Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Languages: English
Types: Article
In the contemporary business environment, to adhere to the need of the customers, caused the shift from mass production to mass-customization. This necessitates the supply chain (SC) to be effective flexible. The purpose of this paper is to seek flexibility through adoption of family-based dispatching rules under the influence of inventory system implemented at downstream echelons of an industrial supply chain network. We compared the family-based dispatching rules in existing literature under the purview of inventory system and information sharing within a supply chain network. The dispatching rules are compared for Average Flow Time performance, which is averaged over the three product families. The performance is measured using extensive discrete event simulation process. Given the various inventory related operational factors at downstream echelons, the present paper highlights the importance of strategically adopting appropriate family-based dispatching rule at the manufacturing end. In the environment of mass customization, it becomes imperative to adopt the family-based dispatching rule from the system wide SC perspective. This warrants the application of intra as well as inter-echelon information coordination. The holonic paradigm emerges in this research stream, amidst the holistic approach and the vital systemic approach. The present research shows its novelty in triplet. Firstly, it provides leverage to manager to strategically adopting a dispatching rule from the inventory system perspective. Secondly, the findings provide direction for the attenuation of adverse impact accruing from demand amplification (bullwhip effect) in the form of inventory levels by appropriately adopting family-based dispatching rule. Thirdly, the information environment is conceptualized under the paradigm of Koestler's holonic theory.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. Cachon, G.P., & Fisher, M. (2000). Supply chain inventory management and the value of shared information. Management Science, 46(8), 1032-1048.
    • 2. Chang, Y., & Makatosoris, H. (2001). Supply chain modelling using simulation. International Journal of Simulation, 2(1), 24-30.
    • 3. Chen, F. (1998). Echelon reorder points, installation reorder points, and the value of centralized demand information. Management Science, 44(2), 1221-1234.
    • 4. Chen, Y.M., Liao, C.C., & Prasad, B. (1998). A systematic application of virtual enterprising through knowledge management techniques. Journal of Concurrent Engineering Research and Application, 6(3), 225-244.
    • 5. Christopher, M., & Towill D. (2001). An integrated model for the design of agile supply chains. International journal of physical distribution logistic management, 31(4), 235- 246.
    • 6. Christopher, M., & Lee, H. (2004). Mitigating supply chain risk through improved confidence. International Journal of Physical Distribution and Logistics Management, 34(5), 388-396.
    • 7. Dev, N.K., Caprihan, R., & Swami, S. (2011). A case study of redesign of supply chain network of a manufacturing organization. Journal of Advances in Management Research, 8(2), 195-212.
    • 8. Dev, N.K., & Shankar, R. (2012). Design of fractal information coordination system in a supply chain network. International Journal of Services and Operations Management, 12(1), 1-19.
    • 9. Dev, N.K., Caprihan, R., & Swami, S. (2012). Strategic positioning of push-pull boundary within a supply chain: An ordering policy co-ordination perspective. Operations and Supply Chain Management, 5(1), 42-53.
    • 10. Dominici, G. (2008). Holonic Production System to Obtain Flexibility for Customer Satisfaction. Journal of Service Science and Management, 1(3), 251-254.
    • 11. Fernando, J.M.M., & Sichman, J.S. (2010). Oil industry supply chain management as a holonic agent based distributed constraint optimization problem. In proceedings of 19th European conference on Artificial intelligence, August 17, 2010, Lisbon, Portugal.
    • 12. Fletcher, M., Garcia-Herreros, E., Christensen, J. H., Deen, S. M., & Mittman, R. (2000). An open architecture for holonic cooperation and autonomy. In Proceedings of 11th international Workshop on Database and Expert Systems Applications, pp. 224-230.
    • 13. Frazier, G.V. (1996). An evaluation of group scheduling heuristics in a flow-line manufacturing cell. International Journal of Production Research, 34(4), 959-976.
    • 14. Georges, M.R.R., Franco, G.N., & Batocchio, A. (2009). Extending holonic manufacturing systems to achieve the virtual supply chain domain. Journal of Operations and Supply Chain Management, 2(2), 47-55.
    • 15. Goletz, T., & Ferreira, J.J.P. (2000). Enacting dynamic distribution networks-the DAMASCOS project. In: Camarinha-Matos, L.M., Afsarmanesh, H., Erbe, H. (Eds.), Advances in Networked Enterprises-Virtual Organizations, Balanced Automation and Systems Integration. Kluwer Academic Publishers, Dordrecht, 73-80.
    • 17. Hamedi, M., Esmaeilian, G.R., Ismail, N., & Ariffin, M.K.A. (2012). Capability-based virtual cellular manufacturing systems formation in dual-resource constrained settings using Tabu search. Computers and Industrial Engineering, 62, 953-971.
    • 18. Huang, B., Gou, H., Liu, W., Li, Y., & Xie, M. (2002). A framework for virtual enterprise control with the holonic manufacturing paradigm. Computers in Industry, 49(3), 299-310.
    • 19. Kannan, V. R., & Ghosh, S. (1996). Cellular manufacturing using virtual cells. International journal of operations and production management, 16(5), 99-112.
    • 20. Kara, S., & Kayis, B. (2004). Manufacturing flexibility and variability: An overview. Journal of Manufacturing Technology Management, 115(6), 466-478.
    • 21. Kelton, W.D., Sadowski, R.P., & Swets, N.B. (2010). Simulation with Arena. 5th ed. New York: McGraw-Hill.
    • 22. Ko, H.H., Kim, J., Kim, S.S., & Baek, J.G. (2010). Dispatching rule for non-identical parallel machines with sequence-dependent setups and quality restrictions. Computers and Industrial Engineering, 59, 448-457.
    • 23. Koestler, A. (1967). The ghost in the machine. Arkana, London.
    • 24. Kotak, D., Wu, S., Fleetwood, M., & Tamoto, H. (2003). Agent-based holonic design and operation environment for distributed manufacturing. Computers in Industry, 52(2), 95- 108.
    • 25. Lau, R.S.M., & Yam, R.C.M. (2005). A case study of product modularization on supply chain design and coordination in Hong Kong and China. Journal of manufacturing technology management, 16(4), 432-446.
    • 27. Li, X., & Y. Chen. (2010). Impacts of supply disruptions and customer differentiation on a partial-backordering inventory system. Simulation Modelling Practice and Theory, 18(5), 547-557.
    • 29. Longo, F., Mirabelli, G., & Papoff, E. (2005). Modelling analysis and simulation of a supply chain devoted to support pharmaceutical business retail. In the proceedings of 18th International Conference on Production Research, Salerno, Italy.
    • 30. Mahmoodi, F., Tierney, E.J., & Mosier, C.T. (1992). Dynamic group scheduling heuristics in a flow-through cell environment. Decision Sciences, 23(1), 61-85.
    • 31. Mahmoodi, F., & Martin, G.E. (1997). A new shop-based and predictive scheduling heuristic for cellular manufacturing. International Journal of Production Research, 35(2), 313-326.
    • 32. Masuchun, W., Davis, S., & Patterson, J.W. (2004). Comparison of push and pull control strategies for supply network management in a make-to-stock environment. International Journal of Production Research, 42(20), 4401-4419.
    • 33. Mohebbi, E. (2004). A replenishment model for the supply-uncertainty problem. International Journal of Production Economics, 87(1), 25-37.
    • 34. Mosier, C.T., Elvers, D.A., & Nelly, D. (1984). Analysis of group technology scheduling heuristics. International Journal of Production Research, 22(5), 857-875.
    • 35. Narasimhan, R., & Carter, J.R. (1998). Linking business unit and material sourcing strategies. Journal of Business Logistics, 19(2), 155-171.
    • 36. Nomden, G., Van Der, D.J., & Slomp, J. (2008). Family-based dispatching: anticipating future jobs. International journal of Production Research, 46(11), 73-97.
    • 37. Olhager, J., & Persson, F. (2006). Simulating production and inventory control system: A learning approach to operational excellence. Production Planning and Control, 17(2), 113-127.
    • 38. Sahin, F., & Robinson, E.P. (2002). Flow coordination and information sharing in supply chain: review, implications, and directions for future research. Decision Sciences, 33(4), 505-536.
    • 39. Samvedi, A., & Jain, V. (2011). Studying the impact of various inventory policies on a supply chain with intermittent supply disruptions. In the proceedings of the 2011 Winter Simulation Conference, Phoenix, AZ, 1641-1649.
    • 40. Sensi, G. De, Longo, F., & Mirabelli, G. (2008). Inventory policy analysis under demand patterns and lead times constraints in a real supply chain. International Journal of Production Research, 46(24), 6997-7016.
    • 41. Sheffi, Y., & Rice, J. B. (2005). A supply chain view of the resilient enterprise. MIT Sloan Management Review, 47(1), 41-48.
    • 43. Suda, H. (1989). Future factory system formulated in Japan. Japanese Journal of Advanced Automation Technology, 1(1), 15-25.
    • 44. Suwanruji, P., & Enns, S.T. (2006). Evaluating the effects of capacity constraints and demand patterns on supply chain replenishment strategies. International Journal of Production Research, 44(21), 4607-4629.
    • 45. Tang, C.S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103(2), 451-488.
    • 46. Thomas, D.J., & Griffin, P.M. (1996). Coordinated supply chain management. European Journal of Operations Research, 94(1), 1-15.
    • 47. Van Brussel, H., Valckenaers, P., Bongaerts, L., & Wyns, J. (1995). Architectural and systems design issues in holonic manufacturing systems. In Proceedings of the 3rd IFAC/ IFIP/IFORS Workshop on Intelligent Manufacturing Systems IMS'95, 1-6.
    • 48. Van der Zee, D.-J. (2013). Family based dispatching with batch availability. International Journal of Production Research, 51(12), 3643-3653.
    • 49. Vernadat, F.B. (1997). Enterprise Modeling and Integration: Principles and Applications. London, U.K.: Chapman & Hall.
    • 50. Wadhwa, S., Mishra, M., Chan, F.T.S., & Ducq, Y. (2008). Effects of information transparency and cooperation on supply chain performance: a simulation study. International Journal of Production Research, 48(1), 145-166.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article