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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 Branch

Integrated Supply Chain (ISC)


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Operations Research at IBM 2006 IBM Corporation2

Todays DiscussionIntroduction: Supply Chain Management & IBMs 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


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Summary

Questions


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Summary

Questions


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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


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OR Community and Examples

Center of ExcellenceCenter of Excellence

(IBM Research)(IBM Research)

SGTGPSG

Business Units

Advanced Planning Systems

Supply/Demand Process

Network Optimization


Informal

Network

Block Scheduling for Classroomsand Instructors

Improve utilization and decrease costsPenalty function, MIP (using OSL, C++)Used through 7 cycles (over 4+ years)Model size:

62,010 columns90,002 rows (273,392 nonzeros)

Well-accepted, will spread to Europe

MD Network DesignLogic packaging vendor offered alternate locationsSpreadsheet model, "What's Best" MIP$650K savings identifiedExtensions to full logic network and other products

SSD SourcingManufacturing Strategy groupAssigning flows from multiple manufacturinglocations to multiple customer sites

LP and MIP


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Embedding tools within processes for decision support

Investment Matrix, FEATCorporate-Wide Supply/Demand ProcessInterlock: Supply Support Decision

Unbiased ForecastAlternative PerspectivesSupply Support DecisionRisk (lost sales versus inventory)Maximize Expected PTI

Design for LogisticsEnable Designers At Decision TimeConsider Total Product CostHeuristics and Model

Inventory Targeting in an Assemble-To-Order Environment

Simulation to Model S390 Supply ChainExpress Targets as DOS by CommodityWeekly Review of Actuals versus Targets


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Most OR Practice Successes in IBM Leveraged Multiple Roles

Depth in ORThinking

Very deep

Shallow

Literacy in IBMs business

Deep andBroad

Little toNone

General(broadly familiar)

Academia

IBM Research

ISC TechnicalLeaders

ISC Practitioners& Executives


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North

America

95% NAsuppliers

Poughkeepsie53.5% Volume

20 CDCs

LatinAmerica

Brazil

Sumare

Europe

Middle EastAfrica

95% EuropeanSuppliers

(Less MCM)

CDCs

Montpellier46.5%Volume

Fujisawa

CDCs

Ireland

FabricatedParts

.

Japan

AsiaPacific

Key Strategy: Fab/Fulfillment

Simulation modeling to explorebehavior of BTP/CTO supply chain

70% of business transacted on IBM servers

Cyclic demand production challenges

Inventory management: High $ parts by commodity

S390 Inventory Analysis


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S390 Simulation Project: Inventory StudyFulfillment Center

Feature1

BOX(MTM)

power

memory

.

.

.

Fabrication

MCMs

FeatureK

When managing a measurement, we need toknow where we expect it to be...


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ObjectivesSteps

Confirm objectives

Build modelGather dataCleanse dataValidate model

Test hypothesesDraw conclusionsAnalyticalBusiness 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 featureratio forecasting accuracy would impactCMOS inventory turns.

3. Establish the impact on required CMOSinventory of fab/fulfillment versusconsumptive pull replenishment.


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Fulfillment Center BOMs:Replenishment

Lead Times:

Fab BOMs:

ForecastsBox/MES:

Serviceability:

Testing Lead Times --Danielle Fields

Dave Pearson (IE)

Debby Carelli

Don Gunvalsen

Jeff 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 ExtractFed by SAP

Identifying Feature P/NsLarry Fox

Identifying FC P/NsDon Gunvalsen

Jeff Benedict

Monthly ForecastsLarry Fox's spreadsheets

SCE files (20-day process)

Monthly Actuals

COATS data extracts

(custom SQL)

Jim Curatolo

Brian Kuhn

Wendy Sell

Roger Tsai/Pete Weber

Bethesda DB

CAD=CRAD for CRADwithin 3 weeks (80%)

100% otherwise

(custom SQL)

EMLS ExtractFound incorrect

(empty system LTs)

Debby CarelliDenny Slocum

SAPPull vs Non-pull

In PracticeNick Kulick (pwr)

Mike O'Dowd (DASD)

Sue CozalinoRon Shields

Transportation Lead Times:Jeff Schmitt


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Validation against historical actuals, builds confidence inthe model

04/20/98

05/04/98

05/18/98

06/02/98

06/23/98

Average

October

Mayto

11/02/98

11/09/98

11/16/98

Date

0

10

20

30

40

50

60

70

Millions$

AverageIn

ventory

PWR_SUPP

PWR_MECHMEMORY

LOGIC

CMOS AVG High Dollar Inventory

Actuals Simulation

$0

$10

$20

$30

$40

$50

$60

$70

Millions

AverageInventory

ofHighDollarIMPACT

Parts

PWR_SUPP

PWR_MECH

MEMORY

LOGIC

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 %


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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

DaysofSupply

LOGIC DOS forpDOS2QA

Three Replications


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Patterns emerged for each commodity

1 3 5 7 9 11 13

Week of Quarter

10

15

20

25

30

35

40

45

50

DOS

LOGIC DOS

1 3 5 7 9 11 13

Week of Quarter

10

20

30

40

50

60

DOS

MEMORY DOS

1 2 3 4 5 6 7 8 9 10 11 12 13

Week of Quarter

10

20

30

40

50

60

DOS

PWR_MECH DOS

1 2 3 4 5 6 7 8 9 10 11 12 13

Week of Quarter

10

15

20

25

30

35

40

45

DOS

PWR_SUPP DOS


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1 5 9 13

Week of Quarter

10

15

20

25

30

35

40

45

50

D

OS

LOGIC DOS


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


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As Is versus Consumptive Pull

$0

$10

$20

$30

$40

$50

$60

$70

Millions

Inventory

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

Million

s

Invento

ry

PWR_SUPP

PWR_MECH

MEMORY LOGIC

Lead Time Reduction: Consumptive Pull

30%13%

11% 6% 6% 6%

Sensitivity AnalysisSensitivity AnalysisUsing consumptive pull modelUsing consumptive pull model

(max savings)(max savings)

Using fab/fulfillment modelUsing 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 Constraints

Quantifies cost of strategy/costQuantifies cost of strategy/costof skewof skew

29% more expensive overall29% more expensive overall

74% savings possible for74% savings possible for

PWR_MECHPWR_MECH

Additional Observations


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Summary

Questions


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Manufacturing:"We have excessparts 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 / salesas finished goods (saleable) product, to condition demand and consume the excess

Optimization aspect appears as

a straightforward LinearProgramming application

Production Planning LP toolalready 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 expiredeffectivity dates (billpresent but no demandon parts)

1.A) Excess at acomponent level unknownto Manufacturing

1.B) Card bills missing

(outsourced)

3. Bills missingentirely for parts in

excess

ETIS relates planningitems to p/n viahistory (ratios)

5. Which parts arecalled out by whichfeatures isorder-dependent

4. Inadequate historycauses artificial 'zero'ETIS ratios

SG ATS/ 03

rev7/22/01

6.Penny parts6000 of these

7.C-source(consigned)


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Feature Translation: creating pseudo bills of material and appending

to existing structuresfc 1234 formodel ABC

Parts uniqueto ABC

New billstructures tobe added...

...connect toexisting billstructures

fc 1234 forall models


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Device Code to Bill Structure Example

EXAMPLE (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 PUBS

9406;***;Q01;0014;00017G0071;***;(CONH.R1)PRE-GA PUBS

75G2720 17G0071 46G0063

0014_1700014_

ML

6

0014_

50

S

0014_

53

0

0014_

51

0

0014_

50

0

0014_

53

S

0014_M10

On all models other than those listed, device code0014 requires either one unit per of 46G0063 (formodels S1*,S20,60*,62*, and 720) or one unit per of17G0071 (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 FunctionDependent on sales price (to maximize profit) but prices unavailableUse a scaling factor k and

maximizek *(excess consumed) sum (cost of additional purchases)

k small k large

Minimize addl 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 millionHardened and offered commercially to clients (first sale 2005)


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Summary

Questions


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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


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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 unitvolumes

Low unitvolumes

DRAMs

Custom Logic(ASICs)

RS6000

Common function,product acrossmany customers

Unique function,product for eachcustomer

HDDs

PSG

AS/400

S390

PSDSSD

Key Question: Is it more efficient to have inventoryand idle factory capacity, or to sell the product atwhatever price the market will bear?

AmenableAmenable

to Auctionto Auction


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Auctioning as an Additional Channel

Three Key Parameters Drive the DynamicsPercentage of total revenue targeted through the auction channel

Percentage of current channel demand cannibalized by auction sales

Percentage auction price effectiveness

These Key Inputs Are Unknown

Able to be estimated from pilotingCannibalization and auction price effectiveness are outcomes

% revenue targeted translates through the other parameters into resultant auction revenue

Brand- (product-) specific

ApproachEstimate reasonable average values for these parameters, and then testsensitivity across a range of values by randomly simulating differentcombinations.

Total Revenue

Auction Revenue =

targeted revenue times

price effectiveness

CannibalizedRevenue

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 costacross more revenue) and from selling excess inventory.

The incremental profits and revenues are fairly robust across a widerange of cannibalization and auction prices:

m

illion$

Revenue potential

m

illion$

Net change in profit


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Auctioning versus "Working Off" in Supply Chain

DecisionsRecognize an "Excess Supply" SituationDetermine Whether to Auction or Work Off

Determine How to Auction

Timing

Relative to Building the Box

Relative to Calling the Missed Demand

Factors:

Component part leadtime kperiods

Price takedown Pk

Cost of inventory c

Cost of production Selling expense: usual channel(s) s

Selling expense: auctions a

Waiting penalty wk

Price received through auctioning product (random variable) Pa

Before

Missing

Forecast

After

Missing

Forecast

Before

Building

Product

After

Building

Product

Auction if we can get at least Pa ,so that the margin is at least what we couldget by working it off through the supply chain

Pa - c - - a m Pk- c - - s - wk

...OR

HERE

START

HERE


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0 1 2 3 k k+1-1

Decide onand/or Conduct

Auctioning

Recognize

"Excess"

Declare "on-hand"and net out of

demand

Demand Plans

booked (reforecast

and netted on-hand)

Requirements Passed

to Suppliers

Materials Received from

Suppliers (leadtime=k periods)

After Missing Forecast and

Before Building Product

Auction at minimum opening bid of Pa = Pk - s + a - wkwhere wk = cki if I have already purchased the component inventory

I t t d S l Ch i


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Difference Equation ApproachSell through the auction, and sell through working off, with certainprobabilities (< 1):

Let Ci = maximum return given one unit (box) of excess supply inperiod i, then

IssuesAuction (market) price distributions may be poorly understood

Probabilities (of selling one item at price Pk in period i+k) unknown

Ci = max {maxrPr(Pa m r){E(Pa | Pa m r) - c - - a } + Pr(Pa < r)(w1 + Ci+1 ),(Pk+i - c - - s - wk+i )Pr(sell it in period i+kfor Pk) + ( Ci+k - wk+i ) Pr(don't sell it)}

andClast = scrap value (for some well-defined period in the future)

Then, solve for C0

Dynamic Programming Approach to the Auctioning Decision

I t t d S l Ch i


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Todays Discussion

Introduction: Supply Chain Management & IBMs Integrated Supply Chain





Summary

Questions



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Enabling OR Application within Industry

Depth in OR

Thinking

Very deep

Shallow

Literacy in IBMs business

Deep andBroad

Little toNone

General(broadly familiar)

Academia

IBM Research

ISC Technical

Leaders

ISC Practitioners& Executives

Functionembedded in

S/W instantiation

Wrapper Concept:

OSL / WIT / SCE / AtS

DES / BPMAT / AMT



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In Summary

Supply chain management is a dynamic, exciting, growingapplication area

Data management (gathering, cleansing, workarounds) is acritical 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


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