SupplyChain Management: A View of the Future

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Supply-Chain ManagementA View of the Future

• Leroy B. Schwarz
• Krannert School of Management
• Purdue University
• Supported by e-Enterprise Center at Discovery Park

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Outline

• Supply-Chain Management of Yesterday
• How Modeled
• How Practiced
• Supply-Chain Management of Today
• How Practiced
• How Modeled

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Outline (cont.)

• Introduce Paradigm called
• IDIB Portfolio
• Describe My Vision of the Futureof SCM
• Provide an Overview of 2 Projects
• Collaborative Decision-Making and Implementation
• Secure Supply-Chain Collaboration

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SCM Models of Yesterday

• Took Centralized Perspective
• Assumed Single, Systemwide Objective Function
  F(x1, x2, x3, ...)
• Assumed System Information was
• Available
• Omnipresent
• Assumed Implementation was Contractible

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• Typical Results
• Characteristics of the Optimal Policy for Special
  Structures
• Clark Scarf, 60
• Schwarz, 73
• Examination of Heuristics for More General
  Structures
• Clark Scarf, 62
• Roundy, 85

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SCM Practice of Yesterday

• Single-Owner Chains Took a Centralized
  Perspective
• Single Objective Function F(x1, x2, x3, ...)
• De-Centralized Decision-Making
• Information Not Available or, at best,
  Asymmetric
• Implementation De-Centralized NOT Contractible

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• Consequently
• Supply Chains Managed as Separate Entities,
  regardless of their ownership
• Ex. Local Objective Functions F1(x1),
  F2(x2), ...
• Examples
• USAF Logistics Command Consumable Inventory
  System
• IBM Service-Parts Inventory System

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Consequences of this

• Huge Buffers
• Raw, WIP, and Finished-Goods Inventories
• Capacity Buffers (e.g., understated capacity)
• Leadtime Buffers (e.g., overstated leadtime)

9
Yesterdays Relationship Mismatched

• Models
• Too Specialized
• Required More Information than Practice Had
• Practice
• Inexperienced with Models Computers
• Confused by Models
• Suspicious of Models

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SCM Practice Today

• The Beginnings of Real SCM for Single-Owner
  Chains
• Ex Wal-Marts Retail Link
• Targets Partners OnLine
• Capabilities
• Broadcast SKU-level Data Across the Chain
• Observe Status Implemetation Contractible

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• Results
• Huge Reductions in Buffers Lower Operating
  Costs
• Improved Competitiveness
• Lower Prices
• More Customization
• Higher Availability

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• Development of Technologies to Support
  Multiple-Owner SCM
• Internet is Providing Experience
• E-Markets
• Providing Buyer-Supplier Linkages
• Data Standardization e.g. RosettaNet
• Beginnings of SCM for Multiple-Owner Supply
  Chains
• VMI, Quick Repsonse
• VICS CPFR Campaign

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• Huge Challenges for Multi-Owner Chains
• Multiple often Conflicting Objective
  Functions
• Technical Difficulties in Sharing Information
• SKU Identification
• Time-Frame
• Fear about Information Sharing
• Vertical Leakage
• Horizontal Leakage

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SCM Models of Today

• Models with Multi-Ownership, Competing Objective
  Functions, and Asymmetric Information
• Roots in Economics
• 1980s Work of Monahan, Pasternak
• Contemporary Work
• Supply-Chain Coordination with Contracts, G.
  Cachon (forthcoming)
• Information-Sharing and Supply-Chain
  Coordination, F. Chen (forthcoming)

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• Models for Assessing the Impact of Decentralized
  Decision-Making and/or Asymmetric Information
• Ex Lee, et al. Bullwhip Paper (MS 434)
• Results
• Assessments of Agency Loss
• Non-bathtub Shaped Loss Functions
• Contracting Mechanisms to Improve/Optimize
  Performance

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Relationship Today Out of Step

• Models beginning to include ownership and
  private-information issues, but
• Little Work on How to Share Information or How to
  Collaborate on Decision-Making or Implementation
• Ignoring the Development of More Sophisticated
  Centralized Models

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Relationship Today Out of Step

• Practice ready to Dance but No Model Partner
• Using simple models based on pull down menus in
  ERP systems
• Swimming in Data, but uncertain about how to
  use it

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What About the Future of SCM?
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First.......
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The IDIB Portfolio

• a.k.a.
• The Information, Decision-Making, Implementation,
  Buffer Portfolio

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Managing anything can be viewed as 4 related
activities

• Getting Information
• Making Decisions
• Implementing Decisions
• Buffering against Imperfections in information,
  decision-making, or implementation

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Every Management System is, in fact, 4
Sub-Systems

• The Information System provides information
• The Decision-Making System makes decisions
• The Implementation System implements decisions
• The Buffer System copes with imperfections in
  information, decision-making, or implementation

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Each Sub-System has Cost and Quality
Characteristics

• The Information System
• Quality Characteristics
• Accuracy
• Leadtime
• Aggregation Level
• Horizon
• Etc.
• Cost Increasing and Marginally-Increasing
  with Quality

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Each ... Characteristics (cont.)

• The Decision-Making System
• Quality Characteristics
• Optimality i.e., how good?
• Leadtime i.e., how long to make?
• Etc.
• Cost Increasing and Marginally-Increasing
  with Quality

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Each ... Characteristics (cont.)

• The Implementation System
• Quality Characteristics
• Accuracy i.e., conformance to decision
• Leadtime i.e., how long to implement
• Etc.
• Cost Increasing and Marginally-Increasing
  with Quality

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Each ... Characteristics (cont.)

• The Buffer System
• Quality Characteristics
• Form
• Robustness
• Etc.
• Cost Increasing and Marginally-Increasing
  with Quality

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IDIB Portfolio?

• Like a Financial Portfolio, the IDIB System
  requires an investment of Dollars
• Like a Financial Porfolio, each Subsystems
  Characteristics Should Complement the
  Characteristics of the Others
• Ex Robust Buffer System Complements an
  Inaccurate Information System
• Ex Tradeoffs Among Buffer Sub-Systems

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Managing the IDIB Portfolio....

• .... means changing the nature and quality of its
  4 sub-systems so that total portfolio cost
  which includes the cost of imperfect buffering
  is minimized
• This is NOT Rocket Science!

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Most Operations-Research Models Ignore the IDIB
Portfolio

• Example The Newsvendor Model
• Information-System Quality Assumed
• Implementation is Ignored
• Select Decision-Rule to Minimize Buffer-System
  Cost

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IDIB Portfolio View of Newsvendor Problem

• The Problem is that acquistion/production
  decsion must be made before demand occurs
• What if
• Production was instantaneous?
• Production Decision and Implementation Leadtime
  Horizon of Known Demand?

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What is the Value-Added of the IDIB Paradigm?

• Vantage Point on the Majority of
  Operations-Research Models
• Vantage Point on Past/Present Practice
• Vantage Point on the Future

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1st Axiom of the IDIB Portfolio

• Given an existing IDIB Portfolio, increasing the
  quality of one of its components typically
  facilitates decreasing the quality of at least
  one of its other three components while
  maintaining the same level of customer service
• the Tradeoff Axiom

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Examples

• In a (Q,r) system
• If all leadtimes are fixed, then the
  information-system, decision-making, and
  implementation leadtimes tradeoff one-for-one
• If any of these leadtimes are variable, then
  reducing their variance facilitates reducing
  safety stock (buffer) inventory

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Examples from Practice

• Schneider National
• Increasing Quality of I, D, and I Reducing B
  improving service
• Manufacturer Making Transition from a Push
  (e.g., MRP) to Pull (e.g., JIT)
• Reducing Buffer Inventory, increasing Buffer
  Capacity
• Domestic Manufacturer Outsourcing to Off-Shore
  Supplier
• Reducing Implementation Quality (Leadtime)
  Increasing Buffer Inventory

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The IDIB Perspective on State-of-the-Art Practice
in SCM

• Involves the sharing of past, present, and
  future-oriented information between
  buyer-supplier pair and/or
• Involves delegation of decision-making or
  implementation to the supplier
• .....So, then what is the future.......?

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2nd Axiom of the IDIB Portfolio

• Investment to improve the quality of any single
  component of the IDIB Portfolio will, over some
  range, decrease total cost of the Portfolio but,
  beyond some quality level, increase total cost of
  the Portfolio
• Do-Nothing-in-Excess Axiom

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The Future of Supply-Chain Management Involves
Collaborative Decision-Making and/or
Implementation
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Why?

• For Supply Chains that already share information,
  the returns from additional information sharing
  are diminishing
• For Supply Chains that are already delegating
  some decision-making, the returns from additional
  delegation are marginally diminishing

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Two Personal Projects

• Models for Collaborative Decision-Making
• How to Improve Decision-Making and Implementation
  Based on Shared Information
• Protocols for Secure Supply-Chain Management
• How to Improve Decision-Making and Implementation
  without Sharing Information

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Models for Collaborative Supply-Chain
Decision-Making

• with
• Vinayak Deshpande
• Jennifer Ryan

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Starting Point is Collaborative Planning,
Forecasting, and Replenishment (CPFR)
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What is CPFR?

• A process model, shared by the buyer and
  supplier, through which inventory status-,
  forecast-, and promotion-oriented information are
  shared and replenishment decisions generated

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The 9 Process Steps

• Step 1
• Develop Front-End Agreement Roles,
  Measurement, Readiness
• Step 2
• Create Joint Business Plan Strategies and
  Tactics
• Step 3
• Create Sales Forecast Buyer or Supplier
• Step 4 Identify Exceptions for Sales Forecast

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The 9 Process Steps

• Step 5
• Resolve/Collaborate on Exception Items
• Step 6 Create Order Forecast
• Step 7 Identify Exceptions for Order
  Forecast
• Step 8 Resolve/Collaborate on
  Exception Items
• Step 9 Order Generation

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CPFR Whos Behind it?
Federated Department Stores
CORNING Consumer Products
Staples
FIELDCREST CANNON
JCPenney
Mead School Office
Schnuck Markets
Benchmarking Partners
QRS
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CPFR History

• 95/96 Wal-Mart Warner-Lambert CFAR
  Pilot
• 97 VICS Develops CPFR Initiative
• 98 VICS CPFR Guidelines Published
• 99 Pilots Between
• Kimberly-Clark K-Mart,
• PG Meier, Target, Wal-Mart
• Nabisco Wegmans, etc.
• 001st Production Rollout K-Mart

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CPFRs Future

• n-Tier Collaboration
• Extension to Include Master-Scheduling Decisions
• Include Transportation

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Research Topics in CPFR

• Process Model How and Where does the CPFR model
  (e.g., forecast collaboration) fit into the
  supply-chain process?
• Front-End Agreements How Should agreements be
  structured, performance measured, and benefits
  shared?
• Data Sharing How should data be shared
  (aggregation/disaggregation issues)?
• Exception Processing What constitutes an
  exception?

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Secure Supply-Chain Collaboration

• with
• Mikhail Atallah
• Vinayak Deshpande

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The Starting Point....

• Information Asymmetry is one of the major
  sources of inefficiency in Managing Supply Chains
• Wrong Investment in Capacity
• Misallocation of Resources
• Distorted Prices
• Reduced Customer Service
• Unnecessary Additional Costs

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.... there are Very Good Reasons for Keeping
Private Information Private

• Fear that Supply-Chain Partner will Take
  Advantage of Private Information
• Fear that Private Information will Leak to a
  Competitor

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So, then, the Obvious Question...
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Is it possible to enjoy the benefits of
Information-Sharing without Disclosing Private
Information?

• It Depends

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If the Value of Private Information is the
Information Itself, then..

• ...obviously, information must be disclosed for
  value to be created

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But, if the Value of Private Information is a
Decision .......

• ...then it is possible to create value without
  Disclosing Private Information

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Example

• In CPFR
• Determine agreed-upon planned orders without
  sharing forecasts, etc.

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Secure Multi-Party Computation

• SMC is Decades Old
• Elegant Theory
• General Results w.r.t. Existence, Complexity,
  etc.
• Recently, Practical Protocols for Specific
  Problems
• Ex. Electronic Voting
• Information Retrieval

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SMC Paradigm

• Alice has Private Information XA
• Bob has Private Information XB
• Want to Determine f(XA, XB)
• f(XA, XB) is well defined
• No Trusted Third Party
• Provide f(XA, XB) to Alice, Bob, both, or Neither

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We are Developing Secure Multi-Party Protocols
for Supply-Chain Management

• Secure Supply-Chain Collaboration

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More Specifically...

• ...we are developing protocols to enable
  Supply-Chain Partners to Make Decisions that
  Cooperatively Achieve Desired System Goals
  without Revealing Private Information

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Our Goals

• Develop and Apply SSCC Protocols to Some
  Well-Known SCM Problems
• Simple e-Auction Scenarios
• Simple Capacity-Allocation Scenarios
• Bullwhip Scenarios
• Compare Effectiveness of Protocols vs.
  non-cooperative decision-making

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Our Goals (cont.)

• Develop Proof-of-Concept Software
• Examine Security versus Cost Tradeoffs

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Ex Capacity Allocation

• Single Supplier N Retailers Single Sales Period
• Supplier has constant marginal production cost,
  but fixed capacity, K
• Retailers operate in non-competing markets each
  retailer i has private information, qi, about its
  market that influences its order to the supplier
  Supplier has prior Pr(q)
• If SOrdersi K, Supplier Uses Pre-announced
  Allocation Mechanism

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Cachon and Lariviere (MS, 99)

• Examine this scenario from perspective of the
  retailers in non-cooperative setting
• Linear Demand Market-Clearing Price, r(q)
• r(q) qi - q
• Several Very Interesting Results
• Retailers will over-order even if Pareto
  allocation mechanism is used
• Supplier and Supply-Chain Profit can increase if
  a truth-telling mechanism is replaced by
  manipulable one.

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Deshpande Schwarz (02)

• Examine this scenario and a newsvendor scenario
  from perspective of maximizing Supply-Chain
  Profit assuming truth-telling
• Derive conditions under which two commonly-used
  allocation mechanisms maximize supply-chain
  profit
• Our SSCC Protocols use these mechanisms without
  revealing the retailers qis

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Allocation Mechanisms

• Supplier has Capacity K
• Retailers place orders q1, q2, q3,..qN
• Assume Sqi K
• Linear Allocation qi qi - (Sqi- K)/N
• Proportional Allocation qi qi (K/ Sqi )

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Proportional Allocation Protocol

• 1. Retailers choose a random R
• 2. Every retailer sends its Rqi to Supplier
• 3. System computes
• D (RSqi/K)
• and sends it to all the retailers
• 4. Every retailer computes its allocation
• qi Rqi/D
• and sends to supplier

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Notes

• We are assuming that retailers will tell the
  truth i.e., reveal the quantity they truly want
  (one that is consisent with their qi)
• Supply Chain Profit will be reduced if they dont
• Contracting Mechanisms will be Required

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Notes

• The Supplier Learns each Retailers qi, but not
  qi
• Supplier Might be able to Infer qi
• Shipping Proxies

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We Have Only Just Begun...

• Tough Issues to Deal with
• SMC Complexities e.g.,
• How to Deal with Collusion
• Computational Complexity (e.g., simultaneity)
• Supply-Chain Modeling Complexities e.g.
• Contracting/Incentive Issues
• SSCC Complexities e.g.,
• Inverse Optimization
• Bobs Objective is fB(xA, xB) Alices is fA((xA,
  xB)

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Discussion....