Managing the new product development (NPD) is a challenging mission, and most researches would argue that design is fundamentally linked to intentional action and it cannot emerge out of complexity. ...In fact, its complexity is generated by a large number of entities and actors which cooperate simultaneously with an unpredictable way to understand what customers want and then design product with diverse objectives in mind. A slight change in one activity may cause tremors everywhere. Within a dynamic environment and in order to meet concurrently these challenges, several researchers have implemented design for X (DFX) techniques. Regarding the availability of numerous DFX, the decision as to which one to apply remains absent. Hence, the purpose of this paper is to present a comprehensive overview of the most prominent DFX techniques with respect to sustainability dimension as well as the cost ownership and product differentiation strategies. In addition to that, complex product necessitates the consideration of integrated DFX to optimize product life cycle from a more holistic perspective. In this respect, the paper addresses a systematic review from 1980 to 2018 by investigating and discussing the past and current research of each DFX techniques as well as for integrated ones. The key problems and issues that future DFX research should address have been identified and discussed in this paper.
With the growth of sustainability challenges, the automotive is regarded as one of the most important and strategic industries in the manufacturing sector. Reducing time in the product development ...process, seeking higher product quality, maintaining sustainable products, lowering product cost in the manufacturing process, and fulfilling customers’ requirements are the key factors of the success of a company. To achieve these requirements, automotive companies must consider the use of new sustainable models that ensure design efforts, customer, and societal needs from product ideation until its end-of-life. To do so, the leading companies adopt Design for X (DFX) as a concurrent approach, which considers several issues through different factors Xs. However, with the modified applications for various domains, several researchers have developed many DFX techniques. This multiplicity makes it difficult for researchers and practitioners to keep up with DFX development. Hence, the aim of this paper is first to use mixed and different techniques to organize and select the most prominent DFXs that consider quality and customer satisfaction strategies in designing automotive product. Second, a conceptual framework called, Design for Relevance (DFRelevance) is introduced. It addresses the design factors (guidelines) of each DFX and their associated modules to facilitate the collaboration between designers and all the project team during the whole product lifecycle. Furthermore, a modeling approach based on unsupervised learning is used to accomplish DFRelevance concerns. The aim of this approach is to cluster similar modules into homogenous groups to facilitate the simultaneous implementation of the concurrent engineering strategy.
The cold start problem is a potentiel problem in Recommender Systems (RSs). It concerns the inability of the system to infer recommendaation for new users or new items about wich it has not enough ...iformation. Specifically, when an item is new, the system may fail to perform well due to the insufficiency of available information for this item. The most common solution addressed in the literature consists in combining the content and collaborative information under a single RS. However these hybrid solutions inherit the classical problems of natural language ambiguity and don’t exploit semantic knowledge in their items representations. In this paper, we propose a hybrid RS composed of three modules to surpass those weaknesses. The first one is rested on a powerful content clustering algorithm; which uses a Hybrid Features Selection Method (HFSM). It combines statistical and semantic relevant features to get the maximum profit from the content of items. The second module is the Collaborative Filtering (CF) one, which depends only on users’ ratings. The third one combines the previous modules to solve the problem of missing values in CF approach and to handle new-item issue. The proposed hybrid Recommender is evaluated against traditional item-based CF in different settings: no cold-start situation and a simulation of a new-item scenario (an item with few/ no ratings). The conducted experiments show the ability of the proposed hybrid recommender to deliver more accurate predictions for any item and its outperformance on the classical CF approach, which fails in cold-start situations.
Is Supply Chain a complex system? Raaidi, Safaa; Bouhaddou, Imane; Benghabrit, Asmaa
MATEC Web of Conferences,
01/2018, Letnik:
200
Journal Article, Conference Proceeding
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Nowadays, industries are continually looking to implement new subsidiaries in different continents, in order to better fulfill their customers’ needs, generate the best products in the shortest time ...and cheaper than their competitors. Achieving these goals is no longer related to the company itself, but to all partners in the supply chain. This justifies the need for efficient and judicious management of the whole supply chain, through the collective intervention of all its actors. Needless to say, a supply chain is a system made up of a set of suppliers, producers, subcontractors, retailers, wholesalers and customers, between whom material, information and financial flows are exchanged. Management of these flows is becoming increasingly difficult and constitutes the main source of the supply chain complexity. In order to alleviate this problem and improve supply chain performance, it is necessary to model it, taking into consideration its characteristics, which make it a complex system. Hence, the scoop of this paper is to prove that supply chain is a complex system, by highlighting its most relevant characteristics that make it such a system. Complex means what is braided together or woven together. If we separate the elements, we get acquaintance elements, but we lose their interactions. Within this trend, our contribution subscribes with its ultimate purpose modelling supply chain as complex system.
Across a wide variety of fields and especially for industrial companies, data are being collected and accumulated at a dramatic pace from many different resources and services. Hence, there is an ...urgent need for a new generation of computational theories and tools to assist humans in extracting useful information from the rapidly growing volumes of digital data. A well-known fundamental task of data mining to extract information is clustering. However, with the modified applications for various domains, several researchers have developed and have provided many clustering algorithms. This complexity makes it difficult for researchers and practitioners to keep up with clustering algorithms development. As a result, finding appropriate algorithms helps significantly to organize information and extract the correct answer from different queries of the databases. In this respect, the aim of this paper is to find the appropriate clustering algorithm for sparse industrial dataset. To achieve this goal, we first present related work that focus on comparing different clustering algorithms over the past twenty years. After that, we provide a categorization of different clustering algorithms found in the literature by matching their properties to the 4V’s challenges of Big data which allow us to select the candidate clustering algorithm. Finally, using internal validity indices, K-means, agglomerative hierarchical, DBSCAN and SOM have been implemented and compared on four datasets. In addition, we highlighted the best performing clustering algorithm that gives us the efficient clusters for each dataset.
Currently, researchers have been particularly interested in Industry 4.0 (I4.0) as a combination of novel revolutionary digital technologies. This evolution is transforming how organizations operate ...to promote sustainable and circular practices. Consequently, this research empirically investigates the capabilities of I4.0 adoption to establish a sustainable and circular supply chain. The study investigates the impact of organizational factors on the deployment of I4.0 technologies to achieve sustainability objectives. Furthermore, this approach combines empirical and qualitative analyses within a methodological framework to investigate the relationship between organizational factors, I4.0 adoption, sustainable, and circular practices. In this context, survey responses have been collected from 323 supply chain professionals in the textile sector in Morocco. Consequently, employing structural equation modeling, our results reveal a strong link between I4.0 adoption and the promotion of circular and sustainable approaches. The study finds a positive association between I4.0 adoption, technological readiness, and environmental responsiveness while highlighting the challenges posed by organizational practices on I4.0 implementation in developing countries. This research contributes to the theoretical foundations of the potential influence of I4.0 technologies, and it provides practical contributions for stakeholders to effectively embrace I4.0 technologies and thereby drive more sustainable and efficient practices.
Given the growing global emphasis on sustainable transportation systems, this research presents a comprehensive approach to achieving economic, social, and environmental efficiency in transport ...within the waste management sector. To address the different challenges of sustainable transportation issues, this paper presents a hybrid multi-criteria decision-making (MCDM) approach that incorporates the analytic hierarchy process (AHP) along with data envelopment analysis (DEA) for sustainable route selection. By leveraging the strengths of both methods, this approach reconciles conflicting requirements and diverse perspectives, facilitating effective decision making. This paper involves identifying relevant criteria for route evaluation, engaging waste management company experts and stakeholders in pairwise comparisons using AHP. Furthermore, DEA is used to calculate route efficiency based on the inputs and outputs of the system. These evaluations enable the identification of the most effective and sustainable routes. This proposed methodology empowers decision makers and transportation policymakers to develop an effective decision-making tool for addressing waste transportation challenges in developing countries. The study contributes to the growing body of research on sustainable waste management practices and provides insights for waste management companies and decision makers on how to optimize waste transportation routes while reducing economic, social, and environmental impacts.
Purpose
The health crisis has highlighted the shortcomings of the industry sector which has revealed its vulnerability. To date, there is no guarantee of a return to the “world before”. The ability ...of companies to cope with these changes is a key competitive advantage requiring the adoption/mastery of industry 4.0 technologies. Therefore, companies must adapt their business processes to fit into similar situations.
Design/methodology/approach
The proposed methodology comprises three steps. First, a comparative analysis of the existing CPSs is elaborated. Second, following this analysis, a deep learning driven CPS framework is proposed highlighting its components and tiers. Third, a real industrial case is presented to demonstrate the application of the envisioned framework. Deep learning network-based methods of object detection are used to train the model and evaluation is assessed accordingly.
Findings
The analysis revealed that most of the existing CPS frameworks address manufacturing related subjects. This illustrates the need for a resilient industrial CPS targeting other areas and considering CPSs as loopback systems preserving human–machine interaction, endowed with data tiering approach for easy and fast data access and embedded with deep learning-based computer vision processing methods.
Originality/value
This study provides insights about what needs to be addressed in terms of challenges faced due to unforeseen situations or adapting to new ones. In this paper, the CPS framework was used as a monitoring system in compliance with the precautionary measures (social distancing) and for self-protection with wearing the necessary equipments. Nevertheless, the proposed framework can be used and adapted to any industrial or non-industrial environments by adjusting object detection purpose.
The COVID-19 pandemic and the Russian-Ukrainian war revealed vulnerabilities in supply chains, emphasizing the need for resilience in multiple industries. While Industry 4.0 is valuable, it cannot ...fully address complex supply chain challenges. Therefore, exploring additional drivers like Operations Management has become imperative. More clearly, the adoption of Operations Management tools and practices helps implement and use enabling technologies more effectively. In this context, the aim of the paper is to conduct a literature review to study supply chain resilience considering these two concepts. In other words, the objective of the work consists in examining the effectiveness of the mentioned combination. The paper presents a conceptual framework for enhancing supply chain resilience by exploring the potential of Industry 4.0 and Operations Management. The paper also underlines the positive impact issued from hybridizing both solutions to achieve resilience. As it discusses enhancing features of each concept aside the other. The paper provides a basic model for researchers to further detailed studies, and for managers to bringing the proposed framework from theory to practice.
An improved RDF data Clustering Algorithm Eddamiri, Siham; Zemmouri, El Moukhtar; Benghabrit, Asmaa
Procedia computer science,
2019, 2019-00-00, Letnik:
148
Journal Article
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Linked data has been officially approved as one of the best useful sources for background information that publishing and connecting structured data on the Web. Therefore, Machine Learning community ...should respond with a clear package of methods and best practices to bring this type of data into the field. Relatively little attention has been paid in the literature to the possible union of both linked data and Machine Learning. In order to overcome this carelessness and achieve this joining, we should focus little more on RDF data. The main purpose of this article, is to present a typical Machine Learning Pipeline on RDF data and an improved version of RDF clustering algorithm based on Candidate Description by using different similarity measures and agglomerative hierarchical techniques.
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