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Material flow management is an increasingly difficult activity in today's ever-more-complex manufacturing systems. New approaches are offered by the industrial internet of things (IoT), which is envisioned as a network of active devices that are capable of wireless communication. However, the dependence of IoT devices on battery energy presents a maintenance challenge. The paper proposes a self-organisation-based approach to improve the overall energy efficiency of workpiece localisation using IoT devices. A simulation study is performed to show the effects of transmitter effectiveness, material flow dynamics, and system scale on the energy efficiency. [ABSTRACT FROM AUTHOR]

Cyber-physical production systems are transforming traditional, hierarchical control structures into distributed ones in which the elements offer and consume services with a high degree of autonomy. This paper proposes an agent-based approach to distributed control for production environments in which the agents are only able to interact with a part of the whole system. It hypothesises that the performance of the agent network can be improved through learning and communication. A description of the approach is presented and illustrated with a simulation case of distributed control for an industrial compressed-air system. [ABSTRACT FROM AUTHOR]

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 fault-detection 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. [ABSTRACT FROM AUTHOR]

Robustness is an important success factor for production networks in which the operation of enterprises is subjected to an uncertain environment. In this paper, the robustness of networks is studied as a function of network size. The study is performed through a simulation experiment in which the uncertain environment is modelled by introducing perturbations in demand. The decision-making model mimics the behaviour of socially connected human subjects. The results show how robustness and production rate are affected by system size and social network structure, and how this is relevant for the design and operation of future manufacturing systems. [ABSTRACT FROM AUTHOR]

Abstract: In the context of distributed manufacturing, existing models for the management of orders and work systems assume that the entirety of information about the state of the production environment is known. Instead, this paper studies the implications of networking in an environment in which no element possesses information about the entire state. An experiment in the form of a production simulation game is designed and carried out to explore the emergence and behaviour of such a network. Network structure and dynamics as well as the social aspects of networking are discussed and evaluated. [Copyright &y& Elsevier]

Abstract: Anomaly detection in material flow refers to the detection of irregular patterns in shop floor behaviour. As anomalies often translate to harmful effects on a larger scale, it is important to detect them in due time. In the paper, the manufacturing shop floor is considered as a dynamic network with work systems acting as its nodes and with material flow creating connections between them. Regularities and irregularities are defined as specific patterns within the network. The approach is studied through simulation and applied to an industrial case study. The results suggest potential use for decision making in production management. [Copyright &y& Elsevier]

Abstract: Modern theories propose autonomous structures as building blocks of next-generation manufacturing systems. However, their size and scope are not agreed upon and remain a subject of research. The paper presents a method for discovering autonomous structures within existing manufacturing systems. Firstly, it is shown how a complex network model of a manufacturing system can be obtained. Then, a method for discovering structure in complex networks is applied in order to find cohesive subnetworks – candidates for the formation of autonomous work systems. The approach is illustrated in a case study of engineer-to-order production. [Copyright &y& Elsevier]

Abstract: Complexity has been identified as a ubiquitous and ever increasing property of manufacturing systems. Conventional theories of management lack the tools to describe, analyse, and manage complexity, and can, in turn, no longer cope with the issues it gives rise to. New approaches are offered by complexity science, namely by computational mechanics. In the paper, a method for complexity assessment is proposed and illustrated on real industrial data. The results of the presented case study suggest a distinct relationship between complexity and throughput, and indicate that the tool used has a major impact on complexity. [Copyright &y& Elsevier]

Abstract: Manufacturing networks open new possibilities and potentials in design, development and production of complex high-tech products while they combine good characteristics of large companies with advantages of SMEs. In order to manage the structural complexity of a manufacturing network, the paper proposes the business-to-manufacturing network B2MN approach based on the market mechanism. Next, the network nodes in terms of the autonomous work system (AWS) are conceptualized. AWS encapsulates functionalities and competencies related to its management and manufacturing operations, as well as its autonomous information system, which supports autonomous decision-making and cooperation in the network. The case study illustrates the implementation of the AWS concept in industry. [Copyright &y& Elsevier]