Improvement of Business Performance by Enhancing the Productivity and Profitability of Production Lines

Document Type : Research Paper

Authors

1 Associate Professor, Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G.C., Tehran, Iran

2 Ph.D student of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G.C., Tehran, Iran

Abstract

Due to the competitiveness of production over the past years, managers have been trying to improve their business performance by increasing productivity and profitability. Maintenance plays an important role in increasing the efficiency and effectiveness of production lines, reducing costs and improving quality. In this paper, a model is presented for the simulation of JIT production systems in different operating conditions with various maintenance policies. The simulation model is designed for a production line that includes five work stations. The results show that the use of CBM can improve the productivity and profitability of production lines.

Keywords


1-   Ahmad, M.O., Dennehy, D., Conboy, K., & Oivo, M. (2017). Kanban in Software Engineering: A Systematic Mapping Study, The Journal of Systems & Software, 133, 121-140.
2-   Alrabghi, A., & Tiwari, A. (2016). A Novel Approach for Modelling Complex Maintenance Systems Using Discrete Event Simulation. Reliability Engeering & System Safety, 154, 160-170.
3-   Arab, A., Ismail, N., & Lee, L.S. (2013). Maintenance scheduling incorporating dynamics of production system and real-time information from workstations, Journal of Intelligent Manufacturing, 24:4, 695-695.
4-   Ayo-Imoru, R.M., & Cilliers, A.C. (2018). A survey of the state of condition-based maintenance (CBM) in the nuclear power industry, Annals of Nuclear Energy, 112, 177-188.
5-   Azimi, P., Farajpoor Nazari, M., Esmati, A., & Farzin, A. (2013). Optimization via simulation & Enterprise Dynamics tutorial. Islamic Azad University of Qazvin press. Qazvin.
6-   Bin, L., Shaomin, W., Min, X., & Way, K. (2017). A condition-based maintenance policy for degrading systems with age- and state-dependent operating cost, European Journal of Operational Research, 263:3, 879-887.
7-   Chung, Ch. A. (2003). Simulation modeling handbook: a practical approach, CRC press, Inc. Boca Raton, FL, USA, ISBN 0-8493-1241-8.
8-   Davoodi, S.M.R., Jolai, F., Mohaghar, A., & Mehregan, M.R. (2015). Designing a multi-Level Multi-Product inventory simulation model and comparing it with the selected models; Case: Iran steel industries, Journal of Industrial Management Perspective, 5:19, 9-38.
9-   Dekker, R. (1996). Applications of maintenance optimization models: a review and analysis, Reliability Engineering and System Safety, 52:3, 229-240.
10-   Duffuaa, S., Ben-Daya, M., Al-Sultan, K., & Andijani, A. (2001). A generic conceptual simulation model for maintenance systems, Journal of Quality in Maintenance Engineering, 7:3, 207-219.
11-   Esmaeli, M., & Heidari, A. (2012). Key factors for success in establishing a pull production system, Journal of Industrial Management Perspective, 6, 45-66. (in persian)
12-   Hou, T., & Hu, W. (2011). An integrated MOGA approach to determine the Pareto-optimal kanban number and size for a JIT system, Expert Systems with Applications 38, 5912–5918.
13-   Houshmandi Maher, M., & Amiri, M., (2012). A model for Order Allocation in Multi Supplier, Multi Product and Multi Period Situation, Considering Incremental Discounts. Journal of Business Administration Researches. 4:7, 122-146.
14-   Morton, J. M., Maier, P., & Trinder, P. (2016). JIT-Based Cost Analysis for Dynamic Program Transformations, Electronic Notes in Theoretical Computer Science, 330, 5–25.
15-   Mostafaa, S., Dumrakb, J., & Soltan, H. (2015). Lean maintenance roadmap. Procedia Manufacturing, 2, 434–444.
16-   Motallebi, S., & Zandieh, M. (2017). Determination of Inventory Management Policies in Process Manufacturing: Using Discrete Event Simulation. Journal of Industrial Management Perspective, 26, 83-108. (In persian)
17-   Nicolai, R.P., & Dekker, R. (2008). Optimal maintenance of multi-component systems: a review, in Kobbacy, K. and Murthy, D. N. (eds.) Complex system maintenance handbook, Springer, London, 263-286.
18-   Nowakowski, T., & Werbinka, S. (2009). On problems of multicomponent system maintenance modelling, International Journal of Automation and Computing, 6:4, 364-378.
19-   Oyarbide-Zubillaga, A., Goti, A., & Sanchez, A. (2008). Preventive maintenance optimisation of multi-equipment manufacturing systems by combining discrete event simulation and multi-objective evolutionary algorithms, Production Planning and Control, 19:4, 342-355.
20-   Savsar, M. (1997). Simulation analysis of maintenance policies in just-in-time production systems. International Journal of Operations & Production Management, 17:3, 256-266.
21-   Sharma, A., Yadava, G., & Deshmukh, S. (2011). A literature review and future perspectives on maintenance optimization, Journal of Quality in Maintenance Engineering, 17:1, 5-25.
22-   Van Horenbeek, A., Buré, J., Cattrysse, D., Pintelon, L., & Vansteenwegen, P. (2013). Joint maintenance and inventory optimization systems: a review, International Journal of Production Economics, 143:2, 499–508.
23-   Warrington, L., Jones, J.A., & Davis, N. (2002). Modelling of maintenance, within discrete event simulation, Annual Reliability and Maintainability Symposium, The International Symposium on Product Quality and Integrity, 260-268.
24-   Zolikhaei Sayyar, L., Mosavi, E., pouya, M., & Naderi Mahdeei, K. (2017). Improving the Performance of Cooperative Businesses through Redesigning their Productive Capacity: A Case Study of Consumer Cooperatives in Hamedan. Journal of Business Administration Researches. 9:17, 1-30.