Author(s)

Safiye Turgay ¹, Kemal Furkan Dinçer ¹

Affiliation(s)

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

Corresponding Author

Safiye Turgay

ABSTRACT

In the fast-paced and competitive landscape of fast-moving consumer goods (FMCG) industries, effective inventory allocation plays a pivotal role in ensuring optimal supply chain performance. Traditional deterministic inventory models often fail to account for the inherent uncertainties and fluctuations in consumer demand and lead times, leading to suboptimal allocation decisions. To address this challenge, a stochastic model has emerged as a powerful approach that considers demand and lead time as probabilistic variables. Through a comprehensive case study of a leading FMCG manufacturer, the effectiveness of the stochastic model is demonstrated in achieving improved inventory allocation decisions. The results indicate that embracing the stochastic model empowers FMCG companies to make informed and data-driven decisions when allocating inventory resources. By embracing uncertainty and variability, businesses can develop more robust inventory allocation strategies that align with real-world demand patterns and lead times. As supply chains continue to face dynamic challenges, the stochastic model represents a valuable tool for FMCG industries seeking to optimize their inventory allocation practices and thrive in the ever-evolving consumer landscape.

KEYWORDS

Inventory Control, Deterministic Model, Stochastic Model, Optimization, FMCG-Fast Moving Consumer Goods

CITE THIS PAPER

Safiye Turgay, Kemal Furkan Dinçer, Optimizing Inventory Allocation for Fast Moving Consumer Goods: A Stochastic Model Approach. Industrial Engineering and Innovation Management (2023) Vol. 6: 77-86. DOI: http://dx.doi.org/10.23977/ieim.2023.061011.

REFERENCES

[1] Kumar, N., Mishra, A. (2013). Gaps between inventory management theory and practice: a critical examination of emerging trends from the FMCG industry, International Journal of Logistics Economics and GlobalisationVol. 5, No. 1, Published Online:June 11, pp 1-14https://doi.org/10.1504/IJLEG.2013.054427
[2] Valbuena, J. N. (2022). Demand forecasting of fast moving consumer goods based on modeling of time series and deep learning methods. Recuperado de: http://hdl.handle.net/20.500.12010/27585.
[3] Husseini, S.M.M., Karimi, B. (2017). Sajadieh, M.S Bi-objective intelligent water drops algorithm to a practical multi-echelon supply chain optimization problem, Journal of Manufacturing Systems, Volume 44, Part 1, July, Pages 93-114
[4] Dong, C., Transchel, S. (2020). A dual sourcing inventory model for modal split transport: Structural properties and optimal solution, European Journal of Operational Research, Volume 283, Issue 3, 16 June, Pages 883-900
[5] Kchaou-Boujelben, M.,  a,  Bensalem, M.,  b,  Jemai, Z., Bi-objective stochastic closed-loop supply chain network design under uncertain quantity and quality of returns, Computers & Industrial Engineering, Volume 181, July, 109308, 2015.
[6] Asgharizadeh, E., Daneshvar, A. (2023). Homayounfar, M. et al. Modeling the supply chain network in the fast-moving consumer goods industry during COVID-19 pandemic. Oper Res Int J 23, 14 (2023). https://doi.org/ 10.1007/s12351-023-00757-x
[7] Pratap, S.,   Jauhar, S.K., Paul, S.K., Zhou, F. (2022). Stochastic optimization approach for green routing and planning in perishable food production, Journal of Cleaner Production, Volume 333, 20 January, 130063
[8] Wang, Y., Assogba, K.,  Liu, Y., Ma, X., Xu, M., Wang, Y. (2018) Two-echelon location-routing optimization with time windows based on customer clustering, Expert Systems with Applications, Volume 104, 15 August, Pages 244-260
[9] Govindan, K., Jafarian, A., Khodaverdi, R., Devika, K. (2014).  Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food, International Journal of Production Economics, Volume 152, June,, Pages 9-28
[10] Escalona, P., Marianov, V., Ordóñez, F.  Stegmaier, R.(2018), On the effect of inventory policies on distribution network design with several demand classes, Transportation Research Part E: Logistics and Transportation Review, Volume 111, March, Pages 229-240
[11] Zhu, S., Hu, X., Huang, K., Yuan, Y. (2021), Optimization of product category allocation in multiple warehouses to minimize splitting of online supermarket customer orders, European Journal of Operational Research, Volume 290, Issue 2, 16 April, Pages 556-571
[12] Zanjani, S.M., Jabalameli, M.S., Pishvaee, M.S. (2021). A resilient-green model for multi-echelon meat supply chain planning, Computers & Industrial Engineering, Volume 152, February, 107018
[13] Asghari, M., Afshari, H.,  Al-e-hashem, S.M.J.H., Fathollahi-Fard, A.M.( 2022), Dulebenets, M.A., Pricing and advertising decisions in a direct-sales closed-loop supply chain, Computers & Industrial Engineering, Volume 171, September, 108439
[14] Nasiri, G.R., Kalantari, M., Karimi, B. (2021), Fast-moving consumer goods network design with pricing policy in an uncertain environment with correlated demands, Computers & Industrial Engineering, Volume 153, March, 106997
[15] Önal, M., Yenipazarli, A., Kundakcioglu, O.E.( 2016), A mathematical model for perishable products with price- and displayed-stock-dependent demand, Computers & Industrial Engineering, Volume 102, December, Pages 246-258
[16] Sahling, F., Hahn, G.J. (2019), Dynamic lot sizing in biopharmaceutical manufacturing, International Journal of Production Economics, Volume 207, January, Pages 96-106
[17] Duan, Q., Liao, T.W.( 2013) A new age-based replenishment policy for supply chain inventory optimization of highly perishable products, International Journal of Production Economics, Volume 145, Issue 2, October, Pages 658-671
[18] Dinçer, F.K., Turgay, S. (2023). Balancing Demand and Supply: Inventory Allocation in FMCG, Industrial Engineering and Innovation Management (Accepted Paper: ID: 101638 in Clausius Scientific Paper Publishing, Canada. 
[19] Prasad, M.M., Dhiyaneswari, J.M., Jamaan, J.R., Mythreyan, S., Sutharsan, S.M. (2020). A framework for lean manufacturing practice in the Indian textile industry. Today: Proc., 10.1016 / j.matpr. 2020.02.979

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