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.
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.
Nowadays, the supply chain faces several challenges, among others, uncertainty relating to demand, stochasticity, and bullwhip effect, as well as external disruptions, risks and crises which can temporarily or durably impact customer’s service, Science has therefore become increasingly interested in an industrial revolution, namely Industry 4.0 which Artificial Intelligence is the most commonly used technology that is capable of revolutionizing many industries and fields. The aim of this article is to review the literature on the Artificial Intelligence applications in Supply Chain and the most used approaches in planning, prediction, purchasing, procurement, transportation and distribution to improve the performance, resilience and efficiency of the Supply chain.
In recent times, globalized supply chains have been disrupted with the venue of the pandemic crisis Covid-19. All countries are working together to deal with this crisis and be sustainable-resilient at the same time especially emerging economies. Supply chains plays a capital role in maintaining the stability of the economy and society. Through this review, we aim to provide an overview about literature of sustainability and resilience of Supply Chains. To take a fresh look of patterns and practices deployed by emerging countries, such as morocco, to build Resilient and Sustainable Supply Chain around a holistic framework. A focus on the existing issues, methodologies used, research gaps that need to be developed is outlined.
Lean manufacturing (LM) and Six sigma (SS) are two methods of continuous improvement that became essential in several industrial sectors. These approaches interest the researchers and also the business managers. Thus, LM and SS success that can be involved simultaneously and systematically is based on a set of Critical Success Factors (CSF). First, we extracted the CSFs discussed in the literature. Then, these CSFs have been projected to those used in the Moroccan automotive industry. For this purpose, we adopted a qualitative research methodology using structured interviews with 12 experts from the Moroccan automobile industry. Through this study, LM and SS implementing characteristics were revealed. Thus, we performed, based on CSF importance and maturity, a bi-dimensional scan that describe Lean manufacturing (LM) and Six sigma (SS) implementation within Moroccan automotive industry.
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