Integrated Supply Chain
IBM Research, Delhi India | April 2006 © 2006 IBM Corporation
Operations Research at IBM Corporation:Integrated Supply Chain Perspective
Dr. Brian Thomas EckDirector of Strategy, IT & Business Transformation, IBM International Holdings, Inc. -- Singapore BranchIntegrated Supply Chain (ISC)
Integrated Supply Chain
Operations Research at IBM © 2006 IBM Corporation2
Today’s DiscussionIntroduction: Supply Chain Management & IBM’s Integrated Supply Chain
Enablers of Successful OR Application:
Demand and Support for OREmbedding in OperationsDifferentiated Roles
Examples of OR at IBM:
Simulation / Inventory Optimization ExampleAvailable to Sell: Resource Allocatione-Auctions Analysis
Summary
Questions
Integrated Supply Chain
Operations Research at IBM © 2006 IBM Corporation3
Today’s DiscussionIntroduction: Supply Chain Management & IBM’s Integrated Supply Chain
Enablers of Successful OR Application:
Demand and Support for OREmbedding in OperationsDifferentiated Roles
Examples of OR at IBM:
Simulation / Inventory Optimization ExampleAvailable to Sell: Resource Allocatione-Auctions Analysis
Summary
Questions
Integrated Supply Chain
Operations Research at IBM © 2006 IBM Corporation4
What is Supply Chain Management?
Supply ChainSupply ChainPartnersPartnersSuppliers
Contract Mfg.
3PL’s
Supply Chain Managers
Exchanges SellSellPlanPlan SourceSource DeliverDeliverMakeMake
CustomerCustomer
ChannelsChannelsRetail
Direct
B2B
Resellers
4 Flows: Product, Information, Work, Cash4 Flows: Product, Information, Work, Cash
DesignDesign
ReturnReturn
What is ISC in IBM?19,000 employees in 61 countries (200+ with Ph.D.s)Responsible for USD$ 40 Billion of IBM cost and expenseShipping 1 Billion kilograms of product annuallyPart of the larger Integrated Operations team
Transformation credited with savings for IBM in excess of USD $20 Billion over first three years (2002-2004)
Integrated Supply Chain
Operations Research at IBM © 2006 IBM Corporation5
Today’s DiscussionIntroduction: Supply Chain Management & IBM’s Integrated Supply Chain
Enablers of Successful OR Application:
Demand and Support for OREmbedding in OperationsDifferentiated Roles
Examples of OR at IBM:
Simulation / Inventory Optimization ExampleAvailable to Sell: Resource Allocatione-Auctions Analysis
Summary
Questions
Integrated Supply Chain
Operations Research at IBM © 2006 IBM Corporation6
Enabling OR Application within Industry
Demand for OR ApplicationCompetitive PressuresMaturity in Organizational ImprovementAwareness of Methods, Skill Base of EmployeesBusiness Improvement Process and Structure
Support for OR ApplicationVirtual CommunityApplication Domain SupportCenter of Excellence Support
Effectiveness of OR ApplicationBusiness insights and OR expertiseEmbedding in Business Processes
Integrated Supply Chain
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OR Community and Examples
Center of ExcellenceCenter of Excellence(IBM Research)(IBM Research)
SGTGPSG
Business Units
Advanced Planning SystemsSupply/Demand Process
Network Optimization
Integrated Supply Chain
InformalNetwork
Block Scheduling for Classrooms and Instructors
•Improve utilization and decrease costs•Penalty function, MIP (using OSL, C++)•Used through 7 cycles (over 4+ years)•Model size:
62,010 columns 90,002 rows (273,392 nonzeros)
•Well-accepted, will spread to Europe
MD Network Design•Logic packaging vendor offered alternate locations•Spreadsheet model, "What's Best" MIP•$650K savings identified•Extensions to full logic network and other products
SSD Sourcing•Manufacturing Strategy group•Assigning flows from multiple manufacturing locations to multiple customer sites•LP and MIP
Integrated Supply Chain
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Embedding tools within processes for decision support
Investment Matrix, FEAT•Corporate-Wide Supply/Demand Process•Interlock: Supply Support Decision•Unbiased Forecast•Alternative Perspectives•Supply Support Decision•Risk (lost sales versus inventory)•Maximize Expected PTI
Design for Logistics•Enable Designers At Decision Time•Consider Total Product Cost•Heuristics and Model•Inventory Targeting in an Assemble-To-Order Environment
Simulation to Model S390 Supply Chain•Express Targets as DOS by Commodity•Weekly Review of Actuals versus Targets
Integrated Supply Chain
Operations Research at IBM © 2006 IBM Corporation9
Most OR Practice Successes in IBM Leveraged Multiple Roles
Depth in OR Thinking
Very deep
Shallow
Literacy in IBM’s business
Deep and Broad
Little to None
General(broadly familiar)
AcademiaIBM Research
ISC Technical Leaders
ISC Practitioners & Executives
Integrated Supply Chain
Operations Research at IBM © 2006 IBM Corporation10
North
America
95% NA suppliers
Poughkeepsie53.5% Volume
20 CDCs
LatinAmerica
Brazil
Sumare
EuropeMiddle EastAfrica
95% European Suppliers
(Less MCM)
CDCs
Montpellier46.5% Volume
FujisawaCDCs
Ireland
Fabricated Parts
.
Japan
AsiaPacific
Key Strategy: Fab/FulfillmentSimulation modeling to explore behavior of BTP/CTO supply chain
70% of business transacted on IBM serversCyclic demand production challengesInventory management: High $ parts by commodity
S390 Inventory Analysis
Integrated Supply Chain
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S390 Simulation Project: Inventory StudyFulfillment Center
Feature1
BOX (MTM)
power
memory
.
.
.
Fabrication
MCMs
FeatureK
When managing a measurement, we need to know where we expect it to be...
Integrated Supply Chain
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ObjectivesSteps
•Confirm objectives•Build model•Gather data•Cleanse data•Validate model•Test hypotheses•Draw conclusions
•Analytical•Business implications
•Present, convince, implement
1. Determine Days of Supply (DOS) levels/targets for high dollar parts, for the "as is" CMOS supply chain.
2. Assess how improvements to feature ratio forecasting accuracy would impact CMOS inventory turns.
3. Establish the impact on required CMOS inventory of fab/fulfillment versus consumptive pull replenishment.
Integrated Supply Chain
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Fulfillment Center BOMs:Replenishment Lead Times:
Fab BOMs:
ForecastsBox/MES:
Serviceability:
Testing Lead Times --Danielle FieldsDave Pearson (IE)Debby CarelliDon GunvalsenJeff Benedict
Testing Yields/Usage --Gisela Hetherington (MCMs)Dave Pearson (general)Mae Ling Chen (non MCM Logic)Kai Wong (Memory)Winston Ralph/Mark Coq (power)
EMLS Extract Fed by SAPIdentifying Feature P/Ns Larry FoxIdentifying FC P/Ns Don Gunvalsen Jeff Benedict
Monthly Forecasts Larry Fox's spreadsheets SCE files (20-day process)Monthly Actuals COATS data extracts (custom SQL)
Jim CuratoloBrian KuhnWendy SellRoger Tsai/Pete Weber
Bethesda DB CAD=CRAD for CRAD within 3 weeks (80%) 100% otherwise (custom SQL)
EMLS Extract Found incorrect (empty system LTs) Debby Carelli Denny SlocumSAP Pull vs Non-pullIn Practice Nick Kulick (pwr) Mike O'Dowd (DASD) Sue Cozalino Ron Shields
Transportation Lead Times:Jeff Schmitt
Integrated Supply Chain
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Validation against historical actuals, builds confidence in the model
04/20/9805/04/9805/18/9806/02/9806/23/98
Average
October
May to
11/02/9811/09/9811/16/98
Date
0
10
20
30
40
50
60
70
Mill
ions
$A
vera
ge In
vent
ory
PWR_SUPPPWR_MECHMEMORYLOGIC
CMOS AVG High Dollar Inventory
Actuals Simulation$0
$10
$20
$30
$40
$50
$60
$70
Mill
ions
Ave
rage
Inve
ntor
y of
Hig
h D
olla
r IM
PA
CT
Par
ts
PWR_SUPPPWR_MECHMEMORYLOGIC
Validation of Simulation ModelCMOS: May through October 1998
LOGICLOGIC 97 %97 %MEMORYMEMORY 96 %96 %PWR_MECHPWR_MECH 86 %86 %PWR_SUPPPWR_SUPP 94 %94 %
OVERALLOVERALL 95 %95 %
Integrated Supply Chain
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Multiple Replications, Demand Mix and VariationTo Test Effect of Fab/Fulfillment (BTP/CTO)
15
20
25
30
35
40
45
50
Day
s of
Sup
ply
LOGIC DOS forpDOS2QA
Three Replications
Integrated Supply Chain
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Patterns emerged for each commodity
1 3 5 7 9 11 13Week of Quarter
10
15
20
25
30
35
40
45
50
DO
S
LOGIC DOS
1 3 5 7 9 11 13Week of Quarter
10
20
30
40
50
60
DO
S
MEMORY DOS
1 2 3 4 5 6 7 8 9 10 11 12 13Week of Quarter
10
20
30
40
50
60
DO
S
PWR_MECH DOS
1 2 3 4 5 6 7 8 9 10 11 12 13Week of Quarter
10
15
20
25
30
35
40
45
DO
S
PWR_SUPP DOS
Integrated Supply Chain
Operations Research at IBM © 2006 IBM Corporation17
1 5 9 13Week of Quarter
10
15
20
25
30
35
40
45
50
DO
S
LOGIC DOS
Integrated Supply Chain
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SQC charts are applied to the residuals to detect when to act
Integrated Supply Chain
Operations Research at IBM © 2006 IBM Corporation19
As Is versus Consumptive Pull$0
$10
$20
$30
$40
$50
$60
$70
Mill
ions
Inve
ntor
y
PWR_SUPP
PWR_MECH
MEMORY LOGIC
Inventory Cost of Fab/Fulfillment
Base All MCM Logic MEM Mech Supp Actuals$0
$10
$20
$30
$40
$50
$60
$70
Mill
ions
Inve
ntor
y
PWR_SUPP
PWR_MECH
MEMORY LOGIC
Lead Time Reduction: Consumptive Pull
30%13%
11% 6% 6% 6%
Sensitivity AnalysisSensitivity AnalysisUsing consumptive pull model Using consumptive pull model (max savings)(max savings)Using fab/fulfillment model Using fab/fulfillment model (much less sensitive)(much less sensitive)
Model run with consumptive pull, Model run with consumptive pull, optimized reorder pointsoptimized reorder points
No Capacity ConstraintsNo Capacity ConstraintsQuantifies cost of strategy/cost Quantifies cost of strategy/cost of skewof skew29% more expensive overall 29% more expensive overall 74% savings possible for 74% savings possible for PWR_MECHPWR_MECH
Additional Observations
Integrated Supply Chain
Operations Research at IBM © 2006 IBM Corporation20
Today’s DiscussionIntroduction: Supply Chain Management & IBM’s Integrated Supply Chain
Enablers of Successful OR Application:
Demand and Support for OREmbedding in OperationsDifferentiated Roles
Examples of OR at IBM:
Simulation / Inventory Optimization ExampleAvailable to Sell: Resource Allocatione-Auctions Analysis
Summary
Questions
Integrated Supply Chain
Operations Research at IBM © 2006 IBM Corporation21
Manufacturing: "We have excess parts inventory."
MFI/FFBM
Features
Planning Items(MTMs, Upgrades, MES loose piece)
Sales: "What do we have in excess?"
Available-to-Sell (AtS)
•Determining how excess parts inventory can be positioned with marketing / sales as finished goods (saleable) product, to condition demand and consume the excess
•Optimization aspect appears as a straightforward Linear Programming application
•Production Planning LP tool already developed in IBM Research (WIT/SCE)Enterprise implosion problem:380K resources, 185K operations, 84K demands, 800K flows, 52 periods (and this doesn't include capacity)
LP formulation: 57 M variables, 24 M constraints, 118 M nonzeros
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Data Issues Dominate Industrial Problem Solving
2. EC causing expired effectivity dates (bill present but no demand on parts)
1.A) Excess at a component level unknown to Manufacturing
1.B) Card bills missing (outsourced)
3. Bills missing entirely for parts in excess
ETIS relates planning items to p/n via history (ratios)
5. Which parts are called out by which features is order-dependent
4. Inadequate history causes artificial 'zero' ETIS ratios
SG ATS/ 03rev7/22/01
6.“Penny parts”6000 of these
7.C-source (consigned)
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Feature Translation: creating pseudo bills of material and appending to existing structures
fc 1234 for model ABC
Parts unique to ABC
New bill structures to be added...
...connect to existing bill structures
fc 1234 for all models
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Device Code to Bill Structure ExampleEXAMPLE (d/c 0014)9406;170;Q01;0014;00075G2720;***;(9401.R1)ESP -DOCS9406;500;Q01;0014;00017G0071;***;(9406.R1)PRE-GA PUBS9406;510;Q01;0014;00017G0071;***;(9406.R1)PRE-GA PUBS9406;530;Q01;0014;00017G0071;***;(9406.R1)PRE-GA PUBS9406;50S;Q01;0014;00017G0071;***;(9406.R1)PRE-GA PUBS9406;53S;Q01;0014;00017G0071;***;(9406.R1)PRE-GA PUBS9406;***;Q01;0014;00046G0063;***;(MILL.R1)PRE-GA PUBS9406;***;Q01;0014;00017G0071;***;(CONH.R1)PRE-GA PUBS
75G2720 17G0071 46G0063
0014_170 0014
_ML6
0014
_50S
0014
_530
0014
_510
0014
_500
0014
_53S
0014_M10
On all models other than those listed, device code 0014 requires either one unit per of 46G0063 (for models S1*,S20,60*,62*, and 720) or one unit per of 17G0071 (for models 840 and SB3).*
On model 170, 0014 requires only 1 per of 75G2720On models 500, 510, 530, 50S, and 53S, only 1 per of part 17G0071 is required.
This is expressed as follows in the SCE format:
"0014_9406ML6";"0000017G0071";1"0014_9406M10";"0000046G0063";1"0014_9406170";"0000075G2720";1"0014_9406500";"0000017G0071";1"0014_9406510";"0000017G0071";1"0014_9406530";"0000017G0071";1"0014_940650S";"0000017G0071";1"0014_940653S";"0000017G0071";1
*using rel3.mfc (bld level) MTMODCNV.R file
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Additional Observations
•Objective Function•Dependent on sales price (to maximize profit) but prices unavailable•Use a ‘scaling factor’ k and
maximize k *(excess consumed) – sum (cost of additional purchases)
k small k large
Minimize add’l payment Maximize using up excess
•Business process design/implementation equally key to success
•Results / Timeline• Jan 2002: Identified problem, data challenges, modeling approach• April 2002: programmed prototype; simple features only• June 2002: production version including simple+1, simple+2 f/c parser• Patent filing late 2002• In 2002, component inventory moved = USD$ 72 million• In 2003, component inventory moved = USD$ 40 million•“Hardened” and offered commercially to clients (first sale 2005)
Integrated Supply Chain
Operations Research at IBM © 2006 IBM Corporation26
Today’s DiscussionIntroduction: Supply Chain Management & IBM’s Integrated Supply Chain
Enablers of Successful OR Application:
Demand and Support for OREmbedding in OperationsDifferentiated Roles
Examples of OR at IBM:
Simulation / Inventory Optimization ExampleAvailable to Sell: Resource Allocatione-Auctions Analysis
Summary
Questions
Integrated Supply Chain
Operations Research at IBM © 2006 IBM Corporation27
e-Auctions to Exploit price/quantity Relationships
Fixed-Price versus Auctions Selling
Reason Not to Auction NewProducts
Auctioning Complements BAU
price
quantity
price
quantity
p0
Q0
price
quantity
p0
Q0
forecasting demand (BAU)
forecasting price (auction)
price
quantity
p0
Q0
Integrated Supply Chain
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Investigation of Product Differences and Value of Auctions
Some Products are a Better Fit for AuctioningKey Driver: How unique is the purchase across customers?
High unit volumes
Low unit volumes
DRAMs
Custom Logic (ASICs)
RS6000
Common function, product across many customers
Unique function, product for each customer
HDDs
PSG
AS/400
S390
PSDSSD
Key Question: Is it more efficient to have inventory and idle factory capacity, or to sell the product at whatever price the market will bear?
Amenable Amenable to Auctionto Auction
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Auctioning as an Additional ChannelThree Key Parameters Drive the Dynamics
Percentage of total revenue targeted through the auction channelPercentage of current channel demand cannibalized by auction salesPercentage auction price effectiveness
These Key Inputs Are UnknownAble to be estimated from piloting
Cannibalization and auction price effectiveness are outcomes% revenue targeted translates through the other parameters into resultant auction revenue
Brand- (product-) specificApproach
Estimate reasonable average values for these parameters, and then test sensitivity across a range of values by randomly simulating different combinations.
Total Revenue
Auction Revenue =targeted revenue times price effectiveness
Cannibalized Revenue
Targeted Auction Revenue
% Cannibalization
Price Effectiveness
10%15%5%
82.5%95%70%
57.5%90%25%
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e-Auctions Appear to be an Attractive Channel
Although auction price effectiveness is less than 100% (70% to 95%), profit margins improve by using free capacity (leveraging fixed cost across more revenue) and from selling excess inventory.
The incremental profits and revenues are fairly robust across a wide range of cannibalization and auction prices:
mill
ion
$
Revenue potential
mill
ion
$
Net change in profit
Integrated Supply Chain
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Auctioning versus "Working Off" in Supply ChainDecisions
Recognize an "Excess Supply" SituationDetermine Whether to Auction or Work OffDetermine How to Auction
Timing
Relative to Building the Box
Relative to Calling the Missed Demand
Factors:Component part leadtime k periodsPrice takedown Pk
Cost of inventory cCost of production Selling expense: usual channel(s) sSelling expense: auctions aWaiting penalty wk
Price received through auctioning product (random variable) Pa
BeforeMissingForecast
AfterMissingForecast
BeforeBuildingProduct
After Building Product
Auction if we can get at least Pa ,so that the margin is at least what we could get by working it off through the supply chain
Pa - c - - a m Pk - c - - s - wk
...ORHERE
STARTHERE
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0 1 2 3 k k+1-1
Decide on and/or Conduct Auctioning
Recognize"Excess"
Declare "on-hand"and net out ofdemand
Demand Plansbooked (reforecastand netted on-hand)
Requirements Passedto Suppliers
Materials Received fromSuppliers (leadtime=k periods)
After Missing Forecast andBefore Building Product
Auction at minimum opening bid of Pa = Pk - s + a - wk
where wk = cki if I have already purchased the component inventory
Integrated Supply Chain
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Difference Equation ApproachSell through the auction, and sell through working off, with certain probabilities (< 1):Let Ci = maximum return given one unit (box) of excess supply in period i, then
IssuesAuction (market) price distributions may be poorly understoodProbabilities (of selling one item at price Pk in period i+k) unknown
Ci = max {maxr Pr(Pa m r){E(Pa | Pa m r) - c - - a } + Pr(Pa < r)(w1 + C i+1 ),
(Pk+i - c - - s - wk+i )Pr(sell it in period i+k for Pk) + ( C i+k - wk+i ) Pr(don't sell it)}
and Clast = scrap value (for some well-defined period in the future)
Then, solve for C0
Dynamic Programming Approach to the Auctioning Decision
Integrated Supply Chain
Operations Research at IBM © 2006 IBM Corporation34
Today’s DiscussionIntroduction: Supply Chain Management & IBM’s Integrated Supply Chain
Enablers of Successful OR Application:
Demand and Support for OREmbedding in OperationsDifferentiated Roles
Examples of OR at IBM:
Simulation / Inventory Optimization ExampleAvailable to Sell: Resource Allocatione-Auctions Analysis
Summary
Questions
Integrated Supply Chain
Operations Research at IBM © 2006 IBM Corporation35
Enabling OR Application within Industry
Depth in OR Thinking
Very deep
Shallow
Literacy in IBM’s business
Deep and Broad
Little to None
General(broadly familiar)
AcademiaIBM Research
ISC Technical Leaders
ISC Practitioners & Executives
Function embedded in
S/W instantiation
“Wrapper” Concept:
OSL / WIT / SCE / AtSDES / BPMAT / AMT
Integrated Supply Chain
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In Summary
Supply chain management is a dynamic, exciting, growing application area
Data management (gathering, cleansing, workarounds) is a critical success factor and often consumes most project resources
Ingredients for success include:► Readiness (maturity, awareness, skill base, burning platform)
► Support community
► Combined OR expertise with business insight– Differentiated roles helpful
Integrated Supply Chain
Operations Research at IBM © 2006 IBM Corporation37
Thank you!
Brian T. [email protected] [email protected]