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Supply-Chain Management: A View of the Future

Supply-Chain Management: A View of the Future

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Supply-Chain Management: A View of the Future

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  1. Supply-Chain Management:A View of the Future Leroy B. Schwarz Krannert School of Management Purdue University Supported by e-Enterprise Center at Discovery Park

  2. Outline • Supply-Chain Management of “Yesterday” • How Modeled • How Practiced • Supply-Chain Management of “Today” • How Practiced • How Modeled

  3. Outline (cont.) • Introduce Paradigm called: “IDIB Portfolio” • DescribeMy Vision of the “Future”of SCM • Provide an Overview of 2 Projects • Collaborative Decision-Making and Implementation • Secure Supply-Chain Collaboration

  4. 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”

  5. 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

  6. 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

  7. 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

  8. Consequences of this: ==> Huge Buffers • Raw, WIP, and Finished-Goods Inventories • Capacity Buffers (e.g., understated capacity) • Leadtime Buffers (e.g., overstated leadtime)

  9. “Yesterday’s” Relationship: “Mismatched” • Models • Too Specialized • Required More Information than Practice Had • Practice • Inexperienced with Models & Computers • Confused by Models • Suspicious of Models

  10. SCM Practice “Today” • The Beginnings of “Real” SCM for Single-Owner Chains • Ex: Wal-Mart’s Retail Link Target’s Partners OnLine • Capabilities • Broadcast SKU-level Data Across the Chain • Observe Status ==> Implemetation “Contractible”

  11. Results: • Huge Reductions in Buffers ==> Lower Operating Costs • Improved Competitiveness • Lower Prices • More Customization • Higher Availability

  12. 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

  13. 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”

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

  15. Models for Assessing the Impact of Decentralized Decision-Making and/or Asymmetric Information • Ex: Lee, et al. “Bullwhip” Paper (MS 43:4) • Results: • Assessments of “Agency Loss” • Non-bathtub Shaped Loss Functions • Contracting Mechanisms to Improve/Optimize Performance

  16. 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

  17. 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

  18. What About the Future of SCM?

  19. First.......

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

  21. “Managing” anything can be viewed as 4 related activities: • Getting Information • Making Decisions • Implementing Decisions • Buffering against Imperfections in information, decision-making, or implementation

  22. Every “Management System” is, in fact, 4 Sub-Systems • TheInformation Systemprovides information • The Decision-Making System makes decisions • The Implementation Systemimplements decisions • TheBuffer Systemcopes with imperfections in information, decision-making, or implementation

  23. 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

  24. 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

  25. 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

  26. Each ... Characteristics (cont.) The Buffer System • Quality Characteristics • Form • Robustness • Etc. • Cost: Increasing and Marginally-Increasing with Quality

  27. IDIB “Portfolio”? • Like a Financial Portfolio, the IDIB System requires an investment of Dollars • Like a Financial Porfolio, each Subsystem’s Characteristics Should Complement the Characteristics of the Others • Ex: Robust Buffer System Complements an Inaccurate Information System • Ex: Tradeoffs Among Buffer Sub-Systems

  28. 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!

  29. 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

  30. 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?

  31. 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

  32. 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”

  33. 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

  34. 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

  35. 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.......?

  36. 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”

  37. The Future of Supply-Chain Management Involves CollaborativeDecision-Making and/or Implementation

  38. 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

  39. 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

  40. Models for Collaborative Supply-Chain Decision-Making with Vinayak Deshpande & Jennifer Ryan

  41. Starting Point is “Collaborative Planning, Forecasting, and Replenishment” (CPFR)

  42. 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

  43. 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

  44. 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

  45. CPFR: Who’s Behind it? Federated Department Stores CORNING Consumer Products Staples FIELDCREST CANNON JCPenney Mead School & Office Schnuck Markets Benchmarking Partners QRS

  46. 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, • P&G & Meier, Target, Wal-Mart • Nabisco & Wegman’s, etc. • ‘00:1st Production Rollout: K-Mart

  47. CPFR’s Future: • “n-Tier” Collaboration • Extension to Include Master-Scheduling Decisions • Include Transportation

  48. 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?

  49. Secure Supply-Chain Collaboration with Mikhail Atallah & Vinayak Deshpande

  50. The Starting Point.... “Information Asymmetry” isone 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