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The evolution of the supply chain has resulted in a growth in the usage of technology and data generated and distributed within the industry. Third-party logistics (3PL) companies operating within the supply chain industry are not maximising the capabilities of data to make wellinformed decisions. The purpose of this paper is to address this gap and to develop a prescriptive, theoretical model for data-driven decision-making (DDDM). To address the gap, a literature review of DDDM in 3PL industry and in other contexts was conducted. The proposed model is built based on the consideration of existing DDDM models and frameworks; data and data analytics principles to collect, store, manage and analyse data; and the Cynefin framework. Existing models and frameworks for DDDM do not provide explicit guidelines on how to apply DDDM in a 3PL and supply chain context. The proposed DDDM model constitutes of three phases, namely: (1) the setup phase, that considers data knowledge and decision-making knowledge; (2) execution phase; and (3) the learning phase. The application of the model in 3PL companies can support the decisionmaking process in these companies, with a consideration of the challenges and opportunities that exist in the supply chain. The decision-makers in 3PLs can thus make better-informed decisions that positively impact their enterprises and the supply chain.

A Data-Driven Decision-Making Model for the Third-Party Logistics (3PL) Industry