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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 ?

Challenge of delivering on the strategy?

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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 Forecasts

AveForecast

StandDev

2 x StandDevStyle Price Laura Caroly

nGreg Wend

yTom Wally

Job Market Director

CSMgr

Product-ion mgr

Product-ion coord

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 1113

Anita $ 93 4400 3300 3500 1500 4200 2875 3296 1047

2094

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

Totals 20000 20000

20000

20000

20000

20000

20000Standard 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 z

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Production Strategy A Account for production minimum

If we assume same wholesale price, we want to produce the mean of a style’s forecast minus the same number of standard deviations of that forecast i.e., i-ki (k is same for all).

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

Sum (-k)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 Std. Dev. Forecast First Period Production Q= -k

Seduced 4017 1113 2837 Assault 2525 680 1804 Electra 2150 807 1295 Anita 3296 2094 1076 Daphne 2383 1394 905 Entice 1358 496 832 Gail 1017 398 606 Isis 1042 646 357 Teri 1100 762 292 Stephanie 1113 1048 2

Totals 20001 10008

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But what about the batch size minimums?

Large production minimums force us to make 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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Strategy B:Categories for Risk Assessment

m= minimum order quantity (600 here)

SAFE: Styles where demand is more than 2X the minimum order quantity (we’ll have a second order commitment)

SOS: Sort of Safe=expected demand is less than minimum order quantity. “If we make ‘em at all, make ‘em first” (have to make minimum)

RISKY: demand is between C1 & C2.

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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

Std. Dev. Forecast

Risk Type

Seduced 4017 1113 Safe Assault 2525 680 Safe Electra 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 give total first period production quantity.

Assess each case by determining the optimal quantities for non-risk suits using Production Quantity = Max(600, i-600-k*i)

Same approach as before (determine the appropriate k so that lot size <10,000)

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Example: Production Quantity = Max(600, i-600-k*i) ; k =.33

Style Avg. of Forecast m Std. Dev. ForecastSeduced 4017 1113 3049.71Assault 2525 680 1700.6Electra 2150 807 1283.69Anita 3296 2094 2004.98Daphne 2383 1394 1322.98Entice 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 from using this risk adjustment

Model’s 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 & operations changes 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 Reduce Markdown and Stock-out Costs

Reducing minimum production lot-size constraints

How ?

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Effect of Minimum Order Quantity 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.

Co

st a

s %

of

Sal

es

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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 as a Function of Reactive Capacity

0

2

4

6

8

10

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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.

Co

st a

s %

of

Sal

es

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Lead Times

Decrease raw material and/or manufacturing 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 to inventory product?

Griege Fabric

Dye Solid Colors Printed

Size 8 Black Electra SKU SKU SKU

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Obtain market information earlier

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Accurate Response Program Using buying committee to develop

probabilistic forecast of demand and variance (fashion risk)

Assess overage and underage costs to develop relative costs of stocking too little or too much

Use Model to determine appropriate initial production quantities (low risk first)

“Read” early demand indicators Update demand forecast Determine final production quantities