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With the intensive development and implementation of information and communication technologies in manufacturing, large amounts of heterogeneous data are now being generated, gathered and stored. Handling large amounts of complex dataoften referred to as big datarepresents a challenge as there are many new approaches, methods, techniques, and tools for data analytics that open up new possibilities for exploiting data by converting them into useful information and/or knowledge.However, the application of advanced data analytics in manufacturing lags behind in terms of penetration and diversity in comparison with other domains such as marketing, healthcare and business, meaning that the available data often remain unexploited. This paper proposes a new conceptual framework for systematically introducing big-data analytics into manufacturing systems. To this end, the paper defines a new stepwise procedure that identifies what knowledge and skills, and which reference models, software and hardware tools, are needed for the development, implementation and operation of data-analytics solutions in manufacturing systems. The feasibility of the proposed conceptual framework is demonstrated in a case study from an engineer-to-order company and by mapping the framework to several previous data-analytics projects.

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In most manufacturing processes the defect rate is very low. Sometimes, only a few parts per million are defective because of a faulty process. For this reason, fault diagnostics is faced with extremely imbalanced data sets and requires large volumes of data to achieve a reasonable performance. This paper explores whether a machine-to-machine approach can be used, in which several work systems share the process data to improve the accuracy of the faultdetection model. The model is based on machine learning and is applied to industrial data from approximately two million process cycles performed on several injection moulding work systems.

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Cyber-physical systems (CPSs) open up new perspectives for the design, development, implementation, and operation of manufacturing systems and will enable a paradigm shift in manufacturing. The objective of this research is to develop a new concept of cyber-physical production systems (CPPSs) and, on this basis, to address the issue of management and control, which is crucial for the effective and efficient operation of manufacturing systems. A new model of CPPS is proposed. The model integrates digitalized production planning, scheduling, and control functions with a physical part of manufacturing system and enables the self-organization of the elements in production. A case study demonstrates feasibility of the approach through the use of simulation experiments, which are based on real industrial data collected from a company that produces industrial and energy equipment. Keywords: cyber-physical production system, production planning and control, self-organization Highlights • A conceptual model of cyber-physical production systems (CPPSs) is developed. • A cyber-physical approach to the production planning and control (PPC) of manufacturing systems is presented. • The presented approach to the production planning, scheduling and the self-organization in the CPPS is demonstrated through the use of simulation experiments.

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