Sport Obermeyer Case

Submitted By connorso
Words: 2445
Pages: 10

Sport Obermeyer Case

John H. Vande Vate
Spring, 2006
1

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

2

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
3

Production Options
• Hong Kong





• Mainland (Guangdong, Lo Village)

More expensive
Smaller lot sizes
Faster
More flexible






Cheaper
Larger lot sizes
Slower
Less flexible

4

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)
5

The Product
• Gender
– Styles
– Colors
– Sizes

• Total Number of SKU’s: ~800

6

Service
• Deliver matching collections simultaneously • Deliver early in the season

7

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!
8

Quotas
• Force delivery earlier in the season
• Last man loses.

9

The Critical Path of the SC





Contract for Greige
Production Plans set
Dying and printing
YKK Zippers

10

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?
11

Production Planning Example





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

12

Sample Problem
Style
Price
Laura
Carolyn
Gail
$ 110.00
900
1,000
Isis
$ 99.00
800
700
Entice
$ 80.00
1,200
1,600
Assault
$ 90.00
2,500
1,900
Teri
$ 123.00
800
900
Electra
$ 173.00
2,500
1,900
Stephanie $ 133.00
600
900
Seduced $ 73.00
4,600
4,300
Anita
$ 93.00
4,400
3,300
Daphne
$ 148.00
1,700
3,500
Total
20,000
20,000

Individual Forecasts
Greg
Wendy
Tom
Wally
Average
Std. Dev 2X Std Dev
900
1,300
800
1,200
1,017
194
388
1,000
1,600
950
1,200
1,042
323
646
1,500
1,550
950
1,350
1,358
248
496
2,700
2,450
2,800
2,800
2,525
340
680
1,000
1,100
950
1,850
1,100
381
762
1,900
2,800
1,800
2,000
2,150
404
807
1,000
1,100
950
2,125
1,113
524
1,048
3,900
4,000
4,300
3,000
4,017
556
1,113
3,500
1,500
4,200
2,875
3,296
1047
2,094
2,600
2,600
2,300
1,600
2,383
697
1,394
20,000
20,000
20,000
20,000
20,000

Cut and Sew Capacity
3000 Units/month
7 month period
First Phase Commitment
10,000 units
Second Phase Commitment
10,000 units

13

Recall the Newsvendor
• Ignoring all other constraints recommended target stock out probability is: 1-Profit/(Profit + Risk)
=8%/(24%+8%) = 25%

14

Ignoring Constraints
Style
Gail
Isis
Entice
Assault
Teri
Electra
Stephanie
Seduced
Anita
Daphne

Mean
Std Dev Recommended Order Quantity
1,017
388
1,278
1,042
646
1,478
1,358
496
1,693
2,525
680
2,984
1,100
762
1,614
2,150
807
2,695
1,113
1048
1,819
4,017
1113
4,767
3,296
2094
4,708
2,383
1394
3,323
26,359 Note This suggests over buying!

Everyone has a 25% chance of stockout
Everyone orders
Mean + 0.6745

P = .75 [from .24/(.24+.08)]
Probability of being less than
Mean + 0.6745 is 0.75
15

Constraints
• Make at least 10,000 units in initial phase
• Minimum Order Quantities

16

Objective