managing uncertainty in the supply chain david simchi-levi professor of engineering systems...
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![Page 1: Managing Uncertainty in the Supply Chain David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology Tel: 617-253-6160 E-mail:](https://reader034.vdocuments.us/reader034/viewer/2022052504/5516eb00550346fe558b4909/html5/thumbnails/1.jpg)
Managing Uncertainty in the Supply Chain
David Simchi-Levi
Professor of Engineering SystemsMassachusetts Institute of Technology
Tel: 617-253-6160E-mail: [email protected]
![Page 2: Managing Uncertainty in the Supply Chain David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology Tel: 617-253-6160 E-mail:](https://reader034.vdocuments.us/reader034/viewer/2022052504/5516eb00550346fe558b4909/html5/thumbnails/2.jpg)
©Copyright 2003 D. Simchi-Levi
Outline of the Presentation
Introduction
Push-Pull Systems
Case Studies High Tech Automotive Electrical Components
![Page 3: Managing Uncertainty in the Supply Chain David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology Tel: 617-253-6160 E-mail:](https://reader034.vdocuments.us/reader034/viewer/2022052504/5516eb00550346fe558b4909/html5/thumbnails/3.jpg)
©Copyright 2003 D. Simchi-Levi
Today’s Supply Chain Pitfalls
• Long Lead Times• Uncertain Demand• Complex Product Offering• Component Availability• System Variation Over Time
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©Copyright 2003 D. Simchi-Levi
The Dynamics of the Supply Chain
Ord
er
Siz
e
Time
Source: Tom Mc Guffry, Electronic Commerce and Value Chain Management, 1998
CustomerDemand
CustomerDemand
Retailer OrdersRetailer OrdersDistributor OrdersDistributor Orders
Production PlanProduction Plan
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©Copyright 2003 D. Simchi-Levi
The Dynamics of the Supply Chain
Ord
er
Siz
e
Time
Source: Tom Mc Guffry, Electronic Commerce and Value Chain Management, 1998
CustomerDemand
CustomerDemand
Production PlanProduction Plan
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©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
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©Copyright 2003 D. Simchi-Levi
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”
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©Copyright 2003 D. Simchi-Levi
Supply Chain Strategies
• Achieving Global Optimization• Managing Uncertainty
– Risk Pooling– Risk Sharing
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©Copyright 2003 D. Simchi-Levi
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
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©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
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©Copyright 2003 D. Simchi-Levi
From Make-to-Stock Model….ConfigurationAssemblySuppliers
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©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
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©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
![Page 14: Managing Uncertainty in the Supply Chain David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology Tel: 617-253-6160 E-mail:](https://reader034.vdocuments.us/reader034/viewer/2022052504/5516eb00550346fe558b4909/html5/thumbnails/14.jpg)
©Copyright 2003 D. Simchi-Levi
Push-Pull Supply ChainsThe Supply Chain Time Line
Low Uncertainty High Uncertainty
CustomersSuppliersPUSH STRATEGY PULL STRATEGY
Push-Pull Boundary
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©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
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©Copyright 2003 D. Simchi-Levi
….to Assemble-to-Order ModelConfigurationAssemblySuppliers
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©Copyright 2003 D. Simchi-Levi
Outline of the Presentation
Introduction
Push-Pull Systems
Case Studies High Tech Automotive Electrical Components
![Page 18: Managing Uncertainty in the Supply Chain David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology Tel: 617-253-6160 E-mail:](https://reader034.vdocuments.us/reader034/viewer/2022052504/5516eb00550346fe558b4909/html5/thumbnails/18.jpg)
©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 InventoryAnalystTM
from LogicTools (www.logic-tools.com)
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How to Read the Diagrams
PART 2DALLAS ($0.50)
PART 1DALLAS ($260)
30
PART 3MONTGOMERY ($220)
15
0
88
2
0
15
5
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
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PART 2DALLAS ($0.50) 0
PART 1DALLAS ($260)
30
PART 6RALEIGH ($3)
PART 4MALAYSIA ($180)
PART 5CHARLESTON ($12)
PART 3MONTGOMERY ($220)
8
15
5
15
x2
88
7
37
70PART 7DENVER ($2.50) 58 4
3
3
28 2
0
PART 2DALLAS ($0.50)
5
PART 1DALLAS ($260) 30
PART 6RALEIGH ($3)
PART 4MALAYSIA ($180)
PART 5CHARLESTON ($12)
PART 3MONTGOMERY ($220)
8
15
5
15
x2
13
7
37
324
3
3
28 2
0
Safety Stock Cost = $74,100/yr
Safety Stock Cost = $45,400/yr (39% savings)
PART 7DENVER ($2.50) 58
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Safety Stock Cost = $53,700/yr (28% savings, 50% reduction in LT)
PART 2DALLAS ($0.50)
0
PART 1DALLAS ($260) 15
PART 6RALEIGH ($3)
PART 4MALAYSIA ($180)
PART 5CHARLESTON ($12)
PART 3MONTGOMERY ($220)
8
15
5
15
50
7
37
324
3
3
28 2
0
PART 7DENVER ($2.50)
58
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©Copyright 2003 D. Simchi-Levi
Comparison of Performance Measures
Scenario
Safety Stock Holding Cost
($/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
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PART 1DAL ($535)
30
PART 2DAL ($55)
55
PART 3DAL ($6) 50
PART 8DAL ($65) 56
PART 10DAL ($35)
38
PART 4DAL ($285)
65
PART 5DAL ($3)
4
PART 6DAL ($18)
46
PART 7DAL ($9) 21
PART 9DAL ($30)
82
PART 23DAL ($30)
50 PART 18DAL ($35)
51 PART 11DAL ($40)
54
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)
12
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)
3
PART 36NJ ($40) 20
PART 37DAL ($8)
10
PART 39TAI ($15) 5
PART 40NZ ($22) 12
PART 41PHI ($32) 6
PART 42PHI ($2)
3
PART 13MEX ($11)
24
PART 14MEX ($4) 10
PART 31SEA ($20)
40
4
1314
6
50
31639
28 3
35
32
6282
3
2
2
1
13
1
2
3
7
4
4
14
3
8
8
3
305612
35
15
12316
3
Safety Stock Cost = $95,000/yr
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PART 1DAL ($535)
30
PART 2DAL ($55)
26
PART 3DAL ($6) 26
PART 8DAL ($65)
26
PART 10DAL ($35) 26
PART 4DAL ($285)
26
PART 5DAL ($3) 4
PART 6DAL ($18)
26
PART 7DAL ($9) 21
PART 9DAL ($30) 26
PART 23DAL ($30)
21 PART 18DAL ($35)
22 PART 11DAL ($40)
25
PART 38NJ ($8)
6
PART 22DAL ($28)
11PART 17DAL ($30)
14
PART 21NZ ($18)
41 PART 16DAL ($21)
25
PART 30PHI ($6)
4
PART 20WAS ($42)
18PART 15DAL ($60)
26PART 29WAS ($40)
12
PART 19DAL ($210) 22 PART 12
DAL ($260) 23
PART 24NJ ($30) 14
PART 25WAS ($75) 13
PART 26DAL ($80)
16
PART 27NJ ($4) 1
PART 28DAL ($12)
16
PART 32NJ ($22) 8
PART 33WAS ($30)
10
PART 34WAS ($25) 10
PART 35NJ ($35)
3
PART 36NJ ($40) 11
PART 37DAL ($8)
9
PART 39TAI ($15)
5
PART 40NZ ($22) 12
PART 41PHI ($32) 6
PART 42PHI ($2)
3
PART 13MEX ($11)
24
PART 14MEX ($4) 10
PART 31SEA ($20)
40
4
1314
6
50
31639
283
35
32
6282
3
2
2
1
13
1
2
3
7
4
4
14
3
8
8
3
305612
35
15
12316
3
Safety Stock Cost = $36,600/yr(62% savings)
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©Copyright 2003 D. Simchi-Levi
Comparison of Performance Measures
Scenario
Safety Stock Holding Cost
($/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
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©Copyright 2003 D. Simchi-Levi
Safety Stock vs. Quoted Lead Time
Safety Stock Cost vs. Quoted Lead Time
$0
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
$70,000
$80,000
$90,000
$100,000
0 20 40 60 80 100
Lead Time Quoted to Customer (days)
Sa
fety
Sto
ck
Co
st
($/y
ea
r)
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
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©Copyright 2003 D. Simchi-Levi
Outline of the Presentation
Introduction
Push-Pull Systems
Case Studies High Tech Automotive Electrical Components
![Page 28: Managing Uncertainty in the Supply Chain David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology Tel: 617-253-6160 E-mail:](https://reader034.vdocuments.us/reader034/viewer/2022052504/5516eb00550346fe558b4909/html5/thumbnails/28.jpg)
©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)
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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)
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 DD
D
D
D
D
D
DD
DD
D
D
D
D
D
D
D
D
DD DD
D D
D DDDD D
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Cy
cle
Sto
ck
SS
(N
LT
& V
ar)
SS
(U
ps
tre
am
SF
)
In t
ran
sit
Sto
ck P art 5
P art 4
P art 3P art 2
P art 1
$0.00
$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
In t
ran
sit
to P
lan
ts
Pla
nt
RM
Pla
nt
FG
Intr
an
sit
to W
hs
e
Wh
se
P art 1
P art 2
P art 3P art 4
P art 4
$0.00
$500.00
$1,000.00
$1,500 .00
$2,000.00
$2,500.00
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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
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Pla
nt
0,
PD
C 0
Pla
nt
0,
PD
C 1
da
y
Pla
nt
0,
PD
C 2
da
ys
Pla
nt
0,
PD
C 3
da
ys
Pla
nt
0,
PD
C 1
We
ek
Pla
nt
0,
PD
C 2
We
ek
s
Pla
nt
0,
PD
C 4
we
ek
s
Total Holding Cost
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
P lant3M,
R D C 0
P lant 0,R D C 1
day
P lant 0,R D C 2
days
P lan t 0,R D C 3
days
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
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IA – Impact of changes in CST to Dealers
Cost Vs CST
$0
$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
Co
st/M
on
th
Cost w ith CST changes at Plants and DCs Cost w ith CST changes only at the DCs
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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)
Co
st
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0
2000
4000
6000
8000
10000
12000
14000
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
0
2000
4000
6000
8000
10000
12000
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
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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
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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
$0
$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
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Outline of the Presentation
Introduction
Push-Pull Systems
Case Studies High Tech Automotive Electrical Components
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US PLANTS
Supply Chain Structure
ASIAN PLANTS
EUROPEANPLANTS
LATIN AMERICAN PLANTS
CA PORT
PHIL PORT
MIAMI PORT
PA DC
CA DC
GA DC
IL DC
TX DC
MFG #1CustomersInventory 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)
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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
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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
24.6%
0.3%
49.0%
9.6%
16.6%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
50.0%
Warehouse Customer In Transit from Plant In Transit betw eenWarehouse
In Transit toCustomer
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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
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
4000000
4500000
5000000
Cycle Stock Safety Stock
WAREHOUSES
Top 20% SKUs Bottom 80% SKUs
0
2000
4000
6000
8000
10000
12000
Cycle Stock Safety Stock
CUSTOMERS
Top 20% SKUs Bottom 80% SKUs
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Inventory Drivers
Inventory by Location Inventory by ReasonP
lant
- W
hse
In T
ran
sit
War
ehou
se
Whs
e -
Wh
se In
Tra
nsi
t
Whs
e -
Cus
t In
Tra
nsit
Cus
tom
er
39-1701
39-2700-169-1200
0
50
100
150
200
250
300
350
400
In Trans itInventory Cycle Stock
Safety Stock
39-1701
39-2700-1
69-1200
0
20
40
60
80
100
120
140
160
180
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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%
0
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
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$100 $95
$81$70
$0
$20
$40
$60
$80
$100
$120
Current Inv Proper Levels OptPositioning
ChangingPolicies
Inve
nto
ry (
in $
MM
)
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
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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
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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
0
1
2
3
4
5
6
7
8
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