Abstract
Smart Manufacturing Systems (SMS) are software systems that identify opportunities for automating manufacturing operations by using Internet of Things (IoT) devices and services connected to machines. An active challenge of SMS is to satisfy the ever-changing conditions of industries, supply networks, and customer needs. To operate effectively, SMS should be flexible enough to perform automatic or semi-automatic adjustments to manufacturing processes in response to unexpected changes, a feature called context awareness. Recent advances in interpreting context data in the semantic web have permitted SMS to understand the active situation of manufacturing processes. This paper presents a literature analysis of context-aware workflow management approaches in the smart manufacturing domain, with a particular focus on semantic web-based approaches published from 2015 to 2022. A Systematic Literature Review (SLR) methodology was applied to analyze the state-of-the-art via the PICOC method. The contributions of this work are (1) an SLR about context-aware workflow management for smart manufacturing systems focusing on semantic web-based approaches, (2) a systematic taxonomy to break down the approaches in conformity based on content and main workflow management function area, and (3) identification of opportunities for improvement in technical features such as context awareness, use case implementation, tools employed, licensing, security, and scalability. A novel architecture and components are also proposed to address the identified active challenges.
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