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Managing Uncertainty in the Supply Chain David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology Tel: 617-253-6160 E-mail:

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Presentation on theme: "Managing Uncertainty in the Supply Chain David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology Tel: 617-253-6160 E-mail:"— Presentation transcript:

1 Managing Uncertainty in the Supply Chain David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology Tel:

2 ©Copyright 2003 D. Simchi-Levi Outline of the Presentation uIntroduction uPush-Pull Systems uCase Studies uHigh Tech uAutomotive uElectrical Components

3 ©Copyright 2003 D. Simchi-Levi Todays Supply Chain Pitfalls Long Lead Times Uncertain Demand Complex Product Offering Component Availability System Variation Over Time

4 ©Copyright 2003 D. Simchi-Levi The Dynamics of the Supply Chain Order Size Time Source: Tom Mc Guffry, Electronic Commerce and Value Chain Management, 1998 Customer Demand Customer Demand Retailer Orders Distributor Orders Production Plan

5 ©Copyright 2003 D. Simchi-Levi The Dynamics of the Supply Chain Order Size Time Source: Tom Mc Guffry, Electronic Commerce and Value Chain Management, 1998 Customer Demand Customer Demand Production Plan

6 ©Copyright 2003 D. Simchi-Levi What are the Causes…. Promotional sales Volume and Transportation Discounts Inflated orders Demand Forecast Long cycle times Lack of Information

7 ©Copyright 2003 D. Simchi-Levi Example: Automotive Supply Chain Custom order takes 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 ©Copyright 2003 D. Simchi-Levi Supply Chain Strategies Achieving Global Optimization Managing Uncertainty –Risk Pooling –Risk Sharing

9 ©Copyright 2003 D. Simchi-Levi 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 From Sequential Optimization to Global Optimization Source: Duncan McFarlane

10 ©Copyright 2003 D. Simchi-Levi A new Supply Chain Paradigm A shift from a Push System... –Production decisions are based on forecast …to a Push-Pull System

11 ©Copyright 2003 D. Simchi-Levi From Make-to-Stock Model…. Configuration Assembly Suppliers

12 ©Copyright 2003 D. Simchi-Levi 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 ©Copyright 2003 D. Simchi-Levi A new Supply Chain Paradigm A shift from a Push System... –Production decisions are based on forecast …to a Push-Pull System

14 ©Copyright 2003 D. Simchi-Levi Push-Pull Supply Chains The Supply Chain Time Line Low Uncertainty High Uncertainty Customers Suppliers PUSH STRATEGY PULL STRATEGY Push-Pull Boundary

15 ©Copyright 2003 D. Simchi-Levi 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 ©Copyright 2003 D. Simchi-Levi ….to Assemble-to-Order Model Configuration Assembly Suppliers

17 ©Copyright 2003 D. Simchi-Levi Outline of the Presentation uIntroduction uPush-Pull Systems uCase Studies uHigh Tech uAutomotive uElectrical Components

18 ©Copyright 2003 D. Simchi-Levi 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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22 ©Copyright 2003 D. Simchi-Levi Comparison of Performance Measures

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25 ©Copyright 2003 D. Simchi-Levi Comparison of Performance Measures

26 ©Copyright 2003 D. Simchi-Levi 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 ©Copyright 2003 D. Simchi-Levi Outline of the Presentation uIntroduction uPush-Pull Systems uCase Studies uHigh Tech uAutomotive uElectrical Components

28 ©Copyright 2003 D. Simchi-Levi 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)

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 Raw Materials Water Pump Kit FG Committed Service Time (months) PDC 1PDC 2PDC 3PDC 7PDC 6PDC 5PDC 4PDC 10PDC 9PDC 8PDC 13PDC 12PDC 11 D DD D DD D DD D D D DD D D D D D D D D DD D D DDDDDD

30 Inventory Drivers Root Cause AnalysisInventory by Location ItemHolding Cost Part 1$1.37 Part 2$0.02 Part 3$0.09 Part 4$0.47 Part 5$0.02

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 Inventory Turns $26.5M$17.2M$34.5M$36.5M$38.3MFree Cash Flow Prioritizing Savings Opportunities

35 Fewer Stock-outs & Improved Inventory Turns SUPPLIERPLANT Raw Materials Finished Goods Safety Stock Savings: 33% CANADAMICHIGANBOSTON NEVADA MINNESOTAW VIRGINADENVERLOS ANGELES ILLINOIS $35.17 $63.25 $35.01 $90.45 $33.45 $35.83 $ $ $43.31 $50.21 $ $ $94.92 $53.19 $30.76 $63.14 $34.68 $48.62 $43.87 $ $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

36 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

37 ©Copyright 2003 D. Simchi-Levi Outline of the Presentation uIntroduction uPush-Pull Systems uCase Studies uHigh Tech uAutomotive uElectrical Components

38 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) (3,1) (2,0) (3,1) CR MFG (3,1) (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 Warehouse – Warehouse Transit Lanes 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 LocationInventory by Reason

43 Sensitivity Analysis A.Customer Holding Cost is not significant (< 0.01%) B.With no Transit Time Variance from the Ports to PA RDC the Cost is reduced by 5% C.Reviewing Inventory Daily at warehouses can reduce Inventory Holding Cost by 14%

44 ©Copyright 2003 D. Simchi-Levi Inventory Savings $19 MM freed cash flow by globally optimizing inventory 5.0 = Inv Turns Could move from the lower quartile to the medium quartiles

45 ©Copyright 2003 D. Simchi-Levi 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

46 ©Copyright 2003 D. Simchi-Levi 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

47 ©Copyright 2003 D. Simchi-Levi


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