1. Sport Obermeyer Case
Prof Mellie Pullman

2. 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

3. Type of Product Typical Operational & Supply Chain Strategies
Cost
Quality
Time (delivery, lead time, etc)
Flexibility (multiple choices, customization)
Sustainability
Sport Obermeyer ?

4. Challenge of delivering on the strategy?

5. Challenges of matching supply to demand
Supply Side
Demand Side

6. Costs & Risks of Over-stock versus Under-stock

7. China Colorado US Retailer November Pre year November March August
Design clothes
Make forecasts
Order textiles & styles
November
Take Orders
Make Fabric
Assemble Clothes
March
Make orders to Sport O.
Las Vegas show
August
Deliver to Colorado
Warehouse
Distribute to retailers
September
Retail Season
February

8. Two Order Periods
How are they different?

9. Risk-Based Production Sequencing Strategy
New Info.
Lead Time to Store
Material Lead time
Speculative Production
Capacity
Reactive Production
Capacity

10. Planning Approach How many of each style to product?
When to produce each style?

11. Buying Committee Forecasts
Ave
Forecast
Stand
Dev
2 x Stand
Style
Price
Laura
Carolyn
Greg
Wendy
Tom
Wally
Job
Market
Director CS
Mgr
Product-ion mgr
Product-ion coord
Sales
Rep VP
Gail
$110
900
1000
1300
800
1200
1017
194
388
Isis
$ 99
700
1600
950
1042
323
646
Entice
$ 80
1500
1550
1350
1358
248
496
Assault
$ 90
2500
1900
2700
2450
2800
2525
340
680
Teri
$123
1100
1850
381
762
Electra
$173
1800
2000
2150
404
807
Stephani
$133
600
2125
1113
524
1048
Seduced
$ 73
4600
4300
3900
4000
3000
4017
556
Anita
$ 93
4400
3300
3500
4200
2875
3296
1047
2094
Daphne
$148
1700
2600
2300
2383
697
1394
Totals
20000
Standard Deviation of demand= 2x Standard Deviation Forecast

12. 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

13. Low Risk Styles We under-produce during initial production so we want:
Least expensive products
Low demand uncertainty
Highest expected demand

14. Standard Normal Distribution - produce m-zs

15. 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., 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

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

17. 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?

18. 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.

19. Compute risk for each style Rank styles by risk
Approach
Compute risk for each style
Rank styles by risk
Figure out the amount of non-risk suits
to produce in the first run

20. Assign Risk

21. 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, mi-600-k*si)
Same approach as before (determine the appropriate k so that lot size <10,000)

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

23. Should we make more suits?
Production minimum order is 10,000?
Pros?
Cons?

24. 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
.18%
1.56%
Total Cost (% of sales)
1.48%
3.30%

25. 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?

26. Operational Changes to Reduce Markdown and Stock-out Costs
Reducing minimum production lot-size constraints
How ?

27. Effect of Minimum Order Quantity on Cost

28. Capacity Changes Increase reactive production capacity
How? Pros and cons?
Increase total capacity
How? Pros and Cons?

29. Stock-out & Mark-down Costs as a Function of Reactive Capacity

30. Lead Times Decrease raw material and/or manufacturing lead times
Which ones?
How?

31. Lead Times Reduce “findings” leads times (labels, button, zippers)
inventory more findings
standardize findings between product groups
more commonality reduced zipper variety 5 fold.

32. Where does it make sense to inventory product?

33. Obtain market information earlier

34. 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