This paper proposes a model of performance indicators for Moroccan Textile industry subcontractors. First, the study reports, through a PMQ questionnaire, the KPIs used and deemed relevant by a sample of 82 companies. Second, the weight and hierarchy of various indicators are developed using Analytical Hierarchy Process (AHP) to release a formula for calculating the overall performance. The study shows that outsourcers and Moroccan manufacturers consider compliance with the schedule and the competence and versatility of the production system as a priority. The formula for calculating the overall performance also includes other dimensions such as quality and human resource development. This should facilitate the selection of the contractor and make it more objective.

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The development of a company requires the development of its human resources. Research has shown that overall performance is measured not only by the economic dimension but also by the social dimension and actions on development processes, in particular, the continuous training process which is the constructor of adequate skills for improvement. The below analysis will be divided into two axes: The first axis aims to define the overall performance through a review of literature. It demonstrates a relationship between the individual’s performance and continuous training as a process of individual development, and cites some performance measurement tools. The second axis examines the literature following specific inclusion criteria, its impact on overall performance, and highlights existing assessment models.

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.

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