A Framework for Optimizing the Supply Chain Performance of a Steel Producer

Ali Diabat, Raid Al-Aomar, Mahmoud Alrefaei, Ameen Alawneh, Mohd Nishat Faisal

Abstract

Supply Chain Management (SCM) is focused on developing, optimizing, and operating efficient supply chains. Efficient supply chains are characterized by cost effective decisions, lean flow and structure, high degree of integration, and well-chosen Key Performance Indicators (KPIs). Although there exists a large body of literature on optimizing individual supply chain elements (transportation, distribution, inventory, location, etc.), the literature does not provide an effective methodology that can address the complexity of the supply chain of a large scale industry such as steel producers. This paper, therefore, builds on existing research methods of supply chain modeling and optimization to propose a framework for optimizing supply chain performance of a steel producer. The framework combines deterministic modeling using Linear Programming (LP) with stochastic simulation modeling and optimization. A holistic LP deterministic optimization model is first used to characterize and optimize the supply chain variables. The model minimizes the annual operating cost of the steel company’s supply chain. Simulation-based optimization with Simulated Annealing is then used to determine the operational levels of the supply chain drivers that meet a desired level of customer satisfaction. The proposed approach is applied to the supply chain of a major steel producer in the Arabian Gulf.

References

  1. Min, H., and Zhou, G., 2002. “Supply chain modeling: Past, present and future,” Computers & Industrial Engineering, 43, 231-249.
  2. Mula, J., Peidro, D., Diaz-Madronero, M., and Vicens, E., 2010. “Mathematical programming models for supply chain production and transport planning,” European Journal of Operational Research 204, 377-390.
  3. Arostegui, M. A., Kadipasaoglu, S. N., Khumawala, B. M., 2006. “An empirical comparison of Tabu Search, Simulated Annealing, and Genetic Algorithms for facilities location problems,” International Journal of Production Economics 103, 742 - 754.
  4. Altiparmak, F., Gen, M., Lin, L., Paksoy, T., 2006. “A genetic algorithm approach for multi-objective optimization of supply chain networks,” Computers & Industrial Engineering 51, 196 - 215.
  5. Jayaraman, V., Ross, A., 2003. “A simulated annealing methodology to distribution network design and management,” European Journal of Operational Research 144, 629 - 645.
  6. Sharma, V., Sahay, B. S., Sardana, G. D., 2008. “An Empirical Assessment of the Impact of SCM Practices on Quality Performance: A Case in the Indian Automobile Industry,” Supply Chain Forum 9(1), 28 - 40.
  7. Walker, K., 2009. “Ingredients for a Successful Supply Chain Management System Implementation,” Supply Chain Forum 10(1), 44 - 50.
  8. Scarsi, R., 2007. “Recovering Supply Chain Cost Efficiency Through Original Logistics Solutions: A Case in the Steel Industry,” Supply Chain Forum 8(1), 74 - 82.
  9. Stevens, G. C., 1989. “Integrating the Supply Chain,” International Journal of Physical Distribution & Logistics Management 19(8), 3-8.
  10. Potter, A., Mason, R., Naim, M., Lalwani, C., 2004. “The evolution towards an integrated steel supply chain: A case study from the UK,” International Journal of Production Economics 89, 207-216.
  11. Chae, B. K., 2009. “Developing key performance indicators for the supply chain: and industry perspective,” Supply Chain Management: An International Journal 14(6), 422-428.
  12. Ulungu, E.L., Teghem, J., and Fortemps, Ph., 1995. “Heuristic for multi-objective combinatorial optimization problems by simulated annealing,” in: J. Gu, G. Chen, Q. Wei, S. Wang (Eds), MCDM: Theory and Applications, Sci-Tech, 229-238.
  13. Alrefaei, M. H., and Ali Diabat., 2009. “A Simulated Annealing Technique for Multi-Objective Simulation Optimization,” Applied Mathematics and Computation, 215, 3029-3035.
  14. Yanling, W., Deli, Y., Guoqing, Y., 2010. “Logistics supply chain management based on multi-constrained combinatorial optimization and extended simulated annealing,” 2010 International Conference on Logistics Systems and Intelligent Management, 188- 192.
  15. Jahangirian, M., Eldabi, T., Naseer, A., Stergiouslas, L. K., Young, T., 2010. “Simulation in manufacturing and business: A review,” European Journal of Operational Research 203, 1-13.
  16. Terzi, S., Cavalieri, S., 2004. “Simulation in the supply chain context: a survey,” Computers in Industry 53, 3- 16.
  17. Longo, F., Mirabelli, G., 2008. “An advanced supply chain management tool based on modeling and simulation,” Computers & Industrial Engineering 54, 570-588.
  18. Diabat, A., Richard, J. P., Codrington, C. W., 2013. “A Lagrangian relaxation approach to simultaneous strategic and tactical planning in supply chain design,” Annals of Operations Research, 203(1), 55- 80.
  19. Jung, J. Y., Blau, G., Pekny, J. F., Reklaitis, G. V., Eversdyk, D., 2004. “A simulation based optimization approach to supply chain management under demand uncertainty,” Computers & Chemical Engineering 28, 2087-2106.
  20. Yoo, T., Cho, H., Yücesan, E., 2010. “Hybrid algorithm for discrete event simulation based supply chain optimization,” Expert Systems with Applications 37, 2354-2361.
  21. Ingalls, R. G., 1998. “The value of simulation in modeling supply chain,” In Proceedings of the 1998 winter simulation conference, Washington DC, 1371-1375.
  22. Metropolis, N., A. Rosenbluth, M. Rosenbluth, A., Teller, E., 1953. “Equation of state calculations by fast computing machines,” Journal of Chemical Physics, 21, 1087-1092.
  23. Eglese, R. W., 1990. “Simulated annealing: a tool for operational research,” European Journal of Operational Research 46(3), 271- 279.
  24. Laarhoven, P. J. M. and E. Aarts, 1987. “Simulated Annealing: Theory and Applications,” D. Reidel Publishing Company, Holland.
  25. Lanner Group Inc., 2011. http://www.lanner.com/ Heidrich, J., 2002. “Implementation of supply chain management systems in steel industry,” Industry Management, 18(5), 46-49.
  26. Chopra, S. and Meindl, P. 2007. Supply Chain management; Strategy, Planning, and Operations. Prentice Hall, New Jersey.
Download


Paper Citation


in Harvard Style

Diabat A., Al-Aomar R., Alrefaei M., Alawneh A. and Faisal M. (2013). A Framework for Optimizing the Supply Chain Performance of a Steel Producer . In Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: SSOS, (ICEIS 2013) ISBN 978-989-8565-59-4, pages 554-562. DOI: 10.5220/0004628405540562


in Bibtex Style

@conference{ssos13,
author={Ali Diabat and Raid Al-Aomar and Mahmoud Alrefaei and Ameen Alawneh and Mohd Nishat Faisal},
title={A Framework for Optimizing the Supply Chain Performance of a Steel Producer},
booktitle={Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: SSOS, (ICEIS 2013)},
year={2013},
pages={554-562},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004628405540562},
isbn={978-989-8565-59-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: SSOS, (ICEIS 2013)
TI - A Framework for Optimizing the Supply Chain Performance of a Steel Producer
SN - 978-989-8565-59-4
AU - Diabat A.
AU - Al-Aomar R.
AU - Alrefaei M.
AU - Alawneh A.
AU - Faisal M.
PY - 2013
SP - 554
EP - 562
DO - 10.5220/0004628405540562