Untitled Document
   
You are from : ( )  
     
Untitled Document
Untitled Document
 

International Journal of Information Technology & Computer Science ( IJITCS )

Abstract :

This paper is concerned with understanding supply chain dynamics using a multi-echelon inventory and order based production control system. The model uses the control theoretic concepts of variables, flows and feedback processes and is implemented using the iThink software package. The impact of information sharing is measured for both the bullwhip effect and inventory variances. Previous similar studies have focused on investigating the impact of information sharing on bullwhip effect. However, little research has been carried out to explore the impact of information sharing for both bullwhip effect and inventory variances. Through simulation experiments, information sharing has been found more helpful for reducing the inventory variances in a multi-echelon supply chain. It has been shown that in a four tier supply chain information sharing can reduce the demand amplification from 20:1 to 8:1 and inventory variances from 300:1 to 120:1. Further, it has been observed that increasing the percentage of information sharing reduces the bullwhip effect along with safety stock and minimizes the backlog cost. These findings will help supply chain operations manager & designers to reduce the cost, minimize order rate and inventory variances across supply chain.

Keywords :

: Information Sharing, Multi-Echelon Supply Chain, Simulation, Bullwhip effect, APIOBPCS

References :

  1. Agarwal, S., Sengupta, R.N. and Shanker, K. (2009), “Impact of information sharing and lead time on bullwhip effect and on-hand inventory”, European Journal of Operational Research, vol. 192, no.2, pp.576-593.
  2. Chan, F.T.S. and Chan, H.K. (2009), Effects of cascade information sharing in inventory and service level in multi-echelon supply chains, International Journal of Business Performance and Supply Chain Modeling, vol. 1, no. 1, pp. 1-7.
  3. Chen, F., Drezner, Z., Ryan, J. K. and Simchi-Levi, D., 2000. Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Management Science, vol. 46, no.3, pp.436-443.
  4. Coppini, M., Rossignoli C., Rossi T. and Strozzi F., 2009. Bullwhip effect and inventory oscillations analysis using the beer game model. International Journal of Production Research, 1-14, iFirst.
  5. Dejonckheere, J., Disney, S.M., Lambrecht, M.R. and Towill, D.R (2004), The impact of information enrichment on the bullwhip effect in supply chains: A control theoretic approach, European Journal of Operational Research, vol.153, no.3, pp. 727-750.
  6. Forrester, J.W. (1961), Industrial Dynamics, MIT Press and John Wiley & Sons, Inc., New York.
  7. Hosoda, T, Disney, SM (2006), On variance amplification in three echelon supply chain with minimum mean squared error forecasting, Omega, vol.34, no.4, pp.344-358.
  8. Houlihan, J.B. (1987), “International supply chain management”, International Journal of Physical Distribution and Materials Management, vol.17, no.2, pp. 51-66. Hussain, M, and Drake, P.R., 2011. Analysis of bullwhip effect with order batching. International Journal of Physical Distribution & Logistics Management, vol.41, no.10, pp.120-142.
  9. Hussain, M, Shoame, A, and Lee,D.M. (2012), “Impact of forecasting methods on variance ratio in Order-up-to level policy”, International Journal of Advance Manufacturing Technology Management, vol. 59, no, 1-4, pp.413-220.
  10.  Mason-Jones, R., Naim, M.M. and Towill, D.R. (1997), The impact of pipeline control on supply chain dynamics. The International Journal of Logistics Management, vol. 8, no.2, pp. 47-61.
  11. Lee, H.L., Padmanabhan, V. and Whang, S. (1997), “Information distortion in the supply chain: The bullwhip effect”, Management Science, vol. 43, no.4, pp. 546-559.
  12. Lee, H.L., So, K.C. and Tang, C.S. (2000), “The Value of information sharing in a two level supply chain”, Management Science, vol. 46, no.5, pp. 626-643.
  13. Lee, H.L., Padmanabhan, V. and Wang, S. (2004), “Information distortion in supply chain: The bullwhip effect”, Management Science, vol. 50, no.12, pp.1875-1886.
  14. Mason-Jones, R. and Towill, D.R., 1997. Information enrichment: Designing the supply chain for competitive advantage. Supply Chain Management, vol.2, no.4, pp.137-148.
  15. Riddalls, C.E. and Bennett, S. (2002), “The stability of supply chains”, International Journal of Production Research, vol. 40, no.2, pp.459- 475.
  16.  Shukla, V., Naim, M.M. and Yaseen, E.A., 2009. ‘Bullwhip’ and ‘backlash’ in supply pipelines. International Journal of Production Research, vol.47, no.23, pp.6477-6497.
  17. Slack, N. and Lewis, M. (2002), Operations Strategy, Prentice Hall, U.K.
  18. Sterman, J. (1989), “Modeling managerial behavior: misperception of feedback in a dynamic decision making experiment”, Management Science, Vol. 35 No. 3, pp. 321-339.
  19. Towill, D.R. (1992), “Supply chain dynamics- The change engineering challenge of the mid 1990s”, Proceedings of  the Institution of Mechanical Engineers, Vol. 206, pp. 233-245.
  20.  Towill, D.R. (1996), “Time compression and supply chain management- A guided tour”, Supply Chain Management, vol. 1 no.1, pp. 15-27.
  21. Wangphanich, P., Kara, S. and Kayis, B., 2009. Analysis of the bullwhip effect in multi-product, multi-stage supply chain systems – a simulation approach. International Journal of Production Research, 1-7, iFirst.
  22. Wilson, C.M., 2007. The impact of transportation disruptions on supply chain performance. Transportation Research, vol.43, no. E, pp.295-320.

Untitled Document
     
Untitled Document
   
  Copyright © 2013 IJITCS.  All rights reserved. IISRC® is a registered trademark of IJITCS Properties.