Risk Pooling

Chap. 14

• Risk Pooling – “Theory”

• Applications

– Differentiation Postponement/Delay

– Location, …

1. On page 1, it said “We can’t run our business with this level of unproductive

assets.” What are these assets?

2. What is “the I-word” referred to?

3. Is the ink-jet printer a commodity or fashionable product?

4. At the European DC, did HP have too much stock or too little stock?

5. What were the symptoms of the problem in the European DC?

6. When customers buy ink-jet printers, is brand/product loyalty playing an

important factor in their choosing which products to buy?

7. How did the Vancouver Division impress visitors? Was the line suitable for high

volume or low volume production? Why?

8. Were Ink-Jet Printers completely built in the Vancouver factory? Can a printer

that was built for the Germany market be directly sold in Italy market? Why? Be

precise.

9. What were the performance evaluation criteria for European DC? Should the DC

manager be concerned about the inventory level?

10. What were the alternatives for resolving the inventory and service crisis?

HP Case - handed out – preparation questions

Coefficient of Variation

CV = Standard Dev / Average demand

Demand series 1: CV1 = 0.5

Demand series 2: CV2 = 2.0

Which is more volatile?

Risk Pooling

• Which sales are more volatile: the regional

sales or sales at the store level?

• Which demand is more volatile : a family

of products or individual members of the

family?

• Do people wait longer in a multiple-

waiting-lines system than a single-waiting-

line system?

• Implication for forecasting ?

Family vs. Individual Products

Time

Sales

A

B

C

N

Family vs. Individual Products

Model Mean Stdv CV

X 42 32 0.78

XX 420 203 0.46

XY 15,830 5,624 0.36

XH 2,301 1,168 0.51

XC 4,208 2,204 0.52

XY 309 103 0.34

Total 23,109 6,244 0.27

Pooling, Profit & Service Level

(An Example)

• Two products (paints) differ only in colour

• It is fast to mix to the required colour upon

receiving orders

• Assume that the demand for each follows a

distribution given by tossing a dice

An Example

• Alternative I: Make to Stock

• Alternative II: Make to Order (for colouring

only)

• Order-up-to inventory replenishment policy

• One season, c=$2.5, p=$12.5, s=0

For Alternative I: 5 units for each

• For Alternative II: ?

Preliminary Calculations

1 2 3 4 5 6

1 1,1 x

2 x

3 x

4 4,3 4,5 x

5 5,4 5,5 x

6 x x x x x xChance of stockout?

Preliminary Calculations

If D1=4, D2 =5, profit = ?;

If D1=6, D2=4, profit = ?.

1 2 3 4 5 6

1 1,1 x

2 x

3 x

4 4,3 4,5 x

5 5,4 5,5 x

6 x x x x x x

• Holding 10 units of “generic” colour -

pigment, the chance of stocking out in a

period is only

3/36 = 1/12 = 0.083

The “risk” of disservice is lowered.

• Even holding 9 units, a higher level of

“service” will be achieved (as compared with

Alternative 1)

Preliminary Calculations

1 2 3 4 5 6

1 2 3 4 5 6 7

2 3 4 5 6 7 8

3 4 5 6 7 8 9

4 5 6 7 8 9 10

5 6 7 8 9 10 11

6 7 8 9 10 11 12

If D1=4, D2 =5, profit = ; If D1=6, D2=4, profit = 0. If

D1=2, D2=6, profit= . Additional cost?

“Theory”

,1|| general,In )(

,1 If )(

,1 If )(

,0 If )(

2)(

)( , Let

, Stdv

,Mean

, :products Two

2221

21

2121

21

21

21

d

c

b

a

xVar

xmeanxxx

xx

Why？

Implications?

• Variety is the “culprit” of high forecasting

errors, and higher forecasting accuracy can be

achieved if only a few varieties are offered

• Since we can not reduce them, we must find a

way to get around

– By postponing mass customisation

– By redesigning the product

• Universal products and common parts

(modules)

Product Variety Proliferation

• Product proliferation exists in various

forms

– global mkt: “protocols”, languages, phases,

elec.

– local mkt: multiple models differ in features &

capacities

– mkting strategies

• Marketing strategy is the major reason

The world of The Long Tail

Pitfalls of increasing product variety

Risk pooling strategies

• The objective of a risk pooling strategy is to redesign the supply chain,

the production process or the product to either reduce the uncertainty

the firm faces or to hedge uncertainty so that the firm is in a better

position to mitigate the consequence of uncertainty.

• Four versions of risking pooling:

– product pooling -- delayed differentiation/postponement

– location pooling

– lead time pooling

• delayed differentiation (HP case)

• consolidated distribution

– capacity pooling

14-18

Postponement

• Key idea - postpone the commitment of WIP into a particular finished product –SKU

• Delay of product differentiation closer to time of sale.

• Prior to point of postponement, only certain degree of aggregate forecast needed

• Individual forecasts more accurate close to time of sale

Postponement Concepts

• Mainly two forms

– Logistics Postponement: moving customisation point

closer to customers - out of mgr functions

– Form Postponement: delaying differentiation point by

standardisation or process re-sequencing

Logistics Postponement

Manufac- Integra- Customi- Locali- Pack-

turing tion sition tion ing

Supply Chain Process

Distribution Centers

Factory

Logistic Postponement by Process

Resequencing: Paint Retail

Colour pigments,

paint mixing,

packaging

Retail sales

Colour pigments,

white paint

Retail sales,

paint mixing

packaging

Nippon: combined

Before module design of the

metal frame

Integration+ship DC

DC+ pannel assembly

Fib

Fab.

Black

White

Black

WhiteIntegration+ship

Dishwasher

Operations Buffer

Log PP: More Examples

• Rheem Manufacturing Co., kept 120 SKUs (heaters)

at its factory. Some were overstocked while other

fall short - only different in several elements

– Using a 3rd party to hold around 10 basic models

and parts

– Filling orders in hours and saving 15% of

inventory cost

• Even Coffee Rosters use it

• Of course, PC mfgrs apply it

• Some done by customers. More real life examples?

PC: indirect model (traditional)

Suppliers

VARsMfr factory Distribution

Centers

Distributor Customers

PC

CompanyOrders

Product

Configuration:

Disk, memory...

Hybrid Model

Suppliers

VARsFactory: core Distribution

Centers

Distributor Customers

PC

CompanyOrders

Product

Assembly

+Configuration

Other examples of delayed

differentiation

• Private label soup manufacturer:

– Problem: many different private labels (Giant, Kroger, A&P, etc)

– Solution: Hold inventory in cans without labels, add label only

when demand is realized.

• Black and Decker:

– Sell the same drill to different retailers that want different

packaging.

– Store drills and package only when demand is realized.

• Nokia:

– Customers want different color phones.

– Design the product so that color plates can be added quickly and

locally. 14-27

Form Postponementby Common Part

Before After

Sometimes called

standardization

PCA FA&T Customizatio

PCA FA&T

Customization

Operations Buffer

Mono

Color

Mono

Color

Mono/Color Printers

No correlation

Form Postponement by Process Reengineering

PCB Insertion

Series of tests and burn-in

Coupon PCB

Common

tests

Customisation

tests

Benetton: Process Reengineering

Purchase Yarn

Dye Yarn

Finish Yarn

Knit Garment Parts

Join Parts

Old Sequence

Purchase Yarn

Knit Garment Parts

Join Parts

Dye Garment

Finish Garment

New Sequence

This process

is postponed

Process Redesign for Supply Chain:

Postponement at Benetton

dye

knit

Dyeing

operations

postponed

Dye yarn only after the season’s fashion preferences become more

established (knit lead-time much longer than dyeing lead-time).

Example: single product; four colors

knit

dye

Outcome: Reduces demand uncertainty & inventory

Demand correlation

• Correlation refers

to how one random

variable’s outcome

tends to be related

to another random

variable’s

outcome.0

2

4

6

8

10

12

14

16

18

20

0 5 10 15 20

0

2

4

6

8

10

12

14

16

18

20

0 5 10 15 20

0

2

4

6

8

10

12

14

16

18

20

0 5 10 15 20

Random demand for two products (x-axis

is product 1, y-axis is product 2). In

scenario 1 (upper left graph) the

correlation is 0, in scenario 2 (upper right

graph) the correlation is -0.9 and in

scenario 3 (the lower graph) the

correlation is 0.90. In all scenarios

demand is Normally distributed for each

product with mean 10 and standard

deviation 3.14-33

Limitations of product

pooling/universal design

• A universal design may not provide key functionality to consumers with

special needs:

– High end road bikes need to be light, high end mountain bikes need to be

durable. It is hard to make a single bike that performs equally well in both

settings.

• A universal design may be more expensive to produce because additional

functionality may require additional components.

• But a universal design may be less expensive to produce/procure because each

component is needed in a larger volume.

• A universal design may eliminate brand/price segmentation opportunities:

– There may be a need to have different brands (e.g., Lexus vs Toyota) and

different prices to cater to different segments.

14-34

Summary: When is PP

Valuable?

• A lot of varieties

• Demand uncertainties over the variety are high

– Negatively correlated, ?

– Positively correlated, ?

• Differentiation is not too costly to perform locally, or

not time consuming

• The “core components” have high value, but

differentiating parts are of low value

Common Obstacles

• Though often design changes do not cost much, people

resist their implementation

• As production cost may increase, prod. people may oppose

to changes

• They also pose challenges to designers

• Indirect cost savings and intangible benefits