This paper outlines a new methodology for developing strategy for supply chain integration of Autonomous Electrical Vehicles (AEV) to the Internet of Things (IoT). The methodology consists of external architecture and internal design that anticipates the business strategy in the development process. The methodology is designed to anticipate the impact of developments in new road transport technologies, such as Tesla Truck or Tesla Pickup. Since the methodology is designed to anticipate the impact of non-existing technologies, it represents green-field analysis. Green-field is defined as a new and non-existent operation. Green-field strategy architecture in this paper is presented as a process of accepting the world and acting upon that version of the world. The results of the analysis are presented as pathways and outcomes, emerging from the interrelated relationship between AEV and IoT. The emerging methodology is applied through two case studies to evaluate the impact to environment, performance and operationalisation. The methodology proposes architecture and design for integrating AEV and IoT in the supply chain strategy, and a set of new evaluation criteria that promote acceptance of Artificial Intelligence (AI) in the design process. The main contribution to knowledge is a new methodology for integrating AEV and the IoT to the supply chains. The paper applies interplay between inductive and deductive case study and grounded theory approach to build upon the concept of supply chain architecture and contribute to knowledge to the topic of formulating green-field integrated AEV- IoT supply chain strategy.
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Subject: Engineering - Industrial and Manufacturing Engineering
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