presentation to company

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


* 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


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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

em

en

t/u

pd

ate

p

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es

s

Relation to existing processes

Stand-alone Dynamic

One-off studies Driving execution

Structural changes

Continuous improvement

“Dynamic value chain”


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


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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


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SOFTWARE ARCHITECTURE FOR ENTERPRISE INVENTORY PLANNING


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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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