The research presented in this paper looks at evaluating RAMI4.0, a Research Architecture (RA) designed for Industry 4.0, through the representation of an existing Cyber-Physical System's (CPSs) key functionality. The use case represented is that of a UK firm refurbishing End of Life (EoL) IT devices for business clients. EoL refurbishment is a domain with many complexities due to an inherent business model which results in varying quantities, types and conditions of received devices. These uncertainties can generally not be addressed until the devices have arrived in the facility and are inspected. RAs are an important tool used in system development to represent functionality, this representation should be high level and allow the easy communication of key concepts for not only client-to-developers and developer-to-developer but also either to an audience. An appropriate RA will help industrialists to understand what Industry 4.0 means to them (i.e. increased flexibility and control) and the functionality of any system potentially being invested in. The results of this research included two proposals for the extension of RAMI4.0 regarding the representation of security and humans within the systems. While Industry 4.0 focusses on CPSs this work also makes a further recommendation that the focus of modelling should be shifted to Cyber-Physical Human Systems (CPHSs) to ensure correct consideration of the humans within the system.

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Cyber-Physical Systems (CPSs) are becoming a significant research focus resulting from advancements in technologies such as the Internet of Things (IoT), Cloud Manufacturing and Intelligent Products. Successful deployment of CPSs has the potential to provide a step change in manufacturing efficiency, flexibility and production yield as envisaged by the fourth industrial revolution or Industry 4.0 paradigm. The realisation of intelligent products and services is in the provisioning of predictive, risk preventative and high-performance manufacturing systems. As part of these manufacturing systems, Returnable Transit Items (RTIs) play a critical role in provisioning robust and efficient means of component (e.g. Work in Progress and finished items) protection and logistics. The research outlined in this paper details how a Returnable Transit Item (RTI) can become an integral part of the Industrie 4.0 vision as an intelligent container that can interact with components, machines and other cyber-physical manufacturing services. This paper discusses a CPS reference architecture for the integration of intelligent containers and presents a hardware and software proof of concept solution suitable for industrial deployments. The paper concludes with feasibility studies utilising the intelligent container for context determination services including the identification of intelligent components and monitoring of logistical handling process (i.e. the detection of collisions, lifting and turns).

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• This is an Accepted Manuscript of a paper published by CRC Press in INTRODUCTIONModelling manufacturing processes which contain human interactions is difficult and can produce unrealistic views of the process. This is because in many companies the actual manufacturing process that takes place is not as planned when human interaction is involved. Human factors can determine what actually happens, the time it takes and what order it happens in. To produce a more reliable representation of the process more information on what is actually happening is required. This can be found by tracking and recording the process using radiofrequency identification (RFID) tags (Weinstein, 2005). From the data produced from these tags the possible paths which products take in the process can be determined and hence the actual manufacturing process can be defined. Furthermore the data can be used to form Markov chains which can determine what future process routes will look like and the probability of each route. Basing future business simulations on these Markov chains can give a more reliable representation of the business. This reduces the risk of modelling inaccuracies and can help to predict future outcomes and run optimisation more accurately.The research performed here studies a company which refurbishes IT products. The company has a business model of the manufacturing process which it expects the products to follow. The company has tracked their products through the refurbishment process using RFID tags to determine what processes each product undertakes and to allow each object to be kept track of. The RFID tags communicate the process information of each product to a database software using RFID tag readers. The information from these RFID tags is used in the work presented here to form a Markov chain representation of the business process routes.Companies' model manufacturing processes for many reasons, including predicting cost (Rehman et al., 1998), predicting resource and material demand and running optimisation studies.When modelling the data produced from the RFID tags the Markov chain produced gives a large variety of process routes. These are not all true reflections of the routes products take. The data mining process from the RFID tag data is also investigated to allow the development of more precise process models. This process allows thresholds to be set for each route. Hence irregular paths are removed.The Markov chain is necessary to simulate the product flow. The process is simulated using the Markov chain produced from the data and the results can be compared to the process simulated based on previous perceptions of the business process. The results produced will include the time taken for each product to be processed, the cost of the process and the final destination of products. The Markov chain ABSTRACT: Optimizing manufacturing processes with inaccurate models of the process will lead to unreliable results. This can be true when there is a strong human influence on the manufacturing process and man...