Background: Nowadays an ever-increasing amount of data is generated which is why companies face the challenge of extracting valuable information from these data streams. An enhanced Information Utilisation carriers the opportunity for improved decision-making. This could address challenges that come along with delayed trucks in inbound logistics and associated warehouse resource planning.

Purpose: This study aims to deepen the understanding of Big Data Analytics capabilities that foster Information Integration and decision support to facilitate Information Utilisation. We apply this to the context of warehouse resource replanning in inbound logistics in case of unexpected short-term deviations.

Method: We conducted a qualitative research study, comprising a Ground Theory approach in combination with an abductive reasoning. Derived from a literature review we adapted a framework and proposed an own conceptual framework after conducting and analysing 14 semi-structured interviews with inbound logistics practitioners and experts.

Conclusion: We identified four interconnected capabilities that facilitate Information Utilisation. Data Generation Capabilities and Data Integration & Management Capabilities contribute to improved Information Integration, establishing a base for subsequent data analytics. Consequently, Data Analytics Capabilities and Data Interpretation Capabilities lead to enhanced decision support, facilitating Information Utilisation.