Change Management System (CMS) Evaluation: A Case Study in a Multinational Manufacturing Company in Malaysia
Changes can be defined as modification of the form, fit or function of an object such as a process or a product. Changes can be positive or negative but in general, making changes show that a company is progressing and improving. A company can choose to take initiative to change or just wait for external forces depending on its necessity or requirement. In some cases, change is not favourable unless it is really necessary as it involves time and money as well as other resources. Due to this, a good change management is necessary so that changes can be monitored effectively. A dynamic and timely change management is important in order to ensure that the company does not fall behind in being competitive in the industry. This study focuses on the evaluation of the change management system in a manufacturing company. Focus is given to the measurement of the change process which has been agreed to be due to cycle time in which an ideal cycle time for the change process is simulated. Based on Monte Carlo simulation, it is figured that the overall cycle time can be improved by 35%. At the same time, other effectiveness measure is also identified to improve the management system of the company.
Anees, M. M., Mohamed, H. E., & Abdel Razek, M. E. (2013). Evaluation of change management efficiency of construction contractors. HBRC Journal, 9(1), 77–85.
Bruno, G. (2016). A Support System to Manage Product and Process Changes in Manufacturing. IFAC-PapersOnLine, 49(12), 1080–1085.
Cichos, D., & Aurich, J. C. (2016). Support of Engineering Changes in Manufacturing Systems by Production Planning and Control Methods. Procedia CIRP, 41, 165–170.
Cunningham, A., Wang, W., Zio, E., Wall, A., Allanson, D., & Wang, J. (2011). Application of delay-time analysis via Monte Carlo simulation. Journal of Marine Engineering & Technology, 10(3), 57-72.
Thomas, O.O. (2014). Change Management and its Effects on Organizational Performance of Nigerian Telecoms Industries: Empirical Insight from Airtel Nigeria. International Journal of Humanities Social Sciences and Education, 1(11), 170–179.
Koch, J., Gritsch, A., & Reinhart, G. (2016). Process design for the management of changes in manufacturing: Toward a Manufacturing Change Management process. CIRP Journal of Manufacturing Science and Technology, 14, 10–19.
Levovnik, D., & Gerbec, M. (2018). Operational readiness for the integrated management of changes in the industrial organizations – Assessment approach and results. Safety Science, 107(April), 119–129.
Li, F., Zhu, Q., Chen, Z., & Xue, H. (2018). A balanced data envelopment analysis cross-efficiency evaluation approach. Expert Systems with Applications, 106, 154–168.
Mahdavi, M. M. M., & Mahdavi, M. (2014). Stochastic lead time demand estimation via monte carlo simulation technique in supply chain planning. Sains Malaysiana, 43(4), 629-636.
Paris, A. S., Tanase, I., Tarcolea, C., & Dragomirescu, C. (2012). Applications of the Monte Carlo method in manufacturing processes. Proceedings in Manufacturing Systems, 7(4), 253-25.
Plehn, C., Stein, F., De Neufville, R., & Reinhart, G. (2016). Assessing the Impact of Changes and their Knock-on Effects in Manufacturing Systems. Procedia CIRP, 57, 479–486.
Raineri, A. B. (2011). Change management practices: Impact on perceived change results. Journal of Business Research, 64(3), 266–272.
Stasis, A., Whyte, J., & Dentten, R. (2013). A critical examination of change control processes. Procedia CIRP, 11, 177–182.
Sujova, A., & Rajnoha, R. (2012). The Management Model of Strategic Change based on Process Principles. Procedia - Social and Behavioral Sciences, 62, 1286–1291.
Wilberg, J., Elezi, F., Tommelein, I. D., & Lindemann, U. (2015). Using a Systemic Perspective to Support Engineering Change Management. Procedia Computer Science, 61, 287–292.
Yin, L., Tang, D., Ullah, I., Wang, Q., Zhang, H., & Zhu, H. (2017). Analyzing engineering change of aircraft assembly tooling considering both duration and resource consumption. Advanced Engineering Informatics, 33, 44-59.