While many supply chain decisions could take advantage of big data, firms struggle with investments into supply chain analytics since they are not able to assess the application areas and benefits of these initiatives. In this paper, a multi-level perspective is provided to assess the value of supply chain data.
The researchers develop a framework that highlights the connections between data characteristics and supply chain decisions with different time horizons (i.e. short-or long-term) as well as different supply chain levels (i.e. individual-firm level or supply-chain level). As data gets more complex in one or more of the 4 V dimensions (i.e. volume, variety, velocity, veracity), firms must assess how to best take advantage of the opportunities offered.
They use the Dutch floriculture sector as a case study for their framework in which we highlight four data analytics applications to improve logistics processes. In the applications , they demonstrate how the data is used to support the decisions at different time horizons and supply-chain levels.
They find that each of the big data's Vs is required differently according to the decisions' characteristics. Based on the findings, applications in other industries and promising directions for future research are discussed.