Coordinating Capacity and Inventory in Supply Chains with High-Value ProductsMurat Erkoc

Associate Professor and Director

UM Center for Advanced Supply Chain Management

Department of Industrial Engineering

University of Miami

Coral Gables, Florida.

E-mail: merkoc@miami.edu

Matching Demand and Supply

Two types of costs occur if supply and demand do not match Supply < Demand => opportunity cost Supply > Demand => Inventory cost

To match the two Manage Supply

Capacity management Inventory management

Manage Demand Marketing/pricing

High-Tech Manufacturing Supply Chain

Rapid innovation & volatile demand

High obsolescence rate on technology

Short product life-cycle

Capital intensive facilities ($2 Billion +)

High capacity costs

Manufacturers are conservative in capacity expansion of any form

Capacity reservation contracts

CM 1Buyer1

BuyerM

Buyer2

Spot Market

CM 2

CM n Supplier K

Supplier 2

Contract Manufacturers

High-Tech Supply Chain Building Blocks

Capacity Reservation

Contract

Supplier 1

Product Life-Cycle

Integrated Channel

Supplier

w p

c V(k)

Buyer Market

F(x)

k

F dxxFkkS0

)()(expected sales given capacity k

Delegation of Control in the Supply Chain

System’s Profit (concave)

Let ko be the system optimal solution

Supplier’s newsvendor profit function

Let k* be the newsvendor solution

)()(*)( kVkScp FI

)()(*)(0 kVkScw Fs

)(*

1*

cwFk

)(1

cpFk

oo

okk *

Double Marginalization- suboptimal

Supplier

w p

c V(k)

Buyer Market

F(x)

Due to double marginalization the system optimal capacity is always greater than the supplier’s newsvendor capacity

Optimal

Competitive Outcome

Reservation Contract with Deductible Fee

For each unit of capacity reserved, the buyer is charged a fee

The fee is later deducted from the purchasing price if the reserved capacity is used by the intended customer

If the buyer’s demand exceeds the reservation amount, the orders are satisfied based on the availability of “extra capacity”

Customer reservations are locked in and some extra capacity may be built during expansion

The wholesale price w and product cost c are set at the “design-win” phase, which are not negotiable

The Sequence of Events

Supplier announces reservation fee (r) Buyer announces how

much to reserve (q)

Supplier decides how much capacity to build (k)

Demand is realized (x)

Buyer is penalized for unused reserved capacity

Supplier’s Capacity Decision

If , then the constraint must be binding

qk

xqErkVkScw Fk

s.t.

),0max()()()( Max

*kq

Buyer’s Incentive to ReserveBuyer never reserves less than k*. In fact, she reserves only if the reservation fee is below a certain threshold, rt. The reservation quantity will be at least qt , where

qt is a function of w and does not depend on p

The buyer reserves iff r rt

)()( *0 kq Bt

B

)(

)()(

t

tt

qF

qFwpr Threshold fee

Threshold Reservation Qty.

The Supplier’s Faces Three Profit Scenarios in sequence as the Buyer’s Margin Decreases

)()(

)()(*

*

kSqS

kVqVcp

t

tt

)()( *kq It

I

r minr*

Reservation Fee

Exp

ec

ted

Pro

fit

Reservation FeeE

xpe

cte

d P

rofi

t

Reservation Fee

Exp

ec

ted

Pro

fit

r min

r min

Scenario 1: High Scenario 2: Medium

Scenario 3: Low

The buyer’s margin must be sufficiently high to justify capacity reservation

Theorem: It is individually rational for the supplier and the buyer to enter the reservation contract if the buyer revenue margin p is no less than the threshold pt as follows:

Capacity reservation may create surplus for the channel

However, except for a few special cases the surplus is sub-optimal

We design two additional coordination mechanisms to achieve optimality Partial-deduction (PD) contract Cost-sharing (Options) contract

System Optimality and Reservations

Inventory Management with Horizontal Model

Subcon

Subcon

Supplier Customer

Supply Demand Buffer

Optimization Model

Distribution Method

Dem

and M

gm

t

Central Regional

Inst

alla

tion

Ech

elo

nV

is. To

CM

WIP

& D

md

1 2

34 Identify best method of distribution and

demand management for outsource models.

Scenarios

Distribution Method

Dem

and M

gm

t

Central Regional

Inst

alla

tion

Ech

elo

nV

is. To

CM

WIP

& D

md

IC IR

EREC

• Central: inventory is staged at central supplier location.

• Regional: inventory is staged at customer regional locations

• Installation: legacy information policy – only CM forecast and orders shared with supplier.

• Echelon: collaborative information policy – CM WIP, inventory and end customer demand on CM shared with supplier.

Installation Policies (No visibility)

Supplier Regional Hubs Sub-Con End Customer

Forecast

Shipments

Supplier Sub-Con End Customer

Installation

Regional

Installation

Central

No Visibility

Supplier Regional Hubs Sub-Con End Customer

Forecast

Shipments

Supplier Sub-Con End Customer

Installation

Regional

Installation

Central

No Visibility

Echelon Policies Visibility on Sub-Con WIP and FGI

Supplier Regional Hubs Sub-Con End Customer

Forecast

Shipments

Supplier Sub-Con End Customer

Echelon

Regional

Echelon

CentralWIP & Inventory

Supplier Regional Hubs Sub-Con End Customer

Forecast

Shipments

Supplier Sub-Con End Customer

Echelon

Regional

Echelon

CentralWIP & Inventory

ObjectivesIdentify the business model that leads to Higher profits Higher service levels for the end customer Lower inventory costs Higher inventory turnover/velocity

Find scenarios that lead to desirable performance combinationsInvestigate the advantages and drawbacks of each model under different parameter settings and demand structures

Per

form

ance

M

easu

res

Approach

Create an Excel Spreadsheet model to simulate the supply chain for the 4 scenarios

Part 1: Decision Making

IC

IR

EC

ER

Part 2: Performance Evaluation (Test Bed)

IC

IR

EC

ER

Input

Parameters

Scenarios

Scenario ParametersDemand forecast refreshed weekly or dailySub-con sharesDemand volatility (% of forecasted demand)Manufacturing lead times for all facilities

Assumed 1-2 weeks at subcontractorsDelivery PerformanceTransportation lead timesCost Parameters

Manufacturing cost Inventory cost Transportation cost Selling price

Input ScreenBASIC SUPPLY CHAIN PARAMETERS (required) COST PARAMETERS

Title: Supply Chain Design AnalysisSubtitle: Starting Value Ending Value Increments

10 Total M anufacturing Cycle Time (whole weeks, 16 maximum) $0.01 Transportation Cost

30.0% 100.0% 10% Standard Devia tion of Week -to-Week Demand Cha nges (as % of demand) $1.16 Ma nufa cturing Cost

11 11 1Base stock (s) 1 M anufacturing Cycle Time for Subcons $0.26 Inventory Cost 0

15Order-up-to Level (S) 2 FG Inventory Tar get $0.20 WIP Cost

25.0% Avg Change in Demand (% of demand) 90% Targeted Shipping P erforma nce $2.50 Price

Apply Target P erforma nce 87% Stopping P erforma nce 7,500 Container Size (# of products)

Fix s 70 M onte Carlo Simulation Iterations

Fix Order-upto Level

Fix Standa r d Deviation

If the cell is not gray, you shouldn't be making entries orchanges … See notes to the right.

Demand Profile 1

(required)

Demand Profile 2

(required)

End C ustomer Demand Profile

Adjusted StandardDeviation

Week / Day

End Item Units Demand

End Item Units Demand

End Item Units Demand

12 103,200 149,400 252,600 238.41%13 103,200 149,400 252,600

14 103,200 149,400 252,600

15 103,200 149,400 252,600

16 86,200 129,200 215,400

17 86,200 129,200 215,400

18 86,200 129,200 215,400

19 86,200 129,200 215,400

20 80,000 110,000 190,000

21 80,000 110,000 190,000

22 80,000 110,000 190,000

23 80,000 110,000 190,000

24 60,000 90,000 150,000

25 60,000 90,000 150,000

26 60,000 90,000 150,000

Run Scenarios

Objective

Run Scenarios

Input Screen (Simulation Parameters)

BASIC SUPPLY CHAIN PARAMETERS (required)

Title: Supply Chain Design AnalysisSubtitle: Starting Value Ending Value Increments

10 Total Manufacturing Cycle Time

30.0% 100.0% 10% Standard Deviation of Week-to-Week Demand Changes (as % of demand)

7 15 1 Minimum Buffer (s) 1 Manufacturing Cycle Time for Subcons

13 Max. Buffer (S) 2 FG Inventory Target

25.0% Average Change in Demand (as % of demand) 90% Targeted Shipping Performance

Apply Target Performance 87% Stopping Performance

Fix Min. Buffer 70 Monte Carlo Simulation Iterations

Fix Max. Bufferl

Fix Standard Deviation

If the cell is not gray, you shouldn't be making entries orchanges … See notes to the right.

Installation CentralInstallation Central

Minimize InventoryMinimize Inventory Objective

Results Screen

Echelon Central Policy Results

Base Stock

Up-to Order Level Lost Sale

Number of Stockouts

Average Inventory Cost

Annual Costs

Total Revenue Annual Profit

Inventory Velocity

FG InventoryVelocity

InventoryTurnover

43 46 10,155 3 $30,443.48 $354,679.47 $1,227,462.46 $2,360,577.78 12.27 42.45 70.6942 43 9,330 3 $29,384.11 $346,889.91 $1,204,598.22 $2,321,947.05 12.52 44.69 72.1044 44 6,932 2 $29,511.71 $348,444.91 $1,207,187.52 $2,321,645.42 12.42 42.08 71.3840 41 9,947 3 $26,131.46 $328,030.94 $1,171,233.42 $2,307,888.41 13.60 46.86 78.1240 40 9,905 3 $26,428.19 $332,628.00 $1,179,902.64 $2,308,970.35 13.49 45.96 77.65

• Red row: highest profit

• Blue row: lowest inventory cost

ExamplesDemand forecast across two quarters from end customer 1(Example 1) and end customer 2 (Example 2)Example 1 (Device Code)

10 weeks of manufacturing lead time Weekly review Two volatility scenarios (30% & 80%) Two CM’s with 40-60 split No transportation delay

Example 2 (Device Code) 41 days of manufacturing lead time Daily review 65% volatility Two CM’s with no obvious pattern in split 3 days of transportation lead time to the CM location

• Higher profits with echelon and central warehousing policies

• More than 35% improvement in inventory cost under echelon policy

• No significant advantage of central warehousing in inventory cost

Policy Comparisons

• Higher profits with echelon and central warehousing policies

• Around 50% improvement in inventory cost under echelon policy

• 10% to 20% improvement in inventory cost under central warehousing

Profits, Inventory and Service Level (Maxtor)

150

200

250

300

350

6600 6650 6700 6750 6800

Profit ($K)

Inv

ento

ry C

ost

($K

)

IR

ER

IC

EC

97% 98%

96% 96%

Profits, Inventory and Service Level (Cisco)

0

10

20

30

40

50

60

70

1900 2000 2100 2200 2300 2400 2500

Profits ($K)

Inv

en

tory

Co

st

($K

)

IRIC

EREC

95%

90%

93%

93%

Inventory Velocity (annual sales/average inventory)

• 30-50% improvement in overall inventory velocity with echelon policy

• FG inventory velocity increases more than 3 fold with echelon policy

• 15-20% improvement in overall inventory velocity with echelon policy

• FG inventory velocity increases around 5 fold with central warehousing

Policy Comparison (bubble size=Inventory cost)

$13.0K$16.0K

$40.0K$36.0K

0

10

20

30

40

50

3 3.5 4 4.5 5 5.5 6 6.5

Overall Inventory Velocity

FG

Inv

ento

ry V

elo

cit

y

IC

EREC

IR

Policy Comparison (bubble size=inventory cost)

$15.5K

$46.0K

$55.5K

$10.8K

0

10

20

30

40

50

60

70

5 7 9 11 13 15

Overall Inventory Velocity

FG

In

ve

nto

ry V

elo

cit

y

IR

IC

ER

EC

Inventory Velocity

Inventory Turnovers and Velocity (Cisco)

0

1020

3040

5060

7080

90

IR IC ER EC

Inventory Turnover

Inventory Velocity

FG Inventory Velocity

InventoryVelocity (Maxtor)

0

10

20

30

40

50

IC IR EC ER

Total Inventory

FG Inventory

• More sensitive to distribution method

• More sensitive to demand management

Test Bed & Sensitivity to Subcon Split Changes

Optimal policies from each model are plugged in the “Test Bed”

Test bed is used to analyze the sensitivity of the business strategies to subcon split changes

The model investigates different scenarios by fluctuating subcon shares in demand

Impact of Split Changes on Inventory Cost

• EC policy is less sensitive to split changes (< 3%)

• Increase can be as high as 11% in IC policy

Increase in Inventory Cost (Maxtor)

-2.00%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Subcon 1 Share

% I

nc

rea

se

IC

EC

ConclusionsModeling allows for rapid identification and subsequent optimization of the dominant drivers for device-specific and customer-specific supply chains

Both demand management and distribution policy have significant impact on the supply chain performance

Demand and manufacturing profiles have dramatic effect on how models perform

With high subcontractor share volatility, highest advantage is gained by moving to central warehousing

With greater demand stability and subcontractor share certainty, highest advantage is gained from moving to echelon policy

Murat Erkoc, Ph.D., Director of CASCME-mail: merkoc@miami.edu

Tel: 305 284 4477 Fax: 305 284 4040

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