Much has been published about potential benefits of the adoption of cyber-physical systems (CPSs) in manufacturing industry. However, less has been said about how such automation systems might be effectively configured and supported through their lifecycles and how application modeling, visualization, and reuse of such systems might be best achieved. It is vitally important to be able to incorporate support for engineering best practice while at the same time exploiting the potential that CPS has to offer in an automation systems setting. This paper considers the industrial context for the engineering of CPS. It reviews engineering approaches that have been proposed or adopted to date including Industry 4.0 and provides examples of engineering methods and tools that are currently available. The paper then focuses on the CPS engineering toolset being developed by the Automation Systems Group (ASG) in the Warwick Manufacturing Group (WMG), University of Warwick, Coventry, U.K. and explains via an industrial case study how such a component-based engineering toolset can support an integrated approach to the virtual and physical engineering of automation systems through their lifecycle via a method that enables multiple vendors' equipment to be effectively integrated and provides support for the specification, validation, and use of such systems across the supply chain, e.g., between end users and system integrators.
Complexity in manufacturing systems still remains a challenge and leads to operational issues and increased production cost. In this paper, drivers of complexity and typical symptoms of complex manufacturing systems are identified. A comprehensive review of studies published within the last two decades to assess manufacturing system complexity are presented. The key contributions of this review are: 1) a classification of complexity assessment methods based on perceived complexity symptoms; 2) a comprehensive review of assessment methods with cross-evaluation to identify appropriate use based on available data; 3) recommendations for the wider academic and industrial community, based on research trends identified in the literature, as to how complexity assessment should be addressed in the future. It is concluded that the assessment of complexity is necessary so that it can be controlled effectively, however the industry suffers from a lack of practical tools to support in this endeavour. [
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