Title: A comparative study of forecasting methods for sporadic demand in an auto service station

Authors: Rahul S. Mor; Jitendra Nagar; Arvind Bhardwaj

Addresses: Department of Food Engineering, National Institute of Food Technology Entrepreneurship and Management, Kundli, Sonepat, India ' Department of Industrial and Production Engineering, National Institute of Technology, Jalandhar, Punjab, India ' Department of Industrial and Production Engineering, National Institute of Technology, Jalandhar, Punjab, India

Abstract: Spare parts are essential in the automobile sector and forecasting of spare parts has always been the vital prospects in an automobile service parts station. In this paper, a comparison and efforts have been made at the service station of a reputed organisation to reduce the errors in demand forecasting of intermittent demand items. The errors are compared for various methods using mean absolute scaled error (MASE) and Syntetos and Boylan approximation (SBA) method which exhibited the least error for intermittent demand and lumpy demand pattern. While single exponential smoothing method is used for smooth and erratic demand pattern. All calculations are done in MS Excel and solver tool to find optimal values of smoothing parameters.

Keywords: demand forecasting; intermittent demand; sporadic demand; mean absolute scaled error; MASE; solver; auto service station.

DOI: 10.1504/IJBFMI.2019.099009

International Journal of Business Forecasting and Marketing Intelligence, 2019 Vol.5 No.1, pp.56 - 70

Received: 25 Apr 2018
Accepted: 06 Jul 2018
Published online: 12 Apr 2019 *

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