How do you decide what to produce customers will buy? Marshall Fisher
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 High Forecast accuracy High Low Low High Long Short Product variety Product life cycle Functional © 2005 Marshall L. Fisher Innovative And supply strategies differ Factory focus Inventory Strategy Lead-time focus Supplier selection Product-design strategy High utilization Maintain buffer capacity High turns Significant buffer stocks of components and FGs Low cost trumps short lead-time Aggressively shorten lead-time Low cost Integral for max performance at min cost Physically efficient © 2005 Marshall L. Fisher Speed & flexibility Modular to enable postponed differentiation Market responsive Respond quickly to unpredictable demand to minimize stockouts, markdowns, and obsolete inventory © 2005 Marshall L. Fisher Life cycle < 1 year Gross Margin > 35% High Product Variety Functional Products Innovative Products Efficient Supply Chain Supply predictable demand efficiently at lowest cost Life cycle > 2 years Gross Margin < 35% Low Product Variety match mismatch Responsive Supply Chain Need to match supply strategy with product type mismatch match So as to minimize total of two types of costs Raw Materials • Component Manufacturer Retailers Suppliers 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 Consumers Dell reaps benefits from supply chain innovation Dell 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 has grown earnings faster than sales by cutting supply chain costs P&G Net Sales and Net Earnings 1990-98 40000 10000 35000 9000 6000 20000 5000 15000 4000 3000 10000 2000 trendline 1000 0 1998 1997 1996 1995 1994 1993 1992 1991 1990 0 Source: Company 10K reports Note: 1993-94 accounting change makes series discontinuous. © 2005 Marshall L. Fisher Net Earnings ($M) Net Sales ($M) 7000 25000 5000 During 94-98 8000 30000 Net Sales grew 22.3% Net Earnings grew 71% 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 © 2005 Marshall L. Fisher Sold 4 Initial forecasts are highly inaccurate Black Voodoo © 2005 Marshall L. Fisher Measuring the cost of over and under supply Orders Production Lost sales Full price = $200 4000 4 2000 2000 2000 Excess 1996 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 undyed greige goods Cut/Sew Factory Denver Warehouse Retailer 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 1400 High Error 1200 Average Error = 155 units 1000 Average Error = 252 units 800 Forecast Error 600 400 200 Low Error 0 0 110 High Agreement 220 330 Low Agreement Standard Deviation of the Individual Forecasts of a Six Person Committee © 2005 Marshall L. Fisher Obermeyer Committee Forecasts STYLE COLOR # # Wholesale Price LK CO SS CB JD WS WRO TT GW AM COMM COMM AVE STD DEV AS 6220 77.5 64 45 78 9 TOTAL: 6221 479 650 50 180 1359 700 200 300 300 1500 500 500 200 200 1400 350 600 350 500 1800 700 200 300 400 1600 700 200 750 450 2100 930 1450 450 1529 500 350 400 475 200 560 100 475 100 390 800 1900 600 414 300 800 614 300 300 895 300 290 876 400 1990 2799 1300 600 340 840 340 600 510 360 510 2400 1700 481 500 373 361 1715 125 233 253 217 581 500 600 400 400 400 500 300 100 385 400 400 320 1600 1500 1605 275 465 575 542 400 310 150 233 1400 1550 200 300 175 175 850 420 300 516 375 432 600 72 225 1440 1500 421 441 335 288 1485 152 128 138 137 249 700 200 500 600 2000 400 1100 350 400 400 250 500 200 350 500 300 150 1800 2000 1100 550 771 400 375 135 100 725 539 500 150 275 300 1800 1720 1300 400 600 672 300 400 800 200 300 1672 2000 597 303 526 323 1749 232 168 189 150 324 800 600 600 2100 400 300 700 300 740 1300 1000 1310 500 300 150 1400 2100 1225 400 400 320 300 100 385 1015 1415 1750 1291 150 275 1425 1650 150 233 400 310 760 1310 2028 1015 200 300 1420 1240 72 225 432 600 887 1244 323 1499 288 335 316 440 150 375 137 138 94.5 64 45 78 76 TOTAL: 6222 9.5 64 45 9 49 TOTAL: Color forecasts 9 45 49 64 76 78 © 2005 Marshall L. Fisher 900 700 300 900 175 175 Historical Distribution of Forecast Errors Follows the Normal Bell-Shaped Curve © 2005 Marshall L. Fisher The Normal Distribution Accurately Models Demand Uncertainty at Obermeyer Pandora Parka 15% 33% 33% 2% 2% 740 15% 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 $ 200 Profit Margin 100 30 25 _____ $ 45 Markdown Price $ 120 Loss 100 35 _____ ($15) Less Supplier charges Sales Commission Inventory carrying/Delivery Less Supplier charges Inventory carrying/delivery © 2005 Marshall L. Fisher Cost of Under Production Cost of Over Production 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 Gain if we sell © 2005 Marshall L. Fisher Loss if we don’t sell 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 Order 50% in April Order 50% in November Forecasts Product Sketches Forecast Committee 800 Ski Retailers Retailers order in Feb & April Results of a parallel test show profit increase equal to 1.8% of sales Optimization Model Decisions Total Production (Units) 124,805 Demand Over-Production (Units) Under-Production (Units) Over-Production Cost (% of Sales) Under-Production Cost (% of Sales) Total Cost (% of Sales) © 2005 Marshall L. Fisher 121,432 103,831 22,036 25,094 792 1.30% 0.18% 1.48% 103,,831 7,493 1.74% 1.56% 3.30% Legacy Process Decisions 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 Accurate Forecasts Track & improve the accuracy of forecasts that drive decisions e.g. parts lead time demand © 2005 Marshall L. Fisher Responsive Supply Chain Optimize cost of lost margin, carrying and obsolescence Create a framework for inventory efficiency e.g. common parts, short lead times, efficient small lot production
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