Identification of Demand through Statistical Distribution Modeling for Improved Demand Forecasting
Demand functions for goods are generally cyclical in nature with characteristics such as trend or stochasticity. Most existing demand forecasting techniques in literature are designed to manage and forecast this type of demand functions. However, if the demand function is lumpy in nature, then the general demand forecasting techniques may fail given the unusual characteristics of the function. Proper identification of the underlying demand function and using the most appropriate forecasting technique becomes critical. In this paper, we will attempt to explore the key characteristics of the different types of demand function and relate them to known statistical distributions. By fitting statistical distributions to actual past demand data, we are then able to identify the correct demand functions, so that the most appropriate forecasting technique can be applied to obtain improved forecasting results. We applied the methodology to a real case study to show the reduction in forecasting errors obtained.
Forecasting, Lumpy, Distribution, Time Series
Computer Sciences | Management Information Systems | Operations Research, Systems Engineering and Industrial Engineering
Intelligent Systems and Decision Analytics
Business Intelligence Journal
IIU Press and Research Centre
CHOY, Murphy and CHEONG, Michelle Lee Fong.
Identification of Demand through Statistical Distribution Modeling for Improved Demand Forecasting. (2012). Business Intelligence Journal. 5, (2), 260-266. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1437