Abstract
High variability is a harmful factor for manufacturing performance that may be originated from multiple sources and whose effect might appear in different temporary levels. The case study analysed in this chapter constitutes a paradigmatic case of a process whose variability cannot be efficiently controlled and reduced. It also displays a complex behaviour in the generation of intermediate buffers. Simulation is employed as a tool for detailed modelling of elements and variability components capable of reproducing the system behaviour. A multilevel modelling approach to variability is validated and compared to a conventional static model in which process parameters are kept constant and only process cycle dependant variations are introduced. Results show the errors incurred by the simpler static approach and the necessity of incorporating a time series model capable of simulating the autocorrelation structure present in data. A new layout is proposed and analysed by means of the simulation model in order to assess its robustness to the present variability. The new layout removes unnecessary process steps and provides a smoother response to changes in the process parameters.
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References
Penker, A., Barbu, M.C., Gronald, M.: Bottleneck analysis in MDF-production by means of discrete event simulation. International Journal of Simulation Modelling 6(1), 49–57 (2007)
Mertens, K., Vaesen, I., Löffel, J., Kemps, B., Kamers, B., Zoons, J., Darius, P., Decuypere, E., De Baerdemaeker, J., De Ketelaere, B.: An intelligent control chart for monitoring of autocorrelated egg production process data based on a synergistic control strategy. Computers and Electronics in Agriculture 69(1), 100–111 (2009)
Nachtwey, A., Riedel, R., Mueller, E.: Flexibility oriented design of production systems. In: 2009 International Conference on Computers & Industrial Engineering, pp. 720–724 (July 2009)
Schultz, K.: Overcoming the dark side of worker flexibility. Journal of Operations Management 21(1), 81–92 (2003)
Bendoly, E., Prietula, M.: In ‘the zone’: The role of evolving skill and transitional workload on motivation and realized performance in operational tasks. International Journal of Operations & Production Management 28(12), 1130–1152 (2008)
Powell, S.G., Schultz, K.L.: Throughput in Serial Lines with State-Dependent Behavior. Management Science 50(8), 1095–1105 (2004)
Schultz, K.L., Juran, D.C., Boudreau, J.W.: The effects of low inventory on the development of productivity norms. Management Science 45(12), 1664–1678 (1999)
Arakawa, K., Ishikawa, T., Saito, Y., Ashikaga, T.: Individual differences on diurnal variations of the task performance. Computers Ind. Engineering 27(1-4), 389–392 (1994)
Baines, T., Mason, S., Siebers, P.-O., Ladbrook, J.: Humans: the missing link in manufacturing simulation? Simulation Modelling Practice and Theory 12(7-8), 515–526 (2004)
Aue, W.R., Arruda, J.E., Kass, S.J., Stanny, C.J.: Brain and Cognition Cyclic variations in sustained human performance. Brain and Cognition 71(3), 336–344 (2009)
Fletcher, S.R., Baines, T.S., Harrison, D.K.: An investigation of production workers’ performance variations and the potential impact of attitudes. The International Journal of Advanced Manufacturing Technology 35(11-12), 1113–1123 (2006)
Buzacott, J.: The impact of worker differences on production system output. International Journal of Production Economics 78(1), 37–44 (2002)
Neumann, W.P., Winkel, J., Medbo, L., Magneberg, R., Mathiassen, S.E.: Production system design elements influencing productivity and ergonomics: A case study of parallel and serial flow strategies. International Journal of Operations & Production Management 26(8), 904–923 (2006)
Schultz, K.L., Schoenherr, T., Nembhard, D.: An Example and a Proposal Concerning the Correlation of Worker Processing Times in Parallel Tasks. Management Science 56(1), 176–191 (2009)
Mason, S.: Improving the design process for factories: Modeling human performance variation. Journal of Manufacturing Systems 24(1), 47–54 (2005)
Shaaban, S., Mcnamara, T.: Unreliable Flow Lines with Jointly Unequal Operation Time Means, Variabilities and Buffer Sizes. In: Proceedings of the World Congress on Engineering and Computer Science, vol. II (2009)
D’Angelo, A.: Production variability and shop configuration: An experimental analysis. International Journal of Production Economics 68(1), 43–57 (2000)
Inman, R.R.: Empirical Evaluation of Exponential and Independence Assumptions in Queueing Models of Manufacturing Systems *. Production and Operations Management 8(4), 409–432 (1999)
Colledani, M., Matta, A., Tolio, T.: Analysis of the production variability in multi-stage manufacturing systems. CIRP Annals - Manufacturing Technology 59(1), 449–452 (2010)
He, X., Wu, S., Li, Q.: Production variability of production lines. International Journal of Production Economics 107(1), 78–87 (2007)
Young, T.M., Winistorfer, P.M.: The effects of autocorrelation on real-time statistical process control with solutions for forest products manufacturers. Forest Products Journal 51(11/12), 70–77 (2001)
Mertens, K., et al.: An intelligent control chart for monitoring of autocorrelated egg production process data based on a synergistic control strategy. Computers and Electronics in Agriculture 69(1), 100–111 (2009)
Mittler, M.: Autocorrelation of Cycle Semiconductor Manufacturing Times in Density. In: Proceedings of the 1995 Winter Simulation Conference, pp. 865–872 (1995)
del Rio Vilas, D., Crespo Pereira, D., Crespo Mariño, J.L., Garcia del Valle, A.: Modelling and Simulation of a Natural Roofing Slates Manufacturing Plant. In: Proceedings of The International Workshop on Modelling and Applied Simulation, vol. (c), pp. 232–239 (2009)
Rego Monteil, N., del Rio Vilas, D., Crespo Pereira, D., Rios Prado, R.: A Simulation-Based Ergonomic Evaluation for the Operational Improvement of the Slate Splitters Work. In: Proceedings of the 22nd European Modeling & Simulation Symposium, vol. (c), pp. 191–200 (2010)
Alfaro, M., Sepulveda, J.: Chaotic behavior in manufacturing systems. International Journal of Production Economics 101(1), 150–158 (2006)
Clymer, J.R.: Simulation-based engineering of complex systems, 2nd edn. Wiley, Hoboken (2009)
R. D. C. Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing (2005)
Pfaff, B.: VAR, SVAR and SVEC Models: Implementation Within R Package vars. Journal of Statistical Software 27(4), 1–32 (2008)
Trapletti, A., Hornik, K.: tseries: Time Series Analysis and Computational Finance (2009), http://cran.r-project.org/package=tseries (accessed 2011)
Schultz, K.L., Schoenherr, T., Nembhard, D.: An Example and a Proposal Concerning the Correlation of Worker Processing Times in Parallel Tasks. Management Science 56(1), 176–191 (2009)
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Pereira, D.C., del Rio Vilas, D., Monteil, N.R., Prado, R.R. (2012). Simulation and Highly Variable Environments: A Case Study in a Natural Roofing Slates Manufacturing Plant. In: Bangsow, S. (eds) Use Cases of Discrete Event Simulation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28777-0_9
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DOI: https://doi.org/10.1007/978-3-642-28777-0_9
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