Reduction of production disturbances of a shoemaking industry through a discrete event simulation approach

Document Type : Research Paper


Industrial Engineering, Mechanical and Industrial Engineering Faculty, Bahir Dar Institute of Technology, Bahir Dar University,Bahir Dar, Ethiopia


This study presents a reduction of production disturbances of a shoemaking industry through discrete event simulation approach. The study is conducted at Peacock Shoe factory found in Addis Ababa, Ethiopia.  This factory faces line balancing problem that becomes production disturbance for its assembly lines. Detail time study is carried out for the selected shoe model using stopwatch. Assembly process chart is used to understand the chronological sequence of assembly operations. Arena input analyzer is used to fit the input data, and K-S test is conducted to validate the goodness of fit. Hence, a simulation model for existing stitching, and lasting and finishing assembly lines are developed after taking basic simulation assumptions. The model is verified by checking a coding error of SIMAN language through try and error and validated by comparing its output with real system. Production disturbance (bottleneck) assembly line and operations are identified based on parameters such as average waiting time, WIP, production rate, capacity utilization and total flow time. To alleviate line balancing problem, five scenarios are proposed, and the detail what if the analysis is done using Arena simulation software. Scenario five is selected to reduce the level of production disturbances of the stitching assembly line. This scenario reduces the average waiting time and WIP from 2118.28 to 417.05 sec. and 252 to 85 respectively. Scenario one is selected to reduce the level of production disturbances of existing lasting and finishing assembly line. This scenario reduces the average waiting time and WIP from 2026.91 to 641.26 sec. and 169 to 65 respectively. 


Main Subjects

Aggarwal, V., Padhi, S. S., and Bhatnagar, V. (2011). Performance improvement in parallel manufacturing systems through scenario analysis and optimal design of parameters. International Journal of Production Research, 13(6), 85–107.
a H. D. (1960).  An Introduction to Mathematical Statistics (vol.2). Michigan: Ginn.
Corte, P., Onieva, L. and Guadix, J. (2010). Optimizing and simulating the assembly line balancing problem in a motorcycle manufacturing company: a case study. International Journal of Production Research, 48(3), 3637–3656.
Daniel, K., Amare, M. and Solomon, T. (2010). Assembly line balancing using simulation technique in  a garment manufacturing firm. Journal of EEA, 27(1), 69-80.
Drucker,  P. F. (1990). The emerging theory of manufacturing.  Harvard Business Review, 68 (3), 94-102.
Eryilmaz, M .S. , Kusakci, A. O. , Gavranovich, H. and Findik, F. (2012). Analysis of shoe manufacturing factory by simulation of production processes. Journal of Soft Computing, 1(3), 120- 127.
Garza-Reyes, J. A., Eldridge, S., Barber, K. D., Soriano –Meier, H. (2010). Overall equipment effectiveness (OEE) and process capability (PC) measures: A relationship analysis. International Journal of Quality & Reliability Management, 27 (1), 48 – 62.
Groover, P.  (2000). Automation, Production Systems, and Computer-Integrated manufacturing 2nd Ed., Delhi, Pearson Education.
Hassan, M. M., Gruber, S. (2008). Application of discrete-event simulation to study the paving operation of asphalt concrete. Construction Innovation.  Journal of Information, Process, Management, 8(1), (109-118).
Ingemansson, A., Bolmsjo, G. S. (2005). Improved efficiency with production disturbance reduction in manufacturing systems based on discrete-event simulation. Journal of manufacturing technology management, 15(3), (267 -274).
James, C., Putra, A. P. , Anggono, N. and  Chen, J. (2014). Simulation modeling and analysis for stitching line of footwear industry.International Conference on Industrial Engineering and Operations Management. Bali, Indonesia.
Kuivanen, R. (1996). Disturbance control in flexible manufacturing. International journal of human factors in manufacturing, 6(1), (41-56).
Law, A. M. and Kelton, W. D. (2000). Simulation modeling and analysis 3rd ed. Boston, McGraw-Hill.
Mohamad, E., Salleh, M. R., Nordin, N. A. (2012). Simulation study towards productivity improvement for assembly line. Journal of Human Capital Development,5(1), 59-69.
Padhi, S.S., Wagner, S. M., Niranjan, T. T. and Aggarwal, V. (2013). A simulation-based methodology to analyse production line disruptions.International Journal of Production Research, 51(6), 1885–1897.
Padhi, S. S., Mohapatra, P. K. J. (2010). Process evaluation of award of work contracts in a government department, International journal of electronic governance, 2(3), 118 -130.
Quintero, L. A., Conway, P. P., Velandia, D. M. S., West, A. A. (2011).  Root cause analysis support for quality improvement in electronics manufacturing, Assembly automation, 31(1), 38-46
Shang, J. S., Li, S. and Tadikamalla, P. (2004). Operational design of a supply chain system using the taguchi method, response surface methodology, simulation and optimization.International Journal of Production Research, 42(18), 3823 – 3849.
Smet, R. D., Gelders, L. and Pintelon, L. (1997). Case studies on disturbances registration for contineos improvement. Journal of quality in maintenance engineering, 3(2), 91-108.
Toledo, T., Koutsopoulos, A. D., Ben-Akiva, M. E., Burghout, W., Andreasson, I ., Johansson ,T.and Lundin, C. (2003). Calibration and Validation of Microscopic Traffic Simulation Tools: Stockholm Case Study. Transportation Research Record (1831): 65-75.
Temesgen, G. and Nahom, M. (2014). Modeling and performance analysis of manufacturing systems in footwear industry.  Science, Technology and Arts Research Journal, 3(1), 132-141.
Wu, B. (1989). Manufacturing systems design and analysis: Context and techniques 2nd  Ed. London ,Chapman and Hall.