operations research at ibm corporation: integrated supply chain...

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)


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








Summary

Questions



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)








Summary

Questions



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



OR Community and Examples

Center of ExcellenceCenter of Excellence(IBM Research)(IBM Research)

SGTGPSG

Business Units

Advanced Planning SystemsSupply/Demand Process

Network Optimization


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



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



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



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



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...



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.



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



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 %



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



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



1 5 9 13Week of Quarter

10

15

20

25

30

35

40

45

50

DO

S

LOGIC DOS



SQC charts are applied to the residuals to detect when to act



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








Summary

Questions



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



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)



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



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



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)








Summary

Questions



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



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



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%



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



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



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



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








Summary

Questions



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



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



Thank you!

Brian T. [email protected] [email protected]

operations research at ibm corporation: integrated supply chain...

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