Dynamic TOC-based approach to planning and controlling accessories in MTO environments

Document Type : Research Article

Authors

Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran

Abstract

In make-to-order systems, customers expect to have more freedom to choose the accessories. But demand variations and internal disorders induce some uncertainties. Hence, a different inventory system is needed for such items that dynamically manage those variations. In this paper, a dynamic approach based on the theory of constraints is proposed for inventory planning and control of accessories. First, the risk of processing time variation is balanced while keeping cycle time balancing. Second, the ribbons of buffer control charts are determined by a buffer planning model in which a multi-criteria ABC analysis is used to apply different customer service levels. To detect demand variations and monitor the buffer, trend of consumption in each monitoring window is carefully traced. Also, simulation-based procedures are recommended to update control ribbons. Comparing the performance of proposed approach to common methods using the data of an automobile company as well as several random test problems confirms that it can significantly reduce the costs and improve the efficiency of inventory system.

Keywords

Main Subjects

References

References:
1. Hopp, W.J. and Spearman, M.L., Factory Physics: Foundation of Manufacturing Management, MacGraw Hill Companies, First Ed (2001).
2. Buchel, A. "Stochastic material requirements planning for optional parts", Int. J. Prod. Res., 21(4), pp. 511- 527 (1983).
3. Wang, T.-Y. and Hu, J.-M. "An inventory control system for products with optional components under service level and budget constraints", Eur. J. Oper. Res., 189(1), pp. 41-58 (2008).
4. Wang, T.-Y. and Hu, J.-M. "Heuristic method on solving an inventory model for products with optional components under stochastic payment and budget constraints", Expert Syst. Appl., 37(3), pp. 2588-2598 (2010).
5. Lee, D. and Lee, C.-Y. "A heuristic algorithm for continuous review inventory system with necessary and optional components", Int. J. Adv. Comput. Tech., 5(12), pp. 183-190 (2013).
6. Thomopoulos, N.T. "Line balancing-sequencing for mixed-model assembly", Manage. Sci., 14(2), pp. B- 59-B-75 (1967).
7. Thomopoulos, N.T. "Mixed model line balancing with smoothed station assignments", Manage. Sci., 16(9), pp. 593-603 (1970).
8. Udayakumar, P., Risk Oriented Stochastic Assembly Line Balancing, Wichita State University (2010).
9. Sharma, S., Mittal, P., and Agrawal, P. "Risk analysis in assembly line balancing: A study", Int. J. Appl. Eng. Tech., 1(2), pp. 71-76 (2014).
10. Keyvani, P. and Lotfi, M.M. "A risk-based heuristic algorithm for mixed model assembly line balancing problem", in 9th International Iranian Operation Research Conference, Shiraz University of Technology, p. 5 (2016).
11. Gardiner, S.C. and Blackstone Jr, J.H. "The theory of constraints and the make-or-buy decision", J. Supp. Chain Manage., 27(3), p. 38 (1991).
12. Balakrishnan, J. and Cheng, C.H. "The theory of constraints and the make-or-buy decision: an update and review", J. Supp. Chain Manage., 41(1), pp. 40-47 (2005).
13. Ramanathan, R. "ABC inventory classification with multiple-criteria using weighted linear optimization", Comput. Oper. Res., 33(3), pp. 695-700 (2006).
14. Flores, B.E. and Whybark, D.C. "Implementing multiple criteria ABC analysis", J. Oper. Manage., 7(1-2), pp. 79-85 (1987).
15. Ernst, R. and Cohen, M.A. "Operations related groups (ORGs): a clustering procedure for production/ inventory systems", J. Oper. Manage., 9(4), pp. 574-598 (1990).
16. Flores, B.E., Olson, D.L., and Dorai, V. "Management of multicriteria inventory classification", Math. Comput. Model., 16(12), pp. 71-82 (1992).
17. Guvenir, H.A. and Erel, E. "Multicriteria inventory classification using a genetic algorithm", Eur. J. Oper.Res., 105(1), pp. 29-37 (1998).
18. Partovi, F.Y. and Anandarajan, M. "Classifying inventory using an artificial neural network approach", Comput. Indust. Eng., 41(4), pp. 389-404 (2002).
19. Zhou, P. and Fan, L. "A note on multi-criteria ABC inventory classification using weighted linear optimization", Eur. J. Oper. Res., 182(3), pp. 1488-1491 (2007).
20. Chen, J.-X. "Peer-estimation for multiple criteria ABC inventory classification", Comput. & Oper. Res., 38(12), pp. 1784-1791 (2011).
21. Ng, W.L. "A simple classifier for multiple criteria ABC analysis", Eur. J. Oper. Res., 177(1), pp. 344-353 (2007).
22. Hadi-Vencheh, A. "An improvement to multiple criteria ABC inventory classification", Eur. J. Oper. Res., 201(3), pp. 962-965 (2010).
23. Babai, M., Ladhari, T., and Lajili, I. "On the inventory performance of multi-criteria classification methods: empirical investigation", Int. J. Prod. Res., 53(1), pp. 279-290 (2015).
24. Yuan, K.-J., Chang, S.-H., and Li, R.-K. "Enhancement of theory of constraints replenishment using a novel generic buffer management procedure", Int. J. Prod. Res., 41(4), pp. 725-740 (2003).
25. Kaijun, L. and Wang Yuxia, W.X. "Research on inventory control policies for nonstationary demand based on TOC", Int. J. Comput. Intel. Syst., 3(Supp01), pp. 114-128 (2010).
26. Reyes, J., Alvarez, K., and Vasquez, R. "Dynamic buffer management for raw material supply in the footwear industry", J. Indust. Intel. Inf., 4(1), pp. 1-8 (2016).
27. Goldratt, E.M., Cox, J., and Whitford, D., The Goal: A Process of Ongoing Improvement, North River Press Great Barrington, MA (1992).
28. Blackstone, J.H. "Theory of constraints-a status report", Int. J. Prod. Res., 39(6), pp. 1053-1080 (2001).
29. Gundogar, E., Sari, M., and Kokcam, A.H. "Dynamic bottleneck elimination in mattress manufacturing line using theory of constraints", Springerplus, 5(1), pp. 1276 (2016).
30. Bernardi de Souza, F. and Pires, S.R. "Theory of constraints contributions to outbound logistics", Manage. Res. Rev., 33(7), pp. 683-700 (2010).
Scientia Iranica
Volume 26, Issue 5
Transactions on Industrial Engineering (E)
September and October 2019
Pages 2885-2903

Files

Cited by

History

  • Receive Date: 23 June 2017
  • Revise Date: 16 December 2017
  • Accept Date: 23 June 2018

Share

How to cite

Statistics