Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science


DOI: 10.5937/jaes0-31532 
This is an open access article distributed under the CC BY 4.0
Creative Commons License

Volume 20 article 927 pages: 254-263

Purba Daru Kusuma*
Computer Engineering, Telkom University, Bandung, Indonesia

Meta Kallista
Computer Engineering, Telkom University, Bandung, Indonesia

In the vendor-managed inventory (VMI) system, the vendor takes over responsibility for managing customer inventory so that delivery is not based on the order but the customer’s inventory condition. It makes the vendor becomes a dominant entity, and customers are supplied by its own vendor exclusively. That is why most studies in VMI implement a single-vendor-single-customer or single-vendor-multi-customer scenario. In certain conditions, this exclusiveness can increase lost sales. Besides, most of them implement a single product scenario. In this work, we develop VMI model for the multi-vendor-customer-product scenario. This model is developed based on the collaborative multi-agent system. The relationship between vendors and customers is many-to-many. This work aims to reduce lost sales and maintain efficiency in the inventory. The continuous review (r, Q) policy is used as the replenishment model. The simulation result shows that the collaborative model creates higher sales, lower lost sales, and competitive inventory than the non-collaborative one. The lost sales is 50 to 75 percent lower. The sales percentage is 17 to 27 percent higher. The total retailers’ stock is 20 to 38 percent higher. The total vendors’ stock is 11 to 30 percent lower. The total stock in the supply chain in the collaborative model is 2 to 16 percent higher. The number of retailers is directly proportional to the total vendor’s stock and total supply chain stock gaps; inversely proportional to the lost sales gap; and not related to the sales percentage and total retailers’ stock gaps.

View article

1. Joseph, J.F., Sundarakani, B., Hosie, P., Nagarajan, S. (2010). Analysis of Vendor Managed Inventory Practices for Greater Supply Chain Performance. International Journal on Logistics Economics and Globalization, vol. 2, no. 4, 297-315.

2. Taleizadeh, A.A. (2017). Vendor-managed Inventory System with Partial Backordering for Evaporating Chemical Raw Material. Scientia Iranica, vol. 24, no. 3, 1483-1492.

3. Erikshammar, J., Wetterblad, J., Wallin, J., Herder, M., Svensson, T. (2013). Vendor Managed Inventory: A Sawmills Potential Offering for Builders Merchants. Lulea University of Technology.

4. Gronalt, M., Rauch, P. (2008). Vendor Managed Inventory in Wood Processing Industries – a Case Study. Silva Fennica, vol. 42, no. 1, 101-114.

5. Phong, H.T., Yenradee, P. (2020). Vendor Managed Inventory for Multi-Vendor Single-Manufacturer Supply Chain: A Case Study of Instant Noodle Industry. Engineering Journal, vol. 24, no. 6, 91-107.

6. Sari, K. (2007). Exploring the Benefits of Vendor Managed Inventory. International Journal of Physical Distribution & Logistics Management, vol. 37, no. 7, 529-545.

7. Casino, F., Dasaklis, T.K., Patsakis, C. (2019). Enhanced Vendor-Managed Inventory through Blockchain. Proc. of 4th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Piraeus, Greece.

8. Hadiguna, R.A., Jaafar, H.S., Mohamad, S. (2011). A Model for Vendor Managed Inventory by Applying the Economic Order Quantity with Fuzzy Demand. International Journal of Enterprise Network Management, vol. 4, no. 4.

9. Zachariassen, F., de Haas, H., Burkland, S. (2014). Vendor Managed Inventory: Why You Need to Talk to Your Supplier. Journal of Industrial Engineering and Management, vol. 7, no. 4, 831-856.

10. Salem, R.W., Elomri, A. (2017). Vendor Managed Inventory (VMI): From Theory to Practical Implementation – A Literature Review. International Journal on Supply Chain Management, vol. 6, no. 1, 68-93.

11. Taleizadeh, A.A., Shokr, I., Konstantaras, I., Vafaeinejad, M. (2020). Stock Replenishment Policies for a Vendor-Managed Inventory in a Retailing System. Journal of Retailing and Consumer Services, vol. 55.

12. Khajehnezhad, M. (2018). Integrating Supply Chain with Vendor Managed Inventory and Joint Replenishment Policies. Proc. of the International Conference on Industrial Engineering and Operations Management, Paris.

13. Poorbagheri, T., Niaki, S.T.A. (2014). Vendor Managed Inventory of a Single-Vendor Multiple-Retailer Single-Warehouse Supply Chain under Stochastic Demands. International Journal of Supply and Operations Management, vol. 1, no. 3, 297-313.

14. Guan, R., Zhao, X. (2010). On Contracts for VMI Program with Continuous Review (r,Q) Policy. European Journal of Operational Research, vol. 207, no. 2, 656-667.

15. Adamu, I.I. (2017). Reorder Quantities for (Q, R) Inventory Models. International Mathematical Forum, vol. 12, no. 11, 505-514.

16. Pan, A., Hui, C.L., Ng, F. (2014). An Optimization of (Q, r) Inventory Policy Based on Health Care Apparel Products with Compound Poisson Demands. Mathematical Problems in Engineering, 1-9.

17. Castellano, D. (2015). Stochastic Reorder Point-Lot Size (r, Q) Inventory Model under Maximum Entropy Principle. Entropy, vol. 18, no. 16, 1-18.

18. Singha, K., Buddhakulsomsiri, J., Parthanadee, P. (2017). Mathematical Model of (R, Q) Inventory Policy under Limited Storage Space for Continuous and Periodic Review Policies with Backlog and Lost Sales. Mathematical Problems in Engineering, 1-9.

19. Sung, C.S, Oh, G.T. (1987). (r, Q) Policy for a Single-Product Production/Inventory Problem with a Compound Poisson Demand Process. Journal of the Operations Research Society of Japan, vol. 30, no. 2, 132-149.

20. Moon, I., Gallego, G. (1994). Distribution Free Procedures for Some Inventory Models. Journal of the Operational Research Society, vol. 45, 651-658.

21. Andresson, J., Jornsten, K.O., Nonas, S.L., Sandal, L.K., Uboe, J. (2013). A Maximum Entropy Approach to the Newsvendor Problem with Partial Information. European Journal of Operational Research, vol. 228, no. 1, 190-200.

22. Gallego, G., Katircioglu, K. Ramachandran, B. (2007). Inventory Management under Highly Uncertain Demand. Operations Research Letters, vol. 35, no. 3.

23. Waters, D. (2003). Inventory Control and Management. Willey, West Sussex.

24. Goyal, S.K. (1985). Economic Order Quantity under Conditions of Permissible Delay in Payments. Journal of the Operational Research Society, vol. 36, no. 4, 335-338.

25. Russel, S., Norvig, P. (1995). Artificial Intelligence – A Modern Approach. Prentice Hall, New Jersey.

26. Wooldridge, M. (2002). An Introduction to Multi Agent System. Wiley.

27. Glavic, M. (2006). Agents and Multi-Agent Systems: A Short Introduction for Power Engineers. University of Liege, Liege.

28. Balaji, P.G., Srinivasan, D. (2010). An Introduction to Multi-Agent Systems. Srinivasa, D., Jain, L.C. Innovations in Multi-Agent Systems and Applications – 1. Springer-Verlag, Berlin.

29. Lhaksmana, K.M., Murakami, Y., Ishida, T. (2018). Role-Based Modeling for Designing Agent Behavior in Self-Organizing Multi-Agent Systems. International Journal of Software Engineering and Knowledge Engineering, vol. 28, no. 1, 79-96.

30. Gamoura, S.C., Derrouiche, R., Ouzrout, Y., Bouras, A. (2003). Multi Agent Supply Chain Architecture to Optimize Distributed Decision Making. Proc. of 7th Conference on Systemic and Informatics, Florida.

31. Pal, K., Karakostas, B. (2014). A Multi Agent-Based Service Framework for Supply Chain Management. Procedia Computer Science, vol. 32, 53-60.

32. Zgaya, H., Zoghlami, N., Hammadi, S., Bretaudeau, F. (2009). IFAC Proceedings Volumes, vol. 42, no. 4, 1026-1031.