Adaptive exponential smoothing versus conventional approaches for lumpy demand forecasting: case of production planning for a manufacturing line

Abstract

Production planning in a lumpy demand environment can be tenuous, with potentially costly forecasting errors. This paper addresses the issue of selecting the smoothing factor used in lumpy demand forecasting models. We propose a simple adaptive smoothing approach to replace the conventional industrial practice of choosing a smoothing factor largely based on the analyst or engineer's experience and subjective judgment. The Kalman filter approach developed in this study processes measurements to estimate the state of a linear system and utilises knowledge from states of measurements and system dynamics. Performances of an array of forecasting models that have been shown to work well in lumpy demand environments are compared with respect to the proposed adaptive smoothing factor and the conventional smoothing constant across a spectrum of lumpy demand scenarios. All models using the adaptive smoothing factor based on Kalman filter weighting function generate smaller errors than their conventional counterparts, especially under high lumpiness demand environments. Our proposed approach is particularly useful when production management is concerned about simplicity and transferability of knowledge due to constant personnel turnaround and low retention rate of expertise.

Customer demand prediction of service-oriented manufacturing incorporating customer satisfaction

“…Çakanyildirim and Roundy (2002) laid out a scheme estimating the variance and correlation of forecast errors and modelling the evolution of forecasts over time. To cope with the production planning problem in a lumpy demand environment, Quintana and Leung (2007) proposed a simple adaptive smoothing approach to replace the conventional industrial practice of choosing a smoothing factor largely based on the analyst or engineers' experience and subjective judgement. Hong et al (2008) identified for major factors influencing the service part demand forecasting and developed a stochastic demand forecasting method.…”

Adaptive forecasting and financial risk estimation

“…The use of adaptive exponential smoothing for lumpy demand forecasting is considered in [12]. It showed substantial advantages over some conventional approaches used in practice due to appropriate selecting the model smoothing factor.…”

Intermittent demand forecasting: An empirical study on accuracy and the risk of obsolescence

“…However, despite the comparatively low contribution to the total turnover, these slow moving Stock Keeping Units (SKUs) may constitute up to 60% of the total stock value (Johnston et al 2003;Quintana and Leung 2007). In addition, spare parts that have become ubiquitous in modern societies, are almost invariably 'intermittently moving'.…”