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parka, also known as the trench coat or a raincoat, outdoor sports enthusiasts is one of the essential equipment. Both urban and leisure family, or ordinary weekend picnic lovers, whatever you do in the long-distance hiking and mountain climbing, or professional adventure, climbing, or even seven or eight kilometers of mountain climbing, one for their "all-weather "parka are your must-have.
Coping vs^ith Demand Uncertainty at Sport Obermeyer Longtime industry player Klaus Obermeyer charac- us to generate multistage, risk-hased produetion terizes the skiwear market as extremely fickle. What schedules.' possible use could formal statistical methods have in To implement the approach described ahove, we bad such an unpredictable setting? You'd he surprised. The to estimate the mean and tbe standard deviation. For trick lies in realizing that although demand for each produets with extensive historical demand data, tbose product can be highly uncertain, the distrihution of parameters ean be estimated using statistieal meth- demand follows a discernible pattern. ods. However, with only a judgmental forecast avail- At Sport Ohermeyer, we found that demand data ahle, we had to devise a different approach. We started followed a normal distribution, whieh is defined hy its by asking each member of Sport Ohermeyer's huying mean [average) and its standard deviation [a measure committee to provide us with an individual forecast of the dispersion, or "width," of the distrihution and for eaeh product. hence of the level of demand uncertainty). We treated tbe average of the committee members' The graph "Prohahle Sales of the Pandora Parka" forecasts as tbe mean of the demand distrihution. We shows a forecast distrihution based on the demand estimated the standard deviation for eaeh style to be predietions of the huying committee. The area under twice the standard deviation of the buying commit- the eurve hetween two points is equal to or greater tee's forecasts. We decided to scale by a factor of two than the probability of demand beeause the average standard devia- falling between those points. (For tion of actual forecasting errors in example, the shaded area repre- preceding seasons was twice that of sents the probahility that demand tbe buying committee's foreeasts. exceeds 1,28.S units.) If Sport Ober- We believed that foreeasts would meyer were to have only one op- tend to be more accurate for those portunity to produce Pandora par- styles for which the buying com- kas, we would use this eurve in mittee members bad similar fore- the following manner to find the casts-that is, those whose fore- produetion quantity that maxi- casts had a low standard deviation. mizes expected profitability by bal- Tbis hypotbesis was eonfirmed aneing the risks of overproduction with actual data from the 1992- and underproduction. 1993 season. The close fit hetween actual and predicted forecasting er- For the Pandora parka, Sport rors gave us a solid basis for deter- Obermeyer earns $14.50 in mar- mining wbicb products were safe to produce before ad- ginal profit for each unit sold and loses S.S.OO for ditional sales data became available and which were eaeh unit produeed and not sold. The company should not. Using this information along with detailed data keep producing parkas as long as it expects the gain about minimum lot sizes and other production con- from eaeh parka to exeeed the loss. Expected profits straints, we formulated an appropriate risk-based pro- are maximized hy producing up to the point where the duction sequenee for Sport Obermeyer. expected marginal gain from producing a parka is roughly equal to the expected marginal loss from pro- [ust as quick-response and just-in-time programs ducing that parka. For the Pandora, that occurs when cannot realize tbeir full potential without correspond- tbe company produces 1,285 parkas, hecause the ex- ing changes in planning systems, neitber should those peeted gain from producing the 1,285th parka is ap- changes in analytical approach exist in isolation. Im- proximately equal to the expected loss from producing provements in supply ehain speed and flexihility are that parka. That is, the probability of selling tbe essential to achieving tbe full potential of an accurate I,285tb parka (25,7%) multiplied hy the profit if the response program.^ company sells that parka IS14.5O| is roughly equal to the probahility of not selling it (74.3%) multiplied hy 1, Fiir a descriptiiin of the mode!, see Marshall L, Fisher and Ananth Raman, "Thu ValuL; of Quick Response for Supplying Fashion Prod- what tbe eompany loses ii it makes it and cannot sell ucts; Analysis and AppliciStion," Departmt-nt of Operations and Infor- it ($5.00), mation Management Working Paper No. 92-10-03, The Wharton School, University of Pennsylvania, 1992, This analysis illustrates two critical components of 2. For a description of such .supply chain changes, see Janice H. Ham- an accurate response program: assessing a prohability mond, "Quick Response in Retail/Manufacturing Channels," in distribution for demand, and estimating the eosts of CJohahzaiion. Tecbnoiag.y, and Competition: The Fufnon of Comput- ers and Telecomnninicntions in the 1990s, ed. Stephen P. Bradley, Icr- stoekouts and markdowns. We have embedded tbis ry A, Hausman, and Richard L, Nolan (Boston: Harvard Business basic logic into a sopbisticated algorithm that allows School Press, 199,1), p. 18S, 90 HARVARD BUSINESS REVIEW May-|une 1994
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