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In this paper, a co-operative distributed process mining system (CDPMS) is developed to streamline the workflow along the supply chain in order to offer shorter delivery times, more flexibility and higher customer satisfaction with learning ability. The proposed system is equipped with the 'distributed process mining' feature which is used to discover the hidden relationships among each working decision in distributed manner. This method incorporates the concept of data mining and knowledge refinement into decision making process for ensuring 'doing the right things' within the workflow. An example of implementation is given, based on the case of slider manufacturer. [ABSTRACT FROM AUTHOR]

Attempts to introduce a quality measurement scheme (QMS) that is able to assess the immediate feedback of customers globally in real time, followed by a data mining process, which is an interactive process that involves assembling the data into a format conducive to producing a multi-dimensional analysis using an online analytical processing (OLAP) approach. In addition, an XML schema, which provides a universal syntax to facilitate the exchange of data, is used in the design of the QMS to support the data mining process. To validate the feasibility of QMS in real industrial situations, a case example is covered, showing promising test results based on the proposed scheme. [ABSTRACT FROM AUTHOR]

In today's competitive environment, rapid advances in economic globalization and information technology have forced many organizations to anticipate and respond to increasing volatility and competitive pressures. Numerous researches have focused on developing techniques on the analysis, design and management of whole logistics chains in particular in the domain of data mining, data analysis and data classification. While the data mining techniques and computational intelligence techniques are good independently for solving specific tasks, they can be synergized through the formation of an integrated and unified model, which can take advantage of the goods and offset the flaws of the two techniques. In this paper, an intelligent information infrastructure, which is characterized by its ability to encompass a rich collection of knowledge representation formalisms for dealing with a logistics information flow problem, is presented. Such intelligent information infrastructure includes a two-step strategy that embraces the combination of online analytical processing and neural networks to support knowledge discovery. In addition, extensible markup language is used to support the overall infrastructure, in order to facilitate the seamless data interchange within an enterprise. [ABSTRACT FROM AUTHOR]