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

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  • What is shown here is how divergent these various forecasts are in relation to real demand. Why?? Because they are developed independently from each other and are dated, and unconnected to each other and the daily fluctuations in the market
  • What is shown here is how divergent these various forecasts are in relation to real demand. Why?? Because they are developed independently from each other and are dated, and unconnected to each other and the daily fluctuations in the market

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

  • Managing Uncertainty in the Supply Chain David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology Tel: 617-253-6160 E-mail: dslevi@mit.edu
  • Outline of the Presentation
    • Introduction
    • Push-Pull Systems
    • Case Studies
      • High Tech
      • Automotive
      • Electrical Components
  • Today’s Supply Chain Pitfalls
    • Long Lead Times
    • Uncertain Demand
    • Complex Product Offering
    • Component Availability
    • System Variation Over Time
  • 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
  • The Dynamics of the Supply Chain Order Size Time Source: Tom Mc Guffry, Electronic Commerce and Value Chain Management, 1998 Customer Demand Production Plan
  • What are the Causes….
    • Promotional sales
    • Volume and Transportation Discounts
    • Inflated orders
    • Demand Forecast
    • Long cycle times
    • Lack of Information
  • 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”
  • Supply Chain Strategies
    • Achieving Global Optimization
    • Managing Uncertainty
      • Risk Pooling
      • Risk Sharing
  • From Sequential Optimization to Global Optimization Source: Duncan McFarlane Procurement Planning Manufacturing Planning Distribution Planning Demand Planning Sequential Optimization Supply Contracts/Collaboration/Integration/DSS Procurement Planning Manufacturing Planning Distribution Planning Demand Planning Global Optimization
  • A new Supply Chain Paradigm
    • A shift from a Push System...
      • Production decisions are based on forecast
    • … to a Push-Pull System
  • From Make-to-Stock Model…. Configuration Assembly Suppliers
  • 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
  • A new Supply Chain Paradigm
    • A shift from a Push System...
      • Production decisions are based on forecast
    • …to a Push-Pull System
  • Push-Pull Supply Chains The Supply Chain Time Line Customers Suppliers Low Uncertainty High Uncertainty PUSH STRATEGY PULL STRATEGY Push-Pull Boundary
  • 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
  • ….to Assemble-to-Order Model Configuration Assembly Suppliers
  • Outline of the Presentation
    • Introduction
    • Push-Pull Systems
    • Case Studies
      • High Tech
      • Automotive
      • Electrical Components
  • 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 & fiberglass  circuit board  enclosure  processor
    • Case study concerns a number of 27,000 SKUs
    • The case study employed InventoryAnalyst TM from LogicTools (www.logic-tools.com)
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  • Comparison of Performance Measures
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  •  
  • Comparison of Performance Measures
  • 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
  • Outline of the Presentation
    • Introduction
    • Push-Pull Systems
    • Case Studies
      • High Tech
      • Automotive
      • Electrical Components
  • 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 InventoryAnalyst from LogicTools (www.logic-tools.com)
  • 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
  • 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
  • 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
  • IA – Impact of changes in CST to Dealers
  • IA – Impact of Supplier CST
  • 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
  • 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
  • IA – Supplier Choice
    • Supplier 1:
      • 4 week CST
      • 95% Service Level
      • Lead Time to Proc. Plant: ½ Day
    • Supplier 2:
      • 2.5 week CST
      • 98% Service Level
      • Lead Time to Proc. Plant: 1 week
  • Outline of the Presentation
    • Introduction
    • Push-Pull Systems
    • Case Studies
      • High Tech
      • Automotive
      • Electrical Components
  • US PLANTS Supply Chain Structure ASIAN PLANTS EUROPEAN PLANTS LATIN AMERICAN PLANTS 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)
  • 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
  • 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
  • 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
  • Inventory Drivers Inventory by Location Inventory by Reason
  • Sensitivity Analysis
    • Customer Holding Cost is not significant (< 0.01%)
    • With no Transit Time Variance from the Ports to PA RDC the Cost is reduced by 5%
    • Reviewing Inventory Daily at warehouses can reduce Inventory Holding Cost by 14%
  • Inventory Savings $19 MM freed cash flow by globally optimizing inventory 5.0 5.0 = Inv Turns 5.3 6.2 7.1 Could move from the lower quartile to the medium quartiles
  • Lessons Learned
    • Globally optimizing inventory can have a dramatic impact
      • Take advantage of risk pooling and inventory positioning
    • Identifying inventory drivers is not easy
      • Many policies and practices were causing poor inventory turnover ratio
      • Can be done with an inventory model
      • Highlights areas for improvement
  • Lessons Learned Manufacturing company inventory turns Heuristics Calculation Global Optimization
    • Service Level not always met
    • Excess Inventory at some location
    • Safety Stock at each node calculated independently
    • Few factors considered
    • Service Level not always met
    • Safety Stock at each node depends on attributes of all nodes
    • Most complete model available
    • Positions safety stock across the network
  •