Effect of manpower factor on semiautomatic production line completion time: A system dynamics approach
DOI:
https://doi.org/10.15282/jmes.11.2.2017.1.0235%20Keywords:
Causal loop diagram; stock flow diagram; system dynamics; completion time; semiautomatic production line.Abstract
Completion time in a manufacturing sector is the time required to complete a product in sequence process during production operation. In a semiautomatic production line, manpower factors such as fatigue and pressure are two significant influences on completion time. However, it is found that previous studies lack the concern to include manpower factor in completion time. Hence, this paper develops a causal loop diagram and stock flow diagram to simulate the influence of manpower factor on the completion time in a semiautomatic production line. In this research, a well-known audio speaker manufacturer is selected as a case company. As a result, it is found that the preparation time for materials has a great impact on fatigue and pressure as it contributes the highest percentage of deviation from the completion time base run with 72.22%. Finally, a policy regarding completion time improvement is recommended to the management to enhance their production performance.
References
Fisel J, Arslan A, Lanza G. Changeability focused planning method for multi model assembly systems in automotive industry. Procedia CIRP. 2017;63:515-20.
Sridhar S, Anandaraj B. Balancing of production line in a bearing industry to improve productivity. The Hilltop Review. 2017;9:10.
Ab Rashid M, Mohamed NN, Rose AM, Kor K. Simulation study of a vehicle production line for productivity improvement. Journal of Mechanical Engineering and Sciences. 2015;8:1283-92.
Sikora CGS, Lopes TC, Magatão L. Traveling worker assembly line (re) balancing problem: Model, reduction techniques, and real case studies. European Journal of Operational Research. 2017;259:949-71.
Russell RS, Taylor-Iii BW. Operations management along the supply chain: John Wiley & Sons; 2008.
Araújo W, Silva F, Campilho R, Matos J. Manufacturing cushions and suspension mats for vehicle seats: A novel cell concept. The International Journal of Advanced Manufacturing Technology. 2017;90:1539-45.
Busogi M, Ransikarbum K, Oh YG, Kim N. Computational modelling of manufacturing choice complexity in a mixed-model assembly line. International Journal of Production Research. 2017:1-15.
Hanisah NH, Sarif M. Ergonomic problems and job stress: A study among workers at nichicon (m) sdn. Bhd: Universiti Utara Malaysia; 2012.
Varnavsky A. The automated system for prevention of industrial-caused diseases. Journal of Physics: Conference Series: IOP Publishing; 2017. p. 012169.
Nawawi R, Deros BM, Daruis DD, Ramli A, Zein R, Joseph L. Effects of payment method on work control, work risk and work-related musculoskeletal health among sewing machine operators. Journal of Mechanical Engineering and Sciences. 2015;9:1705-13.
Chahal V, Narwal M. Impact of lean strategies on different industrial lean wastes. International Journal of Theoretical and Applied Mechanics. 2017;12:275-86.
Collewet M, Sauermann J. Working hours and productivity. Labour Economics. 2017.
Ferjani A, Ammar A, Pierreval H, Elkosantini S. A simulation-optimization based heuristic for the online assignment of multi-skilled workers subjected to fatigue in manufacturing systems. Computers & Industrial Engineering. 2017;112:663-74.
Öner-Közen M, Minner S, Steinthaler F. Efficiency of paced and unpaced assembly lines under consideration of worker variability–a simulation study. Computers & Industrial Engineering. 2017;111:516-26.
Mussa YM. A system dynamics model for operations management improvement in multi-plant enterprise [Master Thesis]2009.
Aslam T. Analysis of manufacturing supply chains using system dynamics and multi-objective optimization: University of Skövde; 2013.
Guo G, Ryan SM. Risk-averse stochastic integer programs for mixed-model assembly line sequencing problems. 2017.
Li Z, Tang Q, Zhang L. Two-sided assembly line balancing problem of type i: Improvements, a simple algorithm and a comprehensive study. Computers & Operations Research. 2017;79:78-93.
Abidin NZ, Zabidi NZ, Karim KN. System dynamics model of research performance among academic staff. Journal of Telecommunication, Electronic and Computer Engineering. 2017;9:121-8.
Behjat S, Salmasi N. Total completion time minimisation of no-wait flowshop group scheduling problem with sequence dependent setup times. European Journal of Industrial Engineering. 2017;11:22-48.
He N, Qiao Y, Wu N, Qu T. Total completion time minimization for scheduling of two-machine flow shop with deterioration jobs and setup time. Advances in Mechanical Engineering. 2017;9:1687814017698887.
Jamil M, Razali NM. Simulation of assembly line balancing in automotive component manufacturing. IOP Conference Series: Materials Science and Engineering: IOP Publishing; 2016. p. 012049.
Yamazaki Y, Shigematsu K, Kato S, Kojima F, Onari H, Takata S. Design method of material handling systems for lean automation—integrating equipment for reducing wasted waiting time. CIRP Annals-Manufacturing Technology. 2017;66:449-52.
Dev NK, Shankar R, Choudhary A. Strategic design for inventory and production planning in closed-loop hybrid systems. International Journal of Production Economics. 2017;183:345-53.
Aalaei A, Davoudpour H. A robust optimization model for cellular manufacturing system into supply chain management. International Journal of Production Economics. 2017;183:667-79.
Jasiulewicz-Kaczmarek M, Bartkowiak T. Improving the performance of a filling line based on simulation. IOP Conference Series: Materials Science and Engineering: IOP Publishing; 2016. p. 042024.
Ahmarofi AA, Ramli R, Zainal Abidin N. Predicting completion time for production line in a supply chain system through artificial neural networks. International Journal of Supply Chain Management. 2017;6:82-90.
Schäfer R, Chankov S, Bendul J. What is really “on-time”? A comparison of due date performance indicators in production. Procedia CIRP. 2016;52:124-9.
Tyagi N, Tripathi R, Chandramouli A. Single machine scheduling model for total weighted tardiness. Indian Journal of Science and Technology. 2016;9:1-8.
Azadeh A, Darivandi Shoushtari K, Saberi M, Teimoury E. An integrated artificial neural network and system dynamics approach in support of the viable system model to enhance industrial intelligence: The case of a large broiler industry. Systems Research and Behavioral Science. 2014;31:236-57.
Sheehan E, Braun A-T, Kuhlmann T, Sauer A. Improving material efficiency for ultra-efficient factories in closed-loop value networks. Procedia CIRP. 2016;40:455-62.
Chen S-H. The influencing factors of enterprise sustainable innovation: An empirical study. Sustainability. 2016;8:425.
García JM, Sterman J. Theory and practical exercises of system dynamics: Juan Martín García; 2006.
Chan KW. A system dynamics approach to improve visibility and performance in a supply chain system: Universiti Utara Malaysia; 2009.
Sterman JDJD. Business dynamics: Systems thinking and modeling for a complex world2000.
bitrus Goyol A, Dala BG. Causal loop diagram (cld) as an instrument for strategic planning process: American university of nigeria, yola. International Journal of Business and Management. 2013;9:77.
Inam A, Adamowski J, Halbe J, Prasher S. Using causal loop diagrams for the initialization of stakeholder engagement in soil salinity management in agricultural watersheds in developing countries: A case study in the rechna doab watershed, pakistan. Journal of Environmental Management. 2015;152:251-67.
Mehrjerdi YZ, Bioki TA. System dynamics and artificial neural network integration: A tool to evaluate the level of job satisfaction in services. International Journal of Industrial Engineering. 2014;25:13-26.
Kamath NH, Rodrigues LL. Simultaneous consideration of tqm and tpm influence on production performance: A case study on multicolor offset machine using sd model. Perspectives in Science. 2016;8:16-8.
Senge PM, Forrester JW. Tests for building confidence in system dynamics models. System dynamics, TIMS studies in management sciences. 1980;14:209-28.
Barlas Y. Formal aspects of model validity and validation in system dynamics. System dynamics review. 1996;12:183-210.
Khalili S, Mohammadzade H, Fallahnezhad M. A new approach based on queuing theory for solving the assembly line balancing problem using fuzzy prioritization techniques. Scientia Iranica Transaction E, Industrial Engineering. 2016;23:387.
Yang S, Arndt T, Lanza G. A flexible simulation support for production planning and control in small and medium enterprises. Procedia CIRP. 2016;56:389-94.
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