Enhancing Supply Chain Integration and Agility : A Case Study of Warehouse Management System (WMS) Implications in the Dairy Industry

The purpose of this study is to explore the role of WMS in enhancing supply chain integration and agility. It identifies specific WMS capabilities that facilitate inter- and intra-organizational integration, enabling companies to operate more smoothly and adapt effectively to changes in supply chains. The research centers on Arla Foods, a company in the dairy industry, where fast and accurate operations are crucial due to product perishability. This study demonstrates how WMS provides value beyond basic warehouse tasks. The study uses a qualitative single-case study design. Data is collected through semi-structured interviews with 10 employees at Arla Foods. Thematic analysis is used to identify patterns related to WMS capabilities and their impact on supply chain integration and agility. The findings connect to existing literature to develop a practical, theory-based framework. The research identifies six key WMS capabilities: enhanced inventory visibility, integrated data sharing, workflow automation, logistics coordination, real-time response, operational flexibility, resource management, and proactive decision-making with predictive analytics. These capabilities enable stronger integration and improve agility. WMS centralizes data and improves communication, but if only some departments use it or tools are not connected, silos remain, limiting impact. Companies should eliminate parallel work, ensure consistent WMS use across departments, and integrate with digital systems like ERP and TMS for unified information flow that supports coordination and collaboration. The findings emphasize that digital alignment is vital to breaking down silos and fully leveraging WMS’s strategic potential. This study is limited to one dairy company and qualitative interviews, so the findings may not apply universally to all industries or regions. It suggests future research should use multi-case and quantitative methods; explore how WMS interacts with other digital technologies, and apply longitudinal designs to assess long-term impacts.