Real-Time Inventory Optimization Integrating ABC, Holt-Winters, EOQ/ROP, and RFID for Last-Mile Telecom Contractors

Past research on last-mile inventory has approached the problems of forecasting, replenishment policy, and automatic identification as a series of independent solutions that limit the real-time control of dispersed telecom assets. In this studied warehouse, this fragmentation caused 26.6% stockouts, 80% accurate inventory, and 7.2-day replenishment times. This study presented an integrated optimization model comprising ABC prioritization, Holt-Winters seasonal forecasting, EOQ/ROP replenishment rules, and a low-cost RFID-Arduino dataset. Arena discrete-event simulation was used to simulate the model, @Risk performed probabilistic analyses, and functional RFID testing was used to demonstrate efficacy. The results produced 8% stockouts, 4-day replenishment time, 90% inventory accuracy, and a coverage index of 3.79 days (greater means better). The findings increased reinforcement of service continuity, financial resources could be released for reinvestment, and the responsiveness of the operational environment could be improved. For future research, this integrated approach should be presented in other asset-intensive service sectors, and further research into scalability and Sustainability should be conducted in the context of more demanding parameters and levels of volatility.