Developing a protocol for collaborative decision-making in a smart manufacturing environment

Abstract

The Fourth Industrial Revolution places different rapidly advancing technologies like the Internet of Things (IoT), Internet of Services (IoS), Internet of Everything (IoE) and Cyber Physical Systems (CPS) at the centre of developing autonomous manufacturing systems. The development of these systems within the environment of Industry 4.0 expects significant changes in tasks and demands on the human in the manufacturing process and recognises that humans and machines are homogeneous parts of a larger diverse body consisting of collaborative and autonomous components. According to the Industry 4.0 concepts, all objects in the manufacturing world have assimilated processing and communication capabilities which highly affect machine-tomachine communication. However, a considerable consequence is that of the effect it will have on human-to-machine interaction. It is occasional that automated systems are solely autonomous; a level of human interaction is usually present although this challenge is not always considered. In mixed environments, automated systems and humans need to collaborate for the completion of a process. Currently, there exists very little research on how a collaborative decision-making process can be developed such that the worker’s acceptance and adaptation to the process is taken into cognizance. This research identifies the lack of collaborative decision-making processes as a research gap and introduces the problem with an extensive literature review that focuses on the research done in this field, followed by a review of potential models for human technology interaction. A case study of an automated water bottling plant to advance the study in collaborative decision-making is introduced for the execution of several experiments to compare a fully automated approach versus a collaboration between the human operator and the system. A single group experimental approach is used to prove the theory while also identifying where the human will best fit into the automated procedure resulting in an optimized production process. The hypothesis is that the completion time for customer orders will be optimal when the human and the machine collaborate for the completion of the production process.