Title

Identification of Demand through Statistical Distribution Modeling for Improved Demand Forecasting

Publication Type

Journal Article

Publication Date

2012

Abstract

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.

Keywords

Forecasting, Lumpy, Distribution, Time Series

Discipline

Computer Sciences | Management Information Systems | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Decision Analytics

Publication

Business Intelligence Journal

Volume

5

Issue

2

First Page

260

Last Page

266

ISSN

1918-2325

Publisher

IIU Press and Research Centre

Additional URL

http://www.saycocorporativo.com/saycoUK/BIJ/journal/Vol5No2/Article_7.pdf