Abstract
Lead time estimation (LTE) is difficult to carry out, especially within the RFID-enabled real-time manufacturing shopfloor environment since large number of factors may greatly affect its precision. This paper proposes a data mining approach with four steps each of which is equipped with suitable mathematical models to analysis the LTE from a real-life case and then to quantitatively examine its key impact factors such as processing routine, batching strategy, scheduling rules and critical parameters of specification. Experiments are carried out for this purpose and results imply that batching strategy, scheduling rules and two specification parameters largely influence the LTE, while, processing routine has less impact in this case.
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Acknowledgments
Authors would like to acknowledge National Natural Science Foundation of China (61074146), International Collaborative Project of Guangdong (gjhz1005), Modern Information Service Fund 2009 (GDIID2009IS048), Guangdong Department of Science and Technology Fund (2010B050100023). Special acknowledgements would be given to Key Laboratory of Internet of Manufacturing Things Technology and Engineering of Development and Reform Commission of Guangdong Province.
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Zhong, R.Y., Huang, G.Q., Dai, Qy., Zhang, T. (2013). Estimation of Lead Time in the RFID-Enabled Real-Time Shopfloor Production with a Data Mining Model. In: Qi, E., Shen, J., Dou, R. (eds) The 19th International Conference on Industrial Engineering and Engineering Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38391-5_33
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DOI: https://doi.org/10.1007/978-3-642-38391-5_33
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