Application and optimization of artificial intelligence techniques for small to medium sized manufacturing enterprises in an industry 4.0 environment

The ascent of the current industrial revolution known as Industry 4.0 (I40) or smart manufacturing is considerably impacting the manufacturing sector with the utilization of more intelligent and advanced practices such as Artificial Intelligence (AI) methods and Machine Learning (ML) within factories. The successful implementation of intelligent practices promises to significantly increase organization flexibility, improve operations, and increase production throughput. Without a doubt, to remain relevant in the competitive market and ensure their future growth, manufacturing companies should start actively considering and adopting advanced and intelligent I40 solutions for their businesses. Manufacturing small and medium-scaled enterprises (SMEs) represent the backbone of several countries’ economies, actively supporting jobs creations and contributing to Gross Domestic Product (GDP). However, SMEs have been the least appealing in embracing new and advanced technological trends within their production processes, mainly due to a lack of appropriate practical guidance or solutions customized to their environment and limited resources (financial and human) in their organizations. Few of the existing works intending to encourage manufacturing SMEs in adopting new technological trends are high-level frameworks, surveys, or only tackle the design of advanced technological solutions without incorporating the corresponding organizational amendments SMEs need to undergo for a sustainable implementation of advanced and innovative solutions. Inspired by some of the technical and organizational challenges of manufacturing SMEs, we utilize the Design Science Research Methodology (DSRM) to create, illustrate, and assess several AI and innovative techniques that increase manufacturing SMEs’ performance. The AI concepts and advanced technological methods we design in this study improve several existing solutions developed by our predecessors in the research field. Our major research contributions are the creation of a practical guide for the application of various AI techniques and innovative solutions adapted and optimized for manufacturing SMEs such as intelligent predictive maintenance (PM) method, automatic parameter configuration for Supervisory Control And Data Acquisition (SCADA) system, product customization framework, robust communication network prototype, and enhanced safety response mechanism. We implement various advanced technologies like ML, Time-Sensitive Networking (TSN), speech recognition, and more to empower our innovative solutions. Our study is valuable to the SME research field (in academia). It provides new literature that combines the design of advanced and optimized technological trends with organization structural (business model) changes to promote innovation within SMEs, prompting more research in that regard. Since we are motivated by some of the manufacturing SMEs' challenges, our research is also useful to the industry as it provides practical solutions that manufacturing SMEs can adapt and implement to boost their production processes performances.College of Engineering, Science and TechnologyPh. D. (Science, Engineering and Technology