Education, Science, Technology, Innovation and Life
Open Access
Sign In

Balancing Demand and Supply: Inventory Allocation in FMCG

Download as PDF

DOI: 10.23977/ieim.2023.061006 | Downloads: 87 | Views: 799

Author(s)

Kemal Furkan Dinçer 1, Safiye Turgay 1

Affiliation(s)

1 Department of Industrial Engineering, Sakarya University, Sakarya, Turkey

Corresponding Author

Kemal Furkan Dinçer

ABSTRACT

This study delves into the critical aspect of supply chain management—optimal inventory allocation—for the fast-moving consumer goods (FMCG) industry. FMCG companies face the challenge of meeting dynamic customer demands while minimizing excess inventory costs. This abstract highlights key strategies employed by successful FMCG businesses to strike a balance between customer satisfaction and efficient inventory management. Moreover, for seasonal products, a tailored approach to inventory allocation is vital, allowing companies to adjust stock levels to match peak demand and prevent overstocking. The integration of advanced inventory management software facilitates real-time tracking, analysis, and decision-making, streamlining the inventory allocation process. By implementing these strategies and continually refining them based on real-world data, FMCG businesses can achieve optimal inventory allocation, leading to improved customer satisfaction, reduced costs, and an overall boost in supply chain efficiency. This study offers a comprehensive overview of inventory optimization techniques, aiming to assist FMCG companies in navigating the complexities of inventory management and remaining competitive in a fast-paced market.

KEYWORDS

Inventory Allocation, fast-moving consumer goods (FMCG), optimization, inventory model, deterministic approach

CITE THIS PAPER

Kemal Furkan Dinçer, Safiye Turgay, Balancing Demand and Supply: Inventory Allocation in FMCG. Industrial Engineering and Innovation Management (2023) Vol. 6: 41-49. DOI: http://dx.doi.org/10.23977/ieim.2023.061006.

REFERENCES

[1] Huang, T. and Van Mieghem, J. A. (2014), Clickstream Data and Inventory Management: Model and Empirical Analysis. Prod Oper Manag, 23: 333-347. https://doi. org/10. 1111/poms. 12046
[2] Williams, B. D.  and Tokar, T.  (2008). A review of inventory management research in major logistics journals: Themes and future directions, The International Journal of Logistics Management, Vol. 19 No. 2, pp. 212-232. https://doi. org/ 10. 1108/09574090810895960
[3] Nemtajela, N., Mbohwa, C. (2016). Inventory management models and their effects on uncertain demand, 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bali, Indonesia, pp. 1046-1049, doi: 10. 1109/IEEM. 2016. 7798037. 
[4] Nemtajela, N., Charles Mbohwa, C. (2017). Relationship between Inventory Management and Uncertain Demand for Fast Moving Consumer Goods Organisations, Procedia Manufacturing, Volume 8, Pages 699-706.
[5] Wang, C. H., Chen, T. Y. (2022) Combining biased regression with machine learning to conduct supply chain forecasting and analytics for printing circuit board. International Journal of Systems Science: Operations & Logistics 9:2, pages 143-154. 
[6] Cao, J., Jiang, Z., Wang, K. (2017) Customer demand prediction of service-oriented manufacturing using the least square support vector machine optimized by particle swarm optimization algorithm. Engineering Optimization 49:7, pages 1197-1210. 
[7] Ghods, L. Kalantar, M. (2008). Methods for long-term electric load demand forecasting; a comprehensive investigation, IEEE International Conference on Industrial Technology, Chengdu, China, 2008, pp. 1-4, doi: 10. 1109/ ICIT. 2008. 4608469. 
[8] Anna-Lena B., Minner, S., (2012). Safety stock planning under causal demand forecasting, International Journal of Production Economics, Volume 140, Issue 2, December, Pages 637-645. 
[9] Arshinder, K., Kanda, A., Deshmukh, S. G. (2011). A Review on Supply Chain Coordination: Coordination Mechanisms, Managing Uncertainty and Research Directions. In: Choi, TM., Cheng, T. (eds) Supply Chain Coordination under Uncertainty. International Handbooks on Information Systems. Springer, Berlin, Heidelberg. https://doi. org/ 10. 1007/978-3-642-19257-9_3
[10] Cachon, G. P., Lariviere, M. A., (2005) Supply Chain Coordination with Revenue-Sharing Contracts: Strengths and Limitations. Management Science 51(1):30-44. https://doi. org/10.1287/mnsc.1040. 0215
[11] Boyaci, T. and Gallego, G. (2004), Supply Chain Coordination in a Market with Customer Service Competition. Production and Operations Management, 13: 3-22. https://doi. org/10. 1111/j. 1937-5956. 2004. tb00141. x
[12] Croson, R., Donohue, K., Katok, E. and Sterman, J. (2014), Order Stability in Supply Chains: Coordination Risk and the Role of Coordination Stock. Prod Oper Manag, 23:176-196. https://doi.org/10.1111/j.1937-5956.2012. 01422. x
[13] Soroor, J. , Tarokh, M. J.  and Shemshadi, A.  (2009), "Theoretical and practical study of supply chain coordination", Journal of Business & Industrial Marketing, Vol. 24 No. 2, pp. 131-142. https://doi. org/ 10. 1108/ 08858620910931749
[14] Yunfei Chu, Y., You, F., Wassick, J. M., Agarwal, A. (2015) Simulation-based optimization framework for multi-echelon inventory systems under uncertainty, Computers & Chemical Engineering, Volume 73, 2 February, Pages 1-16
[15] Nesim Erkip, N., Hausman, W. H., Nahmias, S. (1990). Optimal Centralized Ordering Policies in Multi-Echelon Inventory Systems with Correlated Demands, Management ScienceVol. 36, No. 3
[16] Mitra, S., Chatterjee, A. K. (2004). Leveraging information in multi-echelon inventory systems, European Journal of Operational Research, Volume 152, Issue 1, 1 January, Pages 263-280
[17] Köchel P., Nieländer U. (2005). Simulation-based optimisation of multi-echelon inventory systems, International Journal of Production Economics, Volumes 93–94, 8 January, Pages 505-513
[18] Nyabwanga, R. N., Ojera, P. (2012). Business Performance for Small-Scale Enterprises in Kenya, Inventory Management Practices, Journal / KCA Journal of Business Management / Vol. 4 No. 1  
[19] Oey, E., Nofrimurti, M. (2017). Lean implementation in traditional distributor warehouse - a case study in an FMCG company in Indonesia, HomeInternational Journal of Process Management and BenchmarkingVol. 8, No. 1, Published Online: December 14, pp 1-15https://doi. org/10. 1504/IJPMB. 2018. 088654
[20] Lummus, R. R., Vokurka, R. J., Alber, K. L. (1998). Strategic Supply Chain Planning, Production And Inventory Management Journal; Alexandria Vol. 39, Iss. 3,  (Third Quarter): 49-58. 
[21] Pereira, M. M., Frazzon, E. M. (2021), A data-driven approach to adaptive synchronization of demand and supply in omni-channel retail supply chains, International Journal of Information Management, Volume 57, April, 102165.
[22] ManMohan S. S., Tang, C. S. (2009). Modeling supply-chain planning under demand uncertainty using stochastic programming: A survey motivated by asset–liability management, International Journal of Production Economics, Volume 121, Issue 2, October, Pages 728-738.

Downloads: 11443
Visits: 273494

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.