Optimizing Throughput, WIP and Cycle Time: A Case Study of Utensils Manufacturing Shop Floor
Abstract
The development of simulation models for improving performances is trendy in manufacturing industries. This paper presents simulation model for evaluating and improving performances of a utensils manufacturing plant. Internal benchmarking is employed for comparison under exactly similar conditions. The data of all input variables is collected and statistical models for each process is developed using Arena input analyzer. The simulation model is then developed using SIMIO to optimize throughput, work-in-process, and cycle time. The experiments have been performed under various scenarios i.e. at different values of the input parameters. The areas for improvement have been highlighted on the basis of results. Furthermore, best and worst case scenarios have been discussed in detail to provide insights for managerial performance improvements by: (1) optimizing inter-arrival time, (2) increasing production rate, and (3) reducing the number of rejected parts without disturbing existing set up of the manufacturing facility.
References
Ajdari, A. & Mahlooji H. 2014. An adaptive exploration-exploitation algorithm for constructing metamodels in random simulation using a novel sequential experimental design." Communications in Statistics-Simulation and Computation 43(5): 947-968.
Amiri, M. & Mohtashami A. 2012. Buffer allocation in unreliable production lines based on design of experiments, simulation, and genetic algorithm. The International Journal of Advanced Manufacturing Technology 62(1-4): 371-383.
Barenji, A. V. & Barenji, R. V. et al. 2016. Flexible testing platform for employment of RFID-enabled multi-agent system on flexible assembly line. Advances in Engineering Software 91: 1-11.
Dehghanimohammadabadi, M. & Keyser T. K. 2017. Intelligent simulation: Integration of SIMIO and MATLAB to deploy decision support systems to simulation environment. Simulation Modelling Practice and Theory 71: 45-60.
Galankashi, M. R. & Fallahiarezoudar, E. et al. 2016. Performance evaluation of a petrol station queuing system: A simulation-based design of experiments study. Advances in Engineering Software 92: 15-26.
Geyik, F. & Dosdoğru, A. T. et. al. 2013. Process plan and part routing optimization in a dynamic flexible job shop scheduling environment: an optimization via simulation approach. Neural Computing and Applications 23(6): 1631-1641.
Moazzami, A. & Galankashi, M. R. et al. 2013. Simulation, modeling and analysis of a petrol station. Int Rev Model Simul (IREMOS) 6(1): 246-253.
Pinto, L. & Silva, P. et al. 2015. A generic method to develop simulation models for ambulance systems. Simulation Modelling Practice and Theory 51: 170-183.
Tako, A. A. & Kotiadis, K. 2015. PartiSim: a multi-methodology framework to support facilitated simulation modelling in healthcare. European Journal of Operational Research 244(2): 555-564.
Wright, D. B. & Osborne, J. E. 2005. Dissociation, Cognitive Failures, and Working Memory. American Journal of Psychology 118(1): 103-114.
Chiang, K., Kuan-Ling, H., Yan-Ming, H., Wen-Jui L., Shao-Kang, H. & Chin-An, W. 2013. Productivity of Taiwan’s 1000 largest companies, Journal of Industrial and Production Engineering, 30:1, 44-53, DOI: 10.1080/10170669.2012.762059.
Nurul, F. H., Mad, I. S., Nor, A. M. L., Mohamed, N. A. A. & Nursyazwani, M. F. 2016. Lean six sigma performance improvement tool for automotive suppliers, Journal of Industrial and Production Engineering, 33:4, 215-235, DOI: 10.1080/21681015.2015.1136966
Yossi, B. & Yuval, C. 2013. Minimising throughput loss in assembly lines due to absenteeism and turnover via work-sharing, International Journal of Production Research, 51:20, 6140-6151, DOI: 10.1080/00207543.2013.807374
Chung-Ho, C., Chao-Yu, C., Chao-Chin, K. & Michael, B. C. K. 2014. Production quantity and specification limits settings by considering specified process capability value, Journal of Industrial and Production Engineering, 31:4, 229-237, DOI: 10.1080/21681015.2014.934505
Nur, A. M., Jeng, F. C., Shahrul, K., Mohd, A. C. & Joshua, P. 2015. Fundamental simulation studies of CONWIP in frontend wafer fabrication, Journal of Industrial and Production Engineering, 32:4, 232 246, DOI: 10.1080/21681015.2015.1045562
James, T. L., Chien-Ming, C., Chun-Chih, C. & Hsin-Ying, F. 2013. Simulation optimization with PSO and OCBA for semiconductor back-end assembly, Journal of Industrial and Production Engineering, 30:7, 452-460, DOI: 10.1080/21681015.2013.860926
Ahmad, N., Ahmed, J., Noriah, Y. & Nurul, H. 2012. Development of Kanban System at Local Manufacturing Company in Malaysia – Case Study, International Symposium on Robotics and Intelligent Sensors 2012 (IRIS 2012)
Curtis, I. & Dimitri, M. 2012. Object-Oriented Discrete Event Simulation Modeling Environment for Aerospace Vehicle Maintenance and Logistics Process, Procedia Engineering 41 1721 – 1726 Conference on System engineering research, Curtis Iwata, Dimitri Mavris ,Eds.: C.J.J. Paredis, C. Bishop, D. Bodner, Georgia Institute of Technology, Atlanta, GA, March 19-22, 2013.
Jong-Kwon, L. & Ju-Jang, L. 1997. Discrete Event Modeling And Simulation For Flow Control In An Automated Highway System, PII: S0968-090X(97)00010-7
Greg Werker, A. S., John, F. & Steven, S. 2009. The use of discrete-event simulation modelling to improve radiation therapy planning processes
Villamizar, J.R., Coelli, F.C., Pereira W.C.A. & Almeida R.M.V.R. (2011). Discrete-event computer simulation methods in the optimisation of a physiotherapy clinic, Physiotherapy 97 71–77
Catherine A., Leonardo, B. H., Philippe, B., Serge, D. & Luc, P. 1997. A two-stage methodology for short-term batch plant scheduling: discrete-event simulation and genetic algorithm, PII: S0098-1354(97)00232-09
Antuela, A. T. & Stewart, R. 2012. The application of discrete event simulation and system dynamics in the logistics and supply chain context, Decision Support System 52 802-815
Thomas, P. & Lars, M. 2014. Simulation-based performance assessment of master planning approaches in semiconductor manufacturing, Omega 46 21-35
Savsar, M. & Al-Jawini, A. 1995. Simulation Analysis of JIT Systems, International Journal of
Production Economics 42 67-78
Savsar, M. 1996. Effects of Kanban Withdrawal Policies and Other Factors on the Performance of JIT Systems- A Simulation Study, International Journal of Production Research 34(10) 2879-2899
Savsar, M. & Abdulmalek, F. 2008. Modeling of a Pull-Push Assembly Control System to Minimize Inventory and Demand Delay Costs, International Journal of Industrial Engineering, 15(1)
Lacksonen, T. 2001. Empirical comparison of search algorithms for discrete event simulation, Computers & Industrial Engineering 40 133±148
Doug, C. & Saeid, N. 2003. Application of discrete event simulation for robust system design of a melt facility, Robotics and Computer Integrated Manufacturing 19 469–477
Spedding, T.A. & Sun, G.Q. 1999. Application of discrete event simulation to the activity based costing of manufacturing systems, Int. J. Production Economics 58 289–301
