An Economic Impact Model for Evaluating the Cost-Benefit of Technology Adoption in Inventory Management

Technology adoption in inventory management is critical for enhancing operational efficiency, reducing shrinkage, and improving customer satisfaction across retail and manufacturing sectors. However, evaluating the economic impact of adopting technologies such as RFID, IoT sensors, AI-driven forecasting systems, and advanced Warehouse Management Systems (WMS) poses challenges due to the complexities of quantifying direct, indirect, and intangible benefits against acquisition and operational costs. This paper presents a systematic literature-based study proposing an Economic Impact Model designed to evaluate the cost-benefit structures of technology adoption in inventory management systematically. Leveraging a Systematic Literature Review (SLR), this study synthesises frameworks and empirical insights from global research while identifying gaps in current models that fail to capture dynamic operational contexts and intangible benefits accurately. The paper outlines the conceptual structure of the proposed model, integrating ROI, Total Cost of Ownership (TCO), and scenario planning for practical application within small, medium, and large enterprise inventory environments. By offering a scalable, technology-agnostic evaluation tool, this study supports decision-makers in aligning investment strategies with operational goals, sustainability imperatives, and digital transformation roadmaps in inventory management.