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The competitive advantage in manufacturing has shifted from the mass production paradigm to the one that is based on fast‐responsiveness and flexibility. A worldwide wave of industrial restructuring and the re‐emergence of “small firms” have paved ways for the evolution of dispersed manufacturing networks in various degrees of complexity. With the rapid advances in internet technology, the emerging Factory‐on‐Demand mode of electronic production will create a greater opportunity for both producers and customers in the co‐creation of products and markets. Such a change will have far‐reaching implications in production practice beyond that of mass customization.

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In today’s competitive industrial environment, it is essential that companies are able to focus on their core activities and collaborate with business partners to achieve the common objective of meeting the best satisfaction of customer demands. However, selecting partners based on accumulated experience may not be effective due to subjective judgement and lack of systematic analysis. This paper attempts to propose a partners benchmarking assessment system (PBAS) which incorporates computational intelligence technologies into partners’ benchmarking process to support decision making. Evidence suggests that the undesirables occur in companies such as extensive delays in the planned schedule, serious quality problems and cost overruns are, to a certain extent, related to the unfulfilled promises of business partners. In this paper, the PBAS is designed to propose an alternative approach to benchmark the business partners based on case‐based reasoning and neural network. To validate the proposed system, a prototype has been developed and tested in an emulated industrial environment. The case example is outlined with analysis of the feasibility of this proposed system based on test results.

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