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Sport Obermeyer Case

Prof Mellie Pullman

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Objectives Supply Chain Choices & Operations Strategy

Product Category challenges Operational changes that reduce costs of

mismatched supply and demand

Coordination Issues in a global supply chain

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Type of Product Typical Operational & Supply Chain

Strategies Cost Quality

Time (delivery, lead time, etc)

Flexibility (multiple choices, customization) Sustainability

Sport Obermeyer ?

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Challenge of delivering on thestrategy?

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Challenges of matching supply

to demand Supply Side Demand Side

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Costs & Risks of Over-stock

versus Under-stock Over-stock Under-stock

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NovemberPre year

February

September

August

March

November

China Colorado US Retailer

Design clothes

Order textiles &styles

Las Vegas show

Warehouse

Distribute to retailers

Make orders to

Sport O.

Make Fabric

Assemble

Clothes

Deliver to Colorado

Take Orders

Make forecasts

Retail Season

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Two Order Periods How are they different?

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Speculative Production

Capacity

Reactive Production

Capacity

New Info. Material Lead time Lead Time to Store

Risk-Based Production

Sequencing Strategy

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Planning Approach How many of each style to product?

When to produce each style?

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Buying Committee ForecastsAve

Forecast

Stand

Dev

2 xStand

DevStyle Price Laura Carolyn Greg Wendy Tom Wally

Job Market

Director

CS

Mgr

Product-ionmgr

Product-ioncoord

Sales

Rep

VP

Gail $110 900 1000 900 1300 800 1200 1017 194 388

Isis $ 99 800 700 1000 1600 950 1200 1042 323 646

Entice $ 80 1200 1600 1500 1550 950 1350 1358 248 496

Assault $ 90 2500 1900 2700 2450 2800 2800 2525 340 680

Teri $123 800 900 1000 1100 950 1850 1100 381 762

Electra $173 2500 1900 1900 2800 1800 2000 2150 404 807

Stephani $133 600 900 1000 1100 950 2125 1113 524 1048

Seduced $ 73 4600 4300 3900 4000 4300 3000 4017 556 1113Anita $ 93 4400 3300 3500 1500 4200 2875 3296 104

72094

Daphne $148 1700 3500 2600 2600 2300 1600 2383 697 1394

Totals 20000 20000 20000 20000 20000 20000 20000

Standard Deviation of demand= 2x Standard Deviation Forecast

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Team Break out 1 Using the available data, assess the risk

of each suit and come up with a system

to determine: How many of each to style to produce

When to produce each style

Where to make it

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Low Risk Styles

We under-produce during initial

production so we want: Least expensive products

Low demand uncertainty Highest expected demand

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Standard Normal Distribution

-produce m-zs

m

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Production Strategy AAccount for production minimum

If we assume same wholesale price, we want toproduce the mean of a styles forecast minus thesame number of standard deviations of that forecast

i.e., mi-ksi (k is same for all).

Approach: produce up to the same demand percentile (k)for all suits.

Sum (m-ks)each style = 10,000 (meet production minimum)

Determine k for all styles

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Solve for k with total close to 10000(k=1.06)

Style Avg. of Forecast m Std. Dev. Forecast First Period Production

Q= m-ks

Seduced 4017 1113 2837

Assault 2525 680 1804Electra 2150 807 1295

Anita 3296 2094 1076

Daphne 2383 1394 905

Entice 1358 496 832

Gail 1017 398 606Isis 1042 646 357

Teri 1100 762 292

Stephanie 1113 1048 2

Totals 20001 10008

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But what about the batch sizeminimums?

Large production minimums force us tomake either many parkas of a given

style or none.

How do we consider the batch size

minimums for the second order cycle?

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Approach

Compute risk for each style

Rank styles by risk Figure out the amount of non-risk suits

to produce in the first run

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Assign Risk

Style Avg. of Forecast

m

Std. Dev.

Forecast

Risk

Type

Seduced 4017 1113 Safe

Assault 2525 680 SafeElectra 2150 807 Safe

Anita 3296 2094 Safe

Daphne 2383 1394 Safe

Entice 1358 496 Safe

Gail 1017 398 Risky

Isis 1042 646 Risky

Teri 1100 762 Risky

Stephanie 1113 1048 Risky

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Modified Approach

Determine how many styles to make to givetotal first period production quantity.

Assess each case by determining the optimalquantities for non-risk suits usingProduction Quantity = Max(600, mi-600-k*si)

Same approach as before (determine theappropriate k so that lot size

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Example:Production Quantity = Max(600, m

i

-600-k*si

) ; k =.33

Style Avg. of Forecast m Std. Dev. Forecast

Seduced 4017 1113 3049.71Assault 2525 680 1700.6

Electra 2150 807 1283.69

Anita 3296 2094 2004.98

Daphne 2383 1394 1322.98

Entice 1358 496 600 9961.96

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Should we make more suits?

Production minimum order is

10,000?

Pros?

Cons?

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Sport Obermeyer Savings fromusing this risk adjustment

Models Decisions Sport O Decisions

Total Production (units) 124,805 121,432

Over-production (units) 22,036 25,094

Under-production(units)

792 7493

Over-production

(% of sales)

1.3% 1.73%

Under-production

(% of sales)

.18% 1.56%

Total Cost (% of sales) 1.48% 3.30%

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Team Breakout 2

What supply chain & operationschanges can be implemented to reduce

stock-outs and mark-downs? Design, production, forecasting, etc.?

Specific: How are you going to do it,

Actions?

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Operational Changes to ReduceMarkdown and Stock-out Costs

Reducing minimum production lot-sizeconstraints

How ?

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Effect of Minimum OrderQuantity on Cost

5.1 5.15

5.4

5.8

6.4

5

5.2

5.4

5.6

5.8

6

6.2

6.4

6.6

6.8

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0 200 400 600 800 1000 1200

Minimum Order Quantity

S.O./

M.D.

Costas%o

fSales

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Capacity Changes

Increase reactive production capacity

How? Pros and cons?

Increase total capacity

How? Pros and Cons?

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Stock-out & Mark-down Costs asa Function of Reactive Capacity

0

2

4

6

8

10

12

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

Reactive Capacity (as a % of Sales)

S.O./M

.D.

Costas%o

fSales

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Lead Times

Decrease raw material and/ormanufacturing lead times

Which ones?

How?

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Lead Times

Reduce findings leads times (labels,button, zippers)

inventory more findings

standardize findings between product groups

more commonality reduced zipper variety 5 fold.

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Where does it make sense toinventory product?

Griege Fabric

Dye Solid Colors Printed

Size 8 Black Electra SKU SKU SKU

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Obtain market informationearlier

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Accurate Response Program

Using buying committee to developprobabilistic forecast of demand and variance(fashion risk)

Assess overage and underage costs todevelop relative costs of stocking too little ortoo much

Use Model to determine appropriate initialproduction quantities (low risk first)

Read early demand indicators

Update demand forecast

Determine final production quantities