Demand fulfillment and availability analysis of a multi-state production system considering spare part inventory policy

Abstract

Customer demand fulfilment is essential for production systems in the highly competitive market environment. Because of that fact, a reliable production line is necessary for timely demand fulfilment, and the system must maintain an appropriate spare parts inventory to run the production line smoothly. This research aims to study the impact of spare part inventory policy on production line availability and demand fulfilment. This paper considers a steel pipe manufacturing company that runs continuously. This company uses a continuous raw material flow and produces discrete finished items. The company’s production system is multi-state with unique characteristics in which the machines can be operated at partial capacity under certain failures or disturbances. Hence, three states of the production system, namely fail, working under failure, and success (denoted as 0, 0.5, and 1, respectively) are considered. In addition, the company follows s,S policy for its spare parts inventory system. To deal with this problem, a discrete-event simulation approach is developed as the problem environment is complex with interdependency and variability. A multi-level reliability block diagram is also used to build the production system model. The proposed simulation model is run for different scenarios with either cost or performance as the objective measure. Based on the analysis of results, the proposed approach can improve the current operation in terms of both production line availability and demand fulfilment.

Keywords:

• Demand fulfillment
• availability
• spare part inventory policy
• three-state production system
• discrete event simulation

REVIEWING EDITOR:

• Zude Zhou, Wuhan University of Technology, China

SUBJECTS:

• Industrial Engineering & Manufacturing
• Production Systems
• Engineering Management

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Funding

The authors gratefully acknowledge financial support from the Institut Teknologi Sepuluh Nopember for this work, under project scheme of the Publication Writing and IPR Incentive Program (PPHKI) 2023.

Notes on contributors

Nurhadi Siswanto

Nurhadi Siswanto is a faculty member and the Head of Department of Industrial and Systems Engineering at Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia. He earned his PhD degree from University of New South Wales, Canberra, Australia. His research interests include operations research, large-scale optimization, simulation, vehicle routing, inventory routing and modeling of maritime transportation. He has taught courses in Operations Research, Decision Analysis, Discrete Event Simulation, Simulation Modeling, and Quantitative Modeling.

Ahmed Raecky Baihaqy

Ahmed Raecky Baihaqy earned his bachelor’s degree in industrial engineering from Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia. He was active as a laboratory assistant at Quantitative Modelling and Industrial Policy Analysis (QMIPA) Laboratory of Industrial Engineering ITS also had been appointed as coordinator of laboratory assistant. Currently, he is pursuing a master’s degree in industrial engineering at ITS fully funded by Indonesia Endowment Fund. Ahmed has more than 3 years of experience in logistics and supply chain management at one of biggest 3PL companies. His research interests include discrete-event simulation, inventory modelling, reliability modelling, as well as logistics and supply chain.

Mohd Shukor Salleh

Mohd Shukor Salleh is an Associate Professor at the Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka (UTeM). Mohd Shukor has taught various courses, including materials testing and fracture analysis, advanced manufacturing processes, heat treatment, semi-solid metal processing, and CNC machining. He also supervises both master’s and Ph.D. students in various subjects related to materials processing, CNC machining, and liquid processing. He was an editor of many published books, has published numerous articles, and is currently the technical editor for the Journal of Advanced Manufacturing Technology, an international journal published by the faculty. His research interests include semi-solid metal processing, casting, CNC machining, and manufacturing processes.

Ruhul Sarker

Ruhul A Sarker obtained his PhD from Dalhousie University (former TUNS), Canada. He is a Professor in the School of Engineering and IT (SEIT) at UNSW Canberra located at ADFA. He served as the Director of Faculty PG Research (June 2015 to May 2020) and as the Deputy Head of School (Research) of SEIT (2011–2014). Prof. Sarker’s broad research interests are decision analytics, CI / evolutionary computation, operations research, and applied optimization with an emphasis on Augmenting Human Intelligence (AHI). His name appeared on the recent lists of top 2% of world’s scientists-researchers prepared by (i) Stanford University and also (ii) Elsevier/Scopus/digitalcommonsdata (Research fields: Artificial Intelligence and Operations Research).