Cellular manufacturing systems considering human characteristics

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For more than three decades, group technology and cellular manufacturing have been considered as solutions to many manufacturing problems. Implementing cellular manufacturing has helped many organizations move toward lean manufacturing by reducing setups, material handling, work-in-process, and manufacturing lead times. These benefits are usually achieved by grouping parts and products with similar characteristics into families, and grouping dissimilar machines that share the process of part families into machine cells. One issue that is likely to be overlooked is the human aspects of the cellular manufacturing. Few studies have addressed the allocation of operators to cells by taking a sequential approach to this problem. That is, first part families and machine cells are formed. Then, operators are assigned to cells. This approach, however, may not fully address the problem, as many operators may be allocated to inappropriate cells where their skills are not fully utilized. Operators may have different skills and different preferences towards jobs. Also, their productivity depends on their experience on the job. Failure to consider these important elements will result in loss of motivation and interest for operators, and low productivity for the system. This research proposes a simultaneous approach that considers operators' skill match and their experience and preference while designing machine cells and part families. The proposed research will examine the existing performance measures in order to extend them to the multi-dimensional problem of interest. It will also revisit many existing machine cell/part family formation techniques to investigate their performance for the machine-part-operator grouping problem and will propose a generalized grouping problem with a wide range of applicability. Using the proposed performance models, the best grouping method will be selected.