Transcript Sport Obermeyer

Sport Obermeyer Case
Prof Mellie Pullman
1
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
2
Type of Product

Typical Operational & Supply Chain
Strategies






Cost
Quality
Time (delivery, lead time, etc)
Flexibility (multiple choices, customization)
Sustainability
Sport Obermeyer ?
3
Challenge of delivering on the
strategy?
Challenges of matching supply
to demand

Supply Side

Demand Side
5
Costs & Risks of Over-stock
versus Under-stock

Over-stock

Under-stock
6
China
November
Pre year
Colorado
US Retailer
Design clothes
Make forecasts
November
Take Orders
Order textiles &
styles
Make Fabric
March
August
September
Assemble
Clothes
Deliver to Colorado
Las Vegas show
Make orders to
Sport O.
Warehouse
Distribute to retailers
Retail Season
February
7
Two Order Periods

How are they different?
8
Risk-Based Production
Sequencing Strategy
New Info.
Material Lead time
Lead Time to Store
Speculative Production
Capacity
Reactive Production
Capacity
9
Planning Approach


How many of each style to product?
When to produce each style?
10
Buying Committee Forecasts
Style
Price
Job
Laura
Caroly
n
Greg
Wendy
Tom
Wally
Market
Director
CS
Mgr
Product
-ion
mgr
Product
-ion
coord
Sales
Rep
VP
Ave
Forecast
Stan
d
Dev
2x
Stand
Dev
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
104
7
2094
Daphne
$148
1700
3500
2600
2600
2300
1600
2383
697
1394
20000
20000
20000
20000
20000
20000
20000
Totals
Standard Deviation of demand= 2x Standard Deviation Forecast
11
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
12
Low Risk Styles

We under-produce during initial
production so we want:

Least expensive products

Low demand uncertainty
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Highest expected demand
13
Standard Normal Distribution
- produce m-zs
m
14
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.
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Sum (m-ks)each style = 10,000 (meet production minimum)
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Determine k for all styles
15
Solve for k with total close to 10000
(k=1.06)
Style
Avg. of Forecast m Std. Dev. Forecast
Seduced
Assault
Electra
Anita
Daphne
Entice
Gail
Isis
Teri
Stephanie
Totals
4017
2525
2150
3296
2383
1358
1017
1042
1100
1113
20001
1113
680
807
2094
1394
496
398
646
762
1048
First Period Production
Q= m-ks
2837
1804
1295
1076
905
832
606
357
292
2
10008
16
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?
17
Strategy B:
Categories for Risk Assessment

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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
19
Assign Risk
Style
Seduced
Assault
Electra
Anita
Daphne
Entice
Gail
Isis
Teri
Stephanie
Avg. of Forecast
m
4017
2525
2150
3296
2383
1358
1017
1042
1100
1113
Std. Dev.
Forecast
1113
680
807
2094
1394
496
398
646
762
1048
Risk
Type
Safe
Safe
Safe
Safe
Safe
Safe
Risky
Risky
Risky
Risky
20
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)
21
Example:
Production Quantity = Max(600, mi-600-k*si) ; k =.33
Style
Seduced
Assault
Electra
Anita
Daphne
Entice
Avg. of Forecast m
4017
2525
2150
3296
2383
1358
Std. Dev. Forecast
1113
680
807
2094
1394
496
3049.71
1700.6
1283.69
2004.98
1322.98
600
9961.96
22
Should we make more suits?

Production minimum order is
10,000?

Pros?

Cons?
23
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%
24
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?
25
Operational Changes to Reduce
Markdown and Stock-out Costs


Reducing minimum production lot-size
constraints
How ?
26
S.O./M.D. Cost as % of Sales
Effect of Minimum Order
Quantity on Cost
7
6.8
6.6
6.4
6.2
6
5.8
5.6
6.4
5.8
5.4
5.2
5
5.4
5.15
5.1
0
200
400
600
800
1000
1200
Minimum Order Quantity
27
Capacity Changes

Increase reactive production capacity


How? Pros and cons?
Increase total capacity

How? Pros and Cons?
28
S.O./M.D. Cost as % of Sales
Stock-out & Mark-down Costs as
a Function of Reactive Capacity
12
10
8
6
4
2
0
0
10 20 30 40 50 60 70 80 90 100 110 120 130 140 150
Reactive Capacity (as a % of Sales)
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Lead Times

Decrease raw material and/or
manufacturing lead times

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Which ones?
How?
30
Lead Times

Reduce “findings” leads times (labels,
button, zippers)


inventory more findings
standardize findings between product groups

more commonality reduced zipper variety 5 fold.
31
Where does it make sense to
inventory product?
Griege Fabric
Dye Solid Colors
Size 8 Black Electra
Printed
SKU
SKU
SKU
32
Obtain market information
earlier
33
Accurate Response Program

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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
34
Determine final production quantities