ChatGPT in third-party logistics – The game-changer or a step into the unknown?

Abstract

PurposeThis study aims to examine the influence of ChatGPT on third-party logistics (3PL) operators, specifically in the context of enhancing the value of services they presently provide. Design/methodology/approachThis research employed a combination of survey analyses and a case study on an international logistics operator. Within this framework, specific services were identified and contrasted between the operator's current performance and a hypothetical scenario involving the implementation of ChatGPT. For the majority of the study, the open-source version of ChatGPT was utilized. However, for forecasting purposes, the proprietary GPT-3.5 version was employed. The intention behind this methodology was to highlight the capabilities of the basic ChatGPT, showcasing its applicability as a widely accessible tool, while recognizing the necessity of GPT-3.5 for more sophisticated numerical data analyses. FindingsThe findings revealed that 3PL managers are informed about ChatGPT's capabilities and recognize its potential utility in logistics operations. The case study further underscored ChatGPT's competency in generating actionable insights which can guide managers in enhancing their value-added services. Research limitationsThe primary constraint in this study was the limited number of expert participants. This limitation was due to stringent criteria set by the researchers in selecting experts. However, this also represents an avenue for more comprehensive research in the future. Subsequent studies might also explore other chatbot variations and delve deeper into the practical application of ChatGPT in automating interactions between 3PL entities, service beneficiaries, and the end customers of these services. Value of the paperThis manuscript augments the current body of research concerning the application of ChatGPT in the realm of value-added services offered by logistics operators in the context of contract logistics. Moreover, it provides empirical evaluations of several use cases juxtaposed with existing solutions.