Title: Analysis of the performance of competing models for aggregate demand forecasting using observable data characteristics

Authors: N. Jayasree; T. Radha Ramanan; R. Sridharan

Addresses: Department of Production Engineering, Government Engineering College Thrissur, Kerala, India ' School of Management Studies, NIT Calicut, Kerala, India ' Department of Mechanical Engineering, NIT Calicut, Kerala, India

Abstract: This paper provides an analysis of the performance of two simplified approaches for forecasting the aggregate demand for a product family consisting of two interrelated items. The correlated demand characteristic of the product family is modelled by bivariate vector IMA(1, 1) process. The forecasting approaches chosen for evaluation are univariate scheme and aggregate scheme. The performances of these methods are analysed by theoretically determining the forecast mean square error (MSE) in terms of observable data characteristics. Further, subsequent evaluation of percentage changes of these MSEs with respect to the MSE of original bivariate model has been conducted to determine how far the forecast MSE values deviate from the original model. Through numerical experimentation, the necessary and sufficient conditions in terms of observable data characteristics are determined for the two approaches to be equal in terms of forecast MSE of the original bivariate model.

Keywords: aggregate demand forecast; vector IMA(1, 1) model; observable data characteristics.

DOI: 10.1504/IJISE.2020.108548

International Journal of Industrial and Systems Engineering, 2020 Vol.35 No.4, pp.410 - 432

Received: 07 Oct 2018
Accepted: 25 Dec 2018
Published online: 17 Jul 2020 *

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