How do you decide what to How do you decide what to produce when you don’t know produce when you don’t know what your customers will buy?what your customers will buy?
Marshall FisherUPS Professor, The Wharton
SchoolCofounder and Chairman, 4R
Systems
ISC/SCLC SPRING 2006 MEETINGHilton Marco Island APRIL 29, 2006
© 2006 Marshall L. Fisher
Cost of lost sale
Risk of obsolescence
Forecast accuracy
Product variety
Product life cycle
High
Low
Low
Low
High
High
High
Low
ShortLong
Functional Innovative
Products differ
© 2005 Marshall L. Fisher
And supply strategies differ
Factory focus
Inventory Strategy
Lead-time focus
Supplier selection
Product-design strategy
Low cost trumps short lead-time
High utilization
High turns
Low cost
Maintain buffer capacity
Significant buffer stocks of components and FGs
Speed & flexibility
Aggressively shorten lead-time
Modular to enable postponed differentiation
Integral for max performance at min cost
Physically efficient
Market responsive
© 2005 Marshall L. Fisher
match
matchmismatch
mismatch
Life cycle > 2 yearsGross Margin < 35%Low Product Variety
Functional Products
Life cycle < 1 yearGross Margin > 35%High Product Variety
Innovative ProductsR
esp
on
sive
S
up
ply
Ch
ain
Eff
icie
nt
Su
pp
ly C
hai
n
Supply predictable demand efficiently at lowest cost
Respond quickly to unpredictable demand to minimize stockouts, markdowns, and obsolete inventory
Need to match supply strategy with product type
© 2005 Marshall L. Fisher
So as to minimize total of two types of costs
• Physical Production/Distribution Costs
– Production Costs
– Transportation Costs
– Facility Utilization rates
– Inventory carrying cost on pipeline and cycle stocks
• Supply/Demand Mismatch Costs
– Lost revenue and profit margin when supply is less than demand
– Product and parts scrapped or sold at a loss when supply exceeds demand
– Inventory carrying cost on safety stocks
Raw Materials
Component
Suppliers
Manufacturer
Retailers Consumers
© 2005 Marshall L. Fisher
Dell reaps benefits from supply chain innovation
“Supply chain management is what it’s all about“ Tom Meredith, Chief Financial
Officer of Dell
Source: Open manufacturing Online, July 28, 1998
Dell
S&P 500
© 2005 Marshall L. Fisher
P & G has grown earnings faster than sales by cutting supply chain costs
0
5000
10000
15000
20000
25000
30000
35000
40000
1990
1991
1992
1993
1994
1995
1996
1997
1998
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Net
Sale
s ($
M)
Net
Earn
ings
($M
)
trendline
Source: Company 10K reportsNote: 1993-94 accounting change makes series
discontinuous.
During 94-98Net Sales grew 22.3%
Net Earnings grew 71%
P&G Net Sales and Net Earnings 1990-98
© 2005 Marshall L. Fisher
A page from Sport Obermeyer’s product catalog
© 2005 Marshall L. Fisher
Next year’s catalog
© 2005 Marshall L. Fisher
Obermeyer’s styles are fashion-forward and change every year
© 2005 Marshall L. Fisher
Factories in China
DC in Denver
800 Ski Retailers
The Obermeyer supply chain stretches from Asia to Aspen
Obermeyer’s planning calendar is driven by when it snows
© 2005 Marshall L. Fisher
Which parka family sold best?
Black Voodoo sold 4000
Sold 4
© 2005 Marshall L. Fisher
Initial forecasts are highly inaccurate
Black Voodoo
© 2005 Marshall L. Fisher
Measuring the cost of over and under supply
Orders 4000 4
Production 2000 2000
Lost sales 2000 Excess 1996
Full price = $200 Markdown price = 130 Variable cost = 150
Lost sales cost $50 x 2000 =
$100,000Excess cost $20 x 1996 = $39,920
Initial forecasts are highly inaccurate …
but improve dramatically with just a little sales data
© 2005 Marshall L. Fisher
Early write
• Bring 25 (out of 800) largest retailers to Aspen in February. Accounts for 20% of sales.
• Put them up at the Ritz Carlton
• They ski with Klaus Obermeyer, an industry icon
• They get an advance preview of the line
• They order early
Lead time reduction
• Fabric dyer lead time of several months was a problem for Obermeyer
• Dyer has long lead time on greige goods and needed to keep their capacity utilized year round but can change colors overnight
• Obermeyer can predict total annual sales and sales of basic colors, but can’t predict fashion colors
Solution
• Offer dyer one year commitment on greige goods and capacity
• Dye basic colors early in year and fashion colors late in season on few days notice
Fabric Producer
Fabric Dyer
Cut/Sew Factory
Denver Warehouse
Retailer
undyed greige goods Sport Obermeyer
Asia
Consumer
© 2005 Marshall L. Fisher
Revised planning calendar
© 2005 Marshall L. Fisher
Sample buying committee projections
Which product is more predictable?
PandoraParka
EnticeShell
Carolyn
Laura Tom Kenny Wally Wendy Average
Std. Dev.
1200
1500
1150 1250 1300 1100 1200 1200 65
700 1200 300 2075 1425 1200 572
© 2005 Marshall L. Fisher
0
200
400
600
800
1000
1200
1400
0 110 220 330
Standard Deviation of the IndividualForecasts of a Six Person Committee
Forecast Error
HighError
LowError
High Agreement Low Agreement
AverageError =155 units
AverageError =252 units
The committee process allowed us to forecast forecast accuracy
© 2005 Marshall L. Fisher
STYLE COLOR Wholesale COMM COMM# # Price LK CO SS CB JD WS WRO TT GW AM AVE STD DEV
MEN'S PARKAS6220 77.5
64 479 700 500 400 475 600 414 300 600 340 481 12545 650 200 500 200 560 800 614 300 840 340 500 23378 50 300 200 100 475 300 895 300 600 510 373 2539 180 300 200 100 390 290 876 400 360 510 361 217
TOTAL: 1359 1500 1400 800 1900 1990 2799 1300 2400 1700 1715 581
6221 94.5 64 350 700 500 600 400 275 465 200 420 300 421 15245 600 200 400 400 500 575 542 300 516 375 441 12878 350 300 300 100 385 400 310 175 432 600 335 13876 500 400 400 400 320 150 233 175 72 225 288 137
TOTAL: 1800 1600 1600 1500 1605 1400 1550 850 1440 1500 1485 2496222 9.5
64 700 700 400 1100 350 550 771 400 400 600 597 23245 200 200 400 400 250 375 135 100 672 300 303 1689 750 500 500 200 350 725 539 500 400 800 526 189
49 450 600 500 300 150 150 275 300 200 300 323 150TOTAL: 2100 2000 1800 2000 1100 1800 1720 1300 1672 2000 1749 324
9 930 800 700 300 740 1015 1415 900 760 1310 887 316
45 1450 600 1300 1000 1310 1750 1291 700 2028 1015 1244 44049 450 600 500 300 150 150 275 300 200 300 323 15064 1529 2100 1400 2100 1225 1425 1650 900 1420 1240 1499 37576 500 400 400 400 320 150 233 175 72 225 288 13778 350 300 300 100 385 400 310 175 432 600 335 138
Obermeyer Committee Forecasts
Color forecasts
© 2005 Marshall L. Fisher
Historical Distribution of Forecast Errors Follows the Normal Bell-Shaped Curve
© 2005 Marshall L. Fisher
The Normal Distribution Accurately Models Demand Uncertainty at Obermeyer
Probability Distribution for Sales of PandoraMean = 1200 Standard Deviation = 230
© 2005 Marshall L. Fisher
Pandora Parka
1200 1430 1660970740
33%33%15% 15%
2%2%
Cost of Under and Over Production
Pandora Parka
Wholesale Price Less Supplier charges Sales Commission Inventory carrying/Delivery
Profit Margin
Markdown Price Less Supplier charges Inventory carrying/delivery
Loss
$ 200
100 30 25 _____ $ 45 Cost of Under Production
$ 120 100 35 _____ ($15) Cost of Over Production
© 2005 Marshall L. Fisher
Probabilistic Break Even AnalysisProduce to the point whereProbability we sell x Gain if we sell = Probability we don’t sell x Loss if we don’t sell
.25 x 45 = .75 x 15
Pandora Parka
1200 1430
Probability = .25
Gain if we sell Loss if we don’t sell
© 2005 Marshall L. Fisher
Production Decision if we can only buy once
Accurate Response Optimization Model
• Demand Distributions
• Cost of Stockouts & Excess Inventory
• Production Capacity and Minimum Constraints
Minimize Expected Costof Stockouts
& Excess Inventory
• Risk Adjusted Production Commitments by Style/Color
• Can be used as Simulation Model to measure the impact of better information or increased supply chain flexibility
© 2005 Marshall L. Fisher
Desk top tool run by user
Factories in China
DC in Denver
800 Ski Retailers
Product Sketches
Forecast Committee
Forecasts
Order 50% in
November
Order 50% in April
Retailers order in
Feb & April
Results of a parallel test show profit increase equal to 1.8% of sales
Optimization Model
Decisions
Legacy Process
Decisions
Total Production (Units) 124,805 121,432
Demand 103,831 103,,831
Over-Production (Units) 22,036 25,094
Under-Production (Units) 792 7,493
Over-Production Cost (% of Sales) 1.30% 1.74%
Under-Production Cost (% of Sales) 0.18% 1.56%
Total Cost (% of Sales) 1.48% 3.30%
© 2005 Marshall L. Fisher
Retailers Loved the New Program!
© 2005 Marshall L. Fisher
Obermeyer process at World, a major Japanese retailer
© 2005 Marshall L. Fisher
Right Buy: Commercial Implementation of the Obermeyer concept
Elements of the Obermeyer process Which of these would be useful in your company?• Early orders are highly predictive
• Early write -> bring 25 largest retailers to Aspen to order early
• Cut lead times on expensive, long lead time component – dyed fabric
• Use committee forecast process to forecast forecast errors
• Risk based production sequencing– Replace point forecast by probability distribution– Make predictable volume early.– Set production volumes based on likely forecast
accuracy and cost of over and under production.
How to think about supply chain improvement
Product Availability
Responsive Supply Chain
Inventory
Accurate Forecasts
How does product availability drive revenue?
Optimize cost of lost margin, carrying and obsolescence
Track & improve the accuracy of forecasts that drive decisions e.g. parts lead time demand
© 2005 Marshall L. Fisher
Create a framework for inventory efficiency e.g. common parts, short lead times, efficient small lot production