Using Big Data Analytics to Optimise Inventory Management in the U.S. E-Commerce Warehouses

This study examined how Big Data Analytics (BDA) can be used to optimise inventory management in the warehouses of the U.S. e-commerce companies especially targeting Small and Medium-sized Enterprises (SMEs). With the fast digitalization of logistics, a large number of SMEs are having difficulties with integrating BDA because of resource shortage, technical capability, and an inability to quantify the profit. The research was anchored on the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT) assessing the level of BDA implementation, the effect it had on inventory performance as well as the barriers and enablers to its implementation process. Quantitative survey was employed, where 343 inventory managers, analysts, and supervisors in Illinois provided data, which was analysed using SPSS version 27. The results indicated that BDA is very popular with a great impact of improvement in inventory turnover, stock accuracy, and consequential order fulfilment time and a good beta coefficient of 0.358, which reflects positive effect being moderate. Among the key obstacles, the integration challenges were listed, as well as high costs and low availability of skilled workforce, whereas the major enablers were stated to be support of top management, robust IT base and training of the employees. BDA is both strategic resource and dynamic capability that ensures agility and competitiveness. The study recommends that the SMEs make investments in the development of their workforce and IT modernization. Also, policymakers should offer incentives to facilitate the reduction of adoption prices. Longitudinal and cross-sector research studies should be studied in future academic research.