Integrated Supply Chain
Operations Research at IBM Corporation:
Integrated Supply Chain Perspective
Dr. Brian Thomas Eck
Director of Strategy, IT & Business Transformation,
IBM International Holdings, Inc. -- Singapore Branch
Integrated Supply Chain (ISC)
IBM Research, Delhi India | April 2006
© 2006 IBM Corporation
Integrated Supply Chain
Today’s Discussion
Introduction: Supply Chain Management & IBM’s Integrated Supply Chain
Enablers of Successful OR Application:
Demand and Support for OR
Embedding in Operations
Differentiated Roles
Examples of OR at IBM:
Simulation / Inventory Optimization Example
Available to Sell: Resource Allocation
e-Auctions Analysis
Summary
Questions
2
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
Today’s Discussion
Introduction: Supply Chain Management & IBM’s Integrated Supply Chain
Enablers of Successful OR Application:
Demand and Support for OR
Embedding in Operations
Differentiated Roles
Examples of OR at IBM:
Simulation / Inventory Optimization Example
Available to Sell: Resource Allocation
e-Auctions Analysis
Summary
Questions
3
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
What is Supply Chain Management?
Customer
Partners
Supply Chain
Suppliers
Contract Mfg.
Channels
4 Flows: Product, Information, Work, Cash
3PL’s
Retail
Direct
Supply Chain Managers
Exchanges
Design
Plan
Source
Make
Deliver
B2B
Sell
Resellers
Return
What is ISC in IBM?
ƒ
ƒ
ƒ
ƒ
19,000 employees in 61 countries (200+ with Ph.D.s)
Responsible for USD$ 40 Billion of IBM cost and expense
Shipping 1 Billion kilograms of product annually
Part of the larger Integrated Operations team
ƒ Transformation credited with savings for IBM in excess of USD
$20 Billion over first three years (2002-2004)
4
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
Today’s Discussion
Introduction: Supply Chain Management & IBM’s Integrated Supply Chain
Enablers of Successful OR Application:
Demand and Support for OR
Embedding in Operations
Differentiated Roles
Examples of OR at IBM:
Simulation / Inventory Optimization Example
Available to Sell: Resource Allocation
e-Auctions Analysis
Summary
Questions
5
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
Enabling OR Application within Industry
Demand for OR Application
ƒCompetitive Pressures
ƒMaturity in Organizational Improvement
ƒAwareness of Methods, Skill Base of Employees
ƒBusiness Improvement Process and Structure
Support for OR Application
ƒVirtual Community
ƒApplication Domain Support
ƒCenter of Excellence Support
Effectiveness of OR Application
ƒBusiness insights and OR expertise
ƒEmbedding in Business Processes
6
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
OR Community and Examples
Informal
Network
Center
Center of
of Excellence
Excellence
(IBM
(IBM Research)
Research)
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
Integrated Supply Chain
Advanced Planning Systems
Supply/Demand Process
Network Optimization
Business Units
PSG
7
TG SG
Operations Research at IBM
•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
© 2006 IBM Corporation
Integrated Supply Chain
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
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Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
Most OR Practice Successes in IBM Leveraged Multiple Roles
Very deep
Academia
IBM Research
ISC Technical
Leaders
Depth in OR
Thinking
ISC Practitioners
& Executives
Shallow
Little to
None
General
(broadly familiar)
Deep and
Broad
Literacy in IBM’s business
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Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
S390 Inventory Analysis
70% of business transacted on IBM servers
Cyclic demand production challenges
Inventory management: High $ parts by commodity
Ireland
.
Montpellier
Fabricated
Parts
North
America
46.5%
Volume
95% European
Suppliers
(Less MCM)
Poughkeepsie
Japan
CDCs
53.5% Volume
Europe
Middle East
Africa
95% NA
suppliers
CDCs
Asia
Pacific
Fujisawa
Brazil
20 CDCs
Sumare
Latin
America
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Operations Research at IBM
Key Strategy: Fab/Fulfillment
Simulation modeling to explore
behavior of BTP/CTO supply chain
© 2006 IBM Corporation
Integrated Supply Chain
S390 Simulation Project: Inventory Study
Fabrication
Fulfillment Center
MCMs
Feature1
power
.
.
.
BOX
(MTM)
memory
FeatureK
When managing a measurement, we need to
know where we expect it to be...
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Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
Objectives
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.
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Operations Research at IBM
Steps
•Confirm objectives
•Build model
•Gather data
•Cleanse data
•Validate model
•Test hypotheses
•Draw conclusions
•Analytical
•Business implications
•Present, convince, implement
© 2006 IBM Corporation
Integrated Supply Chain
Replenishment
Lead Times:
Fulfillment Center BOMs:
EMLS Extract
Fed by SAP
EMLS Extract
Found incorrect
(empty system LTs)
Debby Carelli
Denny Slocum
Identifying Feature P/Ns
Larry Fox
Fab BOMs:
Pull vs Non-pull
In Practice
Nick Kulick (pwr)
Mike O'Dowd (DASD)
Sue Cozalino
Ron Shields
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)
Transportation Lead Times:
Jeff Schmitt
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Operations Research at IBM
Jim Curatolo
Brian Kuhn
Wendy Sell
Roger Tsai/Pete Weber
Identifying FC P/Ns
Testing Lead Times --
SAP
Forecasts
Box/MES:
Don Gunvalsen
Jeff Benedict
Monthly Forecasts
Larry Fox's spreadsheets
SCE files (20-day process)
Monthly Actuals
COATS data extracts
(custom SQL)
Serviceability:
Bethesda DB
CAD=CRAD for CRAD
within 3 weeks (80%)
100% otherwise
(custom SQL)
© 2006 IBM Corporation
Integrated Supply Chain
Validation against historical actuals, builds confidence in
the model
CMOS AVG High Dollar Inventory
60
50
PWR_SUPP
PWR_MECH
MEMORY
LOGIC
40
30
20
10
Validation of Simulation Model
0
14
LOGIC
LOGIC
MEMORY
MEMORY
PWR_MECH
PWR_MECH
PWR_SUPP
PWR_SUPP
97
97 %
%
96
96 %
%
86
86 %
%
94
94 %
%
OVERALL
OVERALL
95
95 %
%
Operations Research at IBM
Average Inventory of High Dollar IMPACT Parts
11/16/98
11/09/98
11/02/98
May to
October
Average
06/23/98
06/02/98
05/18/98
05/04/98
04/20/98
Date
CMOS: May through October 1998
$70
$60
$50
Millions
Average Inventory
Millions $
70
PWR_SUPP
PWR_MECH
MEMORY
LOGIC
$40
$30
$20
$10
$0
Actuals
Simulation
© 2006 IBM Corporation
Integrated Supply Chain
Multiple Replications, Demand Mix and Variation
To Test Effect of Fab/Fulfillment (BTP/CTO)
LOGIC DOS for
pDOS2QA
Three Replications
50
Days of Supply
45
40
35
30
25
20
15
15
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
Patterns emerged for each commodity
LOGIC DOS
PWR_MECH DOS
50
60
45
50
40
40
DOS
DOS
35
30
30
25
20
20
15
10
10
1
3
5
7
9
11
13
1
2
3
4
5
6
7
8
9
10
Week of Quarter
Week of Quarter
MEMORY DOS
PWR_SUPP DOS
11
12
13
11
12
13
45
60
40
50
35
DOS
DOS
40
30
30
25
20
20
15
10
10
1
3
5
7
Week of Quarter
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Operations Research at IBM
9
11
13
1
2
3
4
5
6
7
8
9
10
Week of Quarter
© 2006 IBM Corporation
Integrated Supply Chain
LOGIC DOS
50
45
40
DOS
35
30
25
20
15
10
1
5
9
13
Week of Quarter
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Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
SQC charts are applied to the residuals to detect when to act
18
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
Additional Observations
Inventory Cost of Fab/Fulfillment
$70
No
No Capacity
Capacity Constraints
Constraints
Quantifies
Quantifies cost
cost of
of strategy/cost
strategy/cost
of
of skew
skew
29%
29% more
more expensive
expensive overall
overall
74%
74% savings
savings possible
possible for
for
PWR_MECH
PWR_MECH
Lead Time Reduction: Consumptive Pull
$70
PWR_SUPP
$60
MEMORY
LOGIC
Inventory
Millions
$50
13%
11%
6%
6%
6%
Logic
MEM
Mech
Supp
30%
$30
$40
$30
$20
$10
$0
As Is versus Consumptive Pull
PWR_SUPP
MEMORY
LOGIC
PWR_MECH
Sensitivity
Sensitivity Analysis
Analysis
Using
Using consumptive
consumptive pull
pull model
model
(max
(max savings)
savings)
Using
Using fab/fulfillment
fab/fulfillment model
model
(much
(much less
less sensitive)
sensitive)
PWR_MECH
$40
$50
Inventory
Millions
Model
Model run
run with
with consumptive
consumptive pull,
pull,
optimized
optimized reorder
reorder points
points
$60
$20
$10
$0
Base
19
All
MCM
Operations Research at IBM
Actuals
© 2006 IBM Corporation
Integrated Supply Chain
Today’s Discussion
Introduction: Supply Chain Management & IBM’s Integrated Supply Chain
Enablers of Successful OR Application:
Demand and Support for OR
Embedding in Operations
Differentiated Roles
Examples of OR at IBM:
Simulation / Inventory Optimization Example
Available to Sell: Resource Allocation
e-Auctions Analysis
Summary
Questions
20
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
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
21
Sales: "What do we have in excess?"
Planning Items
(MTMs, Upgrades,
MES loose piece)
Features
MFI/FFBM
Manufacturing:
"We have excess
parts inventory."
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
Data Issues Dominate Industrial Problem Solving
ETIS relates planning
items to p/n via
history (ratios)
4. Inadequate history
causes artificial 'zero'
ETIS ratios
5. Which parts are
called out by which
features is
order-dependent
2. EC causing expired
effectivity dates (bill
present but no demand
on parts)
1.B) Card bills missing
(outsourced)
1.A) Excess at a
component level unknown
to Manufacturing
3. Bills missing
entirely for parts in
excess
6.“Penny parts”
6000 of these
7.C-source
SG ATS/ 03
(consigned)
rev7/22/01
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Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
Feature Translation: creating pseudo bills of material and appending
to existing structures
fc 1234 for
model ABC
Parts unique
to ABC
fc 1234 for
all models
New bill
structures to
be added...
...connect to
existing bill
structures
23
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
Device Code to Bill Structure Example
EXAMPLE (d/c 0014)
9406;170;Q01;0014;00075G2720;***;(9401.R1)ESP -DOCS
9406;500;Q01;0014;00017G0071;***;(9406.R1)PRE-GA PUBS
9406;510;Q01;0014;00017G0071;***;(9406.R1)PRE-GA PUBS
9406;530;Q01;0014;00017G0071;***;(9406.R1)PRE-GA PUBS
9406;50S;Q01;0014;00017G0071;***;(9406.R1)PRE-GA PUBS
9406;53S;Q01;0014;00017G0071;***;(9406.R1)PRE-GA PUBS
9406;***;Q01;0014;00046G0063;***;(MILL.R1)PRE-GA PUBS
9406;***;Q01;0014;00017G0071;***;(CONH.R1)PRE-GA PUBS
0014_53S
0014_50S
0014_530
0014_170
0014_510
This is expressed as follows in the SCE format:
0014_500
On model 170, 0014 requires only 1 per of 75G2720
On models 500, 510, 530, 50S, and 53S, only 1 per
of part 17G0071 is required.
0014_ML6
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).*
0014_M10
"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
75G2720
17G0071
46G0063
*using rel3.mfc (bld level) MTMODCNV.R file
24
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
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)
25
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
Today’s Discussion
Introduction: Supply Chain Management & IBM’s Integrated Supply Chain
Enablers of Successful OR Application:
Demand and Support for OR
Embedding in Operations
Differentiated Roles
Examples of OR at IBM:
Simulation / Inventory Optimization Example
Available to Sell: Resource Allocation
e-Auctions Analysis
Summary
Questions
26
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
e-Auctions to Exploit price/quantity Relationships
Fixed-Price versus Auctions Selling
quantity
quantity
Q0
p0
price
price
Reason Not to Auction New
Products
quantity
quantity
forecasting
demand (BAU)
Q0
Q0
forecasting
price (auction)
p0
price
p0
price
Auctioning Complements BAU
27
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
Investigation of Product Differences and Value of Auctions
Some Products are a Better Fit for Auctioning
Key Driver: How unique is the purchase across customers?
High unit
volumes
DRAMs
HDDs
Custom Logic
(ASICs)
PSG
Amenable
to Auction
PSD
Low unit
volumes
SSD
AS/400
RS6000
S390
Common function,
product across
many customers
Unique function,
product for each
customer
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?
28
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
Auctioning as an Additional Channel
Three Key Parameters Drive the Dynamics
Percentage 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 piloting
Cannibalization and auction price effectiveness are outcomes
% revenue targeted translates through the other parameters into resultant auction revenue
Brand- (product-) specific
Approach
Estimate reasonable average values for these parameters, and then test
sensitivity across a range of values by randomly simulating different
combinations.
10%
5%
15%
Targeted Auction Revenue
Auction Revenue =
targeted revenue times
price effectiveness
82.5%
70%
Cannibalized
Revenue
95%
Price Effectiveness
57.5%
25%
Total Revenue
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Operations Research at IBM
90%
% Cannibalization
© 2006 IBM Corporation
Integrated Supply Chain
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:
Net change in profit
30
million $
million $
Revenue potential
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
Auctioning versus "Working Off" in Supply Chain
Decisions
Recognize an "Excess Supply" Situation
Determine Whether to Auction or Work Off
Determine How to Auction
After
Building
Product
Timing
After
Missing
Forecast
Relative to Building the Box
Relative to Calling the Missed Demand
Factors:
START
HERE
Before
Building
Product
...OR
HERE
Before
Missing
Forecast
Component part leadtime k periods
Price takedown
Cost of inventory
Pk
c
Cost of production
Selling expense: usual channel(s)
Selling expense: auctions
Waiting penalty
s
a
wk
Price received through auctioning product (random variable)
Pa
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 31
- a
m
Pk - c -
Operations Research at IBM
- s - wk
© 2006 IBM Corporation
Integrated Supply Chain
Recognize
"Excess"
-1
Decide on
and/or Conduct
Auctioning
Declare "on-hand"
and net out of
demand
1
0
After Missing Forecast and
Before Building Product
2
3
k
k+1
Demand Plans
booked (reforecast
and netted on-hand)
Requirements Passed
to Suppliers
Materials Received from
Suppliers (leadtime=k periods)
Auction at minimum opening bid of Pa = Pk - s + a - wk
where wk = cki if I have already purchased the component inventory
32
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
Dynamic Programming Approach to the Auctioning Decision
Difference Equation Approach
Sell 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
Ci = max
(Pk+i - c -
{max Pr(Pa m r){E(Pa | Pa m r) r
c-
- a } + Pr(Pa < r)(w1 + C i+1 ),
}
- 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
Issues
Auction (market) price distributions may be poorly understood
Probabilities (of selling one item at price Pk in period i+k) unknown
33
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
Today’s Discussion
Introduction: Supply Chain Management & IBM’s Integrated Supply Chain
Enablers of Successful OR Application:
Demand and Support for OR
Embedding in Operations
Differentiated Roles
Examples of OR at IBM:
Simulation / Inventory Optimization Example
Available to Sell: Resource Allocation
e-Auctions Analysis
Summary
Questions
34
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
Enabling OR Application within Industry
Function
embedded in
S/W instantiation
Very deep
Academia
“Wrapper” Concept:
IBM Research
OSL / WIT / SCE / AtS
DES / BPMAT / AMT
ISC Technical
Leaders
Depth in OR
Thinking
ISC Practitioners
& Executives
Shallow
Little to
None
General
(broadly familiar)
Deep and
Broad
Literacy in IBM’s business
35
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
In Summary
ƒ Supply chain management is a dynamic, exciting, growing
application area
ƒ Data management (gathering, cleansing, workarounds) is a
critical success factor and often consumes most project resources
ƒ Ingredients for success include:
►
Readiness (maturity, awareness, skill base, burning platform)
►
Support community
►
Combined OR expertise with business insight
– Differentiated roles helpful
36
Operations Research at IBM
© 2006 IBM Corporation
Integrated Supply Chain
Thank you!
Brian T. Eck
BrianEck@sg.ibm.com or
Drbteck@yahoo.com.sg
37
Operations Research at IBM
© 2006 IBM Corporation