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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