Abstract
This paper is aimed at reducing two of the eight types of wastes under lean manufacturing. The targeted wastes are overproduction and waiting time wastages. Appendices were used to opinionate various departments in addition to the WIP data, regarding the major factors causing the waiting time delays, their impacts on performance, and the constraints to implementation of Kanban techniques under different departments. The paper also provides for a separate hybrid application developed to assist the merchandising department in its activities by overcoming the problem of unaccountability of fabrics, leading to overlapping, and ambiguous order placement leading to capital losses. The paper also provided for the basis of replacing every department’s activity scheduling process from considering forecasted demands to consider actual demand and thus increasing efficiency. This is accompanied by providing a mechanism to track the raw material down the production line for better analysis. The paper provides for a central tracking platform with all the departments linked together making it possible to analyze the demand and the resources at hand, thus providing an opportunity to plan the department’s future activities such that the wastages are minimum, efficiency is maximum, waiting time is reduced, and so is the lead time leading to customer satisfaction. Visual analysis of various factors in the production line and with their help makes the detection of bottlenecks quite efficient. It also is an effective management platform in terms of resources as well as manpower and highlights the problems through lead time analysis. The paper adopts the Kanban pull production technique to address these wastes. This paper also encompasses the just-in-time (JIT) concept of Toyota Production System (TPS).
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Yadav, A., Jha, G. (2022). Developing an Integrated Hybrid App to Reduce Overproduction and Waiting Time Using Kanban Board. In: Skala, V., Singh, T.P., Choudhury, T., Tomar, R., Abul Bashar, M. (eds) Machine Intelligence and Data Science Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 132. Springer, Singapore. https://doi.org/10.1007/978-981-19-2347-0_19
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DOI: https://doi.org/10.1007/978-981-19-2347-0_19
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