Spare Parts Demand Forecasting in Energy Industry

Yaqin, Alvin Muhammad 'Ainul (2019) Spare Parts Demand Forecasting in Energy Industry. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

This paper deals with spare parts demand forecasting problem in energy industry. Forecasting parts demand has its own challenges because in general spares demand is characterized by high variation in its demand size and in its inter-demand interval. In this study, two forecasting approaches to deal with spare parts demand are proposed: in the base approach, traditional time series forecasting methods and machine learning methods are combined using stacked generalization; in the improved approach, external information is utilized to improve the predictions from the base approach, resulting in more accurate predictions. To test the performance of these approaches, a case study in a natural gas liquefaction company is provided in this research. In the case study, these approaches are employed to forecast the monthly demand of parts used in the company’s maintenance operations. Several traditional time series forecasting methods (including Simple Moving Average, Single Exponential Smoothing, Croston’s Method, Syntetos-Boylan’s Approximation, and Teunter-Syntetos-Babai’s Method) and several machine learning methods (including Multiple Linear Regression, Elastic Net, Neural Network, Support Vector Machine, and Random Forests) are also utilized in the case study to compare the performance of the proposed approaches. In the end, results showed that the approaches proposed in this paper are promising.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Demand Forecasting, Spare Parts, Stacked Generalization, External Information
Subjects:	Q Science > QA Mathematics > QA276 Mathematical statistics. Time-series analysis. Failure time data analysis. Survival analysis (Biometry)
Divisions:	Faculty of Industrial Technology > Industrial Engineering > 26101-(S2) Master Thesis
Depositing User:	Yaqin Alvin Muhammad 'Ainul
Date Deposited:	22 Jul 2026 04:12
Last Modified:	22 Jul 2026 04:12
URI:	http://repository.its.ac.id/id/eprint/67846

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