SMU Data Science Review

Forecasting Spare Parts Sporadic Demand Using Traditional Methods and Machine Learning - a Comparative Study

Bhuvana Adur Kannan, Southern Methodist UniversityFollow
Ganesh Kodi, Southern Methodist UniversityFollow
Oscar Padilla, Southern Methodist UniversityFollow
Dough GrayFollow
Barry C. SmithFollow

Abstract

Sporadic demand presents a particular challenge to traditional time forecasting methods. In the past 50 years, there has been developments, such as, the Croston Model [3], which has improved forecast performance. With the rise of Machine Learning (ML) there is abundant research in the field of applying ML algorithms to predict sporadic demand [8][12][9]. However, most existing research has analyzed this problem from the demand side [17]. In this paper, we tackle this predictive analytics challenge from the supply side. We perform a comparative analysis utilizing a spare parts demand dataset from an Original Equipment Manufacturer (OEM). Since traditional measurements of forecast are unsuitable for sporadic demand data because of its sparse nature, we propose a novel method to forecast performance measurement which incorporates the trade-off of economic gains and obsolescence risks incurred.

Recommended Citation

Adur Kannan, Bhuvana; Kodi, Ganesh; Padilla, Oscar; Gray, Dough; and Smith, Barry C. (2020) "Forecasting Spare Parts Sporadic Demand Using Traditional Methods and Machine Learning - a Comparative Study," SMU Data Science Review: Vol. 3: No. 2, Article 9.
Available at: https://scholar.smu.edu/datasciencereview/vol3/iss2/9