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In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, such as product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection and so on. Data mining has emerged as an important tool for knowledge acquisition in manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with an special emphasis on the type of functions to be performed on data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been applied to the abstracts and keywords of 150 identified literatures to identify the research gaps and find the linkages between knowledge area, knowledge type and data mining tools and techniques applied.

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This discussion paper aims to set out the key challenges and opportunities emerging from distributed manufacturing (DM). We begin by describing the concept, available definitions and consider its evolution where recent production technology developments (such as additive and continuous production process technologies), digitisation together with infrastructural developments (in terms of IoT and big-data) provide new opportunities.To further explore the evolving nature of DM, the authors, each of whom are involved in specific applications of DM research, examined within a workshop environment emerging DM applications involving new production and supporting infrastructural technologies. This paper presents these generalizable findings on DM challenges and opportunities in terms of products, enabling production technologies, and the impact on the wider production and industrial system. Industry structure and location of activities are examined in terms of the democrat impact on participating network actors.The paper concludes with a discussion on the changing nature of manufacturing as a result of DM, from the traditional centralised, large scale, long lead-time forecast driven production operations, to a new DM paradigm where manufacturing is a decentralised, autonomous near end-user driven activity. A forward research agenda is proposed that considers the impact of DM on the industrial and urban landscape.