ISC/SCLC SPRING 2006 MEETING Hilton Marco Island APRIL 29, 2006 How do you decide what to produce when you don’t know what your customers will buy? Marshall Fisher UPS Professor, The Wharton School Cofounder and Chairman, 4R Systems © 2006 Marshall L. Fisher

Products differ Cost of lost sale Low High Risk of obsolescence Low Forecast accuracy High Low Low High Product variety Long Short Product life cycle Functional Innovative © 2005 Marshall L. Fisher

And supply strategies differ Maintain buffer capacity Factory focus High utilization Significant buffer stocks of components and FGs Inventory Strategy High turns Low cost trumps short lead-time Aggressively shorten lead-time Lead-time focus Low cost Speed & flexibility Supplier selection Integral for max performance at min cost Modular to enable postponed differentiation Product-design strategy Physically efficient Market responsive © 2005 Marshall L. Fisher

Need to match supply strategy with product type Life cycle > 2 years Gross Margin < 35% Low Product Variety Functional Products Life cycle < 1 year Gross Margin > 35% High Product Variety Innovative Products match Supply predictable demand efficiently at lowest cost Efficient Supply Chain mismatch match Respond quickly to unpredictable demand to minimize stockouts, markdowns, and obsolete inventory Responsive Supply Chain mismatch © 2005 Marshall L. Fisher

So as to minimize total of two types of costs Raw Materials Component Suppliers Manufacturer Retailers Consumers 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 © 2005 Marshall L. Fisher

Dell reaps benefits from supply chain innovation S&P 500 “Supply chain management is what it’s all about“ Tom Meredith, Chief Financial Officer of Dell Source: Open manufacturing Online, July 28, 1998 © 2005 Marshall L. Fisher

P&G Net Sales and Net Earnings 1990-98 P & G has grown earnings faster than sales by cutting supply chain costs P&G Net Sales and Net Earnings 1990-98 During 94-98 Net Sales grew 22.3% Net Sales ($M) Net Earnings ($M) Net Earnings grew 71% trendline Source: Company 10K reports Note: 1993-94 accounting change makes series discontinuous. © 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

The Obermeyer supply chain stretches from Asia to Aspen Factories in China DC in Denver 800 Ski Retailers

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 = $100,000 Excess 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 Asia Fabric Producer Fabric Dyer Cut/Sew Factory Denver Warehouse Retailer undyed greige goods Consumer Sport Obermeyer 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 © 2005 Marshall L. Fisher

Revised planning calendar © 2005 Marshall L. Fisher

Sample buying committee projections Which product is more predictable? Std. Dev. Carolyn Laura Tom Kenny Wally Wendy Average Pandora Parka 1200 1150 1250 1300 1100 1200 1200 65 Entice Shell 1500 700 1200 300 2075 1425 1200 572 © 2005 Marshall L. Fisher

The committee process allowed us to forecast forecast accuracy High Error Average Error = 155 units Average Error = 252 units Low Error High Agreement Low Agreement © 2005 Marshall L. Fisher

Obermeyer Committee Forecasts Color forecasts © 2005 Marshall L. Fisher

Historical Distribution of Forecast Errors Follows the Normal Bell-Shaped Curve © 2005 Marshall L. Fisher

Probability Distribution for Sales of Pandora The Normal Distribution Accurately Models Demand Uncertainty at Obermeyer Pandora Parka 15% 33% 33% 15% 2% 2% 740 970 1200 1430 1660 Probability Distribution for Sales of Pandora Mean = 1200 Standard Deviation = 230 © 2005 Marshall L. Fisher

Cost of Under and Over Production Pandora Parka Wholesale Price Less Supplier charges Sales Commission Inventory carrying/Delivery Profit Margin Markdown Price Inventory carrying/delivery Loss $ 200 100 30 25 _____ $ 45 Cost of Under Production $ 120 35 ($15) Cost of Over Production © 2005 Marshall L. Fisher

Production Decision if we can only buy once Probability = .25 Pandora Parka 1200 1430 Probabilistic Break Even Analysis Produce to the point where Probability 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 Loss if we don’t sell Gain if we sell © 2005 Marshall L. Fisher

Accurate Response Optimization Model Demand Distributions Cost of Stockouts & Excess Inventory Production Capacity and Minimum Constraints Minimize Expected Cost of 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 Order 50% in November Order 50% in April Retailers order in Feb & April Forecasts Product Sketches Forecast Committee

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 How does product availability drive revenue? Inventory Optimize cost of lost margin, carrying and obsolescence Accurate Forecasts Responsive Supply Chain Track & improve the accuracy of forecasts that drive decisions e.g. parts lead time demand Create a framework for inventory efficiency e.g. common parts, short lead times, efficient small lot production © 2005 Marshall L. Fisher