Integrated demand forecasting and planning model for repairable spare part: an empirical investigation

Abstract

Efficient resource management methods are essential for spare parts used in the maintenance and repair of equipment. Forecasting plays a critical role in planning, especially under demand uncertainty. Existing works regarding spare parts with intermittent demand focus on the mere forecasting model while integrating the planning and forecasting models are not sufficiently investigated. We examine the interaction between two models to optimise planning and forecasting decisions and prevent sub-optimality. This paper presents two mathematical models, including a planning model that determines stock level, spare part order assignment to suppliers, equipment repair assignment, and the number of intervals over the planning horizon. The second model is the forecasting model by Support Vector Machine (SVM). Considering uncertainty, demand estimation is performed by piecewise linearisation considering the optimal number of intervals in the planning model used in forecasting. An interactive procedure is developed to optimise models. We use an empirical investigation from an oil company providing the spare part supply chain data. The analyses show that demand estimation by piecewise method and integrating the decisions optimises the cost, improves the forecasting accuracy, and planning performance. Moreover, we offer several insights to practitioners that shed light on spare part planning and forecasting decisions.

KEYWORDS:

• Forecasting
• inventory management
• planning
• spare part
• support vector machine

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data used in this research are available from the corresponding author upon reasonable request.

Notes

1 Line-Replaceable Unit.

2 Shop-Replaceable Unit.

3 Independent and identically distributed.

Additional information

Notes on contributors

Vahid Babaveisi

Vahid Babaveisi is a Ph.D. candidate at the Industrial Engineering faculty, Iran University of Science and Technology, Iran. His thesis concerns an oil company's repairable spare part supply chain network design and planning. He also holds an M.Sc. in industrial engineering and is an optimisation software instructor. His research field includes the mathematical modelling of the supply chain, optimisation algorithms, developing decision support systems, and innovation. He is also working on webGIS systems to improve supply chain performance and enable fast decision-making.

Ebrahim Teimoury

Ebrahim Teimoury is an Associate Professor of Industrial Engineering at the Iran University of Science and Technology. He received his Ph.D. from the Iran University of Science and Technology in 2000 and initiated his work as a faculty member at SIE in 2001. His research vision is concentrated mainly on Supply Chain Management. He teaches Supply Chain Management, E-supply Chain Management, Socio-economic Systems Modelling, Systems Engineering, Queuing Theory, Probability Theory, and Mathematical Statistics. He is the author (or co-author) of more than 100 scientific papers and a referee for more than five international scientific journals

Mohammad Reza Gholamian

Mohammad Reza Gholamian is an Associate Professor in the Department of Industrial Engineering at Iran University of Science and Technology (IUST), Tehran, Iran. He received his Ph.D. in 2005. He teaches courses such as Inventory Control, Decision Analysis, Engineering Economics, Computer Aided Industrial Engineering, and Management Information Systems (MIS) for undergraduate students and Materials and Inventory Management in Supply Chain, Multiple Criteria Decision Making (MCDM), and Supply Chain Network Design (SCND), Operations Research, Decision Theory, Operation Research Relationship Management (CRM), and Design and Evaluation of e-Business Systems for graduate students. He has more than 100 papers in scientific journals.

Bahman Rostami-Tabar

Bahman Rostami-Tabar is a senior lecturer of Management Science at Cardiff Business School (CARBS), Cardiff University, U.K. He holds a Ph.D. in Industrial Engineering from the University of Bordeaux, France. Bahman launched the ‘Democratising Forecasting’ in 2018 sponsored by the International Institute of Forecasters (IIF) to deliver a series of workshops in developing economies that promotes the importance of forecasting and ‘train the trainers’ on forecasting principles using R. This initiative has recently been extended to train over 100 data analysts within the National Health Service (NHS, UK) in collaboration with the NHS-R community. Bahman has also created the Forecasting for Social Good (FSG) initiative sponsored by IIF that aims to explore and expose how (where) forecasting can have some positive societal impact.