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In the real world, 'optimal' solutions for many production process problems do not exist. In such circumstances, 'best practice' is the realistic outcome for which practitioners aim. The reasons for this stem from many causes, including that data associated with production processes are often corrupted and/or missing. These types of processes usually rely heavily on the subjective input of the process workers on the shop floor (tacit knowledge). This paper outlines how the use of mixed-mode modelling has been utilised to help solve these types of problems. The industry examples used in the paper incorporate the concept of Communities of Practice (CoPs) in the mixed-mode models that are developed as a means of capturing tacit knowledge and incorporating it into the solution process. Additionally, CoPs need to sit comfortably within the culture and values of the organisation and employee groups, and must be clearly owned and facilitated by the community of workers whose knowledge is to be shared. Finally, CoPs should be presented as opportunities to share, compare, and learn so that a 'craft' is not lost or diminished.

ABSTRACTNew dynamic benchmarks are developed as performance metrics for operational activities in the ingot mill of an aluminium smelter. This new type of high‐level performance measure enables both a systematic and a holistic appraisal of operations performance that is not possible at present. The benchmarks also allow changes in performance of each distinct resource group to be clearly identified. The method for computing such measures is based on a wide range of shop‐floor data, and possibility theory is used to model the soft characteristics of much of the input. Fuzzy performance estimates are dynamically updated with special attention to combining the different forms of uncertainty that exist in the input information. Although these procedures are site specific, the methodology is applicable to other types of manufacturing systems.