risk pooling –“theory”eclt5940/protected/risk_pool.pdf · 2010-11-25 · risk pooling...

Risk Pooling

• Risk Pooling – “Theory”

• Applications

– Differentiation Postponement/Delay

– Location, …

– Read 11.4 (“Impact of Aggregation …”)

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?


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


If D1=4, D2 =5, profit = 90-2.5 = 87.5; If D1=6, D2=4,

profit = 90-2.5 = 87.5. If D1=2, D2=6, profit=70-7.5=62.5

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)


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 = 90-2.5 = 87.5;

If D1=6, D2=4, profit = ? If D1=2, D2=6, profit?


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 = 90-2.5 = 87.5; If D1=6, D2=4,

profit = 100. If D1=2, D2=6, profit=80-5=75.

Additional cost?

1 2 3 4 5 6

1 2 3 4 5 6 7 8 9 10 11 12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

“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？

Negative

correlation

Why？

Positive

correlation

Why？

No correlation

Sub-summary

• Pooling allows excess demand of one

product to cancel insufficient demand of

another ==> increase service level / reduce

inventory

• Pooling allows excess demand of one

location to cancel insufficient demand of

another ==> increase service level / reduce

inventory

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

Product variety is increasing

• Crest toothpastes have 35 options

(flavors and package sizes)

• HP workstations have 500,000 options

(RAM cards, video cards, graphic cards,

monitors, disk drives, etc..)

• GM cars have 20,000,000 versions (color,

interior combinations, drive train

configurations, and option choices)

Pitfalls of increasing product variety

Demand may not increase

total demand spread over more SKUs

Forecasting nightmare

High manufacturing cost

High inventory cost

Poor availability of product to customers

Product support and service costs

“Mark-down” sales at the end of product life

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

14-27

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?

Reebok (2006)

• Demand for jerseys averages 30,000 per week or 1.5

million each year. The different choices of team name,

player name, color scheme, and size makes it extremely

difficult to predict demand of an individual item during the

pre-season.

• At a price of $25 for a long-sleeve t-shirt or $250 for an

authentic jersey, the cost of lost sales is greater than the

cost to ship, unpack, finish and reship a jersey from a local

finishing center.

• The blank jerseys arrive in the US and are ready for screen

printing and embroidering at Reebok DC

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. 14-38

HP Case

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

Form Postponement by Process Reengineering

PCB Insertion

Series of tests and burn-in

Coupon PCB

Common

tests

Customisation

tests

The US company:

design and

distribution

How to Fulfill Orders?

滿足客戶定單的模式?

• New or low demand products - MTO

• Mature products, several of them sharing a few

core components，high total vol. －ATO

• Mature products, high vol., without common

cores -- MTS“Divide and

conquer”

Several Ａ+Ｂ+Ｃ products share a

core component核心基件

某核心件

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

ＸＹＺ１０１

ＡＢＣ２０３

IＷＶ－００９1

ＸＹＢ－０１

Total/sum

US DCsell-thru

CV of SKU’s: 0.75-1.34, while CV of agg. ~ 0.72

Benetton: Process Reengineering 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

Example: BenettonExample: Benetton• A world leader in knitwear

Dyeing vats for the finished knitted product. Wool Plant in Castrette, near Treviso. Knitting

division. Computerized knitting loom capable of

automatically producing the most complex product

designs

Knitting Dyeing

Process Redesign for Supply Chain:Process Redesign for Supply Chain:

Postponement at BenettonPostponement 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 & inventoryOutcome: Reduces demand uncertainty & inventory

Modular vs. Integral DesignModular vs. Integral Design

One-to-one mapping between functional elements and components

One-to-one mapping between functional elements and components

Interfaces between components not coupled

Complex mapping from functional elements to components

Complex mapping from functional elements to components

Interfaces between components are coupled

Modular design

Integral design

integralmodular

Why is Modular Design Preferred?Why is Modular Design Preferred?

• Example: Chrysler （2000？）

It needs to renew its Durango and Cherokee lines.

Currently, each car has very little component commonality with the other,

since both use integral designs. Chrysler is considering a modular platform

design, in which 60% of the components, in terms of dollar value (chassis,

transmission, underbody, etc.) are common to the new Cherokee and

Durango.

Suppose the monthly demand for the Cherokee, in 000s, N(50,202), for the

Durango is N(40,202). Assume

US$15,000/car to manufacture, and that lead-time across components is

constant at one month (for simplicity). Consider annual holding cost of a

component to be 12% of the component value. Assume a 95% CSL.

Illustration of Chrysler Product Illustration of Chrysler Product

StrategiesStrategies

Current:

Integral

Designs

Proposed:

Modular

Design

http://www.todaysclassic.com/images/class05.jpg

Solution to the Chrysler ExampleSolution to the Chrysler Example

Integral Design:

50, 20, 40, 20, 1, 1.64C C D DAVG STD AVG STD L z

2 2

CSafety stock = 1.64 20 (1) 33Cz STD L

2 2

DSafety stock 1.64 20 (1) 33z STD L

Total safety stock: 33 + 33 = 66

Modular Design: 250 40 90, 2 20 28.3AVG STD

2 2Safety stock = 1.64 28.3 (1) 46.4z STD L

Monthly inventory holding cost savings = (66-46.4)*15,000*0.60*(0.12/12) =1,764 (in 000s),

or US$ 21 million per year!

Downside-effect!

External/internal commonality

Postponement Driver 1:

Product Life Cycle

Value of

Postponement

Time

Ramp-up On-going End-of-Life

Fcst Error

Invent. Risk

Shortage Cost

High Lower High

Low Medium High

High Lower Low

When is PP Valuable?

Postponement Value &

Product Life

Total Value of

Postponement

Life Length of Product


Demand Forecast Uncertainty

Uncertainty Postponement Value

Demand Higher for higher

Variability variability

Option Higher if negative;

Correlation lower if strongly positive

Forecast Horizon Higher for longer horizon

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 3.14-57

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.

14-58

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 ObstaclesCommon 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

No beyond !

Product versioning

• When pure group pricing is infeasible, firms use other

strategies to differentiate prices. The most notable of these

is designing or developing goods (services, virtual or real)

that may have only minor differences but enable the seller

to exploit diff in price sensitivity among consumers

• Inferior/superior variants

• Inferior goods

– Shell, Modil/Exxon sell excess petro at lower prices to “no-brand”

independent dealers who use their own brands

– Many brand-names sell to “President’s Choice” /Park’n Shop’s

brand

Inferior goods (continued)

Damaged goods – e.g., some stores want to get rid of some stuff by

discount, but … Then, they “damage” them, …

degrading, or disabling a standard good to sell low

Intel: 486SX processor began with 486DX -- full

functioning processor, then dsabled the math co-

processor, to produce a chip is inferior, but more

expensive to produce (1991, DX/SX $588/333); many

software packages come with “professional, educational,

…” versions, most just like “chips”

The concept of damaging” a good seems somewhat

bizarre. However, it works

Superior goods: the recent example: Mengniu Milk

Product line extensions – inferior + superior extensions

PC/Notebooks: 98-99% of the parts/software installed the same:

diff performance – vertical lines

Horizontal line -- Coca-Cola: Classical Coke, Diet Coke, Cherry

Coke, … the same price

QuickBooks (Intuit) Price

Basic edition $199.95

Pro 299.95

Premier 299.95

Enterprise 3,500

Pro-edition: “all the features of basic plus

“advanced tools and customerison options to

boost efficiency/accuracy”; Premier –”Pro +

comprehensive tools for greater insight into

your biz”; Enterprise – “Our most

comprehensive biz mgmt tool for growing biz,

with all the features of Pro & Premier.” In all

likelihood, a tiny diff…


Product Life Cycle

Value of

Postponement

Time

Ramp-up On-going End-of-Life

Fcst Error

Invent. Risk

Shortage Cost

High Lower High

Low Medium High

High Lower Low

Postponement Value &

Product Life

Total Value of

Postponement

Life Length of Product


Demand Forecast Uncertainty

Uncertainty Postponement Value

Demand Higher for higher

Variability variability

Option Higher if negative;

Correlation lower if strongly positive

Forecast Horizon Higher for longer horizon

Initial forecast

• Inaccurate

• Over-estimate

Sport Obermayer: Revisited

Demand Forecast

Demand Forecast

Forecast incorporated

with first 20% of sales

data

• Relatively accurate

forecast

• Allows a better

prediction of the actual

demand

Demand Forecast

Forecast incorporated

with first 80% of sales

data

• Data obtained after the

Las Vegas show

• Very close to the actual

demand

Accurate ResponseAccurate Response

• AR: an approach to the entire forecasting,

planning, and production process

• AR is a risk-based production sequencing

strategy whereby production of low-risk

products employs speculative prod. capacity

and prod. of higher-risk products is postponed

until additional mkt inform. has been

gathered.


• Applicable to the context of limited capacity and min.

lot size

Time

Time to Retail

Trad. Dolls New Dolls


• Product portfolio consists of two types:

– Items that are fairly predictable or costs with forecasting errors are relatively lower

– Those whose forecasting is based on gambling

• Those in relatively predictable category sh’d be made the furthest in advance in order to reserve greater mfg cap. for making unpredictable items closer to the selling season

• This planning strategy allows companies to make smaller quant. of the unpredictable products in advance, see how well diff. items fare early in the selling season


• Using the production capacity at early stage to fairly predictable items and then 100% to least predictable ones -- risk based prod. Sequencing– Classifying two types of categories - maybe just one

– Identifying demand “indicators” to improve forecast

– Instituting a system for tracking forecasting error

• When the forecast was made, on what inform. it was based, and its level of detail (SKU or agg.)?

– Examples

• Sports Obermeyer

• National Bicycle (Japan)

Summary

• Risk-based SC planning: what is low-risk or

high-risk product? All else being equal,

– Product price

– Uncertainty

– Expected demand quantity

• Min order quantity

– Category 1: expected demand > 2 times of min Q

– Category 2: less than min Q

– Category 3: in between

Summary

• Above are “passive” approach

• Operational changes

– Generic components, holding certain materials and

components can reduce SC costs

– Min. order quantity

– Reactive cap.

– Materials lead times

– Material commonality to improve flexibility

– Obtaining market info earlier?

• Similarities/dis-similarities between SO & LF?

Summary

• Intelligent forecast process

– obtain early signal

– forecast uncertainty as input for prod. planning

– forecast update

• Power of risk-based prod planning

• Use inventory to establish de-coupling point in supply chain

• Material commonality to improve flexibility

risk pooling –“theory”eclt5940/protected/risk_pool.pdf · 2010-11-25 · risk pooling...

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