Industrial Engineering and Management Systems

Korean Institute of Industrial Engineers (대한산업공학회)
권호 표지
DOI QR코드

DOI QR Code

Integrated Simulation Modeling of Business, Maintenance and Production Systems for Concurrent Improvement of Lead Time, Cost and Production Rate

  • Paknafs, Bahman (School of Industrial Engineering and Centre of Excellence for Intelligent Based Experimental Mechanics, College of Engineering, University of Tehran) ;
  • Azadeh, Ali (School of Industrial Engineering and Centre of Excellence for Intelligent Based Experimental Mechanics, College of Engineering, University of Tehran)
  • Received : 2016.10.02
  • Accepted : 2016.11.29
  • Published : 2016.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

The objective of this study is to integrate the business, maintenance and production processes of a manufacturing system by incorporating errors. First, the required functions are estimated according to the historical data. The system activities are simulated by Visual SLAM software and the required outputs are obtained. Several outputs including lead times in different dimensions, total cost and production rates are computed through simulation. Finally, data envelopment analysis (DEA) is utilized in order to select the best option between the defined scenarios due to the multi-criteria feature of the problem. This is the first study in which the lead times, cost and production rates are simultaneously considered in the integrated system imposed of business, maintenance and production processes by incorporating errors. In the current study, the major bottlenecks of the system being studied are identified and suggested different strategies to improve the system and make the best decision.

Keywords

References

  1. Adler, N., Friedman, L., and Sinuany-Stern, Z. (2002), Review of Ranking Methods in Data Envelopment Analysis Context, European Journal of Operation Research, 140(2), 249-265, doi:10.1016/S0377-2217(02)00068-1.
  2. Andersen, P. and Petersen, N. C. (1993), A Procedure for Ranking Efficient Units in Data Envelopment, Analysis Management Science, 39(10), 1261-1294.
  3. Azadeh, A., Darivandi, K., and Fathi, E. (2012), Diagnosing, simulating and improving business process using cybernetic laws and the viable System model: The Case of a purchasing process, Systems research and behavioral science syst. Res., 29, 66-86. https://doi.org/10.1002/sres.1102
  4. Azadeh, A., Ghaderi, S. F., and Izadbakhsh, H. (2008), Integration of DEA and AHP with computer simulation for railway system improvement and optimization, Applied Mathematics and Computation, 195, 775-785. https://doi.org/10.1016/j.amc.2007.05.023
  5. Azadeh, A., Haghnevis, M. and Khodadadegan, Y. (2008), Design of the Integrated Information System, Business and Production Process by Simulation, Journal of the American society for information science and technology, 59(2), 216-234. https://doi.org/10.1002/asi.20712
  6. Azadeh, A., Sheikhalishahi, M., Firozi, M., and Khalili, S. M. (2013), An Integrated Fuzzy Simulation-Fuzzy DEA Approach for Optimum Maintenance Planning, International Journal of Computer Integrated Manufacturing.
  7. Banker, R. D., Charnes, A. and Cooper, W. W. (1984), Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis, Management Science, 30(9), 1078-1092, doi:10.1287/mnsc.30.9.1078.
  8. Bouvard, K., Artus, S., Berenguer, C. and Cocquempot, V. (2011), Condition-based dynamic maintenance operations planning and grouping: Application to commercial heavy vehicles, Reliability engineering and system safety, 96, 601-610. https://doi.org/10.1016/j.ress.2010.11.009
  9. Charnes, A., Cooper, W. W., and Rhodes, E. (1978), Measuring the efficiency of decision making units, European Journal of Operational Research, 2, 429-444. https://doi.org/10.1016/0377-2217(78)90138-8
  10. Chen, H. and Papazafeiropouloa, A. (2013), Supply chain integration in the IT manufacturing sector: how integration technologies adoption can improve efficiency, Int. J. Applied Systemic Studies, 5(1/2).
  11. Datta, P. P. and Roy, R. (2010), Cost modelling techniques for availability type service support contracts: A literature review and empirical study, CIRP Journal of Manufacturing Science and Technology, 3, 142-157. https://doi.org/10.1016/j.cirpj.2010.07.003
  12. Goldsman, D., Nelson, B. L., Opicka, T. and Pritsker, A. A. B. (1999), Ranking and selection project: experiences from a university industry collaboration, Simulation conference proceedings, 1, 83-92.
  13. Gulledge, T., Hiroshige, S. and Iyer, R. (2010), Condition-based maintenance and the product improvement process, Computers in industry, 61, 813-832. https://doi.org/10.1016/j.compind.2010.07.007
  14. Hashemi, M., Shahmorad-Moghanloo, S., and Behboudi, D. (2014), Review the allocation of production lines in shifts with minimizing energy costs approach in Tehran Pegah Company, Int. J. Operational Research, 19.
  15. Heo, G. and Park, J. (2010), A framework for evaluating the effects of maintenance-related human errors in nuclear power plants, Reliability Engineering and System Safety, 95, 797-805. https://doi.org/10.1016/j.ress.2010.03.001
  16. Jahangirian, M., Eldabi, T. l., Naseer, A., Stergioulas, L. K., and Young, T. (2010), Simulation in manufacturing and business: A review, European Journal of Operational Research, 203, 1-13. https://doi.org/10.1016/j.ejor.2009.06.004
  17. Jin, Y., Kurooka, T., Yamashita, Y., and Nishitani, H. (2003), Modeling and Simulation of Human Errors in Plant Operations, Process Systems Engineering, 1280-1285.
  18. Kim, J. and Park, J. (2012), Reduction of test and maintenance human errors by analyzing task characteristics and work conditions, Progress in Nuclear Energy, 58, 89-99. https://doi.org/10.1016/j.pnucene.2012.02.002
  19. Kirwan, B. (1992), Human error identification in human reliability assessment. Part 1: Overview of approaches, Applied Ergonomics, 23(5), 299-318. https://doi.org/10.1016/0003-6870(92)90292-4
  20. Kjaer, A. P. (2003), The integration of business and production processes, IEEE Control Systems Magazine, 23(6), 50-58.
  21. Law, C. C. H., Chen, C. C., and Wu, B. J. P. (2010), Managing the full ERP life-cycle: Considerations of maintenance and support requirements and IT governance practice as integral elements of the formula for successful ERP adoption, Computersin Industry, 61, 297-308. https://doi.org/10.1016/j.compind.2009.10.004
  22. Liu, F. H. and Tsai, L. C. (2004), Ranking of DEA Units with a Set of Weights to Performance Indices, Paper was prepared for The Fourth International Symposium on DEA, 4-6 September, at Aston University.
  23. Liu, H., Hwang, S. L., and Liu, T. H. (2009), Economic assessment of human errors in manufacturing environment, Safety Science, 47, 170-182. https://doi.org/10.1016/j.ssci.2008.04.006
  24. Minh, N., Khanh, P., and Tuan, P. (2012), A New Approach for Ranking Efficient Units in Data Envelopment Analysis and Application to a Sample of Vietnamese Agricultural Bank Branches, American Journal of Operations Research, 2(1), 126-136. https://doi.org/10.4236/ajor.2012.21015
  25. Oke, S. A. and Charles-Owaba, O. E. (2007), A fuzzy linguistic approach of preventive maintenance scheduling cost optimization, Kathmandu university journal of science, engineering and technology, I(III).
  26. Paknafs, B. (2013), Modeling and simulation of business and operation and maintenance process with make span, cost and manpower simultaneous criteria, Master of Science Thesis, Tehran, Iran: University of Tehran, Faculty of Engineering.
  27. Pinjala, S. K., Pintelon, L., and Vereecke, A. (2006), An empirical investigation on the relationship between business and maintenance strategies, Int. J. Production Economics, 104, 214-229. https://doi.org/10.1016/j.ijpe.2004.12.024
  28. Pritsker, A. B., Sigal, C. E., and Hammesfahr, R. D. J. (1989), Network models for decision support, New York: Prentice-Hall.
  29. Pritsker, A. B. (1990), Papers, experiences and perspectives, Lafayette, Indi-ana: Systems Publishing Corporation.
  30. Pritsker, A. B., Oreilly, J. J., and LaVal, D. K. (1997), Simulation with Visual Slam and AweSim, New York: John Wiley and Sons.
  31. Rooney, J. J., Vanden Heuvel, L. N., and Lorenzo, D. K. (2002), Reduce Human Error: How to analyze near misses and sentinel events, determine root causes and implement corrective actions, quality progress, 27-36.
  32. Roux, O., Jamali, M. A., Kady, D. A., and Chatelet, E. (2008), Development of simulation and optimization platform to analyze maintenance policies performances for manufacturing systems, International, Journal of Computer Integrated Manufacturing-Industrial Engineering and Systems, 407-414.
  33. Tone, K. (2002), A Slacks-Based Measure of Efficiency in Data Envelopment Analysis, European Journal of Operational Research, 143(1), 32-41, doi:10.10 16/S0377-2217(01)00324-1. https://doi.org/10.1016/S0377-2217(01)00324-1
  34. Sueyoshi, T. and Goto, M. (2010), Methodological comparison between two unified (operational and environmental) efficiency measurements for environmental assessment, European Journal of Operational Research, 684-693.
  35. Yelling, E. and Machulack, G. (1996), A comparison of simulation modeling roles: business process reengineering VS. Manufacturing, SCSI, San Diego, CA, VSA, 29-33.
  36. Wen, M., You, C., and Kang, R. (2010), A new ranking method to fuzzy data envelopment analysis, Computers and Mathematics with Applications, 59, 3398-3404. https://doi.org/10.1016/j.camwa.2010.02.034