Managing Uncertainty in the Supply Chain David Simchi-Levi Professor of Engineering Systems...

Managing Uncertainty in the Supply Chain

David Simchi-Levi

Professor of Engineering SystemsMassachusetts Institute of Technology

Tel: 617-253-6160E-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

Source: Tom Mc Guffry, Electronic Commerce and Value Chain Management, 1998

CustomerDemand

Retailer OrdersRetailer OrdersDistributor OrdersDistributor Orders

Production PlanProduction Plan

The Dynamics of the Supply Chain

Source: Tom Mc Guffry, Electronic Commerce and Value Chain Management, 1998

CustomerDemand

Production PlanProduction 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

Procurement Planning

ManufacturingPlanning

DistributionPlanning

DemandPlanning

Sequential Optimization

Supply Contracts/Collaboration/Integration/DSS

Procurement Planning

ManufacturingPlanning

DistributionPlanning

DemandPlanning

Global Optimization

From Sequential Optimization to Global Optimization

Source: Duncan McFarlane

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….ConfigurationAssemblySuppliers

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

forecast

• …to a Push-Pull System

Push-Pull Supply ChainsThe Supply Chain Time Line

Low Uncertainty High Uncertainty

CustomersSuppliersPUSH STRATEGY PULL STRATEGY

Push-Pull Boundary

forecast

• …to a Push-Pull System– Parts inventory is replenished based

on forecasts– Assembly is based on accurate

customer demand

….to Assemble-to-Order ModelConfigurationAssemblySuppliers

Introduction

Push-Pull Systems

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 InventoryAnalystTM

from LogicTools (www.logic-tools.com)

How to Read the Diagrams

PART 2DALLAS ($0.50)

PART 1DALLAS ($260)

PART 3MONTGOMERY ($220)

A Gray Box is a processing stage

Number under the box is the processing time

Cost in the box is the value of the product

Number on the lane is the transit time

Number in the white box is the commitment time to the next stage

Bins indicate safety stock levels- more Red

means more safety stock, empty means no

safety stock

PART 2DALLAS ($0.50) 0

PART 1DALLAS ($260)

PART 6RALEIGH ($3)

PART 4MALAYSIA ($180)

PART 5CHARLESTON ($12)

70PART 7DENVER ($2.50) 58 4

PART 1DALLAS ($260) 30

PART 6RALEIGH ($3)

Safety Stock Cost = $74,100/yr

Safety Stock Cost = $45,400/yr (39% savings)

PART 7DENVER ($2.50) 58

Safety Stock Cost = $53,700/yr (28% savings, 50% reduction in LT)

PART 1DALLAS ($260) 15

PART 6RALEIGH ($3)

PART 7DENVER ($2.50)

Comparison of Performance Measures

Scenario

Safety Stock Holding Cost

($/yr)

Lead Time to Customer

(days)

Cycle Time (days)

Inventory Turns

(turns/yr)1: Baseline $74,100 30 105 1.22: Optimization $45,400 30 105 1.43: Shorten Lead Time $53,700 15 105 1.3

PART 1DAL ($535)

PART 2DAL ($55)

PART 3DAL ($6) 50

PART 8DAL ($65) 56

PART 10DAL ($35)

PART 4DAL ($285)

PART 5DAL ($3)

PART 6DAL ($18)

PART 7DAL ($9) 21

PART 9DAL ($30)

PART 23DAL ($30)

50 PART 18DAL ($35)

51 PART 11DAL ($40)

PART 38NJ ($8) 8

PART 22DAL ($28) 23 PART 17

DAL ($30) 26

PART 21NZ ($18) 41

PART 16DAL ($21) 81

PART 30PHI ($6) 4

PART 20WAS ($42)

18 PART 15DAL ($60)

26PART 29WAS ($40)

PART 19DAL ($210) 61 PART 12

DAL ($260) 62

PART 24NJ ($30) 16

PART 25WAS ($75) 52

PART 26DAL ($80) 25

PART 27NJ ($4) 1

PART 28DAL ($12) 17

PART 32NJ ($22) 10

PART 33WAS ($30) 42

PART 34WAS ($25) 49

PART 35NJ ($35)

PART 36NJ ($40) 20

PART 37DAL ($8)

PART 39TAI ($15) 5

PART 40NZ ($22) 12

PART 41PHI ($32) 6

PART 42PHI ($2)

PART 13MEX ($11)

PART 14MEX ($4) 10

PART 31SEA ($20)

305612

Safety Stock Cost = $95,000/yr

PART 1DAL ($535)

PART 2DAL ($55)

PART 3DAL ($6) 26

PART 8DAL ($65)

PART 10DAL ($35) 26

PART 4DAL ($285)

PART 5DAL ($3) 4

PART 6DAL ($18)

PART 7DAL ($9) 21

PART 9DAL ($30) 26

PART 23DAL ($30)

21 PART 18DAL ($35)

22 PART 11DAL ($40)

PART 38NJ ($8)

PART 22DAL ($28)

11PART 17DAL ($30)

PART 21NZ ($18)

41 PART 16DAL ($21)

PART 30PHI ($6)

PART 20WAS ($42)

18PART 15DAL ($60)

26PART 29WAS ($40)

PART 19DAL ($210) 22 PART 12

DAL ($260) 23

PART 24NJ ($30) 14

PART 25WAS ($75) 13

PART 26DAL ($80)

PART 27NJ ($4) 1

PART 28DAL ($12)

PART 32NJ ($22) 8

PART 33WAS ($30)

PART 34WAS ($25) 10

PART 35NJ ($35)

PART 36NJ ($40) 11

PART 37DAL ($8)

PART 39TAI ($15)

PART 40NZ ($22) 12

PART 41PHI ($32) 6

PART 42PHI ($2)

PART 13MEX ($11)

PART 14MEX ($4) 10

PART 31SEA ($20)

305612

Safety Stock Cost = $36,600/yr(62% savings)

Comparison of Performance Measures

Scenario

Safety Stock Holding Cost

($/yr)

Lead Time to Customer

(days)

Cycle Time (days)

Inventory Turns

(turns/yr)1: Baseline $95,000 30 86 1.52: Optimization $36,600 30 86 1.8

Safety Stock vs. Quoted Lead Time

Safety Stock Cost vs. Quoted Lead Time

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

$70,000

$80,000

$90,000

$100,000

0 20 40 60 80 100

Lead Time Quoted to Customer (days)

Baseline Cost

Optimized Cost

For a given lead-time, the optimized supply chain provides reduced costs

For a given cost, the optimized supply chain

provides better lead-times

Introduction

Push-Pull Systems

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

Raw Materials

Water Pump Kit FG

Committed Service Time(months)

PDC 1 PDC 2 PDC 3

PDC 10

PDC 13 PDC 12 PDC 11

D DDDD D

ck P art 5

P art 4

P art 3P art 2

P art 1

$1,000.00

$2,000.00

$3,000.00

$4,000.00

$5,000.00

Inventory Drivers

Root Cause AnalysisInventory by Location

Item Holding Cost

Part 1 $1.37

Part 2 $0.02

Part 3 $0.09

Part 4 $0.47

Part 5 $0.02

P art 1

P art 2

P art 3P art 4

P art 4

$500.00

$1,000.00

$1,500 .00

$2,000.00

$2,500.00

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

Total Holding Cost

P lant3M,

R D C 0

P lant 0,R D C 1

P lant 0,R D C 2

P lan t 0,R D C 3

P lant 0,R D C 1W eek

P lant 0,R D C 2W eeks

P lan t 0,R D C 4w eeks

P lant To W arehous e Ho ld ing C os t

P lant To P lan t Holding C os t

W arehous e Ho ld ing C os t

P lant O utbound Hold ing C os t

P lant Inbound Ho ld ing C os t

IA – Impact of changes in CST to Dealers

Cost Vs CST

$2,000

$4,000

$6,000

$8,000

$10,000

0 5 10 15 20 25 30 35 40 45

Committed Service Timefrom PDC to Dealers

Cost w ith CST changes at Plants and DCs Cost w ith CST changes only at the DCs

IA – Impact of Supplier CST

Cost Vs Supplier CST for Oil Filter

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

0 5 10 15 20 25 30

Committed Service Time (days)

Current Baseline (Plant0, PDC 0 CST)

Only PDCshold Inventory

ReducedSupplier LT

ReducedVariability

Increased PDCCST

18.4 16.2 20.2 20.7 21.213.9 Inventory Turns

$26.5M $17.2M $34.5M $36.5M $38.3M Free Cash Flow

Prioritizing Savings Opportunities

Baseline Only PDCs holdInventory

Reduced SupplierLT

Reduced Variability IncreasedCustomer CST

Plant Inbound Holding Cost Plant Outbound Holding Cost

Warehouse Holding Cost Plant To Plant Holding Cost

Plant To Warehouse Holding Cost

Fewer Stock-outs & Improved Inventory Turns

SUPPLIER PLANT

Raw Materials

Finished Goods

Safety Stock Savings: 33% CANADA

MICHIGAN

BOSTON

NEVADA

MINNESOTA

W VIRGINA

DENVER

LOS ANGELES

ILLINOIS

$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

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

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

Supplier 1 Supplier 2

Supplier Comparison

Plant To Warehouse Holding Cost

Plant To Plant Holding Cost

Warehouse Holding Cost

Plant Outbound Holding Cost

Plant Inbound Holding Cost

Introduction

Push-Pull Systems

US PLANTS

Supply Chain Structure

ASIAN PLANTS

EUROPEANPLANTS

LATIN AMERICAN PLANTS

CA PORT

PHIL PORT

MIAMI PORT

MFG #1CustomersInventory Allowed

Inventory Not Allowed

(35,4)

(15,3)

(10,2)

CR MFG

(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

• 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-PARDC-CA

RDC-GARDC-IL

RDC-TXMFG #1

MFG #2

Across the Supply Chain

Across Warehouses

Warehouse Customer In Transit from Plant In Transit betw eenWarehouse

In Transit toCustomer

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

500000

1000000

1500000

2000000

2500000

3000000

3500000

4000000

4500000

5000000

Cycle Stock Safety Stock

WAREHOUSES

Top 20% SKUs Bottom 80% SKUs

Cycle Stock Safety Stock

CUSTOMERS

Top 20% SKUs Bottom 80% SKUs

Inventory Drivers

Inventory by Location Inventory by ReasonP

39-1701

39-2700-169-1200

In Trans itInventory Cycle Stock

Safety Stock

39-1701

39-2700-1

69-1200

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%

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

A B CW arehouse To Cus tomer Holding Cos t W arehouse To W arehouse Holding Cos t P lant To W arehouse Holding Cos t Cus tom er Holding Cos t W arehouse Holding Cos t

$100 $95

$81$70

Current Inv Proper Levels OptPositioning

ChangingPolicies

Inventory Savings$19 MM freed cash

flow by globally optimizing inventory

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

• Safety Stock at each node depends

on attributes of all nodes• Most complete model available• Positions safety stock across the

network

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