Integration of AI into Manufacturing Execution System (IMES)

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Canada's manufacturing sector has to become more competitive and claim a larger share of the global market, currently at about 1%. The introduction of Industry 4.0 with intelligent and adaptable production techniques to Canadian manufacturers can offer significant advantages for realizing this objective. These techniques seek to integrate automation technologies with data analytics to make intelligent manufacturing decisions that are robust to perturbations and flexible to changes in production. This proposal describes the research partnership between UBC laboratories and NTWIST Inc., which in collaboration with research and industry collaborators in Germany, aim to design and implement an Intelligent Manufacturing Execution System (IMES) for broad applications in the manufacturing sector. The proposed IMES is a generic and adaptable multiagent production coordination and automation system that can be utilized by different industries to facilitate the digitalization of their production processes and faster progression towards Industry 4.0. The research responds to the need for seamless and rapid integration of artificial intelligence with machine learning and data analytics capabilities into a factory. Such capabilities are currently absent in even highly advanced MES systems. The applied artificial intelligence expertise of the UBC team and the domain expertise of NTWIST in broad industries will be incorporated to accelerate digitalization of manufacturing in Canada. Specifically, the project contributes to i) the research and development of a novel data-driven decision-making paradigm for IMES; ii) rapid integration of a machine learning system for controlling subsystems through training of their agents in an OPC UA driven manufacturing system using the developed IMES; and iii) implementation of the developed IMES in a scaled-down cyberphysical production testbed. The proposed IMES will ultimately be put to test in both Canadian and German factories which will be brought into the project by NTWIST and our German collaborators, respectively.