1. 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

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 to PA RDC the Cost is reduced by 5%

Reviewing Inventory Daily at warehouses can reduce Inventory Holding Cost by 14%

44. 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 45. 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. 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.