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 , 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 . However, most existing research has analyzed this problem from the demand side . 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.
Adur Kannan, Bhuvana; Kodi, Ganesh; Padilla, Oscar; Gray, Dough; and Smith, Barry C.
"Forecasting Spare Parts Sporadic Demand Using Traditional Methods and Machine Learning - a Comparative Study,"
SMU Data Science Review: Vol. 3:
2, Article 9.
Available at: https://scholar.smu.edu/datasciencereview/vol3/iss2/9
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