11

Sport Obermeyer Case

John H. Vande VateSpring, 2006

2 2

Issues Question: What are the issues

driving this case? How to measure demand uncertainty

from disparate forecasts How to allocate production between the

factories in Hong Kong and China• How much of each product to make in each

factory

3 3

Describe the Challenge Long lead times:

It’s November ’92 and the company is starting to make firm commitments for it’s ‘93 – 94 season.

Little or no feedback from market First real signal at Vegas trade show in

March Inaccurate forecasts

Deep discounts Lost sales

4 4

Production Options Hong Kong

More expensive

Smaller lot sizes

Faster More flexible

• Mainland (Guangdong, Lo Village)

– Cheaper– Larger lot sizes– Slower– Less flexible

5 5

The Product 5 “Genders”

Price Type of skier Fashion quotient

Example (Adult man) Fred (conservative, basic) Rex (rich, latest fabrics and technologies) Beige (hard core mountaineer, no-nonsense) Klausie (showy, latest fashions)

6 6

The Product Gender

Styles Colors Sizes

Total Number of SKU’s: ~800

7 7

Service Deliver matching collections

simultaneously Deliver early in the season

8 8

The Process Design (February ’92) Prototypes (July ’92) Final Designs (September ’92) Sample Production, Fabric & Component orders

(50%) Cut & Sew begins (February, ’93) Las Vegas show (March, ’93 80% of orders) SO places final orders with OL OL places orders for components Alpine & Subcons Cut & Sew Transport to Seattle (June – July) Retailers want full delivery prior to start of season

(early September ‘93) Replenishment orders from Retailers

Quotas!

9 9

Quotas Force delivery earlier in the season Last man loses.

10 10

The Critical Path of the SC Contract for Greige Production Plans set Dying and printing YKK Zippers

11 11

Driving Issues Question: What are the issues driving

this case? How to measure demand uncertainty

from disparate forecasts How to allocate production between the

factories in Hong Kong and China• How much of each product to make in each

factory How are these questions related?

12 12

Production Planning Example Rococo Parka Wholesale price $112.50 Average profit 24%*112.50 = $27 Average loss 8%*112.50 = $9

13 13

Sample ProblemStyle Price Laura Carolyn Greg Wendy Tom Wally Average Std. Dev 2X Std DevGail 110.00$ 900 1,000 900 1,300 800 1,200 1,017 194 388 Isis 99.00$ 800 700 1,000 1,600 950 1,200 1,042 323 646 Entice 80.00$ 1,200 1,600 1,500 1,550 950 1,350 1,358 248 496 Assault 90.00$ 2,500 1,900 2,700 2,450 2,800 2,800 2,525 340 680 Teri 123.00$ 800 900 1,000 1,100 950 1,850 1,100 381 762 Electra 173.00$ 2,500 1,900 1,900 2,800 1,800 2,000 2,150 404 807 Stephanie 133.00$ 600 900 1,000 1,100 950 2,125 1,113 524 1,048 Seduced 73.00$ 4,600 4,300 3,900 4,000 4,300 3,000 4,017 556 1,113 Anita 93.00$ 4,400 3,300 3,500 1,500 4,200 2,875 3,296 1047 2,094 Daphne 148.00$ 1,700 3,500 2,600 2,600 2,300 1,600 2,383 697 1,394 Total 20,000 20,000 20,000 20,000 20,000 20,000 20,000

Cut and Sew Capacity3000 Units/month

7 month period

First Phase Commitment10,000 units

Second Phase Commitment10,000 units

Individual Forecasts

14 14

Recall the Newsvendor Ignoring all other constraints

recommended target stock out probability is:

1-Profit/(Profit + Risk) =8%/(24%+8%) = 25%

15 15

Ignoring ConstraintsStyle Mean Std Dev Recommended Order QuantityGail 1,017 388 1,278 Isis 1,042 646 1,478 Entice 1,358 496 1,693 Assault 2,525 680 2,984 Teri 1,100 762 1,614 Electra 2,150 807 2,695 Stephanie 1,113 1048 1,819 Seduced 4,017 1113 4,767 Anita 3,296 2094 4,708 Daphne 2,383 1394 3,323

26,359 Note This suggests over buying!

Everyone has a 25% chance of

stockoutEveryone orders

Mean + 0.6745

P = .75 [from .24/(.24+.08)]Probability of being less thanMean + 0.6745 is 0.75

16 16

Constraints Make at least 10,000 units in initial

phase Minimum Order Quantities

17 17

Objective for the “first 10K” First Order criteria:

Return on Investment:

Second Order criteria: Standard Deviation in Return

Worry about First Order first

Expected Profit

Invested Capital

18 18

First Order Objective Maximize =

Can we exceed return *? IsL(*) = Max Expected Profit - *Invested

Capital > 0?

Expected Profit

Invested Capital

19 19

First Order Objective Initially Ignore the prices we pay Treat every unit as though it costs

Sport Obermeyer $1 Maximize =

Can we achieve return ? L() = Max Expected Profit - Qi >

0?

Expected ProfitNumber of Units

Produced

20 20

Solving for Qi For fixed, how to solveL() = Maximize Expected Profit(Qi) - Qi

s.t. Qi 0 Note it is separable (separate decision each Q) Exactly the same thinking! Last item:

Profit: Profit*Probability Demand exceeds Q Risk: Loss * Probability Demand falls below Q

Set P = (Profit – )/(Profit + Risk) = 0.75 –/(Profit + Risk)

Error here: let p be the wholesale price, Profit = 0.24*pRisk = 0.08*pP = (0.24p – )/(0.24p + 0.08p) = 0.75 - /(.32p)

21 21

Solving for Qi Last item:

Profit: Profit*Probability Demand exceeds Q Risk:Risk * Probability Demand falls below Q Also pay for each item

Balance the two sides:Profit*(1-P) – = Risk*P

Profit – = (Profit + Risk)*P So P = (Profit – )/(Profit + Risk) In our case Profit = 24%, Risk = 8% so

P = .75 – /(.32*Wholesale Price)How does the order quantity Q change with ?

Error: This was omitted. It is not

needed later when we calculate cost as, for

example, 53.4%*Wholesale price, because it

factors out of everything.

22 22

0

200

400

600

800

1000

1200

1400

-3 2 7 12 17 22 27

Q as a function of

QDoh!

As we demand a higher return, we can acceptless and less risk that the item won’t sell. So,

We make less and less.

23 23

Let’s Try ItStyle Mean Std Dev Recommended Order Quantity

Wholesale Price Order Quantity at Return

Gail 1,017 388 1,278 110.00$ 749 1778.1474%Isis 1,042 646 1,478 99.00$ 471Entice 1,358 496 1,693 80.00$ 568Assault 2,525 680 2,984 90.00$ 1767Teri 1,100 762 1,614 123.00$ 697Electra 2,150 807 2,695 173.00$ 2005Stephanie 1,113 1048 1,819 133.00$ 658Seduced 4,017 1113 4,767 73.00$ 0Anita 3,296 2094 4,708 93.00$ 1148Daphne 2,383 1394 3,323 148.00$ 1938

26,359 10,000

Min Order

Quantities!

Adding the Wholesale price brings returns in line with expectations: if we can make $26.40 = 24% of

$110 on a $1 investment, that’s a 2640% return

24 24

And Minimum Order QuantitiesMaximize Expected Profit(Qi) - Qi

M*zi Qi 600*zi (M is a “big” number)

zi binary (do we order this or not)

If zi =1 we order at least

600If zi =0 we

order 0

25 25

Solving for Q’sLi() = Maximize Expected Profit(Qi) - Qi

s.t. M*zi Qi 600*zi zi binary

Two answers to consider:zi = 0 then Li() = 0zi = 1 then Qi is easy to calculateIt is just the larger of 600 and the Q that gives P = (profit

- )/(profit + risk) (call it Q*)Which is larger Expected Profit(Q*) – Q* or 0?Find the largest for which this is positive. Forgreater than this, Q is 0.

26 26

Solving for Q’sLi() = Maximize Expected Profit(Qi) - Qi

s.t. M*zi Qi 600*zi zi binary

Let’s first look at the problem with zi = 1Li() = Maximize Expected Profit(Qi) - Qi

s.t. Qi 600 How does Qi change with ?

27 27

Adding a Lower Bound

Q

0

200

400

600

800

1000

1200

1400

0 5 10 15 20 250

200

400

600

800

1000

1200

1400

0 5 10 15 20 25

28 28

Objective Function How does Objective Function

change with ?Li() = Maximize Expected Profit(Qi) –

Qi

We know Expected Profit(Qi) is concave

$0

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

- 500 1,000 1,500 2,000 2,500 3,000 3,500

As increases, Q decreases

and so does the

Expected Profit

When Q hits its lower bound, it remains there. After that Li()

decreases linearly

29 29

The Relationships

-$50

$0

$50

$100

$150

$200

$250

0 0.05 0.1 0.15 0.2 0.25

Expected Profit

Capital Charge

L(lambda)

Q reaches minimum

Capital Charge = Expected

Profit

Past here, Q = 0

30 30

Solving for zi

Li() = Maximize Expected Profit(Qi) - Qi s.t. M*zi Qi 600*zi zi binary

If zi is 0, the objective is 0If zi is 1, the objective is

Expected Profit(Qi) - Qi

So, if Expected Profit(Qi) – Qi > 0, zi is 1Once Q reaches its lower bound, Li() decreases,

when it reaches 0, zi changes to 0 and remains 0

31 31

Style Mean Std Dev

Recommended Order

QuantityWholesale

Price Lagrange Order Quantity

Min Order

Quantity

Max Order

Quantity Order?Lambda Limit

at 1200Lambda

limit at 600Gail 1,017 388 1,278 110.00$ 717 1864.10% 600 1,278 1 1869% 2478%Isis 1,042 646 1,478 99.00$ 600 600 1,478 1 1505% 1952%Entice 1,358 496 1,693 80.00$ 600 600 1,693 1 1647% 1864%Assault 2,525 680 2,984 90.00$ 1664 600 2,984 1 2160% 2160%Teri 1,100 762 1,614 123.00$ 648 600 1,614 1 1866% 2350%Electra 2,150 807 2,695 173.00$ 1973 600 2,695 1 3937% 4083%Stephanie 1,113 1048 1,819 133.00$ 600 600 1,819 1 1824% 2247%Seduced 4,017 1113 4,767 73.00$ 600 600 4,767 1 1752% 2634%Anita 3,296 2094 4,708 93.00$ 873 600 4,708 1 1928% 2003%Daphne 2,383 1394 3,323 148.00$ 1870 600 3,323 1 3044% 3225%

26,359 10,145

Answers

China

Hong Kong

In China

?

Style Mean Std Dev

Recommended Order

QuantityWholesale

Price Lagrange Order Quantity

Min Order

Quantity

Max Order

Quantity Order?Lambda Limit

at 1200Lambda

limit at 600Gail 1,017 388 1,278 110.00$ 1200 1824.04% 1200 1,278 1 1869% 2478%Isis 1,042 646 1,478 99.00$ 0 0 - 0 1505% 1952%Entice 1,358 496 1,693 80.00$ 0 0 - 0 1647% 1864%Assault 2,525 680 2,984 90.00$ 1714 1200 2,984 1 2160% 2160%Teri 1,100 762 1,614 123.00$ 1200 1200 1,614 1 1866% 2350%Electra 2,150 807 2,695 173.00$ 1988 1200 2,695 1 3937% 4083%Stephanie 1,113 1048 1,819 133.00$ 1200 1200 1,819 1 1824% 2247%Seduced 4,017 1113 4,767 73.00$ 0 0 - 0 1752% 1752%Anita 3,296 2094 4,708 93.00$ 1200 1200 4,708 1 1928% 2003%Daphne 2,383 1394 3,323 148.00$ 1902 1200 3,323 1 3044% 3225%

26,359 10,404

Error: That resolves the question of why

we got a higher return in China with no cost differences!

32 32

First Order Objective: With Prices

It makes sense that the desired rate of return on capital at risk, should get very high, e.g., 1240%, before we would drop a product completely. The $1 investment per unit we used is ridiculously low. For Seduced, that $1 promises 24%*$73 = $17.52 in profit (if it sells). That would be a 1752% return!

Let’s use more realistic cost information.

33 33

First Order Objective: With Prices

Maximize =

Can we achieve return ? L() = Max Expected Profit - ciQi > 0? What goes into ci ? Consider Rococo example Cost is $60.08 on Wholesale Price of

$112.50 or 53.4% of Wholesale Price. For simplicity, let’s assume ci = 53.4% of Wholesale Price for everything from HK and 46.15% from PRC

Expected Profit ciQi

34 34

Return on CapitalHong Kong

Style Mean Std Dev

Recommended Order

QuantityWholesale

Price Lagrange Order Quantity

Min Order

Quantity

Max Order

Quantity Order?Lambda Limit

at 1200Lambda

limit at 600Gail 1,017 388 1,278 110.00$ 608 36.19% 600 1,278 1 31.8% 42.2%Isis 1,042 646 1,478 99.00$ 600 600 1,478 1 28.5% 36.9%Entice 1,358 496 1,693 80.00$ 836 600 1,693 1 38.5% 43.6%Assault 2,525 680 2,984 90.00$ 1808 600 2,984 1 44.9% 44.9%Teri 1,100 762 1,614 123.00$ 0 0 - 0 28.4% 35.8%Electra 2,150 807 2,695 173.00$ 1299 600 2,695 1 42.6% 44.2%Stephanie 1,113 1048 1,819 133.00$ 0 0 - 0 25.7% 31.6%Seduced 4,017 1113 4,767 73.00$ 2844 600 4,767 1 44.9% 44.9%Anita 3,296 2094 4,708 93.00$ 1090 600 4,708 1 38.8% 40.3%Daphne 2,383 1394 3,323 148.00$ 915 600 3,323 1 38.5% 40.8%

26,359 10,000

Style Mean Std Dev

Recommended Order

QuantityWholesale

Price Lagrange Order Quantity

Min Order

Quantity

Max Order

Quantity Order?Lambda Limit

at 1200Lambda

limit at 600Gail 1,017 388 1,278 110.00$ 0 39.87% 0 - 0 36.8% 48.8%Isis 1,042 646 1,478 99.00$ 0 0 - 0 32.9% 42.7%Entice 1,358 496 1,693 80.00$ 1200 1200 1,693 1 44.6% 50.5%Assault 2,525 680 2,984 90.00$ 1889 1200 2,984 1 52.0% 52.0%Teri 1,100 762 1,614 123.00$ 0 0 - 0 32.9% 41.4%Electra 2,150 807 2,695 173.00$ 1395 1200 2,695 1 49.3% 51.1%Stephanie 1,113 1048 1,819 133.00$ 0 0 - 0 29.7% 36.6%Seduced 4,017 1113 4,767 73.00$ 2976 1200 4,767 1 52.0% 52.0%Anita 3,296 2094 4,708 93.00$ 1339 1200 4,708 1 44.9% 46.7%Daphne 2,383 1394 3,323 148.00$ 1200 1200 3,323 1 44.6% 47.2%

26,359 10,000

China

If everything is made in one place, where would

you make it?

35 35

Gail

-$10,000

-$5,000

$0

$5,000

$10,000

$15,000

$20,000

$25,000

0% 10% 20% 30% 40% 50%

Hong KongChina

Expected Profit above Target Rate of Return

Target Rate of Return

Make it in China

Make it in Hong Kong

Stop Making It.

36 36

What Conclusions? There is a point beyond which the smaller

minimum quantities in Hong Kong yield a higher return even though the unit cost is higher. This is because we don’t have to pay for larger quantities required in China and those extra units are less likely to sell.

Calculate the “return of indifference” (when there is one) style by style.

Only produce in Hong Kong beyond this limit.

37 37

Style Mean Std DevRecommended Order Quantity

Wholesale Price

Order Quantity

Using Lambda

Min Order

Quantity

Max Order

Quantity Order Lambda Limit Gail 1,017 388 1,278 110.00$ 0 42.19% 0 - 0 26.9%Isis 1,042 646 1,478 99.00$ 0 0 - 0 27.1%Entice 1,358 496 1,693 80.00$ 1200 1200 1,693 1 44.6%Assault 2,525 680 2,984 90.00$ 1794 1200 2,984 1 52.0%Teri 1,100 762 1,614 123.00$ 0 0 - 0 28.8%Electra 2,150 807 2,695 173.00$ 1283 1200 2,695 1 49.3%Stephanie 1,113 1048 1,819 133.00$ 0 0 - 0 27.1%Seduced 4,017 1113 4,767 73.00$ 2822 1200 4,767 1 52.0%Anita 3,296 2094 4,708 93.00$ 1200 1200 4,708 1 44.9%Daphne 2,383 1394 3,323 148.00$ 1200 1200 3,323 1 44.6%

Gail 1,017 388 1,278 110.00$ 600 600 1,278 1 42.2%Isis 1,042 646 1,478 99.00$ 0 0 - 0 36.9%Entice 1,358 496 1,693 80.00$ 0 0 - 0 43.6%Assault 2,525 680 2,984 90.00$ 0 0 - 0 44.9%Teri 1,100 762 1,614 123.00$ 0 0 - 0 35.8%Electra 2,150 807 2,695 173.00$ 0 0 - 0 44.2%Stephanie 1,113 1048 1,819 133.00$ 0 0 - 0 31.6%Seduced 4,017 1113 4,767 73.00$ 0 0 - 0 44.9%Anita 3,296 2094 4,708 93.00$ 0 0 - 0 40.3%Daphne 2,383 1394 3,323 148.00$ 0 0 - 0 40.8%

10,099

Same Styles Made in Hong Kong

Where to Make What?That little cleverness

was worth 2%

Not a big deal. Make Gail in HK at

minimum

38 38

What Else? Kai’s point about making an amount

now that leaves less than the minimum order quantity for later

Secondary measure of risk, e.g., the variance or std deviation in Profit.

39

2 Sport Obermeyer

40

Sport Obermeyer’s Time Lineand

“Speculative” versus “Reactive” Production

Feb … Oct Nov … Mar April … Aug Sept Oct Nov Dec Jan Feb Mar Apr1992 … 1992 1992 … 1993 1993 … 1993 1993 1993 1993 1993 1994 1994 1994 1994

Line. of 1993-94 Line

8 months

Productionof 1993-94 Line (peak selling in Dec & Jan)

"Reactive"Production

5 months9 months 5 months

"NOW" Initial

Forecast

In Feb 1994, start design of 1995-96 line.

Selling of

In Feb 1993, start design of 1994-95 line.

Las Vegas Revised Forecast 27 Months

1993-94 LineDesign of1993-94

"Speculative"

“Speculative” Production “Reactive” Production

41

Speculative Production:Overstock versus Stockout?

Assume that Sport Obermeyer:is in the Speculative Production phase,forecasts that demand (D) for the Andy parka has a Normal Probability Distribution with a mean of 1000 and a standard deviation of 250, andhas decided that the Andy parka’s Speculative Production should be Q=750.

During the Speculative Production, Sport Obermeyer should be more concerned about

750Q

Pr{Overstock}=Pr{D<Q} =0.159

Pr{Stockout}=Pr{D>Q} =0.841

42

Speculative Production:Guidelines for Choosing a Parka to Produce

In this slide and the next 4 slides, we will assume that Sport Obermeyer is in the Speculative Production phase and must decide whether to produce the Andy parka or the Peter parka.We will also assume that a parka’s demand has a Normal Probability Distribution.We will investigate how this decision is affected by:

the parka’s standard deviation of demand,the parka’s mean demand, andthe parka’s unit cost of production.

43

The Effect of a Parka’sStandard Deviation of Demand

Assume that Andy and Peter have the same unit cost of production andthe same mean demand of 1000,

but thatAndy’s demand has a standard deviation of 100 whilePeter’s demand has a standard deviation of 200.

During Speculative Production, Q

Pr{Overstock}=Pr{D<Q} = Area to Left of Q

44

The Effect of a Parka’sMean Demand

Assume that Andy and Peter have the same unit cost of production andthe same standard deviation for demand of 200,

but thatAndy’s demand has a mean of 1000 whilePeter’s demand has a mean of 1200.

During Speculative Production,

Pr{Overstock}=Pr{D<Q} = Area to Left of Q

Q

45

The Effect of a Parka’sUnit Cost of Production

Assume that Andy and Peter have the same mean demand of 1000 andthe same standard deviation for demand of 1000,

but thatAndy’s demand has unit cost of production of $10 whilePeter’s demand has a unit cost of production of $20.

During Speculative Production,

46

Speculative Production:Summary of Guidelines for Choosing

a Parka to ProduceIn the previous 3 slides, we have seen that a parka is a better candidate for Speculative Production if:

It has a relatively ______ standard deviation of demand.It has relatively ______ mean demand.It has a relatively ______ unit cost of production.

47

Speculative Production:Equalizing over 2 Parkas

the Probability of an OverstockAssume that Andy and Peter have

the same unit cost of productionbut that

Andy’s demand has a mean of 1000 & standard deviation of 250,Peter’s demand has a mean of 2500 & standard deviation of 500.

QUESTION: How can we set the production quantities so thatPr{Overstock of Andy} = Pr{Overstock for Peter}?

Q=2500 – k500Q=1000 - k250

48

Solving Wally’s Sample Problem (on page 8 of the Case)

Using the concept on the previous slide and the sample data in Exhibit 10, we will determine for Wally the order quantity for each style during Speculative Production. To simply, we will assume that:

all 10 styles in the sample problem are made in Hong Kong,no style has a minimum order quantity,all styles have the same unit cost of production, andtotal Speculative Production must be about 10,000 units.

49

Solving Wally’s Sample Problem (with k=0)

Too much!

D E T E R M I N I N G S P E C U L A T I V E P R O D U C T I O N Q U A N T I T I E Sk = 0 < - - - F i n d v a l u e o f k t h a t m a k e s l a s t c o l u m n s u m t o a b o u t 1 0 , 0 0 0

S T A N D A R D F I R S T - P E R I O DM E A N O F D E V I A T I O N P R O D U C T I O N Q U A N T I T YD E M A N D O F D E M A N D

S T Y L EG a i l 1 0 1 7 3 8 8 1 0 1 7I s i s 1 0 4 2 6 4 6 1 0 4 2

E n t i c e 1 3 5 8 4 9 6 1 3 5 8A s s a u l t 2 5 2 5 6 8 0 2 5 2 5

T e r i 1 1 0 0 7 6 2 1 1 0 0E l e c t r a 2 1 5 0 8 0 7 2 1 5 0

S t e p h a n i e 1 1 1 3 1 0 4 8 1 1 1 3S e d u c e d 4 0 1 7 1 1 1 3 4 0 1 7

A n i t a 3 2 9 6 2 0 9 4 3 2 9 6D a p h n e 2 3 8 3 1 3 9 4 2 3 8 3

S u m - - - > 2 0 , 0 0 1 2 0 , 0 0 1 < - - - S u m

),0( kMax

Go toExcel file.

50

Solving Wally’s Sample Problem (with k=2)

D E T E R M I N I N G S P E C U L A T I V E P R O D U C T I O N Q U A N T I T I E Sk = 2 < - - - F i n d v a l u e o f k t h a t m a k e s l a s t c o l u m n s u m t o a b o u t 1 0 , 0 0 0

S T A N D A R D F I R S T - P E R I O DM E A N O F D E V I A T I O N P R O D U C T I O N Q U A N T I T YD E M A N D O F D E M A N D

S T Y L EG a i l 1 0 1 7 3 8 8 2 4 1I s i s 1 0 4 2 6 4 6 0

E n t i c e 1 3 5 8 4 9 6 3 6 6A s s a u l t 2 5 2 5 6 8 0 1 1 6 5

T e r i 1 1 0 0 7 6 2 0E l e c t r a 2 1 5 0 8 0 7 5 3 6

S t e p h a n i e 1 1 1 3 1 0 4 8 0S e d u c e d 4 0 1 7 1 1 1 3 1 7 9 1

A n i t a 3 2 9 6 2 0 9 4 0D a p h n e 2 3 8 3 1 3 9 4 0

S u m - - - > 2 0 , 0 0 1 4 , 0 9 9 < - - - S u m

),0( kMax

Too little!

51

Solving Wally’s Sample Problem (with k=1)

D E T E R M I N I N G S P E C U L A T I V E P R O D U C T I O N Q U A N T I T I E Sk = 1 < - - - F i n d v a l u e o f k t h a t m a k e s l a s t c o l u m n s u m t o a b o u t 1 0 , 0 0 0

S T A N D A R D F I R S T - P E R I O DM E A N O F D E V I A T I O N P R O D U C T I O N Q U A N T I T YD E M A N D O F D E M A N D

S T Y L EG a i l 1 0 1 7 3 8 8 6 2 9I s i s 1 0 4 2 6 4 6 3 9 6

E n t i c e 1 3 5 8 4 9 6 8 6 2A s s a u l t 2 5 2 5 6 8 0 1 8 4 5

T e r i 1 1 0 0 7 6 2 3 3 8E l e c t r a 2 1 5 0 8 0 7 1 3 4 3

S t e p h a n i e 1 1 1 3 1 0 4 8 6 5S e d u c e d 4 0 1 7 1 1 1 3 2 9 0 4

A n i t a 3 2 9 6 2 0 9 4 1 2 0 2D a p h n e 2 3 8 3 1 3 9 4 9 8 9

S u m - - - > 2 0 , 0 0 1 1 0 , 5 7 3 < - - - S u m

),0( kMax

Too much!

52

Solving Wally’s Sample Problem (with k=1.0608)

D E T E R M I N I N G S P E C U L A T I V E P R O D U C T I O N Q U A N T I T I E Sk = 1 . 0 6 0 8 < - - - F i n d v a l u e o f k t h a t m a k e s l a s t c o l u m n s u m t o a b o u t 1 0 , 0 0 0

S T A N D A R D F I R S T - P E R I O DM E A N O F D E V I A T I O N P R O D U C T I O N Q U A N T I T YD E M A N D O F D E M A N D

S T Y L EG a i l 1 0 1 7 3 8 8 6 0 5I s i s 1 0 4 2 6 4 6 3 5 7

E n t i c e 1 3 5 8 4 9 6 8 3 2A s s a u l t 2 5 2 5 6 8 0 1 8 0 4

T e r i 1 1 0 0 7 6 2 2 9 2E l e c t r a 2 1 5 0 8 0 7 1 2 9 4

S t e p h a n i e 1 1 1 3 1 0 4 8 1S e d u c e d 4 0 1 7 1 1 1 3 2 8 3 6

A n i t a 3 2 9 6 2 0 9 4 1 0 7 5D a p h n e 2 3 8 3 1 3 9 4 9 0 4

S u m - - - > 2 0 , 0 0 1 1 0 , 0 0 0 < - - - S u m

),0( kMax

Just right!

53

The Effect of Minimum Order Quantities

Ideally, during Speculative Production, we want to order a specific quantity of a parka style, and then, during Reactive Production, we want to “fine tune” the parka’s remaining supply by ordering as few or as many as the indicated by the revised forecast after Las Vegas.However, a large minimum order quantity for a particular style of parka forces us to order either many parkas or none.Thus, a minimum order quantity significantly reduces the ability to “fine tune” during Reactive Production.

54

Minimum Order Quantities(continued)

Let “Mean” denote a parka’s mean demand.Let “minQ” denote the parka’s minimum order quantity.

Consider the following three cases:0 <= Mean <= minQ <= Mean <= 2minQ <= Mean

Case 1 Case 2 Case 3

During Speculative Production, which of the above three cases are “safe” to order, and which are “risky”?Case 1:

Case 2:

Case 3:

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Recommendations to WallyRECOMMENDATION #1. Improve the demand forecasts made internally by the Buying Committee in November just before Speculative Production.

Instead of using just a simple average of the individual forecasts made by Laura, Carolyn, Greg, Wendy, Tom, & Wally, use a weighted average, with the weights reflecting past accuracy.

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Recommendations to Wally(continued)

RECOMMENDATION #2. Obtain market feedback earlier than Las Vegas, thereby converting some Speculative Production to Reactive Production. Sport Obermeyer can invite selected retailers to come in January to Aspen for an all-expenses-paid “Early Order Weekend”, where there is time for a”sneak preview” of the new line, some recreational skiing and socializing, and then the early placement of orders at a discount.To maximize the value of the market feedback, Sport Obermeyer’s “guest list” should include both large and small retailers and both urban and resort retailers.

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Recommendations to Wally(continued)

RECOMMENDATION #3. Decrease lead times for both raw materials and finished goods, thereby allowing more time to utilize existing capacity.Since the business strategy should emphasize Dependability more than Cost, lead-times can be reduced using some or all of the following methods:

Choose suppliers of raw materials more on the basis of D than C.Expedite orders through information sharing with suppliers.Expedite shipments using faster (but more expensive) shippers.Establish some local (but more expensive) production capacity for “last minute” production.

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Recommendations to Wally(continued)

RECOMMENDATION #3 (continued).

Other ways to reduce lead times include:From the items with long lead times, increase the amount of “safety stock” inventory for those items that are inexpensive (e.g., buttons) and/or shared by many parkas (e.g., black fabric).Simplify the parkas’ designs so that they can share as many components as possible. For example, are 100,000 varieties of zippers really necessary?

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Recommendations to Wally(continued)

RECOMMENDATION #4. Increase production capacity by:

Using more subcontractors,Using more overtime in China, and/orExploring an alliance with a swimwear manufacturer who can “supply” excess capacity when Sport Obermeyer needs it and “consume” capacity when Sport Obermeyer has excess capacity.

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Recommendations to Wally(continued)

RECOMMENDATION #5. Decrease minimum order quantities, thereby improving the ability to “fine tune” during Reactive Production.Minimum order quantities occur because there are long “set-up times” when switching from the production of one style of parka to another, thereby making it uneconomical to have “short runs”.Sport Obermeyer can decrease the minimum order quantities by providing incentives to its suppliers to have more flexible production lines.This increased flexibility can come from:

Improved process design (e.g., a cellular production system).Improved equipment (e.g., more flexible cutting machines).

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Sport Obermeyer’s Relationship with Obersport

In this global supply chain,Sport Obermeyer operates in the US and specializes in the demand side by coordinating activities such as

monitoring fashion trends,designing the parkas, andselling the parkas by entering into relationships with retailers.

Obersport operates in Hong Kong and China and specializes in the supply side by coordinating activities such as

procuring fabric and components (e.g., zippers) andarranging for production using either independent subcontractors or factories of Alpine (a company owned by Obersport’s managing director).

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Sport Obermeyer’sRelationship with Obersport

(continued)Global supply chains are frequently composed of different companies, with each company having a

a different geographical location,a different knowledge seta different skill set, and/ora different set of business relationships.

Sport Obermeyer should NOT eliminate its business relationship with Obersport. Instead, it should retain its relationship and seek to improve the coordination between Sport Obermeyer’s demand-side activities and Obersport’s supply-side activities.