Supply chain visibility in a multi-partner collaboration for distribution logistics – A secondary data research approach

Companies have realized that exchanging logistics data between multiple partners (e.g., shippers, essential customers, logistics service providers, and suppliers) has benefits in terms of optimized processes and reduced costs. Besides vertical collaborations, there are also opportunities for horizontal collaboration between supply chain partners which operate on the same echelon (Verdonck et al. 2013). These coopetitive relationships allow higher resource utilization rates, access to complementary competencies, and potentially share risks (Ferrell et al. 2020). Despite the advantages of coopetition companies tend to reject the idea of sharing their data due to a lack of trust, the connected risk of being exploited, or the objection to antitrust regulations (Basso et al. 2021). The extent to which companies have access to relevant information that can be used for decision-making, and the extent to which this information is shared with supply chain partners, is referred to as supply chain visibility (SCV). The goal of SCV is to reduce risks in the supply chain, improve lead times and performance, and identify supply bottlenecks and quality problems (Apeji and Sunmolam 2022). To facilitate SCV, it is necessary to have verifiable data, standardized data visualization, and a clear recognition of what potential benefits can be generated collaboratively. Aside from technical barriers e.g., related to interoperable and standardized platform solutions for seamless data exchange, companies also experience social barriers such as reduced trust and diminished awareness of a collaborative view or belief (Shin 2020). This research applies an SCV perspective and utilizes secondary data to show a data model collection process in the context of a multi-partner distribution logistics network.