Intelligent Demand Forecasting Approaches for Spare Parts in The Energy Industry

Authors

  • Yu-Chung Tsao National Taiwan University of Science and Technology, Taiwan
  • Alvin Yaqin National Taiwan University of Science and Technology, Taiwan | Institut Teknologi Sepuluh Nopember, Indonesia
  • Jye-Chyi Lu Georgia Institute of Technology, USA
  • Nani Kurniati Institut Teknologi Sepuluh Nopember, Indonesia
  • Nyoman Pujawan Institut Teknologi Sepuluh Nopember, Indonesia

DOI:

https://doi.org/10.23055/ijietap.2024.31.3.8131

Abstract

In this study, two intelligent demand forecasting approaches are used to forecast the spare parts demand in the energy industry. In the first approach, a stacked generalization-based demand forecasting technique that combines traditional time-series forecasting and machine-learning methods is developed. In the second approach, external information (EI) is incorporated into the first one. Thus, the stacked generalization-based demand forecasting technique is used as a base model, and the EI is used to focus on predicting the peak demand; consequently, significant forecast errors can be minimized. A case study of a natural gas liquefaction company is then considered to test the performance of these methodologies. Our results show that the proposed techniques perform significantly better than the previous methods. Compared with the mean absolute scaled error and relative geometric root mean squared error of the company’s forecasts, our intelligent demand forecasting approaches yield a 40.07% (36.81%) and 2.07% (3.40%) increase in butterfly-valve demand forecasting (spiral wound gasket) when no EI is used, and a 57.78% (60.41%) and 5.73% (7.36%) improvement with EI, respectively.

Author Biography

Yu-Chung Tsao, National Taiwan University of Science and Technology, Taiwan

Yu-Chung Tsao is currently a professor and Department Chair in the Department of Industrial Management at National Taiwan University of Science and Technology. Prior to his current position, he was an associate professor in the Sino-US Global Logistics Institute at Shanghai Jiao Tong University and in the Department of Business Management at Tatung University. He was a visiting scholar in the School of Industrial and Systems Engineering at Georgia Institute of Technology. His research interests are in the areas of decision sciences, supply chain management, optimization application, and data analytics.

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Published

2024-06-17

How to Cite

Tsao, Y.-C., Yaqin, A., Lu, J.-C., Kurniati, N., & Pujawan, N. (2024). Intelligent Demand Forecasting Approaches for Spare Parts in The Energy Industry. International Journal of Industrial Engineering: Theory, Applications and Practice, 31(3). https://doi.org/10.23055/ijietap.2024.31.3.8131

Issue

Vol. 31 No. 3 (2024)

Section

Supply Chain Management

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