In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the 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 used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques.

The paper reviews applications of data mining in manufacturing engineering, in particular production processes, operations, fault detection, maintenance, decision support, and product quality improvement. Customer relationship management, information integration aspects, and standardization are also briefly discussed. This review is focused on demonstrating the relevancy of data mining to manufacturing industry, rather than discussing the data mining domain in general. The volume of general data mining literature makes it difficult to gain a precise view of a target area such as manufacturing engineering, which has its own particular needs and requirements for mining applications. This review reveals progressive applications in addition to existing gaps and less considered areas such as manufacturing planning and shop floor control.

Market driven strategies encourage enterprises to produce products that customers want to buy, and therefore can improve an enterprise’s market position. Few organisations make effective use of market, competitor and customer information. Information modelling and intelligent support tools help define product specifications focused on fulfilling customer requirements and facilitating information sharing between members of extended design teams. Design effort can be targeted at particular product features, which yield maximum benefits for customer satisfaction. The Market Driven Design System provides comprehensive, intelligent support, meeting the challenges of effectively modelling, using and sharing valuable, yet imprecise, non-technical market information during product design.

Research into moderators, which support collaborative teams by proactively making team members aware of actions or potential problems which may affect them, began in the 1990s, in the context of supporting collaborations during concurrent engineering projects. This paper provides a background to the evolution of moderators and explores their role in supporting virtual organisations. A collaboration moderator (CM) is an evolution of earlier moderators and is capable of behaving differently for different types of users and therefore caters for the varying requirements of individual users depending on the roles they have in the collaborations. This paper describes the architecture and components of a CM from an implementation perspective. Prototype CMs have been developed during the EU-funded SYNERGY project, and two use cases for which the prototype CMs were implemented as a service (a Pre-Creation use case and an Operational use case) are also discussed in this paper.