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The cosmetics industry is one of the largest contributors to end-of-life waste in Europe due to the wide variety of packaging used for beauty products and the rapid emergence of new products. To tackle this aspect and due to the growing concern and interest in sustainability among consumers and organisations, the personal care industry has started to implement various strategies to reduce its ecological impact, including the implementation of collection programs and adopting a circular approach.

The aim of this master thesis is to evaluate the level of maturity of the beauty sector in terms of green reverse logistics through unsold product and empty packaging recovery management. This study also explores the drivers and barriers experienced by retailers and producers to implement reverse supply chain operations. 

To this end, a qualitative research was conducted by analysing secondary data, conducting a qualitative survey and interviewing European producers. This research concluded that overall, the European industry is naïvely mature in terms of product recovery and empty packaging returns management. It also put forward that there is a significant gap between green reverse logistics management of SMEs and large cosmetics groups. Areas for improvement such as the need to adopt a comprehensive overview of the value chain and the lack of strategy in reverse supply chain management were highlighted.

Environmental sustainability --- Cosmetics --- Product recovery --- Product returns --- Packaging --- Circular economy --- Reverse logistics --- Maturity analysis --- Sciences économiques & de gestion > Production, distribution & gestion de la chaîne logistique

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Recent advancements in computer technology have allowed for designers to have direct control over the production process through the help of computer-based tools, creating the possibility of a completely integrated design and manufacturing process. Over the last few decades, "artificial intelligence" (AI) techniques, such as machine learing and deep learning, have been topics of interest in computer-based design and manufacturing research fields. However, efforts to develop computer-based AI to handle big data in design and manufacturing have not yet been successful. This Special Issue aims to collect novel articles covering artificial intelligence-based design, manufacturing, and data-driven design. It will comprise academics, researchers, mechanical, manufacturing, production and industrial engineers and professionals related to engineering design and manufacturing.

History of engineering & technology --- product service system (PSS) --- availability --- field repair kit --- gradient-based algorithm --- robust genetic algorithm --- warpage --- design of experiments --- fringe pattern --- birefringence --- automatic design --- intelligent optimization method --- CFD --- fluid machinery --- pumps --- multi-function console --- data-driven design --- mismatch equation --- anthropometric measures --- algorithmic approach --- optimal design --- stretchable antenna-based strain sensor --- structural optimization --- structural health monitoring --- dimension reduction --- entropy-based correlation coefficient --- multidisciplinary design and analysis --- uncertainty-integrated and machine learning-based surrogate modeling --- additive manufacturing --- complexity --- modular design --- part consolidation --- product recovery --- product image design --- Kansei Engineering --- integrated decision system --- qualitative decision model --- quantitative decision model --- train seats --- measurement-assisted assembly --- coordination space --- assemblability --- small displacement torsor --- Kriging --- lower confidence bounding --- entropy theory --- product design --- simulation-based design optimization --- convolutional neural network --- object detection --- piping and instrument diagram --- unsupervised learning --- n/a

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Recent advancements in computer technology have allowed for designers to have direct control over the production process through the help of computer-based tools, creating the possibility of a completely integrated design and manufacturing process. Over the last few decades, "artificial intelligence" (AI) techniques, such as machine learing and deep learning, have been topics of interest in computer-based design and manufacturing research fields. However, efforts to develop computer-based AI to handle big data in design and manufacturing have not yet been successful. This Special Issue aims to collect novel articles covering artificial intelligence-based design, manufacturing, and data-driven design. It will comprise academics, researchers, mechanical, manufacturing, production and industrial engineers and professionals related to engineering design and manufacturing.

History of engineering & technology --- product service system (PSS) --- availability --- field repair kit --- gradient-based algorithm --- robust genetic algorithm --- warpage --- design of experiments --- fringe pattern --- birefringence --- automatic design --- intelligent optimization method --- CFD --- fluid machinery --- pumps --- multi-function console --- data-driven design --- mismatch equation --- anthropometric measures --- algorithmic approach --- optimal design --- stretchable antenna-based strain sensor --- structural optimization --- structural health monitoring --- dimension reduction --- entropy-based correlation coefficient --- multidisciplinary design and analysis --- uncertainty-integrated and machine learning-based surrogate modeling --- additive manufacturing --- complexity --- modular design --- part consolidation --- product recovery --- product image design --- Kansei Engineering --- integrated decision system --- qualitative decision model --- quantitative decision model --- train seats --- measurement-assisted assembly --- coordination space --- assemblability --- small displacement torsor --- Kriging --- lower confidence bounding --- entropy theory --- product design --- simulation-based design optimization --- convolutional neural network --- object detection --- piping and instrument diagram --- unsupervised learning --- product service system (PSS) --- availability --- field repair kit --- gradient-based algorithm --- robust genetic algorithm --- warpage --- design of experiments --- fringe pattern --- birefringence --- automatic design --- intelligent optimization method --- CFD --- fluid machinery --- pumps --- multi-function console --- data-driven design --- mismatch equation --- anthropometric measures --- algorithmic approach --- optimal design --- stretchable antenna-based strain sensor --- structural optimization --- structural health monitoring --- dimension reduction --- entropy-based correlation coefficient --- multidisciplinary design and analysis --- uncertainty-integrated and machine learning-based surrogate modeling --- additive manufacturing --- complexity --- modular design --- part consolidation --- product recovery --- product image design --- Kansei Engineering --- integrated decision system --- qualitative decision model --- quantitative decision model --- train seats --- measurement-assisted assembly --- coordination space --- assemblability --- small displacement torsor --- Kriging --- lower confidence bounding --- entropy theory --- product design --- simulation-based design optimization --- convolutional neural network --- object detection --- piping and instrument diagram --- unsupervised learning