efficiencies have resulted in business improvements
such as a 32% reduction in inventory, a 65% reduction in order-fulfillment lead time, and
a threefold increase in responsiveness to customers [3].
[3] Delivering Competitive Advantage Through IT, Intel IT performance Report 2010-2011,
http://www.intel.com/Assets/PDF/general/IntelIT_2011 APREnglishstandard.pdf
2.2
Intel CPU FG Supply Chain
The Central Processing Unit (CPU) is the portion of a computer system that
carries out the instructions of a computer program, and is the primary element carrying
out the computer's functions. The introduction of the microprocessor in the 1970s
significantly affected the design and implementation of CPUs. Since the introduction of
the first commercially available microprocessor (the Intel 4004) in 1970 and the first
widely used microprocessor (the Intel 8080) in 1974, for many years Intel has been the
dominant CPU manufacturer in many market segments. Intel has primarily accomplished
its market leader position through its strengths in microprocessor design, manufacturing,
and marketing. "Intel makes approximately 10 billion transistors per second. Our
factories produce the most advanced computer technology in the world and these
investments will create capacity for innovation we haven't yet imagined," said Brian
Krzanich, Senior Vice President and General Manager of Intel's Manufacturing and
Supply Chain group [4].
There are two major stages in the manufacturing process of microprocessors at
Intel: Semiconductor Fabrication (Fab) and Assembly/Test (ATM). Figure 5 illustrates a
simplified microprocessor manufacturing process at Intel [5]. The Fab stage is the
process of converting raw silicon wafers to finished die. The ATM stage is the process of
assembling and packaging the die into its final microprocessor finished good (FG)
product in either tray or box form factors, as shown in Figure 6.
[4] http://newsroom.intel.com/community/en-za/blog/2010/10/19/intel-announces-multi-billiondollar-investment-in-next-generation-manufacturing-in-us
[5] J. Chow (2004), Analysis of New Approaches to Improve the Customer Responsiveness of
Intel's Microprocessor Supply Chain, Massachusetts Institute of Technology
Fab
4 V00
request
8 Wk
lagmetoAT
s
Eob TPT
1 Wk
1.7 Wk
request ATM TPT
log me
iranst
0,3 W
tranetto
CWr
proeang
ADI
Repenlshmsnt
lead time
(13 weeks 1qtr)
FG
replenishment
lead time
13 weeks)
Figure 5: Manufacturing process and lead times for microprocessor products (2004)
Box Form
Tray Form
Figure 6: Two different packaging form factors for CPU FG products
Intel's main customers in the CPU space are the OEMs (Original Equipment
Manufacturers), and the channel customers that include distributors, resellers, and
retailers all around the world. Figure 7 is a diagram of Intel's Value Chain [6]. The OEMs
represent the larger portion of the company's sales (over 70%), including the major
computer manufacturer such as Dell, Hewlett Packard, IBM, and Lenovo. The
distribution (Disti) channel customer including most major distributors in North America
with hundreds of other scattered throughout Asia and Europe. Intel's boxed processor
shipment volume constitutes approximately 20% of Intel's total CPU shipments.
[6] L.W.Rassey (2003), EnterpriseStrategy: Leveraging the Dynamics and Behaviors in a Supply
Chainfor OperationalExcellence, Massachusetts Institute of Technology
21
Figure 7: Intel CPU distribution channels [6]
Safety stock inventory is held at two main areas:
(1) Die safety stock (ADI - Assembly Die Inventory) is held at each ATM site and new
die units are shipped from Fab sites worldwide
(2) Finished goods (FG) safety stock is held at components central warehouses (CW)
close to the ATM sites and new FG units are provided by the ATM sites
Figure 8 illustrates a simplified CPU FG Supply Chain network. Currently, Intel
component central warehouse (CW/ CWl) provides CPU FG to the box channel
customers (distributors), tray VMI customers (OEMs), and tray channel customers
(distributors).
CW2
CWl
VM4Hub
CWC
90m
Figure 8: CPU FG Supply Chain
VMI Hub Supply Chain
As described previously, Intel deployed the VMI hubs for the CPU Finished Good
(FG) inventory in 2008 to improve customer service levels; the Intel VMI hubs network
diagram is shown in Figure 9. CW represents finished microprocessor inventory at
various assembly/test factory warehouses around the world, which are also known as
components central warehouses. To simplify the finished good CPU supply chain, we
use one virtual component stage to represent the entire non-boxed, finished CPU
inventory within Intel's factory network. VI is the VMI hub at location 1, and CV1
represent a cluster of OEM customers supplied from VI hub.
The customer service improvement could be achieved by forward positioning
inventory so that customers could receive materials in less than 24 hours from the order
placement. The data showed that the average service level was about 80% prior to the
VMI implementation, and 95% post the VMI implementation. Initially, the VMI hubs
might increase the overall supply chain inventory, but the inventory could be reduced
through better demand signals and customer collaborations. In fact, the collaboration
process is a key enabler to a successful VMI implementation. The process requires Intel
and customers working together to create mutual benefit.
VMI Hub
VMI
Customers
Central
Warehouse
Figure 9: VMI hubs network diagram
Boxed Channel Supply Chain
The Channel Supply Planning Operations (CSPO) organization is responsible for
satisfying the branded boxed CPU demands. Figure 10 shows the supply network
diagram for the boxed CPU products [7]. In Figure 10, CW1 means central warehouse
(CW), which represents finished microprocessor inventory at various assembly/test
factory warehouses around the world. At CW1, the microprocessors have completed all
fabrication steps, and the microprocessors are in trays organized by different package
types and processor speeds. Next, processors will be shipped from CW1 to four different
CW2 boxing sites, where the thermal solutions (fan, heat sink, etc) are included into the
retail boxes along with CPUs. The boxing site, typically owned by subcontractor
companies, will ship the boxed products to the nearby Intel finished good warehouses
(CW3) after the completion of the boxing activities. Finally Boxed CPUs are supplied to
Box Channel (Disti channel) customers throughout the world from the CW3.
CWV3
Distributor N4etw~ork
Boxe
acu KF
CW2
Awagrrouses)
(CPU fsoingStes)
FHS
Ternal Soubon)
box ng I-act=r 4
Figure 10: Boxed CPU supply network diagram
2.3
Inventory Optimization Efforts at Intel
"Intel has launched an extremely ambitious effort to remake itselfas a customercentric supply chain" - AMR Research Supply Chain Top 25
As the semiconductor industry continues to evolve at a rapid pace, supply chain
management has become dynamic, complicated and highly sophisticated for Intel.
Computer chips are small, and have an extremely high value to weight ratio. Therefore,
there were only marginal opportunities to improve distribution costs, and even less in
transportation. At the same time, inventory carrying costs represented the preponderance
of total supply chain costs.
Inventory management in a company with the size, scale and complexity like Intel
is particularly difficult. High demand variability, complex manufacturing process, and
long cycle time suggest high inventory requirement and carrying cost.
[7] B. Wieland, P. Mastrantonio, S. P. Willems, and K. G. Kempf, "Optimizing Inventory Levels
within Intel's Channel Supply Demand Operations," future issue, Interfaces.
http://www.informs.org/Pubs/Interfaces/Future-Issues
As a result, Intel has launched several initiatives in an attempt to minimize
inventory cost while maintaining high customer service level. As shown in Figure 11, the
corporate goal was to continue shifting the efficient frontier to the left. Several projects
were proposed to attain this goal, including lowering factory cycle time, lowering
inventory investment, delaying product differentiation, and moving from 100% pushbased to pull-based builds. Also, strategically building inventory ahead of anticipated
demand increases, to even out capacity utilization and avoid stock-out situations. From
Intel's perspective, customer responsiveness, inventory optimization and asset utilization
are the three components of a top-notch supply chain. Nevertheless, the success of the
supply chain management requires leveraging the right people, organization and tools
within the company.
Many inventory optimization initiatives were thus instigated to solve the question
of how much and where Intel should carry inventory to attain the goal of minimizing
inventory cost while maintaining desired customer service level. The objective of these
efforts were to allow inventory target differentiation for each product, each stage and
each site of the supply chain to optimize the total inventory cost. Meanwhile, inventory
analytics prioritize inventory toward market segments with higher service level goals and
products with higher supply variability and/or demand uncertainty.
Tomorrow's
Efficient Frontier
Today's
Efficient Frontier
/
_
2!
2009
2008
V)
2007
Inventory
Figure 11: Efficient Frontier progression at Intel
2.4
Inventory Policy for Safety Stock Target Generation
Over the past several years, Intel has leveraged its sizable supply chain
organization and its extensive operations research to reduce inventory investment
significantly. Three different inventory optimization policies have been employed to
provide the Safety Stock (SS) targets for the CPU Finished Good (FG) Supply Chain.
First, Flat Inventory Policy was used to determine the static VMI SS targets based on
next four weeks' forecast data. Second, a spreadsheet-based Single Echelon Inventory
Optimization (SEIO) model was developed to locally optimize and calculate inventory
targets for each stage and site in the VMI Supply Chain. This SEIO model has proven to
improve inventory efficiency and has established Intel's analytical inventory practices
through the dynamic SS target generation processes. Finally, in 2005-2006 Intel
implemented a Multi Echelon Inventory Optimization (MEIO) model to minimize
inventory cost in the Box Channel Supply Chain. A dynamic programming algorithm has
been used to find the lowest inventory cost solution by considering the cost tradeoffs
across the whole supply network. The MEIO implementation in the Box Channel Supply
Chain has achieved stable service level, higher inventory efficiency, and overall
inventory reduction by 11% [7].
2.4.1 Flat Inventory Policy
The first era of the inventory policy at Intel was "Flat Inventory Policy" or socalled "Rule of Thumb Inventory target". Prior to 2005, most inventory strategies at Intel
were developed by analysts in Microprocessor Marketing and Business Planning
(MMBP) group at the product family level. These flat inventory targets were later carried
out and implemented by planners equally for the finished goods product. For example,
the VMI Hub Guidance, as shown in Figure 12, took product health and product lifecycle
into account and divided different Stock Keeping Units (SKUs) into four color codes,
each has a standard Days of Inventory (DOI) target. The color code orange has lower SS
than green color code products, yellow ramping down between green and orange.
The Safety Stock (SS) target is calculated based on the Hub Guidance flat DOI target,
multiplied by next four week's average forecast data. The problem with such flat
inventory policy is that it has not considered service level, demand uncertainty or the
demand pooling opportunities. The methods for determining the inventory targets were
not analytical or statistical. Also, the SS targets were based on forecast data, which
contained persistent bias and resulted in high inventory at the VMI hubs.
'OREEN criteria
.Sku has good producthealth
or EOL Ifeecele oh ase
*Sku is not in NPM
*ORANGE critena
*Not enougb anventry to support fullSS Sku s in
NPI Ifeevcle thase
-YELLOW critena
-Sku has been EOL (pncefproduct)
-2Months of Yelow pnor to red
-RED critena
*Sku is eitherEOL or
-Not 80% volume
-Conversion to new sku (supply new)
-Red means no mv entory run through the hub
Figure 12: VMI Hub Guidance sample sheet
[7] B. Wieland, P. Mastrantonio, S. P. Willems, and K. G. Kempf, "Optimizing Inventory Levels
within Intel's Channel Supply Demand Operations," future issue, Interfaces.
http://www.informs.org/Pubs/Interfaces/Future-Issues
2.4.2 SEIO implementation at Intel's VMI Supply Chain
Inventory Target Solution (ITS) is a Single Echelon Inventory Optimization
(SEIO) model developed by Intel. ITS employs an analytical single stage, base stock
methodology, it was successfully deployed in substrates with a 25% inventory reduction
and is currently in a live pilot for the Vendor Managed Inventory (VMI) hubs. ITS
incorporates customer service level, replenishment lead time, forecast bias, and forecast
error variability into its algorithm to provide differentiated SS targets recommendation by
product, by hub over time. A simulation study was performed to determine whether such
differentiation of inventory targets by product and market was indeed beneficial. The
simulation results [8] of this back-testing model showed that differentiated targets could
improve the efficient frontier as shown in Figure 13.
Efficient Fronder of ITS compared to Fiat Policy
ITS --
Flat
Ig
A
~0
0
Fill Rat Achieved
Figure 13: SEIO moves the efficient frontier
[8] C. Arnold, A FrameworkforSuccess: Moving OR Projects From Concept to Implementation,
Supply Chain Modeling & Solutions, Intel Corporation
The curve represents the amount of safety stock required to meet any given
customer service level. By shifting the efficient frontier means that we can either reduce
inventory while achieving the same service levels, or increase service levels with the
same inventory.
In 2008, Intel implemented the VMI hubs to improve the service level to its OEM
customers. However, inventories in the VMI hubs were one week greater than the goal by
Q4'2009. A study conducted by Supply Planning Operations (SPO) in Q1'2010 revealed
that the high inventories were attributed to high bias in the customer forecasts leading to
the wrong mix of products in the hubs. A cross functional Hub Taskforce team was
formed to address these issues. Meanwhile, the Supply Modeling and Solutions (SMS)
group conducted a paper pilot and suggested the ITS inventory strategies could help
reduce inventory in the VMI hubs while maintaining desired Service Levels. In
Q2'2010, the Hub Taskforce partnered with the SMS group and conducted a live ITS
pilot on 10 representative SKUs. Figure 14 shows the ITS VMI live pilot result compared
to a control group of similar products in the same VMI locations.
EndAg on Hand inventary tleatve to MAD ThUcal Demand Expressed
as Days of inventory (001)
U
I**1
of SW% Ail "ta%
-0401~SKU
1 2
3 4 5 6 7
^1l "Ut
8 9 10 1112 13 14 15 16 17 18 19 20 2122 23 24 25 (pilot weeks)
Figure 14: Days of Inventory (DOI) for the ITS VMI pilot
Furthermore, the ITS approach can be extended to sequential SEIO optimization.
This sequential SS target setting approach considers individual inventory locations
independent of other upstream or downstream locations. The sequential approach sets
targets at each location to assure a service time of zero (inventory is available when order
is placed) for orders from a downstream location.
2.4.3 MEIO implementation in the Intel Box Channel Supply Chain
"Inventory Optimization is to achieve desired customer service level targets with
lowest possible safety stock inventory cost in the supply chain, using a "precision
strike" approachto the allocation of our inventory investment, targetingareas of
high demand uncertainty and/or high customer service level strategy." -Intel
Inventory Analyst
As mentioned in the earlier chapter, Intel's main customers in the CPU space are
OEMs, and channel customers. While Intel sells microprocessors directly to the largest
computer manufacturers like Dell, Hewlett Packard and Lenovo, the Channel Supply
Planning Operations (CSPO) organization is responsible for fulfilling the boxed CPU
demands of Intel's vast customer network of distributors, resellers, dealers and local
integrators. Intel's boxed processor shipment volume represents approximately 20% of
Intel's total CPU shipments [7].
In 2005, the boxed CPU business group began a multi-echelon inventory
optimization (MEIO) project to improve the efficiency and effectiveness of Channel's
end to end supply chain [7]. After an extensive study, a commercial tool was selected to
provide monthly MEIO solutions. Like Single Echelon Inventory Optimization (SEIO),
MEIO optimizes the boxed CPU supply network and provides differentiated inventory
target to achieve desired service level goals. In addition, MEIO performs a simultaneous,
global optimization over a very large assortment of Stock-Keeping Unit (SKUs) and
achieve the same aggregate service levels with fewer inventories. It optimizes along
multiple echelons of the supply chain, balancing between upstream and downstream
inventory to identify the most globally efficient inventory, thus minimize the total supply
chain cost across the whole boxed CPU supply network.
MEIO incorporates average forecast data, forecast error variability, average and
standard deviation of throughput time per production stage, unit costs per production
stage, transportation costs, inventory holding costs, and customer service level into its
algorithm to provide differentiated Safety Stock (SS) targets by product, by site, and by
stage. In the fourth quarter of 2005, the MEIO tool went live as an integrated component
of the build plan process. Figure 15 reports the actual service levels attained for each
class of managed SKUs beginning in November, 2005 [7]. SKUs planned by the MEIO
tool consistently achieve higher service levels than the set of SKUs managed by planners
(flat inventory policy). The initial result of MEIO implementation in CSPO shows an
11% overall inventory reduction for CSPO (2005-2006) [7].
Box Inventory and Service Level
--
Box MEIOproductsWOI
Box Non-MEIO products WOI
Box MEIO Customer Service Level
--- BoxNon-MEO Customer Service Level
---
am
E
0
monthl
month 10
ohm%
log
month 20
montt 30
#-G
'I
00d AV
Figure 15: SKUs managed by the MEIO tool have significantly higher service levels
than planner-managed SKUs
[7] B. Wieland, P. Mastrantonio, S. P. Willems, and K. G. Kempf, "Optimizing Inventory Levels
within Intel's Channel Supply Demand Operations," future issue, Interfaces.
http://www.informs.org/Pubs/Interfaces/Future-Issues
The CSPO organization has used MEIO modeling to generate inventory targets
for between 200 and 400 product/location combinations every month since
implementation [7]. In addition, a standard process was developed to ensure
collaboration across the various Channel organizations. Although the monthly SS targets
are generated by the Optimizer, the model output could be ratified by the stakeholders
during the Channel Strategy Review Meeting to better align the inventory policy with the
Channel organization's current business objective.
Furthermore, high inventory efficiency and significant annual savings were
achieved through continuous process improvement, including the implementation of
Modified Sigma and Kernel Smoothing methodologies to solve the presence of bias in
the sales forecast data. Figure 16 demonstrates the improvement of 1-Day COR response
(one type of customer service level measurement at Intel) since the implementation of
MEIO program and various process improvement initiatives at Box Channel Supply
Chain. Modified Sigma was developed to adjust forecast error for the heterogeneity and
bias problems, and Kernel Smoothing method was used to improve the relevance of the
forecast error measurements. We will not discuss these methods in detail in this
document, but we refer the reader to publically available references for demand
characterization methodologies in the next chapter, Literature Review.
Inventory Efficiency
<mple
(Higher is better)
Kernel Smoothing
implemented
Modified Sigma
1
4
7
10
-ME1O
13
16
19
--
22
25
28 (weeks)
NonMEO
Figure 16: Modified Sigma & Kernel Smoothing Impact on CSPO Inventory Efficiency
[7] B. Wieland, P. Mastrantonio, S. P. Willems, and K. G. Kempf, "Optimizing Inventory Levels
within Intel's Channel Supply Demand Operations," future issue, Interfaces.
http://www.informs.org/Pubs/Interfaces/Future-Issues
3
Literature Review
The measurement of forecast uncertainty is often called "demand
characterization" and is one of the most important and challenging factors within
inventory optimization. This chapter reviews literatures that described the inventory
optimization problem in Intel's VMI Hub and Box Channel Supply Chains and the
adjustment procedures that were developed to produce appropriate inventory targets in
the presence of forecast bias.
3.1
ITS Approach [9]
ITS is a single echelon optimization tool based on periodic review, base-stock
methodology. It includes demand variability over replenishment time and target service
level into its calculation.
ITS defines Forecast Error (FE) as e, = (l-LTt -
-)FLT,t
where
F-LT,t
is the
demand forecast for week t made in week t -L T, L T is the lead-time between the order
placement to inventory receipt and A, is the actual demand realized in week t. ITS
assumes that the FE comes from a single distribution. A Lognormal or a Normal
distribution was found to be the best fit for the FE of A, / F,LT,t =-
e, depending on
business group. This FE distribution is intended to capture effectively the FE error bias
and variability in terms of u and a- respectively.
[9] A. Mishra, C. Arnold ,VMIDynamic Safety Stock Explore, Supply Chain Modeling &
Solutions, Intel Corporation
ITS demand characterization measures the variability of the forecast error over
the most recent 8 weeks, using a weighted moving average to place higher emphasis on
the forecast error in the most recent weeks. ITS also performs a bias correction of future
demand forecasts using historical Forecast Error distribution. The time series based
approach accounts for short product lifecycles and non-stationary FE as the nature of the
OEM business group. The decision on targeted Service Level is made based on the
news-vendor model formulation using financial parameters considering inventory
overage cost and shortage cost.
The output from ITS is an order up-to target (1*) which is the sum of Cycle Stock
and Safety Stock. I* = Cs + Ss where Cs is cycle stock and Ss is safety stock. Cs
DL where D
is the forecasted demand in next lead-time period and Ss = (Bias
adjustment) + (Variability adjustment). Figure 17 shows the core concept of ITS
methodology [9]. In this figure, RT = LT.
Distribution of Demand over RT~ Normal (p,ar)
aeySte ck
PRT
Order Upto Target
E(1-e)*DRT +zsL*o(1-e)
*DRT
Figure 17: ITS "Order Up-To" Target Generation
[9] A. Mishra, C. Arnold ,VMIDynamic Safety Stock Explore, Supply Chain Modeling &
Solutions, Intel Corporation
=
Next, we'd like to provide an example to illustrate how the ITS calculation works.
Suppose we consider SKU1 with Target service level equal to 95% and with lead time
equal to 1.57 weeks
e
Step 1: calculate e and standard deviation of forecast error based on 8 weeks of
historical data as shown in Figure 18
*
Step 2: calculate s, a weighted moving average methodology that places higher
emphasis on the forecast error in the most recent weeks. This will give us 1-s
=
0.59, and mean error over LT = 1.57*0.59 = 0.92
" Step 3: calculate standard deviation of forecast error over LT = STDEV of (1-e) *
VLT = 1.06
" Step 4: look up ZSL = 1.53
"
Step 5: calculate ITS multiplier = E(1-e) + ZSL* u(1-e). E(1-e) is the mean error
over lead time, and u(1-e) is the Stdev of error over lead time. ITS multiplier
=
0.92+ 1.53* 1.06 = 2.54
*
Step 6: calculate the ITS Order-up-to Target = E(1-e)* DLT + ZSL*
a(1-e)*DLT
ITS multiplier * DLT. Here we define DLT as the forecasted demand in next leadtime period. This will give us ITS Order-up-to target of 799.51 units
" Step 7: calculate the SS target = "ITS Order-up-to Target" - DLT. This will give
us SS
=
799.51 - 494.55 = 304.96 units
e= (Forecast
-Actual
demand)/
Forecast
Actual
Demand
(Weekly)
Forecast
(Weekly)
Week1
100
0
Week2
100
50
126
Week3
Week4
1.00
1.26
0.84
0.37
0.27
(1.10)
2.00
-0.37
42
105
Week5
21
0
1.00
Week6
42
126
(2.00)
Week7
0
0
Week8
42
0
_
-
(0.26)
0.16
50
Week9 (Current WeekLT begins at)
1- e
1.00
s: moving
STDEV of (1 Forecast
average
e)_weekl: over LT (LT (demand
week8 = 1.57)
charaterization)
-
1.00
0.32
2.00
-0.34
1.00
-0.17
-
0.41
494.55
_0.84
WeeklO
315
Week1l
7875
Week12
15750
1
1
1
Week13
28350
1
1
1
1
Week14
28350
1
1
1
L
Figure 18: Example of ITS calculation
36
1
1
3.2
Boxed CPU Demand Characterization
A key component of the MEIO process is Forecast Error (FE) measurement, for
purposes of demand volatility characterization. Or in the case of the off-the-shelf MEIO
software, forecast error is converted in units of the average monthly forecast (F) as shown
below: Coefficient of Variation (COV) = a/F where o is standard deviation of forecast
error, and F is average monthly forecast. This way, the data is independent from the
forecast size and is defined in relative terms.
Graves and Willems (2000) [10] developed a framework for modeling strategic
safety stock in a supply chain that is subject to demand or forecast uncertainty. At Intel,
demand variability is the greatest contribution of the supply chain uncertainty. CPU
products have relatively short life cycles; meaning new products are introduced with
great frequency. Managing product transitions is a particular challenge that exacerbates
demand forecast error. Frequent transitions also make it difficult to collect data and
measure the uncertainty for a specific product in terms of historical FE. Meanwhile, the
forecast data itself also tends to be biased, heterogeneous (scattered data), and with nonparametric densities (no clear data distribution pattern). Even if it does conform to a
particular distribution, it is likely to be non-stationary distributions, which will change
frequently over time. Graves and Willems (2008) [11] introduced a demand model that
permits finding the safety-stock placement in a supply chain that applies to the case of
non-stationary demand.
[10] S. C. Graves, S. P. Willems (2000), "Optimizing Strategic Safety Stock Placement in Supply
Chain", Manufacturing & Service Operations Management, 2 (2000), 68-83
[ 11] S. C. Graves, S. P. Willems (2000) "StrategicInventory Placement in Supply Chains:Non-
StationaryDemand", Vol. 10, No. 2, Spring 2008, pp. 278-287, 2008 INFORMS
Over the years, several continuous process improvement projects were developed
to solve the imperfect forecast data problem in the Box CPU Supply Chain: the product
transition maintenance tool was developed to map multiple products to a particular
market segment to solve for deficient forecast error data for newly launched products; the
MEIO Cube was also developed to improve sample size collection by leveraging multiple
forecast offsets in forecast error calculations; Modified Sigma was developed to adjust
forecast error for the heterogeneity and bias problems. Lastly, a Kernel Smoothing
technique was used to improve the relevance of the forecast error measurements by
weighting the historical data points based on their relative proximity to the current
forecast [12]. Again, this research paper does not intend to discuss Modified Sigma or
Kernel Smoothing technique in detail, but rather to introduce the basic concept of
demand characterization methodologies utilizing by the Intel Box Channel Supply Chain.
However, we refer the reader to the references given by the footnotes in this chapter.
Modified Sigma
Demand planning and collaboration systems are plagued by persistent forecast
bias caused by sales reward systems that encourage wishful thinking or by customers
trying to secure supply they may or may not need. It is common to experience periods of
time where the forecast either consistently exceeds or falls short of the actual demand.
For example, a Channel Forecast Error study conducted by CSPO demonstrated forecast
error bias based on the observation samples. Figure 19 shows the Median forecast
represents 65 units actually sold for every 100 units forecasted.
[12] P. Bloomquist, M. Manary, A. Shihata, B. Wieland (2009), CorrectingHeterogeneous &
Biased ForecastErrorat Intel for Supply Chain Optimization, Intel Channel Supply Demand
Operation , Interfaces C2009
MEIO's initial estimate of demand variation (a2) was calculated from a standard
deviation of forecast errors (SDFE) equation, but SDFE is sensitive to any bias in the
forecast. Figure 20 illustrates how increase in sigma may be affected by the forecast bias.
aSDFE
2
,(F1 - A)
-I
Fst
Qwntile
Fert + Dewuard
100.0% maximum 0.99985
99.5%
0.99751
97.5%
0.98425
90.0%
0.91728
75.0%
quartile 0.79494
5.0%
median 0.644
25.0%
qua
0.44759
10.0%
0.28538
2.5%
0.11765
0.5%
0.03557
0.0%
minimum 0.00647
Figure 19: Intel Channel Forecast Error Summary
250%
=200%
E
.0150%
CO)
-E 100%
50%
0%
0%
10% Forecast Bias 20%
30%
Figure 20: Forecast Bias has significant impact on SDFE value
Since optimization software assumes "well-behaving" error and sets safety stock
targets in large part from the base stock equation
q
+ F~1(a) 0c, a 30% bias equates to
an approximate tripling of the SDFE, which has a linear affect on inventory targets.
However, it was determined that managing the forecast bias by directly modifying
the raw sales forecast data was not an option for the Box Channel Supply Chain because
Sales and Marketing controlled and loaded the data into the manufacturing resource
planning (MRP) system before the planning organization received it. Therefore, the
average forecast demand, with its bias present, was already in the system; the only
adjustment that CSPO could make was to change the inventory target. In 2008, Manary
and Willems presented a set of adjustment procedures that produce appropriate inventory
targets in the presence of forecast bias [13]. Modified Sigma was adopted by CSPO to
adjust the error measure to account for systematic forecast bias. In order to use
optimization techniques the forecast imperfections needed to be addressed through aY.
According to Forecast Error (FE) measurement [13]:
Forecast
Forecast i + Actual Demand i
Modified
Modified Sigma can be presented as:
=
Max
p,
--
0
where Op
p from the distribution of Os,
student-t distribution with a cumulative density of p and degrees of
denotes the quantile point corresponding to
p=
1-a. tp,df is the
freedom coming from the number of historical points to draw from, and p is the average
demand. In the case of extreme over-forecasting, the CSPO MEIO models do not
recommend any SS targets, and for extreme under-forecasting the models call for higher
SS targets than would otherwise be calculated using a standard approach.
[ 13 ] M. Manary and S. Willems (2008), Setting Safety-Stock Targets at Intel in the Presence of
ForecastBias, Interfaces 3 8(2), pp. 112-122, C2008 INFORMS
Kernel Smoothing
All optimization approaches make assumptions about FE accuracy. Standard
approaches typically assume error is centered, and normally distributed. Departures from
this assumption have profound impacts on SS targets; Manary and Willems (2008) [13]
developed an algorithm which eliminates the impact of bias and non-normality of
forecast errors. To check for homogeneity, CSPO compared aModified for multiple service
levels then adapted Bartlett's test (1946) [14]. They anticipated a 5%-10% failure rate,
but the actual failure rate was above 50%. Closer examination revealed two issues: First,
Products displayed significant deviation from Normal behavior. Second, even
"homogeneous" products were heterogeneous when viewed from a different perspective.
Therefore, any solution for estimating FE at Intel should address bias, heterogeneity, and
errors for an individual product potentially coming from multiple (or undefined)
distributions.
To improve the modified sigma method CSPO developed a Kernel Smoothing
technique which does not require FE to be assumed from any one distribution [12]. This
enables customized output for each product at each forecasting level. Combining Kernel
Smoothing with Product Transition Mapping enabled CSPO to generate assumptions that
best fit the actual FE, giving the most weight to observations with similar prior forecasts
and life-cycle stages.
[12] P. Bloomquist, M. Manary, A. Shihata, B. Wieland (2009), CorrectingHeterogeneous &
BiasedForecastErrorat Intelfor Supply Chain Optimization, Intel Channel Supply Demand
Operation, Interfaces C2009
[13] M. Manary and S. Willems (2008), Setting Safety-Stock Targets at Intel in the Presence of
ForecastBias, Interfaces 38(2), pp. 112-122, 02008 INFORMS
[14] Bartlett, M.S., D.G. Kendall (1946), The StatisticalAnalysis of Variances-Heterogeneityand
the Logarithmic Transformation,Journal of Royal Statistical Society, p. 128-138 ©1946
CSPO minimized the impact of bias, heterogeneity, and potential non-parametrics
by isolating a sigma estimate close to the current forecast level. One challenge was a
relatively low number of observations around the current forecast level to draw on as a
population. To compensate for this, CSPO decided to apply a weighting system to FE
observations with decreasing influence as the associated past forecast is further away
from the current forecast. Ultimately, CSPO found the most robust technique for
calculating sigma under biased, heterogeneous and nonparametric error was to apply a
nonparametric approximation of the product error-density function across the full range
of prior forecasts, allowing a Kernel-Smoothing technique to help determine a weighted,
localized error pattern, while not been deterred by non-parametric error [12].
[12] P. Bloomquist, M. Manary, A. Shihata, B. Wieland (2009), CorrectingHeterogeneous &
BiasedForecastError at Intelfor Supply Chain Optimization, Intel Channel Supply Demand
Operation, Interfaces C2009
4
Three Lens Analysis
This chapter details an organizational processes analysis called the three lens
analysis [15] whereby we examine the organization from a strategic design, cultural, and
political perspective. The purpose of the analysis is to provide an objective and unbiased
assessment of Intel's FG CPU Supply Chain organizations and their efforts to implement
the Inventory Optimization initiative. The analysis also allows for a richer understanding
of the different elements that impact an organization and its ability to deal with the
internal and external stresses brought about by change.
4.1
Strategic Design Perspective
The strategic design perspective focuses on: how the flow of tasks and
information is designed; how people are sorted into roles; how these roles are related; and
how the organization can be rationally optimized to achieve its goals [16]. Figure 21
shows the basic processes involved in strategic design. The three key elements of
organizational design are grouping (differentiation), linking (integration), and aligning.
From the strategic design perspective, reasons for organizational ineffectiveness include:
unclear goals, ineffective grouping, inefficient linking, unsuccessful internal alignment,
and poor external fit.
[ 15] J.S. Carrol (2006), Introduction to OrganizationalAnalysis: The Three Lenses, MIT Sloan
[16] Ancona, Kochan, Scully, Van Maanen and Westney, Managingthe Future: Organizational
Behavior and Processes.2nd Edition. South-Western College Publishing (1999)
Assess Environment
(Threats and Opporthnities,
Industry Analysis, Etc.)
Strtegicgn
Assess Organization
(Core competencies.
OrganizatioOal Capabilities)
H Intent
.taeiGrung
Figure 21: Processes in Strategic Design
Grouping (differentiation)
Grouping is a framework that draws boundaries around clusters of tasks or
activities to define jobs, departments, processes. Intel is a large matrix organization that
can be very difficult to navigate. Currently, Intel has several grouping structures in
different levels of the organization; for example, the corporate structure is grouped by
functions, as shown in Figure 22.
Pmesidenta CEO- Paul
S.Otmni
&
igta
Sals ndnt
Marketing
Architecture
Health
TMG (Technology
Manufacturing Group)
Intel capital
Intel ta s
Figure 22: Functional Grouping Structure
Each corporate functional group then further divides into several divisions either
by functions or by products. For example, Supply Planning Operations (SPO), the group
that synchronizes product supply with customer demand, can be represented in product
grouping structure by dividing the planning groups into different product divisions, as
shown in Figure 23. The Channel Supply Planning Operations (CSPO) is responsible for
the demand and fulfillment alignment for Boxed CPU products and the Intel Architecture
Division Planning (IADP) is responsible for all the supply and demand for tray products,
including processors and chipsets. Currently, both CSPO and IADP have their own
supply & demand alignment team, planning team, and operation teams. From an
inventory optimization perspective, CSPO has its own inventory analyst, who is
Legal
Crprate
Affairs
responsible for setting the monthly safety stock strategy for boxed products using MEIO
methodology. IADP does not have its own inventory analyst group, but rather works with
the Supply Chain Modeling & Solutions team and Supply Planning Integration &
Analytics (SPIA) team for the inventory optimization needs; for example, the ITS pilot
for the VMI hub products. To remain unbiased, we created a research project under
SPIA team, which acts as an internal consulting group tasked to drive supply planning
integration and analytics efforts across the Intel Supply Chain.
anning
Channel Supply
(SPIA)
Operations (CSPO)
Planning
on &
Supply &
Demand
Alignrnent
Channel
Planning
Capabilities
Box Factory
f Plnig
an g
401er
Figure 23: Simplified Org Chart for Supply Planning Operations
Linking (integration)
Linking integrates the subdivisions across organizational boundaries. Currently,
CSPO and IADP operate independently with no strong linking mechanism in place.
From an inventory optimization perspective, there are several weaknesses with the
current division structure, including insufficient knowledge sharing, limited career
growth of inventory specialists, strong division affiliations, and difficult product
integration. We will now examine each of these weaknesses in kind.
1. Insufficient Knowledge Sharing: inventory specialists with particular domain
knowledge cannot share their expertise across divisional boundaries due to resource
constraints in the current division structure. This restricts the opportunity for the
IADP and the CSPO to share best practice and conduct knowledge transfers in
inventory optimization.
2. Limited Career Growth of Inventory Specialists: technical specialists feel alienated
from their peers in other divisions and lack the exposure to the growth opportunities
in other organizations.
3. Strong Divisional Affiliations: some employees may feel more allegiance towards
their own department than towards the larger organization. While they discern their
organization's role, they may not understand how their organization relates to the
larger organization's goals.
4. Difficult Product Integration: Intel has multiple product groups and the integration
task is extremely challenging because there is little coordination between different
divisions. The product management task across different division requires regular
sync-ups, but the structure inherently provides little motivation for the product
managers to seek this larger goal.
One potential solution to these issues is to use Functional/ Product Matrix
structure , as shown in Figure 24, which is similar to the structure of Supply Planning
Integration & Analytics (SPIA) group. The SPIA team has experts familiar with the
planning processes, analytics, and data access, who can bring integration solutions by
leveraging the best practices in different supply chains.
Intel
Tray CPU
Chipset
Boxed CPU
Functions
Inventory
Analyst
SPIA
Products
Figure 24: Functional/Product Matrix structure
Aligning
Aligning provides access to ensure that units and individuals posses the necessary
resources and motivation to complete the tasks assigned. Aligning can be achieved by
organization performance measurement systems, individual rewards and incentives,
resource allocation, human resource development, and informal systems and processes.
Overall Intel communicates its strategy very well through several channels, and its suborganizations seek continuous improvement. Last year, a supply shortage issue resulted
in a company-wide effort to reduce its overall pipeline inventory, this shortage issue
provided incentive to use analytics to determine inventory strategy across planning
processes.
By recognizing the linking gap between different divisions, SPO launched several
initiatives to improve collaboration amongst its planning teams. For example, they
created fused quarterly Tray/Box Planning Collaboration Forums as knowledge sharing
platforms. To facilitate these forums, they invited experts to address these supply chain
issues within different business groups. Also, the Supply Chain Strategy (SCS) team
started monthly Supply Chain Tech Forums which fostered the engagement necessary
amongst technologists and supply chain managers to solve the key supply chain
challenges within Intel.
Furthermore, Intel designed many human resource processes to encourage better
alignment in bridging the resource gap and in increasing upward management exposure
across the different business units. Fox instance, individuals commonly take on
temporary relocation assignments in different functional groups. Additionally, individuals
frequently apply for acting manager positions, while their manager is on sabbatical.
These practices successes in promoting career development and creating cross
organizational learning opportunities.
4.2
Political Perspective
"Politicsconstitutes the dark side ofthe organization......An inabilityor an
unwillingness to deal with the politicalaspects of organizations,however, is a
serious handicapfor anyone trying take effective action in an organizational
setting." [16]
The political design perspective focuses on how power and influence are
distributed and wielded; how multiple stakeholders express their different preferences
and get involved in decisions; and how conflicts can be resolved. Its core concepts
revolve around "interest" and "power".
Interest
"Simply recognizing that interests are importantis the first step in developing an
ability to use the politicallens to take more effective action in organizations.The
next step is much harder:analyzing what those interests are and whatpriority
they have for key individuals and collective actors."[16]
[16] Ancona, Kochan, Scully, Van Maanen and Westney, Managing the Future: Organizational
Behavior and Processes. 2nd Edition. South-Western College Publishing (1999)
The goal of our research project was to identify the inefficiency of the current
inventory optimization processes and propose potential solutions that could be
implemented and add value to SPO/ Intel. In order to achieve our goal and build
consensus for the project, we designed a framework to analyze key stakeholder's interests
and their priorities toward using an analytical inventory optimization tool. We created an
informational briefing to introduce our research project and to educate the broader
organization about the value derived from MEIO and the inherent challenges in
optimizing inventory in the supply chain. After delivering this brief, we asked
stakeholders from different organizations within Intel's Supply Chain to answer some
questions about how the MEIO modeling implementation may affect them. Some sample
questions include:
-
What is yourjob at Intel?
*
How does yourjobfit into the current inventory targetsetup process within your
organization?
*
What are the problems! challenge! issues with the current inventory target set up
process in your organization?
-
What drives the problems! challenge! issues, if any?
*
How can an inventory optimization tool help yourjob?
What need to be done in order to make MEIO successful in your organization?
-
What is your concernfor adopting an analyticaltool, such as ITS or MEIO, in
your organization?
*
How do you define success? What output (success criteria)is needed in each
step?
Some insight through stakeholder interviews
At the time, the Microprocessor Marketing and Business Planning (MMBP) group
determined the safety stock target at the product family level. MMBP wields significant
influence within Intel because of its ownership of the P&L for Intel's core business. The
project managers (PM) within MMBP work with Geo Sales around the world to allocate
available supply to create a better balance in their supply and demand alignment. One of
their key responsibilities is to establish an inventory strategy to maximize financial
results by balancing Intel's cost of carrying inventory with its revenue opportunities.
Therefore, the accuracy of the inventory level (shippable target) in the central warehouses
directly impacts MMBP's performance metrics.
During our stakeholder interviews, we learned of several concerns from MMBP
members about adopting an analytical tool like MEIO. First, while lowering inventory
may be important for the PMs, the "explain-ability" of any inventory policy is their top
priority. This is particularly true in the time of a supply shortage because PMs need to be
able to defend and explain the inventory policies they have recommended and make
recovery plans to Intel's executive team. Second, there is currently a very short timeline
between the publications of MMBP's monthly Judge Demand (JD) forecast and the time
that PMs must finalize their inventory policies. Therefore, it is very difficult to receive
buy-in from PMs for any new tool unless the tool proves effective and time efficient.
Another concern voiced by MMBP over this analytical tool is that the historical forecast
error data may not accurately represent future demand uncertainty. Some PMs believe
that human judgment is better suited in providing inventory policy because it reflects the
demand upside and downside faster and more accurately.
These interviews were extremely effective in indentifying the stakeholder's
concerns over, and motivation for, supporting our project. In addition, the interviews also
provided great networking opportunities with various stakeholders within the
organization. We conducted several interviews with members of MMBP, SPIA, CSPO,
IADP, ITS, Planner, and the Geo Sales teams. The insights gathered from the
stakeholder interviews provided an important resource to incorporate into our modeling
and recommendation for implementation, which will be introduced in chapter Eight.
Power
"Power: ability to influence behavior of others. Sources ofpower. personal
characteristics;scarce and valued expertise; pastperformance/trackrecord;
formal position in organization;informalposition in organizationor social
network" [16]
After capturing the collective "interest" and "agenda" from the stakeholders, our
next step was to design frameworks that could take effective actions to influence
stakeholders. We first conducted a stakeholder interest and power mapping exercise to
diagnose the changeability of different stakeholders and their organizational influences,
as shown in Figure 25. Next, we constructed a Capability Analysis Matrix, as shown in
Figure 25, and a Project Commitment Chart, as shown in Figure 27, to find allies and
build a coalition within the organization. As a result, we were able to use these
frameworks, coupled with the insight from the interviews to create action plans with each
stakeholder. These analyses enabled us to construct our inventory model more
effectively based on the relevant information received.
Legend
=Low;
O=Medlum;
- - - = Negative;
Changeability of Applied Influence:
Relationship with Key Players: - -4
-3
-2
-1
+1
+2
+3
+4
+5
+6
+7
+8
+9
=High
-Positive
)
+10
10
10
9
9
8
8
4
4
7
-
6
6
.2
4
3
3
2
2
1
-4
-3
-2
Against
-1
0
+1
+2
+3
Applied Influence
+4
+5
For
+6
+7
+8
+9
+10
Figure 25: Stakeholder Mapping Analysis
[16] Ancona, Kochan, Scully, Van Maanen and Westney, Managing the Future: Organizational
Behavior and Processes. 2nd Edition. South-Western College Publishing (1999)
Ability
Willingness
H
Key Players
1. Mary
H
Influence
M
Tine/Avalability
H
H
L
M
2. Bill
H
3.
Tom
H
H
H
M
4.
Sam
H
H
H
M
5. Amy
M
M
L
M
6. Chris
M
M
L
M
7.
Mike
M
8.
Gill
M
9. Jan
L
10. Ken
M = medium
L= low
L
L
M
L
L
M
H
H
H
L
H
H
L
H= high
Figure 26: Capability Analysis
No
Key Stkhles
15. "y
16. Chris
Bok
I
Help It
LetMtHapen
I
make it
Happen H"
I
I
x
I
x
Manageable
Figure 27: Project Commitment Chart
I
a-
I
0
4.3
Cultural Perspective
"Cultureprovides a template on which meanings are readand actions are
based" [16]
The cultural lens focuses on how history shapes the assumptions and meanings of
different people; how certain practices take on significance and even become rituals; and
how stories and other artifacts shape the pulse of an organization. Intel has long been
known as a company that places a premium on fresh ideas, frank talk, and employee
engagement. Intel cherishes its values of: customer orientation, discipline, quality, risk
taking, great place to work, and result orientation. During our research, we observed
quite a few unique Intel cultural perspectives that impact directly and indirectly to the
point that they may even affect the success of implementing our MEIO project.
Acronyms: While acronyms serve a role at most, if not all, companies, at Intel
they are so prevalent as to be considered promiscuous. Intel has a unique culture of using
acronyms. Not only is there an official company link to an Intel Acronyms dictionary,
there is even an acronym TMA, which stands for "too many acronyms". This reality
creates a significant challenge for the new team members and the external model users.
Safety & Ergonomics: Intel places large emphasis on providing a safe working
environment for its employees. There is a daily group stretching time and a frequent ergo
assessment to promote safety and prevent office injury. At our CSPO and SPIA team
meetings, safety was always the first subject of discussion; SPO provides an early
reporting reward to promote ergo awareness. The plan team implemented many
initiatives to reduce the ergo risk due to mouse over-usage, including a utility installation
to monitor M2K ratio (Mouse Click vs. Keystrokes), a short cut summary list, and several
IT automation projects.
[16] Ancona, Kochan, Scully, Van Maanen and Westney, Managingthe Future: Organizational
Behavior and Processes. 2nd Edition. South-Western College Publishing (1999)
Community service: Intel strongly encourages community involvement through
participation in group-sponsored activities and volunteering in employee's own free time.
Additionally, Intel matches the amount of volunteer hours with financial grants to the
charity organizations. Employees broadly consider Intel as great place to work.
Data Driven: Intel works tirelessly to cultivate a data-driven culture, one in which
data is recognized as a corporate asset essential to managing customer relationships,
increasing efficiency, managing risk and fulfilling the company's strategic objectives. At
Intel, management encourages its employees to express their professional opinions but
subsequently expects them to support those ideas with data.
Intel benefits from this data driven corporate culture, for data frequently resolves
most disputes and encourages constructive confrontation and problem solving.
Additionally, data facilitate collaboration across different teams and functions, since data
trumps intuition. On the flip side, the drawbacks include: skepticism of benchmarking
data, lack of confidence in off-shelve programs, and change resistance. There is a
general belief that "if it's not developed at Intel, it's not good enough to be used here."
Undoubtedly, innovation thrives on such a mentality, but it also creates disbelief in any
outsourcing program, even if it has proven success within another team at Intel.
At the outset of my research, several people cautioned my expectations, for they
forecasted the resistance that I may experience promoting the use of analytics in the
supply chain planning process. This speculation contradicted my original belief, as I
(naively) thought that supply chain managers actively sought new tools to support factbased decision making and that fact-based equates to data-driven. The question
remained: what drove people's rejection in using an analytics tool, like MEIO, given
Intel's data-driven culture?
To gain a better understanding for the reasons behind most team members'
resistance to change, we first examine the issues resident in the current process. The
stakeholder interviews raised the following concerns that: the challenge with the current
process is not lack of useful data, but a lack of actionable insight that can be derived from
the data that already exists; the critical information is not reaching those who need it most
because it remains in silos, and it's not generally accessible throughout the organization;
the data exists, but it's decentralized and all over the place.
The formal IBM CEO, Lou Gerstner states unambiguously, "Nobody likes
change. Whether you are a senior executive or an entry-level employee, change
represents uncertainty and, potentially, pain." [17] Upon reflection, the reasons behind
Intel people's rejection of using analytical tool is the skepticism of the tool's
effectiveness and the uncertainty it could bring.
Also, what makes finding the system-wide, or globally optimal, integrated
solution so difficult? Reasons abound, but "Designingand Managingthe Supply Chain
[17] ", the authors introduce a variety of factors that make this a challenging problem,
including: the supply chain is a complex network; different facilities in the supply chain
frequently have different, conflicting objectives; the supply chain is a dynamic system;
system variations over time.
Recognizing these challenges, we needed to develop technical solutions optimized
not only across the supply chain, but also across the processes associated with the
development of the supply chains. Additionally, to ensure both compliance and
dissemination, we needed to use uniform terminology and standard processes and
integrate them into SPO's current business processes; otherwise people won't use the
modeling data or be able to share it beyond their own divisions.
[ 17] L. Gerstner (2002), Who Says Elephants Can'tDance? : Leading a GreatEnterprise
Through DramaticChange, 2002, p.77
[18] D. Simchi-Levi, P. Kaminsky, E. Simchi-Levi (2008), Designingand Managing the Supply
Chain
In Summary, our three lens analysis helps to provide frameworks for
understanding the complexity of Intel's FG CPU Supply Chain organizations and their
efforts to implement the Inventory Optimization initiative. It also improves our ability to
analyze the dynamics of change initiatives. Finally, the analysis helps us to take more
effective actions in promoting changes that is needed in the Intel Supply Chain
organizations. Intel Supply Chain needs an integrated tool that enables its employees to
make better supply chain decisions based on data analysis. The real benefit of using an
analytical tool is to foster behavioral change. Therefore, empowering people to make
better decisions through analytics must be one of the primary goals of our model
development.
5
Approach and Methodology
Our study investigates reducing inventory cost by applying a Multi Echelon
Inventory Optimization (MEIO) approach to manage Intel's VMI Hub Supply Chain. The
goal of this research is to evaluate the hypothesis that an MEIO approach for inventory
management and replenishment will result in a more efficient use of FG inventory. This
chapter details the approaches and methodologies that we employed to model the Intel
CPU FG Supply Chain.
5.1
Document Current State
The goal of this exercise is to capture and document the current Safety Stock (SS)
target setting processes for the different inventory policies through direct observation,
interviews, process mapping and/or other methodology and also to identify other
opportunities for improvement.
Flat Inventory Policy
Figure 28 shows the current Hub Guidance monthly SS setting process. It starts when the
Microprocessor Marketing and Business Planning (MMBP) PMs update and publish the
Hub Guidance package. Based on the division planners' and Geographic (Geo) sales'
feedback, MMBP PMs made adjustments to the SS target before releasing the final
revision of the Hub Guidance to the Global Supply Analysts (GSA) team. The GSAs
updates the SS into the supply chain management system and starts the hub
replenishment process. The SS targets are based on human judgments; the inventory
optimization methodologies have not been applied.
Replenis
Picture
Figure 28: Hub Guidance monthly Safety Stock targets setting process
SEIO
Figure 29 shows the current weekly ITS SS targets setting process. On Monday,
the ITS data input: replenishment lead time, forecast, service level is pulled into the ITS
Solve to generate SS targets. The SS targets are used to compare to the Hub Guidance
before its release to Hub Ops for review. After Hub ops review the GSA's enter the new
SS targets into the supply chain management system for the ITS Pilot SKU's.
Data Inputs
)
Inventory Optimization
Replenishment LT
Forecast Bs
Forecast Eror
Variabiliy
Customer Sevice
Level
Raillcaion
Implementation
u
Replenishrent
Prcs
m
Trget
Supply Picture
Figure 29: ITS Safety Stock targets setting process
During this process, the SS target is generated by the SEIO tool based on several
supply chain characteristics, thereby minimizing some human judgment errors. ITS has
been proved effective in achieving lower VMI inventory through the ITS pilot (refer to
chapter 2.4.2 SEIO implementation at Intel VMI Supply Chain).
There are both pros and cons to a spreadsheet-based inventory optimization tool,
like ITS. The advantages include ease of use, less upfront development cost, and ability
to expand to sequential SEIO model. However, aside from not having enough automation
due to its status in the pilot stage, there are still limitations for the current ITS tool,
including no multi echelon optimization capability and hard to scale up to be a full
production use tool.
MEIO
Figure 30 illustrates the current monthly MEIO SS targets setting process. The
monthly calculation of the SS targets starts midway through each fiscal month with the
collection of all data inputs required to run the MEIO models. These models are built
and run in a software package, which is an optimization solver that determines the lowest
cost inventory policy for the modeled supply chain. The SS targets are reviewed in the
Channel Strategy meeting that includes MMBP, planning, and Geo Sales before loaded
into the system solver. MEIO modeling has been proved effective in providing better
customer support with lower inventory cost in the Box Channel Supply Chain (refer to
chapter 2.4.3 MEIO implementation in the Intel Box Channel Supply Chain).
Data inpus
:
CsPO Finance
vwentory OpimPraalon
unO
moderms
/
RailIcation
>InIplerneallon>
MMSP, Planning
Figure 30: MEIO Safety Stock targets setting process
The MEIO modeling provides a scientific approach to defining safety stock goals,
and it's a fully commercialized and established technology. It provides "one click away"
ability to do SEIO/Sequential SEIO and MEIO modeling. It's highly modularized, which
allows the CSPO group to add additional demand characterization capability through a
separate module of MEIO. The tool generates cost data that permit us to conduct
Inventory Reduction study on business investment and scenario analysis based on
different supply chain designs.
There are a few challenges, however, for the current MEIO project: the manual
data loading process; higher upfront licensing cost; IT Infrastructure needed to maintain
the off-the-shelf MEIO software servers; limited expertise to drive tool improvements;
hard to do knowledge transfer due to the complex data flow (as shown in Figure 31), and
the level of details involved in the weekly SS target setting up processes (as shown in
Figure 32).
Figure 31: MEIO Data Flow [18]
[19] B. Wieland, High Level Box Inventory Target Setting Process, Intel Corp.
fte inia
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5.2
Model Development
To evaluate the hypothesis that MEIO approach will result in a more efficient use
of FG inventory and lower supply chain cost, we developed a three-step modeling
framework, as shown in Figure 33. In each step, we conducted several experiments,
applying the MEIO model approach, to determine the optimal CPU FG inventory
stocking levels needed to meet customer service level goals for different products and
locations.
Step 1
Single Echelon Optimization (VMI
: nodes)
S
+37)
V3
r
V4
lStep 2
I Multi Echelon Optimization (CW, VMI
I Hub, Box channel)
Is there benefit Inusing METO across VMI and Box
V
Channel Supply Chain?BOCh
Figure 33: A three-step modeling framework was developed to evaluate the MEJO
approach
Step One: Single Echelon Inventory Optimization
Figure 34 shows the network design for Step One modeling. The purposes of this
step are: to validate whether the SS targets generated by the off-the-shelf MEIO software
are similar to the SS target generated by ITS; to understand the difference in model
output and what drives the difference; to identify assumption and gaps in each modeling
steps.
We've developed a total of six models: two ITS models and four MEIO models to
compare the SS target levels using different demand characterization and the SEIO
methodologies. Several experiments were designed and run for each model in 10 CPU
SKUs and five global VMI hub locations. The modeling results are presented in chapter
Six.
VMil
VC1
VM12
VC2
VM13
VC3
VM14
VC4
VM15
VC5
VMI6
VC6
CW
Figure 34: Step One- Single Echelon Inventory Optimization
Step Two: Multi Echelon Optimization (Extended the model to include CWInodes and
the Box Channel Supply Chain)
A three-echelon supply chain was modeled for Intel's CSPO's boxing and
distribution activities. We connected the box MEIO model with the VMI model to see if
the inventory level for the Boxed Channel and VMI can be further reduced. Figure 35
illustrates the network design for Step Two.
Figure 35: Step Two- Multi Echelon Inventory Optimization, including VMI Hub and Box
Channel Supply Chains
Step Three: Multi Echelon Optimization (Extended the model to include Tray Channel)
We further extended the Step Two modeling to include tray disti customers
(Direct Shipped Tray) to see if there is benefit in using MEIO across the entire CPU FG
distribution network. Figure 36 shows the network design for Step Three.
Figure 36: Step Three- Multi Echelon Inventory Optimization, including VMI hub, Box
Channel, and Tray Disti Customer Supply chains
5.3
Study of Inventory Reduction Opportunities
Next, we conducted a cost-benefit analysis from a reduction in inventory
perspective in order to communicate the financial ramifications of the MEIO model to a
larger business audience at Intel. The study is based on the reduction in total inventory
level, in terms of dollars, using different inventory optimization methodologies. The
inventory reduction is calculated in units of inventory, the Days of Inventory (DOI), and
the cost of inventory saved from the three step modeling. The study is used to evaluate
the financial impact of the MEIO modeling, and to determine whether a MEIO program
should be implemented for the entire CPU FG Supply Chain at Intel.
To conduct an inventory reduction study, it is necessary to first determine the
study perspective. This requires carefully defining which benefits and costs are relevant
to the stakeholders. In order to provide a more conservative perspective, we've decided
to show only the accruing cost saving opportunities in addition to the current MEIO
modeling in the Box Channel Supply Chain. Our plan was to model the entire CPU FG
distribution network using the MEIO methodology and compare the result to the current
state. The Current state includes:
*
Heuristic method for 55% of the VMI products and the Direct Shipped Tray
Supply Chain
" SEIO for 45% of the VMI product
*
MEIO for the Box Channel Supply Chain
The problem we encountered with this approach was that the software cannot
model the heuristics associated with the currents state. Therefore we established a
baseline model and compared this baseline model to the MEIO what-if model, which
represents "MEIO modeling for the entire CPU FG supply chain". The baseline model
assumes that both VMI hub and Direct Shipped Tray were currently utilizing SEIO
modeling, while Box Channel is currently using MEIO modeling. The study only
considered the additional cost saving opportunity for including VMI and Direct Ship Tray
into the MEIO what-if model. Figure 37 demonstrates the baseline model and what-if
model for the Inventory Reduction study.
Lurrent btate:
No model available
Heuristic Metho-d
Snl
Baseline Model:
Heuristic
Method
piiain
Assume no savin s between current state and doln SEZO (Conservative
Single
Echelon
Optimization
Multi Echelon Optimization,
MEIO What-if Model: com
Heuristi
ceo
Mehd
are to baseline model
$ingle Echelon OptniM~ation
Figure 37: Baseline model and what-if model for the Inventory Reduction study
Model Implementation and Results
6
This Chapter reports the model implementations, the simulation result, and the
value in merging the optimization models for the VMI and the box supply networks and
expanding the models to include additional inventory locations.
Step 1: ITS vs. the off-the-shelf MEIO software in SEIO model
As previously introduced in the Step One (refer to chapter 5.2), two ITS models
and four SEIO models were constructed. The SEIO models were developed by using the
off-the-shelf MEIO software in SEIO mode. Several experiments were designed and run
for each model in 10 CPU SKUs and five global VMI hub locations.
For the two ITS models:
E = (Forecast - Actual demand)/ Forecast
-
Model 1 (ITS M1): Put 50% bias correction weighting on week T-1 forecast error
and 25% weighting on week T-2 until week T-8. Running average bias correction
methodology is currently used in the ITS VMI pilot.
-
Model 2 (ITS M2): Put even weighting on the forecast error for the past 8 weeks.
No bias correction methodology is applied. For the SEIO models using the off2
I J(Forecast-Actuals)
the-shelf MEIO software:
n-1
-
Model 1 (MEIO Ml): Apply both Modified Sigma & Kernel Smoothing
(Demand Characterization) calculation through a separate module called MEIO
cube with 2 years historical forecast error data before inputting into the off-theshelf MEIO software
-
Model 2 (MEIO M2): Used Modified Sigma only, without Kernel Smoothing
via MEIO cube with 2 years historical forecast error data
-
Model 3 (MEIO M3): Used Modified Sigma only , without Kernel Smoothing via
MEIO cube with 12 weeks historical forecast data
-
Model 4 (MEIO M4): Manually calculated SDFE (standard dev of forecast
Error), using 8 weeks of historical forecast data
Figure 38 shows the comparison results for SS targets using different SEIO and
demand characterization methodologies. All models have achieved the same service
level goals. Figure 39 shows the modeling result by products. Figure 40 & 41
demonstrate the SS target output distribution by models.
1.00
0.90
0.80
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U Average SS...
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ITS M1 ITS M2 Off-the- Off-theshelf
shelf
software software
Ml
M2
Off-theshelf
software
M3
Off-theshelf
software
M4
Figure 38: SS targets using different SEIO and demand characterization methodologies
-+-ITS M1
--
ITS M2
Off-the-shelf M1
Off-the-shelf M2
Off-the-shelf M3
-
A
A
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Figure 41: the SS target output distribution by MEIO tool
The purpose for Step One is to answer the question" whether the SS targets
generated by off-the-shelf MEIO software are similar to the SS target generated by ITS?"
According to the modeling result shown in Figure 38, overall the off-the-shelf MEIO
software yield similar SS targets compared with ITS SS targets in SEIO. However,
judging by Figure 39, there are variations among SKU/location combinations. For
example, ITS models yield significant lower SS targets than the off-the-shelf MEIO
software for product 5, 9 and 10.
Even though both ITS and SEIO use single echelon, periodic review base-stock
model, there are several differences in terms of data input:
Sigma Calculation: ITS calculates volatility of forecast error (not forecast error
itself), and the off-the-shelf MEIO software uses forecast error as input in the
form of Coefficient of Variation (COV)
08225507
* Forecast ErrorHorizon: ITS is set up to use past 8 weeks of forecast error data,
and the off-the-shelf MEIO software conducts demand characterization
methodology based on all the available historical data through a separate module
called MEIO cube
* Bias correction: ITS weights the most current week's forecast error data heavily,
and the off-the-shelf MEIO software adjusts the bias using Modified Sigma
technique
* Order-Up-To SS target setting: ITS uses "Order-up-to target" minus "forecast
over replenishment time" to generate SS target, and the off-the-shelf MEIO
software outputs SS target without calculating an order-up-to level
Let's pick a product (product 5) and conduct a deep-dive study. Table 1 below
shows the SS targets generated through ITS and MEIO off-the-shelf software for product
5's four locations. The table also shows the %delta between the targets. Since location 3
has the largest delta between the two optimization models, we will continue to examine
the forecast error in this location to understand what drove the big SS target level
difference. Figure 42 shows the forecast error distribution for the past 8 weeks in
location 3.
Location 1
Location 2
Location 3
Location4
2.40
0.86
0.51
0.73
1.63
1.89
2.13
1.19
17%
120%
63%
MEO*: Off-the-shelf MEIO software in SEIO mode
Table 1: The SS targets generated through ITS and MEIO off-the-shelf software
Forecast error distribution
past 8 weeks
0.20
(0.20)
1
7
8
(0.40)
(0.60)
(0.80)
(1.00)
Figure 42: Forecast Error distribution for product 5 in location 3
From the forecast error distribution, we know that ITS bias correction puts 75%
weight on the most current two weeks'(week7 and8) forecast error volatility. However,
for the off-the-shelf MEIO software bias correction, Modified Sigma refers to all
historical data in location 3 to figure out the most representative information. This
approach produces a daily COV of 300%. This difference in COV explains the
difference between the SS target suggested by ITS and by the off-the-shelf MEIO
software.
Upon further analysis, as shown in Figure 43, there are different forecast error
distributions across six VMI hubs, which could yield different SS target while adapting
different demand characterization and bias correction techniques. However, to conclude
which demand characterization is better suited for each hub, more data is needed for
further analysis.
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Hub6: DktrbAImofl ff-a)If
Hubs: DistributDn of tf-4~if
S
'6
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,
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455
IM
-4
45
-4
-
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-
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-1
-2
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as
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4.5
all7as
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~~5.45
*4
-
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25
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-Ij
-,I
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05
Figure 43: forecast error distributions across six VMI hubs
MEIO vs. SEIO in the Box Network
Before discussing the results from our Step Two modeling, we'd like to highlight
the result of the MEIO implementation in the Box Channel Supply Chain. We've
compared the results of the MEIO model to that of a single-echelon model on the same
network for Box Channel and found that MEIO provides about 11% inventory savings
while meeting the same service levels. The result yields less inventory with additional
pooling opportunities at cheaper and more flexible upstream locations.
Step Two: Multi Echelon Optimization
In Step Two of our modeling, we extended the SEIO model to include CW nodes
and the Box Channel Supply Chain. We ran the MEIO modeling for 3 offset months.
However, 2 out of the 4 products we chose in month 1 became End of Life (EOL)
products in the end of the monthI. When comparing the results to our baseline model
(SEIO for VMI, and MEIO for Box Channel), we saw an average of 6.44% safety stock
inventory reduction, as shown in Table 2.
Month 1
Month 2
Product 1
-7.6%
Product 2
-2.04%
Product 3
Product 4
-11.30%
-6.04%
Product 5
Month 3
-8.28%
-5.29%
-9.48%
-7.88%
-3.16%
-3.37%
Table 2: Step 2 modeling Safety Stock inventory reduction result
Step Three: Multi Echelon Optimization
In Step Three modeling, we continued to extend the Step Two Modeling
to include Tray Channel Supply Chain. When comparing the results to our baseline
model (SEIO for VMI, MEIO for Box Channel, and SEIO for Tray Channel), we saw and
average of 5.87% safety stock inventory reduction, as shown in Table 3.
Month 1
Month 2
Month 3
Product 1
-5.44%
Product 2
-0.22%
Product 3
-14.88%
-12.20%
-3.74%
Product 4
-1.26%
-6.18%
-4.65%
-5.35%
-4.80%
Product 5
Table 3: Step 3 modeling result
Inventory Reduction using different Optimization Options
Figure 44 demonstrates the inventory reduction result and it was presented in
turns of dollar saving rather than the percentage of saving in inventory levels. The result
was based on inventory reduction opportunity across CPU FG production in Q1'2011,
and the range of inventory reduction was based on different percentage assumptions of
FG products that will flow through VMI hubs in the future. Higher VMI run rate will
result in higher savings. Figure 44 shows the one-time cost reduction in inventory using
MEIO methodology across different part of Intel Supply Chains.
1) The current state assumes that both VMI hub and Direct Shipped Tray are currently
utilizing SEIO modeling, while Box Channel is currently using MEIO modeling.
2) There is a $5-23M one time saving when using ITS for VMI supply chain in
comparing to the result in the current state, as shown in (1).
3) Next, we model both VMI and Box Channel using MEIO methodology. There is an
additional $7-12M reduction in inventory opportunity in addition to using ITS for
VMI, as shown in (2).
4) Finally, we model the entire FG utilizing MEIO methodology. There is an additional
$2-3M reduction in inventory compares to the result shown in (3).
CPU FG Inventory Optimization Options
(SM)
C50
40
imntoy
uctin AvenWg
0
20
20
Curreft State
(Box ME1O only)
+ iTS for VMI
(scaled -up)
+ ME10 for VMI
+ ME0O for alil
CPU FG
Figure 44: Inventory Reduction for different Inventory Optimization Options
We also conducted study to evaluate if there is any inventory reduction
opportunity to risk-pool at VMI instead of at central warehouse (CW). We found an
additional $7-10M value as shown in Figure 45. Chapter 7 will further explore this
subject (risk pooling at VMI) through different network design scenarios. Overall, the
study was extremely helpful in communicating the additional quantitative inventory
reduction opportunities to the Intel management team.
CPU FG Inventory Optimization Options
CSM)
50
C
*2
nenumy
'
ReduciDonange
40
--
kIenta
yeductiinAveage
30
20
10
4
k.
O
Current State
(Box MEIO only)
* ITS for VMI
(scaled -up )
+MEIO for VMI
+
MEIO for all
CPU FG
+
MEIO for alli
CPU FG (Boxing at
VMIl
Figure 45: Inventory Reduction for risk-pooling at VMI hubs
Qualitative Benefit
In addition to the measurable financial benefits to Intel's bottom line, there are
other benefits from implementing the MEIO program that are difficult to value in dollar
terms. A complete analysis should supplement the formal calculation with a list of the
additional outcomes gained, valued within the organization.
First, MEIO enable supply chain managers to determine how much and where to
stage inventory in the distribution network. Secondly, MEIO provides a platform for
inventory strategy collaboration across supply chain partners. The tool provides
baselines for collaborative discussion between operations, planning, marketing, finance,
and sales teams, resulting in higher confidence and less manipulation in the forecast and
planning processes. Third, MEIO provides the baseline for future what-if comparisons
and their effect on inventory strategy. For example, we worked with Intel network design
center of excellence team to explore different network design scenarios in the last month
of our research. The result of this work will be introduced in the next chapter. In
addition, MEIO helps to refine supply chain strategies through exploring different
scenarios, including different strategic build ahead scenarios and Customer Service Level
scenarios.
The Modeling and inventory reduction results were presented to the VP and
Senior Director of Intel's Supply Chain organization in the monthly Management Review
Committee (MRC) meeting.
7
Network Redesign
Toward the end of our research, we had the pleasure to work with Intel's Network
Design Center of Excellence team (OCCND) to evaluate different network design
scenarios for the Intel CPU FG Supply Chain. The objective of the OCCND project was
to determine the efficient and cost effective CPU network by analyzing Channel Tray
Integration with Box Channel and VMI distribution network in order to provide a more
responsive and reliable solution to the Tray and Channel Customers. The goal of the
project was to analyze pooling inventory at specific network nodes using MEIO modeling
approach. Figure 46 shows the collaboration model between OCCND project and MEIO
Inventory Optimization simulations.
OCCND
Logistics Analysis
and Cost Data
OCCND
MEIO Inv
Optimization
NewokIntom
Re-Design
using MEWO
n
Figure 46: OCCND Network Re-design using MEIO approach
Figure 47 illustrates the As-Is MEIO model according to the current CPU FG
network. Figure 48 rearranges the network nodes based on the global network geography
regions. Figure 49 through Figure 53 demonstrate five different "To- Be Scenarios" for
the OCCND project.
VMI Hub
VMI
Customers
VMI Supply Chain
Central
Fan Heatsink
Solution
CPU
Warehouse
Boxed CPU
Customers
Box Channel
Supply Chain
Direct Ship
Disti Customer
Direct Ship
OEM Customer
Direct Ship
Supply Chain
Figure 47: MEIO model based on different supply chain "As-Is Scenario" designs
Region 1
Region 2
Central
Warehouse
c
V4.
V
B03
Region 3
vse
vcs
4
Region 4
Region 5
Figure 48: MEIO model based on global network "As-Is Scenario" design
To - Be Scenario 1
Direct Ship Disti and Direct Ship OEM forward stage in Regionl/2/3
Region 4 and5 remain the same
Region 1
Region 2
Central
Warehouse
Region 3
Figure 49: To- Be Scenario 1 for the OCCND project
To - Be Scenario 2
" Use VMI hub to replenish Boxing Facility for the Box Channel Supply Chain
" Region 4 and5 remain the same
Region 1
F1
Bi
Hi
V2
Region 2
Central
Warehouse
H2
V3
V4
VC
,Region
3
H3
Figure 50: To- Be Scenario 2 for the OCCND project
To - Be Scenario 3
" Use Central Warehouse to replenish Boxing Facility for the Box Channel Supply
Chain in Region 1
" Region 4 and5 remain the same
VC
Region 1
vi
F1
0Bi
-
H1
-V2
v2v
Centra
Regiion 2
-
Warehouse
Region 3
Figure 51: To- Be Scenario 3 for the OCCND project
To - Be Scenario 4
e
Using Boxing Facility to replenish Direct Ship OEM customer in Region 1
" Region 4 and5 remain the same
Region 1
vi
Bi
F
vC
V2
Central
Warehouse
Region 2
Region 3
Figure 52: To- Be Scenario 4 for the OCCND project
To - Be Scenario 5
" VMI and Boxing facilities co-locate in Region 3
" Region 4 and5 remain the same
VC
Region 1
vi
Bi
v2v
V2
Central
Warehouse
Region 2
Region 3
Figure 53: To- Be Scenario 5 for the OCCND project
We ran three different offset months to extend the data sample size. Due to the
data availability issue, different SKUs were included in each Offset month. The
simulation result in Table 4 shows an average of 3.88% total inventory investment
reduction in all 5 network scenarios based on the MEIO modeling compared to the results
from the sequential SEIO modeling.
As- Is
To-Be 1
To-Be 2
To-Be 3
To-Be 4
To-Be 5
Offset month 1
-4.19%
-4.08%
-4.29%
-3.23%
-3.16%
-3.26%
Offset Month 2
-3.95%
-3.85%
-3.73%
-3.83%
-3.75%
-3.83%
Offset month 3
-4.50%
-4.32%
-3.88%
-4.03%
-3.93%
-4.03%
Table 4: MEIO results for 5 different To-Be network scenarios
Figure 54 shows the total inventory investment for "As-Is" scenario and 5
different "To-Be" scenarios. Figure 55 shows the total stock cost for different network
design scenarios. At the first glance, "As-Is" network has lower total Inventory
Investment cost and total Stock Cost compared to any of the "To-Be" scenarios. Among
the "To-Be" scenarios, "To-Be 3" seems to have the lowest Safety Stock cost. However,
it doesn't mean that the original "As- Is" network or "To-Be 3" is the best network.
There are more factors to consider other than the inventory cost in network design.
Moreover, there are other supply chain vehicles and drivers that are still under review for
final network design proposals, including:
e
Inventory Strategy (buffers, including VMI proliferation; factory utilization;
EOL/NPI considerations)
*
Hub Strategy (new hub considerations; pooled hub)
"
Supply chain trade-offs among inventory, cycle time, service level, freight cost,
tax, and lead time
e
Replenishment Strategy
e
Supply Allocation Requirement (what Customers and how do we allocate?)
*
Logistics: product services (partialling; packaging)
Regardless, our modeling result has provided baseline for OCCND analysis from
inventory cost perspective, together with transportation cost and warehouse cost were
supplied to OCCND Network Design Model Analysis to support their upcoming 3 year
proposed network roadmap. The cooperation between OCCND analysis and inventory
optimization team would not happen were it not for the MEIO modeling project.
-----
Offset month 1
Offset
Month
2
-U-Offset month 3
As-Is
To-Be 1 To-Be 2 To-Be 3 To-Be 4 To-Be 5
Figure 54: Total Inventory Investment for different network design scenarios
-*-Offset month 1
-U-Offset month 2
-*-Offset month 3
As-Is
To-Be 1 To-Be 2 To-Be 3 To-Be 4 To-Be 5
Figure 55: Total Stock cost for different network design scenarios
8
Summary of Recommendations and Conclusions
This closing chapter provides a summary of our modeling results, summary of
recommendations, the opportunities for model growth, thoughts on MEIO's future at
Intel, and a general conclusion.
8.1
Summary of Results
According to the modeling result for VMI SEIO comparison, overall the off-the-
shelf MEIO software yields similar SS targets compared with ITS SS targets. Also,
according to our analysis, the cost savings opportunities are around 10% when we expand
the MEIO modelling to different parts of Intel Supply Chain (Box Channel, Tray Direct
Shipped). However, these savings depend heavily upon how well each supply chain is
currently managing its inventory, and how well the specific supply chain will implement
and adopt the methodology into its organization and planning processes. The case study
in CSPO shows more inventory reduction opportunities and better inventory efficient
frontier could be achieved through continuous improvement projects.
8.2
Summary of Recommendations
For a company to be responsive to its customers, its supply chain design and
processes must be aligned in order to support its desired customer service requirement.
Inventory optimization allows Intel to improve its desired customer responsiveness
through an analytical approach in determining the optimal safety stock inventory levels.
The application of using the Multi Echelon Inventory Optimization (MEIO) methodology
to set safety stock levels has tremendous potential to enable Intel to gain more control
over the levels of service that it can provide to its CPU customers, compared to its
heuristic approach of today. In addition, the MEIO approach permits Intel to better align
its overall CPU safety stock inventory mix with the customer demand and the company
strategy. Based upon the results of our research, we suggest the following
recommendations for how Intel can improve its inventory policies within different parts
of the Intel CPU FG Supply Chain.
Our recommendations will be introduced based on the project implementation
pyramid shown in Figure 56. The goal is to align tactical initiative and process
improvement recommendations with Intel's supply chain strategy and organizational
design recommendations.
Time
Commitment
Potential Benefit
Signicant
Omore
LOW F
ISignficant
Figure 56: Project Implementation Pyramid
Strategy and Organizational Design Recommendations
The new Intel supply chain strategy is to transform itself from a"Just say yes"
organization to the four pillars model shown in Figure 57, whereby Intel aims to improve
responsiveness, reduce inventory, improve forecast accuracy, and achieve delivery
performance.
The Four Pillars
Figure 57: the Four Pillars of Intel Supply Chain Strategy
In order to maintain its competiveness, Intel should consider the following
strategy and organizational design recommendations:
e
Utilize key performance indicators to measure performance and to identify
opportunities for continuous improvement within the current supply chain
design;
*
Benchmark inventory position (WOI, Inventory Turns) and Return on
Investment (ROI) on inventory investment internally (with different groups)
and externally (with competitors) to identify potential gaps and share best
practices;
*
Provide ongoing employee training of the inventory optimization knowledge
and skills to maximize the local inventory expert pool;
*
Leverage the expertise of the different parts of the supply chain network;
" Develop metrics to accurately reflect customer service level;
" Devise new managerial incentive schemes that reflect holistic supply chain
(strategic) management;
" Create new support staff, who maintain decision databases and perform
regular analysis of strategic plans.
Process Improvement Recommendations
The recommendations based on process improvement perspective are as follow:
e
Conduct process mapping for the current processes and identify areas for
continuous improvement through A3 (lean) methodology;
" Leverage best practice and develop new cross-organizational business
processes;
*
Implement an annual inventory policy review program to review existing
operations and processes and to identify cost savings alternatives;
" Develop and maintain modeling systems and decision databases that integrate
with Intel's Supply Chain Management Systems;
e
Re-engineer work processes to remove unnecessary steps;
" Utilize computer-based maintenance, inventory control and work order
management systems to streamline supply chain processes;
e
Create new processes for regular review of the Intel's strategic plans including
scenario planning and conflict resolution;
e
Create employee incentives to reward the identification and implementation of
cost reduction ideas in supply chain planning.
Tactical Initiatives Recommendations
The following tactical initiatives are suggested for initial MEIO implementation:
e
Adopt advanced optimization methodologies;
*
Select sample SKUs for CPU FG MEIO pilot project, and start collecting
historical data to set up MEIO cubes;
*
Optimize efficiency by focusing manual effort on constraints and exceptions,
and automate execution of 90%+ of the volume since currently, 90% of
volume and 10% of the SKUs are HVM products (high run rate, high volume
products);
e Determine service level for each SKU: the inventory model we developed in
chapter Five was to determine the optimal inventory policy given a specific
service level target. To maximize the expected supply chain profit, one
possible strategy is to determine service level for each SKU (Simchi-Levi,
Kaminsky 2008) [17];
*
Optimize for high service level with low touch planning and order fulfillment
methods ;
*
Select inventory policy according to product marketing strategy;
*
Develop mathematical techniques for demand characterization, such as:
MEIO VMI Cube Kernel smoothing and Modify Sigma;
*
Develop a robust data infrastructure environment, such as: Automation for
ITS;
e
Run the forecast error, demand, and characterization comparison among
different inventory optimization tools and develop inventory policies
accordingly;
e
Design uniform inventory optimization terminology and develop standard
processes across different FG supply chains;
*
8.3
Integrate MEIO into the build plan process.
Model growth
From our research, we've identified the following two areas for future MEIO
model growth opportunities at Intel:
e
Create an MEIO model for other Intel products, such as Chipset FG products
e
Figure 58 shows the process mapping for the entire Intel Supply Chain [20]. Blue
blocks show the locations for holding inventories. One of the possible model
growth areas is to expand the MEIO model upstream of the Central warehouse
(CW), such as to Assembly Test and Die Sites inventories.
[18] D. Simchi-Levi, P. Kaminsky, E. Simchi-Levi (2008), Designing and managing the Supply
Chain
Fab|/
Sort
what waters to start?
What to ship toCDP, and when?
What to box and sell?
What die to ship to what site?
I
ARDI
CDPy
TRDI
oK1
Ship to customers
Fns
How much to carry in finished goods inventory?
Direct ship to customers
What to assemble?
What to finish
Acronyms
ADI
= Assembly
Ship to customers
Die Inventory
What to forward-stage
in inventory hubs?
CDP= Consolidated Die Prep
TRDI= Tape & Reel Die Inventory
Decisions we need to make.
SFGI = Semi finished goods inventory
Manufacturing
CW = Components Warehouse
Inventory
Hub= Forward-staged inventory location
Figure 58: Intel Supply Chain Map
8.4
MEIO's Future at Intel
Currently, a MEIO special work force, including an ITS pilot team, a box channel
inventory specialist, experts in Supply Planning Integration & Analytics team, and a
Supply Chain Modelling & Solutions team, is collaborating to further explore inventory
optimization policies and optimal solutions that would be able to support much more
complex and dynamic inventory models that reflect Intel's actual inventory system more
closely. The team is currently focusing on how to achieve inventory optimization using
the same "calculator" across the CPU FG Supply Chain without negatively impacting
current business operations.
[21] S.P. Cunningham, C. Mazariegos, et al. (2010), Haas MBA discussion - inventory
management, Intel Corp.
Figure 59 illustrates the "go and no-go" criteria for implementing MEIO project
for the CPU FG Supply Chain [21] with the hypothesis that MEIO proliferation will
improve these measures. However, based on our learning, much of the value is not
achieved immediately upon implementation, but that it takes some time to see
improvement results, thus the project will move on as long as the business is not
negatively impacted.
Based on the modeling results demonstrated in our research team's effort in
promoting using analytics to set inventory policies, Intel's management team approved
resources to support a "CPU FG Inventory Optimization Roadmap" with plans to
implement expansion of the MEIO model into the VMI and the Tray Channel Supply
Chains.
Main criteria
no negative
impact on the
business
*
Measured by the
following data
Success Criteria
Inventory Effic ienc y
reduLced
Need for manual touJch
events has not increased
D control'T
IL
Workload has not
ilcreased
0
saIcontrol1-"
Figure 59: success criteria for MEIO implementation for the CPU FG Supply Chain
[22] M Mentzer (2011), CPUFG Inventory OptimizationRoadmapp, Intel Corp.
Conclusions
8.5
In conclusion, our research indicates that MEIO modeling yields significant
inventory reduction opportunities for the Intel CPU FG Supply Chain. The value
proposition for inventory optimization and using a multi echelon inventory optimization
tool is high. Even reducing the inventory savings to the lower end of the spectrum,
according to our conservative approach introduced in chapter six, will still yield very
attractive returns, making this an easy decision for Intel. However, this methodology is
not a perfect science, as it depends upon a number of assumptions, a few of which are
listed below:
e
Cost savings are not isolated to merely inventory savings but span savings such as
reduced expedite activities and increased services levels, which can lead to greater
customer satisfaction and increases in business over the longer term. However,
this number is more difficult to estimate and thus is not included in our analysis.
" Although this research project selected third party software to test our MEIO
hypothesis, we have not concluded or suggested that Intel should use any off-theshelf MEIO software product rather than building its own integrated MEIO
solution.
Although the MEIO inventory models discussed in our research rely on many
assumptions and limited data, they provided key insights into how Intel's safety stock
inventory needs at FG are potentially affected by CPU demand and demand variability,
demand characterization, lead time, and desired level of customer service. The MEIO
models lay a rich foundation for further research at Intel and may lead to changes in
Intel's strategy for setting FG safety stock levels.
The examples in our research show the value of analytic methodologies in setting
inventory strategies. However, according to our three lens analysis established in chapter
four, this type of change in thinking of using analytics in planning does not happen
overnight. The faster the project can go live, the faster the payback. However, the scope
of functionality required to meet Intel's need must be determined prior to the
implementation. MEIO by itself can be implemented in a few months, but forging
stakeholders collaboration and transforming to a learning organization that use analytics
to make better supply chain decisions can take years. To accelerate the implementation
process, the groups that experienced success in various inventory optimization projects
have responsibilities to share the best practices and lesson learned with other groups.
There are always cost savings opportunities even for a company like Intel that
excels at supply chain planning. Surprisingly, the science of inventory optimization is
relatively young at Intel, and there are still many opportunities to reduce inventory costs
and achieve better desired service level goals. Continuous improvement of existing
supply chain capabilities and further exploration of integrated inventory optimization
methodologies will enhance Intel's inventory position, provide flexibility in its supply
chain planning, and satisfy its growing customer expectations.
With its customer-focused supply chain metrics effort, Intel has made important
progress towards providing world-class customer service. The suggested inventory
policies in our research will drive additional focus and visibility on many crucial, but
often overlooked aspects of Intel's CPU FG Supply Chain. The team working on
evaluating the MEIO implementation must continue to evangelize the importance of
using analytics throughout the Intel Supply Chain and continue to gain support and
endorsement, specifically, from the executive level.
BIBLIOGRAPHY
[I] Logistics Dictionary, http://www.hksunlogistics.com/Homepage2/kclogglossary.htm
[2] Kurt L. Johnson, Inventory Modeling, Intel Technology Journal, Volume 9, Issue 3,
2005
[3] Delivering Competitive Advantage Through IT, Intel IT performance Report 20102011,
http://www.intel.com/Assets/PDF/general/IntelIT_2011 APREnglish standard.pdf
[4] http://newsroom.intel.com/community/en-za/blog/2010/10/19/intel-announces-multibillion-dollar-investment-in-next-generation-manufacturing-in-us
[5] J. Chow (2004), Analysis of New Approaches to Improve the Customer
Responsiveness of Intel's Microprocessor Supply Chain, Massachusetts Institute of
Technology
[6] L.W.Rassey (2003), Enterprise Strategy: Leveraging the Dynamics and Behaviors in
a Supply Chainfor OperationalExcellence, Massachusetts Institute of Technology
[7] B. Wieland, P. Mastrantonio, S. P. Willems, and K. G. Kempf, "Optimizing Inventory
Levels within Intel's Channel Supply Demand Operations," future issue, Interfaces.
http://www.informs.org/Pubs/Interfaces/Future-Issues
[8] C. Arnold, A FrameworkforSuccess: Moving OR ProjectsFrom Concept to
Implementation, Supply Chain Modeling & Solutions, Intel Corporation
[9] A. Mishra, C. Arnold , VMI Dynamic Safety Stock Explore, Supply Chain Modeling &
Solutions, Intel Corporation
[10] S. C. Graves, S. P. Willems (2000), "Optimizing Strategic Safety Stock Placement in
Supply Chain", Manufacturing & Service Operations Management, 2 (2000), 68-83
[11] S. C. Graves, S. P. Willems (2000) "Strategic Inventory Placement in Supply
Chains: Non-StationaryDemand", Vol. 10, No. 2, Spring 2008, pp. 278-287, 2008
INFORMS
[12] P. Bloomquist, M. Manary, A. Shihata, B. Wieland (2009), Correcting
Heterogeneous & Biased ForecastErrorat Intelfor Supply Chain Optimization, Intel
Channel Supply Demand Operation, Interfaces C2009
[ 13 ] M. Manary and S. Willems (2008), Setting Safety-Stock Targets at Intel in the
Presence ofForecastBias, Interfaces 38(2), pp. 112-122, C2008 INFORMS
[14] Bartlett, M.S., D.G. Kendall (1946), The StatisticalAnalysis of VariancesHeterogeneity and the Logarithmic Transformation, Journal of Royal Statistical Society,
p. 128-138 C1946
[15 ] J.S. Carrol (2006), Introduction to OrganizationalAnalysis: The Three Lenses, MIT
Sloan
[16] Ancona, Kochan, Scully, Van Maanen and Westney, Managing the Future:
OrganizationalBehavior and Processes.2nd Edition. South-Western College Publishing
(1999)
[ 17] L. Gerstner (2002), Who Says Elephants Can'tDance? : Leading a GreatEnterprise
Through Dramatic Change, 2002, p.77
[18] D. Simchi-Levi, P. Kaminsky, E. Simchi-Levi (2008), Designingand Managingthe
Supply Chain
[19] B. Wieland, High Level Box Inventory Target Setting Process, Intel Corp.
[20] M. Mentzer, Box MEIO SS Target Setting Flow, Intel Corp
[21] S.P. Cunningham, C. Mazariegos, et al. (2010), HaasMBA discussion - inventory
management, Intel Corp.
[22] M Mentzer (2011), CPU FG Inventory Optimization Roadmapp, Intel Corp.
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