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Towards a learning curve for electric motors production under organizational learning via shop floor data

Abstract : Due to the fierce market competition, organizations should respond quickly to customers’ needs by reducing lead times, or/and lowering operating costs. These objectives can be reached by effectively assessing the workforce capacities. Manufacturing progress function or organizational learning is considered as one of the most important factors that affect workforce capacity. The current paper introduces an examination research that uses factory data to introduce the most appropriate organizational learning model for the manufacture of electric motors. The data used was collected for a period of 42 months for 110 manufacturing processes and 10 different styles of electric motors. By using regression analysis the significant parameters were obtained for 10 learning models. And in order to select the most reliable one, the analytical hierarchy process (AHP) was used after defining the selection criteria. Among most of monovariable learning models listed in literature the model of Wright (1936) is found to be the best one to fit the data, and then comes the model of Knecht (1974). The failure of the other models in fitting the data was also shown.
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El-Awady Attia, Ashraf Megahed, Philippe Duquenne. Towards a learning curve for electric motors production under organizational learning via shop floor data. IFAC-PapersOnLine, Elsevier, 2016, vol. 49 (n°12), pp. 1086-1091. ⟨10.1016/j.ifacol.2016.07.587⟩. ⟨hal-01746110⟩

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