Modeling Lean and Six Sigma Integration using Deep Learning: Applied to a Clothing Company

Abstract

Implementation of Lean and Six Sigma methodologies enable companies to boost their competitiveness and their efficiency. However, the adoption of these approaches is very much restricted in the Textile and Clothing sector in Morocco. In fact, despite all the advances in these methodologies and practical approaches, defining a rational implementation strategy such as the adequate chronology and the prediction of the expected success level are still a part of a fierce debate and an impediment for practitioners. The result is that only 11 companies out of 1,200 Moroccan clothing companies have successfully implemented Lean and Six Sigma. This article, based on an intelligent model, draws up a support tool to the clothing stakeholders, or otherwise aims to successfully integrate Lean and Six Sigma using Deep Learning. The neural network was trained for the prediction of success level rate and customizing of Lean and Six Sigma implementation chronology with the help of weights and maturity of a set of common critical success factors (CSFs). These CFSs were selected as input data. Then, the dataset have been used for training, testing, and validating the neural network model. To evaluate the trained network, 25% of the data have been used and a tuning hyperparameter process has been designed to reinforce the model performance. For the performance indices such as Categorical Cross Entropy (CCE), the defined loss function, accuracy, and precision have been evaluated and optimized. The developed model can then define the adequate chronology and predict success level with an accuracy of 97%. The trained neural network was then applied to a clothing company as a guide to the success of its continuous improvement project.