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A demand aggregation approach for inventory control in two echelon supply chain under uncertainty

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Abstract

In this paper it is discussed that the demand aggregation is an effective approach for reducing inventory levels and the number of facilities under the uncertain supply and demand conditions. Therefore in this paper, an inventory control model is developed incorporating demand aggregation approach for two staged supply chain distribution network under uncertain demand conditions. The two stage of distribution network mainly consists of distributors and retailers. This inventory control model is developed as non-linear programming model with in the different alternatives of distribution networks. The main decision variables of the system are reorder point and the ordering quantity. The prime objective function in this paper is the total cost of system which mainly consists of ordering cost, inventory carrying cost, facility cost, facility operating cost and the cost of shipment. The model is solved for total cost minimization which provides the optimum inventory policy (reorder point and ordering quantity) and the minimum cost. Through this problem best alternative of distribution network is also suggested along with optimum reorder point, ordering quantity and total cost of the system. Some other vital inventory performance parameters besides of ordering quantity and reorder point are also evaluated for the system. These performance parameters are safety stocks, expected shortages per cycle, fill rates, cycle service level, average inventory etc. These performance parameters are evaluated with total cost of the system under different uncertainty levels for a desired service level. This problem also yielded the best network options in given uncertain conditions of demand and supply. This model is formulated for single product and single period. This study mainly focused on the small part of supply chain i.e. distribution network for implementing demand aggregation approach. A case study of a sugar mill distribution network has been performed for validating the industrial applications of the proposed model.

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References

  1. Agrawal, P.: Effect of uncertain and turbulent environment on organizational design. Econ. Bus. J. Enq. Perspect. 5(1), 11–24 (2014)

    Google Scholar 

  2. Angkiriwang, R., Pujawan, I.N., Santosa, B.: Managing uncertainty through supply chain flexibility: reactive vs. proactive approaches. Prod. Manuf. Res. 2(1), 50–70 (2014)

    Google Scholar 

  3. Anupindi, R., Bassok, Y.: Centralization of stocks: retailers vs. manufactures. Manag. Sci. 45(2), 178–191 (1999)

    Article  Google Scholar 

  4. Arnold, J.R.T.: Introduction to Materials Management. Prentice Hall, Upper Saddle River (1998)

    Google Scholar 

  5. Bernstein, F., Decroix, G.A., Wang, Y.: The impact of demand aggregation through delayed component in an assemble to order system. Manag. Sci. 57(6), 1154–1171 (2011)

    Article  Google Scholar 

  6. Bragila, M., Frosolini, M.: Virtual pooled inventories for equipment-intensive industries: an implementation in a paper district. Reliab. Eng. Syst. Saf. 112, 26–37 (2013)

    Article  Google Scholar 

  7. Constantin, A.: Inventory management, service level and safety stock. J. Public Adm. Finance Law X(6), 145–153 (2016)

    Google Scholar 

  8. Christopher, M.: Logistic and Supply Chain Management. Pitman Pub, London (1992)

    Google Scholar 

  9. Charles, J.C., Kumar, R.: A generalization of inventory pooling effect to nonnormal dependent demand. Manuf. Serv. Oper. Manag. 8(4), 351–358 (2006)

    Article  Google Scholar 

  10. Chopra, S., Meindl, P., Kalra, D.V.: Supply Chain Management: Strategy, Planning and operations, 5th edn, pp. 337–344. Pearson Education Inc., London (2013)

    Google Scholar 

  11. Dang, V.T.: Effects of component commonality in multi-component inventory models. OPSEARCH 37(4), 294–315 (2017)

    Article  Google Scholar 

  12. Dai, Y., Fang, S.-C., Ling, X., Nuttle, H.L.W.: Risk pooling strategy in multi-echelon supply chain with price sensitive demand. Math. Methods Oper. Res. 67(3), 391–421 (2008)

    Article  Google Scholar 

  13. Eppen, G.D.: Effects of centralization on expected costs in multi-location newsboy problem. Manag. Sci. 5(5), 498–501 (1979)

    Article  Google Scholar 

  14. Eppen, G., Scharge, L.: Centralized ordering policies in a multi-warehouse system with lead times and random demand. In: Schwarz, L.B. (ed.) Multi-level Production/Inventory System: Theory and Practice, pp. 51–67. North-Holland, Amsterdam (1981)

    Google Scholar 

  15. Etienne, E.C.: Supply chain responsiveness and the inventory illusion. Supply Chain Forum 6(1), 48–65 (2005)

    Article  Google Scholar 

  16. Eynan, A., Fouque, T.: Benefiting from risk pooling effects: internal (component commonality) vs external (demand reshape) efforts. Int. J. Serv. Oper. Manag. 1(1), 90–99 (2005)

    Google Scholar 

  17. Ferrer, G.: Open architecture, inventory pooling and maintenance modules. Int. J. Prod. Econ. 128, 393–403 (2010)

    Article  Google Scholar 

  18. Ganeshan, R.: Managing supply chain inventories: a multiple retailer, one warehouse, multiple supplier model. Int. J. Prod. Econ. 59(1–3), 341–354 (1999)

    Article  Google Scholar 

  19. Gaur, S., Ravindran, A.R.: A bi-criteria model for inventory aggregation problem under risk pooling. Comput. Ind. Eng. 51, 482–501 (2006)

    Article  Google Scholar 

  20. Gerchak, Y., He, Q.M.: On the relation between the benefits of risk pooling and variability of demand. IIE Trans. 35(11), 1027–1031 (2003)

    Article  Google Scholar 

  21. Govindan, K., Fattahi, Md, Keyvanshokooh, E.: Supply chain network design under uncertainty: a comprehensive review and future research directions. Eur. J. Oper. Res. 263(1), 108–141 (2017)

    Article  Google Scholar 

  22. Kamath, B.N., Bhattacharya, S.: Integrated inventory model for similar products under a two-echelon supply chain environment: an empirical Study. OPSEARCH 43(4), 331–357 (2006)

    Article  Google Scholar 

  23. Kang, J.H., Kim, Y.D.: Inventory control in a two level supply chain with risk pooling effect. Int. J. Prod. Econ. 135, 116–124 (2012)

    Article  Google Scholar 

  24. Lee, H.L., Billington, C.: Managing supply chain inventories: opportunities and pitfalls. MIT Sloan Manag. Rev. 33(3), 65–74 (1992)

    Google Scholar 

  25. Lin, C.T., Chen, C.B., Hsieh, H.J.: Effects of centralization on expected profits in a multi-location newsboy problem. J. Oper. Res. Soc. 52(7), 839–841 (2001)

    Article  Google Scholar 

  26. Liao, S.-H., Hsieh, C.-L., Lai, P.-J.: An evolutionary approach for multi-objective optimization of the integrated location—inventory distribution network problem in vendor-managed inventory. Expert Syst. Appl. 38(6), 6768–6776 (2011)

    Article  Google Scholar 

  27. Oeser, G.: Risk pooling in business logistics. In: Deshmukh S. G. (eds.) Risk-Pooling Essentials. Springer Briefs in Business. Springer, Cham (2015)

    Google Scholar 

  28. Ouyang, L.-Y., Chang, H.C.: Mixture inventory model involving setup cost reduction with service level constraints. OPSEARCH 37(4), 327–339 (2000)

    Article  Google Scholar 

  29. Pal, S., Manna, D.K.: A marketing decision problem in single period stochastic inventory model. OPSEARCH 40(3), 230–240 (2003)

    Article  Google Scholar 

  30. Prakash, S., Kumar, S., Soni, G., Jain, V., Rathore, A.P.S.: Closed-loop supply chain network design and modelling under risks and demand uncertainty: an integrated robust optimization approach. Ann. Oper. Res. 5(3), 1–28 (2018)

    Google Scholar 

  31. Prakash, S., Soni, G., Rathore, A.P.S.: A grey based approach for assessment of risk associated with facility location in global supply chain. Grey Syst. Theory Appl. 5(3), 419–436 (2015)

    Article  Google Scholar 

  32. Reddy, G.S.N., Sarma, K.V.S.: A periodic review inventory problem with variable stock dependent demand. OPSEARCH 38(3), 332–341 (2001)

    Article  Google Scholar 

  33. Routroy, S., Kodali, R.: Differential evolution algorithm for supply chain inventory planning. J. Manuf. Technol. Manag. 16(1), 7–17 (2005)

    Article  Google Scholar 

  34. Schmitt, A.J., Sun, S.A., Synder, L.V., Shen, Z.J.: Centralization vs decentralization: risk pooling, risk diversification, and supply chain disruptions. Omega 52, 201–212 (2015)

    Article  Google Scholar 

  35. Shi, J., Zhao, Y.: Component commonality under no hold-back allocation rules. Oper. Res. Lett. 42, 409–413 (2014)

    Article  Google Scholar 

  36. Sobel, M.J.: Risk pooling. In: Chhajed, D., Lowe, T.J. (eds.) Building Institution, pp. 155–174. Springer, Boston (2008)

    Chapter  Google Scholar 

  37. Synder, L.V., Daskin, M., Teo, C.P.: The stochastic location model with risk pooling. Eur. J. Oper. Res. 179, 1221–1238 (2007)

    Article  Google Scholar 

  38. Takai, S., Sengupta, S.: An approach to evaluate the probability of component commonality. J. Mech. Des. 139(7), 74501–74507 (2017)

    Article  Google Scholar 

  39. Vats, P., Soni, G., Rathore, A.P.S., Purohit, J.K.: Risk pooling approach in multi-product multi-period inventory control model under uncertainty. In: 2018 International Conference on Production and Operations Management Society (POMS) (2018)

  40. Wazed, MdA, Ahmed, S., Nukman, Y.: Commonality in manufacturing resource planning issues and models: a review. Eur. J. Ind. Eng. 4(2), 167–188 (2010)

    Article  Google Scholar 

  41. Weber, C.A., Current, J.R., Benton, W.C.: Vendor selection criteria and methods. Eur. J. Oper. Res. 50, 2–18 (1991)

    Article  Google Scholar 

  42. Weng, Z.K.: Risk-pooling over demand uncertainty in the presence of product modularity. Int. J. Prod. Econ. 62, 75–85 (1999)

    Article  Google Scholar 

  43. Xu, K., Evers, P.T.: Managing single echelon inventories through demand aggregation and the feasibility of correlation matrix. Comput. Oper. Res. 30, 297–308 (2003)

    Article  Google Scholar 

  44. Yang, H., Scharge, L.: Conditions that cause risk pooling to increase inventory. Eur. J. Oper. Res. 192(3), 837–851 (2009)

    Article  Google Scholar 

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Correspondence to Gunjan Soni.

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Vats, P., Soni, G., Rathore, A.P.S. et al. A demand aggregation approach for inventory control in two echelon supply chain under uncertainty. OPSEARCH 56, 840–868 (2019). https://doi.org/10.1007/s12597-019-00389-w

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