Purpose -The purpose of this paper is to present a novel approach for identification of machining fixtures, and their elements in an assembly/ disassembly process. Design/methodology/approach -Radio frequency identification (RFID) technology is applied to identification of physical machining fixtures and their basic elements. Findings -Based on comprehensive testing in industrial conditions it was established by this research that the use of RFID technology contributes to significant reduction of assembly/disassembly time of machining fixtures. Practical implications -Practical applicability of RFID technology is emphasized and demonstrated in the paper. The suggested system is proven superior in comparison with conventional methods for identification of fixtures/fixture elements which qualifies it for real industrial application. Originality/value -To the best of authors' knowledge there are no previous reports of successful application of RFID technology on identification of fixtures/fixture elements.
This paper demonstrates a decision support tool for workforce planning and scheduling. The research conducted in this study is oriented on batch type production typical for smaller production systems, workshops and service systems. The derived model in the research is based on historical data from Public utility service billing company. Model uses Artificial Neural Networks (ANN) fitting techniques. A set of eight input indicators is designed and two variants were tested in the model with two different outputs. Several comprehensive parameter setting experiments were performed to improve prediction performances. Real case studies using historic data from public weather database and communal consolidated billing service show that it is difficult to predict the required number of servers-workers in front office. In a similar way, this model is adequate for complex production systems with unpredictable and volatile demand. Therefore, manufacturing systems which create short cycle products, typical for food processing industry, or production for inventory, may benefit of the research presented in this paper. ANN simulation model with its unique set of features and chosen set of training parameters illustrate that presented model may serve as a valuable decision support system in workforce scheduling for service and production systems.
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