Analogous Forecasting of Products with a Short Life Cycle
DOI:
https://doi.org/10.7494/dmms.2010.4.2.71Keywords:
short life cycle, analogous forecasting, measure of similarity, calibrating, adjusting the lengthAbstract
Managing a supply chain for products with a short life cycle, like fashion apparel, high-tech, personal computers, toys, CD’s etc., is challenging for many companies (Fisher and Raman, 1999). Because the life cycles of these products are too short for standard time- series forecasting methods (not longer than one – two years), an important way of overcoming the challenges of managing supply chains for such products is to find appropriate forecasting methodologies. The standard forecasting methods require some historical data, which are often unavailable at the time when the forecasts are being performed for products with a short life cycle (Lin, 2005). The method described in this article allows forecasters to use life cycles of similar, analogous products to arrive at the initial forecasts for the product(s) at hand.
References
Azoury K.S. (1985). Bayes solution to dynamic inventory models under unknown demand distribution. Management Science, 31(9), pp. 1150–1160. DOI: https://doi.org/10.1287/mnsc.31.9.1150.
Burruss J. & Kuettner D. (2002/2003). Forecasting for short-lived products: Hewlett-Packard’s journey. The Journal of Business Forecasting Methods & Systems, 21(4), pp. 9–14. Brak potwierdzonego DOI.
Brzeziński J. (1997). Metodologia badań psychologicznych. Warszawa: Wydawnictwo Naukowe PWN. Brak DOI.
Cieślak M. & Jasiński R. (1979). Miara podobieństwa funkcji. Przegląd Statystyczny, 3–4, pp. 169–173. Brak potwierdzonego DOI.
Cieślak M. (Ed.) (2000). Prognozowanie gospodarcze. Metody i zastosowania. Warszawa: Wydawnictwo Naukowe PWN. Brak DOI.
Dittmann P. (2002). Metody prognozowania sprzedaży w przedsiębiorstwie. Wrocław: Wydawnictwo Akademii Ekonomicznej im. Oskara Langego. Brak DOI.
Duncan G.T., Gorr W.L. & Szczypula J. (2001). Forecasting analogous time series. In: J.S. Armstrong (Ed.), Principles of Forecasting: A Handbook for Researchers and Practitioners, pp. 195–213. Boston: Kluwer Academic Publishers. DOI: https://doi.org/10.1007/978-0-306-47630-3_10.
Fisher M.L. & Raman A. (1996). Reducing the cost of demand uncertainty through accurate response to early sales. Operations Research, 44(1), pp. 87–99. DOI: https://doi.org/10.1287/opre.44.1.87.
Fisher M.L. & Raman A. (1999). Managing short life-cycle products. ASCET: Achieving Supply Chain Excellence Through Technology, 1. Brak potwierdzonego DOI.
Green K.C. & Armstrong J.S. (2007). Structured analogies for forecasting. International Journal of Forecasting, 23(3), pp. 365–376. DOI: https://doi.org/10.1016/j.ijforecast.2007.05.005.
Harpaz G., Lee W.Y. & Winkler R.L. (1982). Learning, experimentation, and the optimal output decisions of a competitive firm. Management Science, 28(6), pp. 589–603. DOI: https://doi.org/10.1287/mnsc.28.6.589.
Johnson G.D. & Thompson H.E. (1975). Optimality of myopic inventory policies for certain dependent demand processes. Management Science, 21(11), pp. 1303–1307. DOI: https://doi.org/10.1287/mnsc.21.11.1303.
Kahn K.B. (2002). An exploratory investigation of new product forecasting practices. Journal of Product Innovation Management, 19(2), pp. 133–143. DOI: https://doi.org/10.1111/1540-5885.1920133.
Kahn K.B. (2006). New Product Forecasting: An Applied Approach. Armonk, NY: M.E. Sharpe.
Kurawarwala A.A. & Matsuo H. (1996). Forecasting and inventory management of short life-cycle products. Operations Research, 44(1), pp. 131–150. DOI: https://doi.org/10.1287/opre.44.1.131.
Lee W.Y., Goodwin P., Fildes R., Nikolopoulos K. & Lawrence M. (2007). Providing support for the use of analogies in demand forecasting tasks. International Journal of Forecasting, 23(3), pp. 377–390. DOI: https://doi.org/10.1016/j.ijforecast.2007.02.006.
Lin F.-J. (2005). Forecasting telecommunication new service demand by analogy method and combined forecast. Yugoslav Journal of Operations Research, 15(1), pp. 97–107. DOI: https://doi.org/10.2298/YJOR0501097L.
Lindsay P.H. & Norman D.A. (1977). Human Information Processing: An Introduction to Psychology. 2nd Edition. New York–London: Academic Press.
Miller B.L. (1986). Scarf’s state reduction method, flexibility, and a dependent demand inventory model. Operations Research, 34(1), pp. 83–90. DOI: https://doi.org/10.1287/opre.34.1.83.
Nikolopoulos K., Goodwin P., Patelis A. & Assimakopoulos V. (2007). Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches. European Journal of Operational Research, 180(1), pp. 354–368. DOI: https://doi.org/10.1016/j.ejor.2006.03.047.
Nowak E. (Ed.) (1998). Prognozowanie gospodarcze. Metody, modele, zastosowania, przykłady. Warszawa: Agencja Wydawnicza PLACET.
Ray W.D. (1982). ARIMA forecasting models in inventory control. Journal of the Operational Research Society, 33(6), pp. 567–574. DOI: https://doi.org/10.1057/jors.1982.119.
Szanduła J. (2002). Metody analogowe w prognozowaniu społeczno-gospodarczym [PhD thesis]. Wrocław: Akademia Ekonomiczna im. Oskara Langego, Katedra Prognoz i Analiz Gospodarczych.
Wu S.D. & Aytac B. (2007). Characterization of Demand for Short Life-Cycle Technology Products. Technical Report No. 07T-005. Bethlehem, PA: Lehigh University. URL: https://engineering.lehigh.edu/ise/research/ise-technical-reports.
