Dynamic small-series fashion order allocation and supplier selection: a ga-topsis-based model

The fashion industry is currently confronted with significant economic and environmental challenges, necessitating the exploration of novel business models. Among the promising approaches is small series production on demand, though this poses considerable complexities in the highly competitive sector. Traditional supplier selection and production planning processes, known for their lengthy and intricate nature, must be replaced with more dynamic and effective decision-making procedures. To tackle this problem, GA-TOPSIS hybrid model is proposed as the methodology. The model integrates Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) evaluation into the fitness function of Genetic Algorithm (GA) to comprehensively consider both qualitative and quantitative criteria for supplier selection. Simultaneously, GA efficiently optimizes the order sequence for production planning. The model's efficacy is demonstrated through implementation on real orders, showcasing its ability to handle diverse evaluation criteria and support supplier selection in different scenarios. Moreover, the proposed model is employed to compute the Pareto front, which provides optimal sets of solutions for the given objective criteria. This allows for an effective demand-driven strategy, particularly relevant for fashion retailers to select supplier and order planning optimization decisions in dynamic and multi-criteria context. Overall, GA-TOPSIS hybrid model offers an innovative and efficient decision support system for fashion retailers to adapt to changing demands and achieve effective supplier selection and production planning optimization. The model's incorporation of both qualitative and quantitative criteria in a dynamic environment contributes to its originality and potential for addressing the complexities of the fashion industry's supply chain challenge

A reinforcement learning based decision support system in textile manufacturing process

This paper introduced a reinforcement learning based decision support system in textile manufacturing process. A solution optimization problem of color fading ozonation is discussed and set up as a Markov Decision Process (MDP) in terms of tuple {S, A, P, R}. Q-learning is used to train an agent in the interaction with the setup environment by accumulating the reward R. According to the application result, it is found that the proposed MDP model has well expressed the optimization problem of textile manufacturing process discussed in this paper, therefore the use of reinforcement learning to support decision making in this sector is conducted and proven that is applicable with promising prospects

Anomaly detection using Long Short Term Memory Networks and its applications in Supply Chain Management

International audienceAnomaly detection has been becoming an important problem in several domains. In this paper, we propose a new method to detect anomalies in time series based on Long Short Term Memory (LSTM) networks. After being trained on normal data, the networks are used to predict interested steps in time series. The difference between the predicted values and observed values is calculated as prediction errors. Then we use a kernel estimator of the quantile function to compute a threshold, which is used to determine anomalous observations. The performance of proposed method is illustrated through an example of anomaly detection of consumer demand in supply chain management. The numerical experiment shows that our approach achieve a higher level of detection accuracy and a lower percentage of false alarm rate compared to the previous One-Class Support Vector Machine method

Seminar as a way to educate engineering students on environmental challenges in the textile industry

The Ecole Nationale Supérieure des Arts et Industries Textiles (ENSAIT) is one of the few schools specialising in materials for the textile industry. Each year it graduates around 110 engineers whose role is to meet the challenges of the sector while respecting the values of the companies and environmental standards. The ENSAIT engineer's course takes place over three years. From the first year of the engineering cycle, a seminar on sustainable development is offered. It is held in the second semester and lasts two full days. The first objective is to make them aware of corporate social responsibility (CSR) issues in companies. The second is to build on the knowledge acquired during the last 6 months to develop the life cycle of a garment and understand the associated impacts. Finally, it is to highlight the different possible strategies based on eco-design, fair trade, taking into account the regulatory constraints. This seminar is based on active pedagogy, where students work in teams and compare their results with each other. It also aims to provide the minimum tools to understand ecodesign strategies and to be an informed fashion consumer, and to become a textile engineer capable of participating in and technically supporting companies' CSR initiatives

Modeling Color Fading Ozonation of Textile Using Artificial Intelligence

International audienceTextile products with faded effect achieved via ozonation are increasingly popular recently. In this study, the effect of ozonation in terms of pH, temperature, water pickup , time and applied colors on the color fading performance of reactive-dyed cotton are modeled using Extreme Learning Machine (ELM), Support Vector Regression (SVR) and Random Forest Regression (RF) respectively. It is found that RF and SVR perform better than ELM in this issue, but SVR is more recommended to be sued in the real application due to its balance predicting performance and less training time

Exemple de module de pédagogie active pour l’enseignement de la supply chain en école d’ingénieur spécialisée

L’enseignement des disciplines relatives à la supply chain s’avère assez complexe notamment par la mise en jeu de différents concepts et les interactions qui régissent une chaîne approvisionnement. Dans les établissements tels que les écoles d’ingénieurs spécialisées, cette discipline est transversale et ne constitue pas la source de motivation principale des étudiants. Il est alors pertinent d’envisager d’autres formes d’enseignement afin de stimuler la motivation des étudiants. Un module basé sur différentes pédagogies actives a été mis en oeuvre à l’ENSAIT, école d’ingénieur textile, à la place des traditionnels cours magistraux, travaux dirigés et pratiques pour l’enseignement des matières liées à la supply chain. Ce module s’appuie sur des séances d’apprentissage par problème, des travaux sous forme d’atelier, des jeux sérieux et des classes inversées. L’utilisation des différents types de pédagogies permet de favoriser tour à tour l‘acquisition des connaissances, la mise en pratique, l’esprit de synthèse ou encore la prise de recul

Sales forecasts in clothing industry: The key success factor of the supply chain management

Like many others, Textile-apparel companies have to deal with a very competitive environment and have to manage consumers which become more demanding. Thus, to stay competitive, companies rely on sophisticated information systems and logistic skills, and especially accurate and reliable forecasting systems. However, forecasters have to deal with some singular constraints of the textile-apparel market such as for instance the volatile demand, the strong seasonality of sales, the wide number of items with short life cycle or the lack of historical data. To respond to these constraints, companies have implemented specific forecasting systems often simple but robust. After the study of existing practices in the clothing industry, we propose different forecasting models which perform more accurate and more reliable sales forecasts. These models rely on advanced methods such as fuzzy logic, neural networks and data mining. In order to evaluate the benefits of these methods for the supply chain and more especially for the reduction of the bullwhip effect, a simulation based on real data of sourcing and forecasting processes is performed and analyzed.Sales forecasts Textile-apparel supply chain Clothing industry Sourcing simulation

A Strategic Location Decision-Making Approach for Multi-Tier Supply Chain Sustainability

Few studies on supply location decisions focus on enhancing triple bottom line (TBL) sustainability in supply chains; they rarely employ objective quantifiable measurements which help ensure consistent and transparent decisions or reveal relationships between business and environmental trade-off criteria. Therefore, we propose a decision-making approach for objectively selecting multi-tier supply locations based on cost and carbon dioxide equivalents (CO2e) from manufacturing, logistics, and sustainability-assurance activities, including certificate implementation, sample-checking, living wage and social security payments, and factory visits. Existing studies and practices, logic models, activity-based costing, and feedback from an application and experts help develop the approach. The approach helps users in location decisions and long-term supply chain planning by revealing relationships among factors, TBL sustainability, and potential risks. This approach also helps users evaluate whether supplier prices are too low to create environmental and social compliance. Its application demonstrates potential and flexibility in revealing both lowest- and optimized-cost and CO2e supply chains, under various contexts and constraints, for different markets. Very low cost/CO2e supply chains have proximity between supply chain stages and clean manufacturing energy. Considering sustainability-assurance activities differentiates our approach from existing studies, as the activities significantly impact supply chain cost and CO2e in low manufacturing unit scenarios.Sustainable Management and Design for Textile

Méthodologie de la prévision des ventes appliquée à la distribution textile

La concurrence et la mondialisation impliquent une gestion très précise de la production et des approvisionnements des acteurs de la filière Textile-Habillement-Distribution. Face aux contraintes liées à la fabrication et à la distribution des produits (délai d'obtention relativement long et durée de vie courte des articles,...), une telle organisation nécessite un système de prévision des ventes adapté aux incertitudes du marché et aux besoins du distributeur. Le caractère incertain des ventes est caractérisé par l'influence de nombreuses variables explicatives difficilement contrôlables et identifiées. Le distributeur doit s'appuyer sur des prévisions à moyen terme (horizon : une saison) afin d'anticiper sa production et ses premiers approvisionnements. Le réajustement de la prévision à court terme (horizon : une à trois semaines) est également nécessaire afin de corriger la planification des réassorts, tout au long de la saison. De nombreux modèles de prévision existent.Cependant, ils sont généralement inadaptés au contexte textile. En effet, leurs capacités d'apprentissage et de modélisation sont souvent limitées sur les historiques courts et perturbés des ventes textiles. L'interprétation et l'intervention de l'utlisateur sont également souvent compliquées avec les modèles classiques. Ainsi, nous proposons un système de prévision, constitué de plusieurs modèles qui abordent des prévisions sur divers horizons et à différents niveaux d'agrégation des ventes. Ce système est basé sur des techniques issues du "soft computing" telles la logique floue, les réseaux de neurones ou les procédures évolutionnistes, autorisant le traitement de données incertaines. Les performances de nos modèles sont ensuite évaluées et analysées sur un jeu de données réelles provenant d'un grand distributeur textile. Enfin, dans le cadre d'un projet nommé AIDE financé par le ministère de l'économie, des finances et de l'industrie, ce système de prévision s'intègre dans un outil d'aide à la décision à destination de chacun des acteurs de la filière textile.LILLE1-BU (590092102) / SudocSudocFranceF