RFID-Enhanced Modified Two-Bin System for Reducing Excess Inventory of FMCG Industry

Shuvojit Das, Gazi Md. Mahbubul Alam Rajin, Md. Nazmul Hasan Sarker, Md. Mahraj Uddin (), Golam Sakaline and Edit Süle
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Shuvojit Das: Department of Industrial and Production Engineering, National Institute of Textile Engineering and Research (NITER), University of Dhaka, Dhaka 1350, Bangladesh
Gazi Md. Mahbubul Alam Rajin: Department of Industrial and Production Engineering, National Institute of Textile Engineering and Research (NITER), University of Dhaka, Dhaka 1350, Bangladesh
Md. Nazmul Hasan Sarker: Department of Industrial and Production Engineering, National Institute of Textile Engineering and Research (NITER), University of Dhaka, Dhaka 1350, Bangladesh
Md. Mahraj Uddin: Department of Industrial and Production Engineering, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh
Golam Sakaline: Department of Industrial and Systems Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
Edit Süle: Department of Corporate Leadership and Marketing, Széchenyi István University, 9026 Győr, Hungary

Logistics, 2025, vol. 9, issue 4, 1-25

Abstract: Background : Globally, in the Fast-Moving Consumer Goods (FMCG) industry, excess inventory results from the bullwhip effect. Earlier, barcode-based two-bin systems were limited by manual scanning; hence, a more responsive system is needed to align the inventory with real-time demand. Prior studies have predominantly concentrated on mitigating demand fluctuations and employed comparatively low-efficiency systems, hindering excess inventory (EI) reduction. Methods : This study proposes identifying research gaps, considering the distributor-manufacturer relationship, and developing an RFID-based modified two-bin system and mathematical model to reduce EI and control over manufacturers’ excessive cost. Results : This study tested through Python-based simulation using historical data from an FMCG manufacturer, and the proposed model achieved a reduction in 67% EI and 73% month-wise holding costs. Moreover, the integration of the Artificial Bee Colony algorithm optimizes rework rates within budget, including reworking shop-floor and holding costs, contributing to a monthly excessive cost reduction of 34–48%, alongside a corresponding 41–44% cumulative excessive cost reduction. Conclusions : Bringing significant implications on digitalized SCM, this study offers a practical and scalable solution for perishable FMCG items facing demand variability and budget constraints. Collectively, this novel perspective bridges research gaps and motivates future research for embedding trend-aligned parameters, enhancing the model’s performance through diverse SCM contexts like safety stock and backorder cost optimization.

Keywords: FMCG; RFID; two-bin; inventory; data-driven decision making; perishable supply chain; excess inventory reduction; artificial bee colony optimization (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2025
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