"How to manage uncertainty in the supply chain, " David

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Transcript of "How to manage uncertainty in the supply chain, " David

  • 1. Managing Uncertainty in the Supply Chain David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology Tel: 617-253-6160 E-mail: [email protected]

2. Outline of the Presentation

  • Introduction
  • Push-Pull Systems
  • Case Studies
    • High Tech
    • Automotive
    • Electrical Components

3. Todays Supply Chain Pitfalls

  • Long Lead Times
  • Uncertain Demand
  • Complex Product Offering
  • Component Availability
  • System Variation Over Time

4. The Dynamics of the Supply Chain Order Size Time Source: Tom Mc Guffry, Electronic Commerce and Value Chain Management, 1998 Customer Demand Retailer Orders Distributor Orders Production Plan 5. The Dynamics of the Supply Chain Order Size Time Source: Tom Mc Guffry, Electronic Commerce and Value Chain Management, 1998 Customer Demand Production Plan 6. What are the Causes.

  • Promotional sales
  • Volume and Transportation Discounts
  • Inflated orders
  • Demand Forecast
  • Long cycle times
  • Lack of Information

7. Example: Automotive Supply Chain

  • Custom order takes 60-70 days
  • Many different products
    • High level of demand uncertainty
  • Dealers inventory does not capture demand accurately
    • GM estimates: Research shows we lose 10% to 11% of sales because the car is not available

8. Supply Chain Strategies

  • Achieving Global Optimization
  • Managing Uncertainty
    • Risk Pooling
    • Risk Sharing

9. From Sequential Optimization toGlobal Optimization Source: Duncan McFarlane ProcurementPlanning Manufacturing Planning Distribution Planning Demand Planning Sequential Optimization Supply Contracts/Collaboration/Integration/DSS ProcurementPlanning Manufacturing Planning Distribution Planning Demand Planning Global Optimization 10. A new Supply Chain Paradigm

  • A shift from a Push System...
    • Production decisions are based on forecast
  • to a Push-Pull System

11. From Make-to-Stock Model. Configuration Assembly Suppliers 12. Demand Forecast

  • The three principles of all forecasting techniques:
    • Forecasts are always wrong
    • The longer the forecast horizon the worst is the forecast
    • Aggregate forecasts are more accurate
      • Risk Pooling

13. A new Supply Chain Paradigm

  • A shift from a Push System...
    • Production decisions are based on forecast
  • to a Push-Pull System

14. Push-Pull Supply Chains The Supply Chain Time Line Customers Suppliers Low Uncertainty High Uncertainty PUSH STRATEGY PULL STRATEGY Push-Pull Boundary 15. A new Supply Chain Paradigm

  • A shift from a Push System...
    • Production decisions are based on forecast
  • to a Push-Pull System
    • Parts inventory is replenished based on forecasts
    • Assembly is based on accurate customer demand

16. .to Assemble-to-Order Model Configuration Assembly Suppliers 17. Outline of the Presentation

  • Introduction
  • Push-Pull Systems
  • Case Studies
    • High Tech
    • Automotive
    • Electrical Components

18. Shifting the Push-Pull Boundary: A Case Study

  • Manufacturer of circuit boards and other high-tech products
  • Sells customized products with high value and short life cycles
  • Multi-stage BOM
    • e.g., copper & fiberglasscircuit boardenclosureprocessor
  • Case study concerns a number of 27,000 SKUs
  • The case study employedInventoryAnalyst TM from LogicTools (www.logic-tools.com)

19. 20. 21. 22. Comparison of Performance Measures 23. 24. 25. Comparison of Performance Measures 26. Safety Stock vs. Quoted Lead Time For a given lead-time, the optimized supply chain provides reduced costs For a given cost, the optimized supply chain provides better lead-times 27. Outline of the Presentation

  • Introduction
  • Push-Pull Systems
  • Case Studies
    • High Tech
    • Automotive
    • Electrical Components

28. Case Study: Spare Part Inventory Optimization

  • INVENTORY STRATEGY
    • Optimal Safety Stock and Base Stock level at each location
    • Optimal Committed Service Time
  • NETWORK DYNAMICS
    • Understanding Inventory Drivers
    • Sensitivity Analysis
    • What-if analysis/Prioritizing Opportunities
  • SOURCING & PRICING
    • Cost implications with different suppliers
    • Supplier Contract Negotiations
    • Differential Pricing

Source: Analysis is done using InventoryAnalystfrom LogicTools (www.logic-tools.com) 29. Spare Part Network with Plant & PDC CST = 0 Supplier 2 Supplier 1 Supplier 4/ Part 1 Supplier 3 Supplier 4/ Part 2 Supplier 4/ Part 3 Water Pump Kit Plant 0.96 1.92 1.92 1.92 0.96 0.96 0 Raw Materials Water Pump Kit FG Committed Service Time (months) D D D D D D D D D D D D D D D D D D PDC 1 PDC 2 PDC 3 PDC 7 PDC 6 PDC 5 PDC 4 PDC 10 PDC 9 PDC 8 PDC 13 PDC 12 PDC 11 D D D D D D D D D D D D D D 30. Inventory Drivers Root Cause Analysis Inventory by Location $0.02 Part 5 $0.47 Part 4 $0.09 Part 3 $0.02 Part 2 $1.37 Part 1 Holding Cost Item 31. IA Impact of relaxing PDC CST

  • CST from Plants is fixed
  • As the CST to dealers increases more inventory is held at the Plants and less at the RDCs

32. IA Impact of changes in CST to Dealers 33. IA Impact of Supplier CST 34. 18.4 16.2 20.2 20.7 21.2 13.9 Inventory Turns $26.5M $17.2M $34.5M $36.5M $38.3M Free Cash Flow Prioritizing Savings Opportunities 35. Fewer Stock-outs & Improved Inventory Turns SUPPLIER PLANT Raw Materials Finished Goods Safety Stock Savings: 33% $35.17 $63.25 $35.01 $90.45 $33.45 $35.83 $136.17 $476.14 $43.31 $50.21 $118.57 $530.09 $94.92 $53.19 $30.76 $63.14 $34.68 $48.62 $43.87 $159.04 $66.89 Current Holding Cost Optimal Holding Cost

  • Optimized Inventory Positioning leads to better
  • Service Levels with lower Inventory Levels

All numbers in 000,000s CANADA MICHIGAN BOSTON NEVADA MINNESOTA W VIRGINA DENVER LOS ANGELES ILLINOIS 36. IA Supplier Choice

  • Supplier 1:
    • 4 week CST
    • 95% Service Level
    • Lead Time to Proc. Plant: Day
  • Supplier 2:
    • 2.5 week CST
    • 98% Service Level
    • Lead Time to Proc. Plant: 1 week

37. Outline of the Presentation

  • Introduction
  • Push-Pull Systems
  • Case Studies
    • High Tech
    • Automotive
    • Electrical Components

38. USPLANTS Supply Chain Structure ASIANPLANTS EUROPEAN PLANTS LATIN AMERICANPLANTS CA PORT PHIL PORT MIAMI PORT PA DC CA DC GA DC IL DC TX DC MFG #1 Customers Inventory Allowed Inventory Not Allowed (4,1) (35,4) (15,3) (10,2) (4,1) (1,0) (3,1) (4,1) (4,1) (3,1) (2,0) (3,1) (3,1) CR MFG (3,1) (4,1) (4,1) (4,1) (4,1) (4,1) (4,1) (Transit Time, Std Dev of Transit Time) 39. Supply Chain Size

  • 76 Plants
  • 10 Warehouses
  • 3105 Customers
  • 8297 Products
  • 8297 Plant Warehouse Transit Lanes
  • 20230 Warehouse Warehouse Transit Lanes
  • 64843 Warehouse Customer Transit Lanes

40. Distribution of Inventory

  • Large part of the Inventory is In Transit
    • Plant to Warehouse
    • Warehouse to Customer
    • Warehouse to Warehouse
  • Most of the Inventory at the Warehouses is in RDC-PA

RDC-PA RDC-CA RDC-GA RDC-IL RDC-TX MFG #1 MFG #2 Across the Supply Chain Across Warehouses 41. Safety Stock and Cycle Stock

  • Top 20% of SKUs account for more than 97% of inventory
  • More Inventory is held at Warehouses than at Customer Locations

42. Inventory Drivers Inventory by Location Inventory by Reason 43. Sensitivity Analysis

  • Customer Holding Cost is not significant (< 0.01%)
  • With no Transit Time Variance from the Ports