What is Missing to Enable Optimization of Inventory
Deployment and Supply Planning?
Professor Sridhar Tayur
Carnegie Mellon University
ANALYTICS FOR A COHERENT ORDER
FULFILLMENT STRATEGY
Availability management
Key policy choices



Promising and meeting order fulfillment
lead times
 Set to maintain or gain market share
Fixed or flexible
Segmentation by product or customer
(e.g. sales vs. rentals)
Capacity management

Stabilizing production rate to maximize
efficiency or flexing capacity to meet
demand


Fixed or flexible capacity
Willingness to subject plant to increased
demand variability
Demand management


Managing sales/order rate variation
Limiting number of allowed “standard”
configurations in build-to-stock environment

Active management of demand
variability (e.g. promotions/incentives)
 Monitoring and managing forecast error
Inventory management

Optimal deployment of inventory to
maximize availability at minimum cost
 Also used to insulate manufacturing
from demand variability


Static or dynamic inventory targets
Rules of thumb vs. product/location/time
specific targets
 Based on total chain or local viewpoint
To achieve maximum
availability at minimum
cost:
 A comprehensive
order fulfillment
strategy must
appropriately define a
coordinated set of
policies for these
interrelated variables
 No one variable can
be managed in
isolation and changing
or fixing one variable
has implications for
the others
Lead time management

Consistent with Lean principles working to reduce supply and in-process
lead-times
 Monitoring and managing lead-time
variability

Active management of lead-times and
lead-time variability
 Incentives and penalties for
performance
©2002 SmartOps Corporation
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ACADEMIC BUILDING BLOCKS:
40+ YEARS OF EVOLUTION, BREAKTHROUGHS, AND APPLICATION
Late 1950s – 1960s
1970s-1980s
1990s
Key
contributors


Clark and Scarf
Arrow, Karlin


Federgruen;Zipkin;
Lee; Cohen; Roundy


Muckstadt;Thomas;Zheng
Glasserman; Tayur
Key
progress

Fundamental issues identified
setting the stage for decades of
research



Early inventory and stochastic*
optimization models created
Searching for simpler
ways of computing
optimal inventory
policies for basic
problems
Stochastic optimization models
developed to explicitly
accommodate supply and
demand variability, multiple
time periods, capacitated,
multi-echelon supply chains

Improved computational
approaches developed
to address larger
problems in “isolation”

Successful “one-off”
application to industrial-size
problems

Breaking of problems into
manageable pieces

Practitioners use rules of thumb
and put pieces together
heuristically
* Stochastic: Involving or containing random or “uncertain” variables (e.g., uncertain demand, lead time, capacity, yield, etc.)
©2002 SmartOps Corporation
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REAL WORLD:
THERE IS SIGNIFICANT INEFFICIENCY IN OUR ECONOMY
U.S. inventories
Estimated
inefficiency
Economic
opportunity
$1.0 trillion
50+%
$500+ billion
Fundamental, persistent forces behind supply chain
inefficiency:

Inability to accommodate and actively manage inherent uncertainty,
variability, and complexity across multi-echelon supply chains

Local vs. global (“total cost”) optimization, metrics, and incentives –
uncoordinated supply chain inventory and cost decisions within enterprises
and across supply chains

Underutilization of current data, systems, and available best practices, e.g.,
lack of dynamic, data driven reviews of “planner variability”
©2002 SmartOps Corporation
What is Missing?
Advanced, practical
value chain planning
and optimization
to
What
is missing?
accommodate and
manage these forces
4
5
CASE STUDY #1: INVENTORY REDUCTION OPPORTUNITY
$ Millions
1200
50
1150
275
875
365
510
Average
inventory
(2000)
Actual
reduction
in 2001
Average
inventory
(2001)
Planned
Average
reduction in inventory
2002
target (2002)
Additional
opportunity
identified
with
SmartOps
Suggested
average
inventory
target (2002)
Source: SmartOps Multistage Inventory Planning and Optimization Software
6
CASE STUDY #1: TYPE OF INVENTORY FOR FY2002:
ONE PRODUCT LINE AT 95% SERVICE LEVEL
11
/4
11 / 20
0
/2
5/ 1
12 200
/1
1
6/
20
01
1/
6/
2
1/ 002
27
/2
2/ 002
17
/2
3/ 002
10
/2
3/ 002
31
/2
4/ 002
21
/2
5/ 002
12
/2
00
6/
2
2/
20
0
6/
23 2
/2
7/ 002
14
/2
00
8/
2
4/
2
8/ 002
25
/2
9/ 002
15
/2
10 002
/6
10 / 20
0
/2
7/ 2
20
02
$ 16,000,000
14,000,000
12,000,000
10,000,000
8,000,000
6,000,000
4,000,000
2,000,000
-
Safety
Safety+Prebuild
Safety+Prebuild+Pipeline
Safety+Prebuild+Pipeline+Cycle
2002 Weekly Sales Forecast
Current Merchandise Inventory
Key Takeaways

The existing supply and demand variability drives the need for significant safety stock for products,
particularly during the peak selling season

Due to capacity constraints, there is also a need for pre-build inventory, meaning that plants will
produce more inventory not because of system uncertainty, but because mean weekly plant
capacity will exceed needed production in future periods
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UNDERSTANDING MODELING APPROACHES
The goal is to pick an
approach that ensures
confidence in the answer,
quick hit improvements,
and sustained execution
Timing/dynamic frequency
Low detail/granularity
High detail/granularity
Quarterly/monthly
Annually/quarterly
Weekly/daily
Planner
N/A
Business Unit
Planning and
Operations
Timed, regular
data loading
Planner
& O.R.
engineer
Data-loader with
manual start
Organization
Data management/update
process
ERP/APS
detailed, dynamic
data inputs
Corporate/
Business Unit
Strategy
Data wizard and
interface
Manual, “metalevel” inputs, click
and drag design
N/A
O.R.
engineer
Relation to existing processes
Stand-alone
Dynamic
One-off studies
Structural
changes
Driving execution
“Dynamic value
Continuous
chain”
improvement
©2002 SmartOps Corporation
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WHAT IS THE OPTIMAL INVENTORY DEPLOYMENT
FOR YOUR BUSINESS?
To enable continuous and sustained
improvement, a comprehensive
approach must accommodate all forms
and purposes of inventory
©2002 SmartOps Corporation
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STOCHASTIC OPTIMIZATION IS NECESSARY

Non-linear
Total Cost
Optimization
–
–
–

Linear and
Integer
Cycle stock
Pre-build stock
Pipeline stock
 Managing uncertainty


Safety stock
Shortfall stock
APS challenges
– Scheduling a factory
– Packing a truck
– Routing a truck
Certain or near-certain
“Deterministic”
Linear,
deterministic
models are not
appropriate for
most critical
inventory
decisions in
multistage,
multi-product,
capacitated,
stochastic
environments
Uncertain
“Stochastic”
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A SUPPLY CHAIN MODELING PROCESS
Commence data
integration
process
Map the
current value
chain
Select relevant
variables,
constraints, and
objective
function

Entire network
or subset
 All nodes or
simplification of
nodes

Initial collection,
cleaning, and
QA of data
Simplifying
 Understand
assumptions to include underlying data
or exclude variables,
assumptions
constraints, or nodes  Ensure data makes
considering quality of
sense in business
answer vs. speed of
and supply chain
answer
terms

Changes to
“nodes” and
“arcs” vs.
changes to
echelons and
BOMs
Review
outputs - send
to operational
system/
process

Post-process
and
summarize
QA outputs
Full, partial, or
no automation
of inputs and
outputs
Selection of
planning
granularity
Select
optimization
algorithms


Days, weeks,
months
 Product hierarchy –
sales model vs. MA
 # of nodes and time
periods
Refresh inputs
Change
structure of
value chain

Scenarios/
what-if
Stationary or nonstationary model (e.g. # of
forecast periods)
 Single or multi-echelon or
hybrid
 Capacitated, uncapacitated
Calculation/
optimization
Manual,
 Aggregation/dis-  Compare results  Design, build, and  Run test cases
exception-based,
aggregation
with expectations
run logical
vs. actual data
or automatic
based on theory
scenarios
 Units/$s/Weeks
 Understand
export of targets  Rounding
and domain
 Test boundary
processing
to planning
expertise
conditions
speed
systems
©2002 SmartOps Corporation
Load data and
pre-process
meta-data

Compute metadata: lead-times,
lead time
variabilites,
forecast disagg.
etc.
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SOFTWARE ARCHITECTURE FOR ENTERPRISE INVENTORY PLANNING
©2002 SmartOps Corporation
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OVERCOMING PRACTICAL DIFFICULTIES
Reality
Possible Approach









Scale
Scope: Many Factors Exist Simultaneously
Data: Existence, Accuracy, Ease of Availability
Silos within Organizations
Multiple Companies in a Supply Chain
Current IT Infrastructure
Existing Execution and Decision Support Tools

Metrics and Measurements
Motivation, Discipline and Incentives
Training and Capability
People: Corporate supply chain and business
planners/super users as well as business unit
planners
Consultants: Internal and External

Professors and Education















Exception Driven Scalable Software
Comprehensive Approach
Pre-processors, Inheritors, Data Loaders
Net Landed Cost View
Collaborative Framework with Trust
‘Bolt-on’s to co-ordinate/synchronize
Productize recent OR/MS Intellectual
Property
Management 101: Track Key
Performance Indicators Dynamically
Culture and Metrics/Bonus Structure
Need to have a Grassroots Revolution
Flexible platform for Multi-tier use and
communication
Do not rely entirely on Spreadsheet
based Optimization!
Appreciate Reality and Train Students to
Handle Reality
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CLOSING REMARKS

Despite ERP and APS investments significant inventory inefficiencies
persist

Fundamental causes of supply chain inefficiency must be addressed:
–
Inherent uncertainty and complexity in multistage supply chains
•
–
Uncoordinated planning decisions
•
–
Total cost optimization by providing visibility and coordination between functional and
external groups
Inconsistent and/or insufficient planning practices
•

Stochastic optimization approach is the appropriate solution
Software can provide a standardized “best planning” solution
All the drivers of inventory must be measured to determine:
–
Optimal inventory targets for all inventory purposes
•
safety, cycle, shortfall, pipeline, pre-build, and merchandising stock
–
Total cost solution to deliver service levels
– Optimal service levels given budget objectives, product margins, and portfolio
of products
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