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What is Missing to Enable Optimization of Inventory Deployment and Supply Planning?
Professor Sridhar TayurCarnegie Mellon University
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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
Capacity management
Stabilizing production rate to maximize efficiency or flexing capacity to meet demand
Demand management
Managing sales/order rate variation Limiting number of allowed “standard”
configurations in build-to-stock environment
Inventory management
Optimal deployment of inventory to maximize availability at minimum cost
Also used to insulate manufacturing from demand variability
Lead time management
Consistent with Lean principles - working to reduce supply and in-process lead-times
Monitoring and managing lead-time variability
Fixed or flexible Segmentation by product or customer
(e.g. sales vs. rentals)
Fixed or flexible capacity Willingness to subject plant to increased
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
Active management of demand variability (e.g. promotions/incentives)
Monitoring and managing forecast error
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
Fundamental issues identified setting the stage for decades of research
Early inventory and stochastic* optimization models created
Breaking of problems into manageable pieces
Practitioners use rules of thumb and put pieces together heuristically
1970s-1980s 1990s
Searching for simpler ways of computing optimal inventory policies for basic problems
Improved computational approaches developed to address larger problems in “isolation”
Stochastic optimization models developed to explicitly accommodate supply and demand variability, multiple time periods, capacitated, multi-echelon supply chains
Successful “one-off” application to industrial-size problems
Clark and Scarf Arrow, Karlin
Federgruen;Zipkin; Lee; Cohen; Roundy
Muckstadt;Thomas;Zheng Glasserman; Tayur
Key progress
Key contributors
©2002 SmartOps Corporation
* Stochastic: Involving or containing random or “uncertain” variables (e.g., uncertain demand, lead time, capacity, yield, etc.)
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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”
REAL WORLD:THERE IS SIGNIFICANT INEFFICIENCY IN OUR ECONOMY
$1.0 trillion 50+% $500+ billion
U.S. inventoriesEstimated inefficiency
Economic opportunity
What is missing?
What is Missing?
Advanced, practical value chain planning and optimization to accommodate and manage these forces
©2002 SmartOps Corporation
5
6
365
510
275115050
875
1200
Average inventory (2000)
CASE STUDY #1: INVENTORY REDUCTION OPPORTUNITY
Actual reduction in 2001
Average inventory (2001)
Planned reduction in 2002
Average inventory target (2002)
Additional opportunity identified with SmartOps
Suggested average inventory target (2002)
$ Millions
Source:SmartOps Multistage Inventory Planning and Optimization Software
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CASE STUDY #1: TYPE OF INVENTORY FOR FY2002: ONE PRODUCT LINE AT 95% SERVICE LEVEL$
-2,000,0004,000,0006,000,0008,000,00010,000,00012,000,00014,000,00016,000,000
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
Annually/quarterly Weekly/dailyQuarterly/monthly
Low detail/granularity High detail/granularity
N/A
N/A
Planner
Planner & O.R. engineer
O.R. engineer
Business Unit Planning and Operations
Corporate/ Business Unit Strategy
Org
an
iza
tio
n
Da
ta m
an
ag
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en
t/u
pd
ate
p
roc
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s
Relation to existing processes
Stand-alone Dynamic
One-off studies Driving execution
Structural changes
Continuous improvement
“Dynamic value chain”
©2002 SmartOps Corporation
ERP/APS detailed, dynamic data inputs
Manual, “meta-level” inputs, click and drag design
SWEET S
POT
The goal is to pick an approach that ensures confidence in the answer, quick hit improvements, and sustained execution
Timed, regular data loading
Data-loader with manual start
Data wizard and interface
Timing/dynamic frequency
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WHAT IS THE OPTIMAL INVENTORY DEPLOYMENT FOR YOUR BUSINESS?
Inventory FormsInventory Purposes
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
Total Cost Optimization
– Cycle stock– Pre-build stock– Pipeline stock
APS challenges– Scheduling a factory– Packing a truck– Routing a truck
Managing uncertainty
Safety stock Shortfall stock
Certain or near-certain
“Deterministic”Uncertain
“Stochastic”
Linear and Integer
Non-linear Linear, deterministic models are not appropriate for most critical inventory decisions in multistage, multi-product, capacitated, stochastic environments
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A SUPPLY CHAIN MODELING PROCESS
Map the current value chain
Select relevant variables, constraints, and objective function
Initial collection, cleaning, and QA of data
Selection of planning granularity
Select optimization algorithms
Commence data integration process
Full, partial, or no automation of inputs and outputs
Entire network or subset
All nodes or simplification of nodes
Simplifying assumptions to include or exclude variables, constraints, or nodes considering quality of answer vs. speed of answer
Understand underlying data assumptions
Ensure data makes sense in business and supply chain terms
Days, weeks, months
Product hierarchy – sales model vs. MA
# of nodes and time periods
Stationary or non-stationary model (e.g. # of forecast periods)
Single or multi-echelon or hybrid
Capacitated, un-capacitated
Load data and pre-process meta-data
Calculation/ optimization
Scenarios/ what-if
QA outputsPost-process and summarize
Review outputs - send to operational system/ process
Change structure of value chain
Run test cases vs. actual data
Understand processing speed
Design, build, and run logical scenarios
Test boundary conditions
Compare results with expectations based on theory and domain expertise
Aggregation/dis-aggregation
Units/$s/Weeks Rounding
Manual, exception-based, or automatic export of targets to planning systems
Changes to “nodes” and “arcs” vs. changes to echelons and BOMs
Compute meta-data: lead-times, lead time variabilites, forecast disagg. etc.
Refresh inputs
©2002 SmartOps Corporation
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SOFTWARE ARCHITECTURE FOR ENTERPRISE INVENTORY PLANNING
©2002 SmartOps Corporation
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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
OVERCOMING PRACTICAL DIFFICULTIES
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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
• Stochastic optimization approach is the appropriate solution
– Uncoordinated planning decisions• Total cost optimization by providing visibility and coordination between functional and
external groups
– Inconsistent and/or insufficient planning practices• 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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