The globalization and the competitiveness are forcing companies to rethink and to innovate their production processes following the so-called Industry 4.0 paradigm. It represents the integration of tools already used in the past (big data, cloud, robot, 3D printing, simulation, etc.) that are now connected into a global network by transmitting digital data. The implementation of this new paradigm represents a huge change for companies, which are faced with big investments. In order to benefit from the opportunities offered by the smart revolution, companies must have the prerequisites needed to withstand changes generated by "smart" system. In addition, new workers who face the world of work 4.0 must have new skills in automation, digitization, and information technology, without forgetting soft skills. This chapter aims to present the main good practices, challenges, and opportunities related to Industry 4.0 paradigm.
Adaptation and innovation are extremely important to the manufacturing industry. This development should lead to sustainable manufacturing using new technologies. To promote sustainability, smart production requires global perspectives of smart production application technology. In this regard, thanks to intensive research efforts in the field of artificial intelligence (AI), a number of AI-based techniques, such as machine learning, have already been established in the industry to achieve sustainable manufacturing. Thus, the aim of the present research was to analyze, systematically, the scientific literature relating to the application of artificial intelligence and machine learning (ML) in industry. In fact, with the introduction of the Industry 4.0, artificial intelligence and machine learning are considered the driving force of smart factory revolution. The purpose of this review was to classify the literature, including publication year, authors, scientific sector, country, institution, and keywords. The analysis was done using the Web of Science and SCOPUS database. Furthermore, UCINET and NVivo 12 software were used to complete them. A literature review on ML and AI empirical studies published in the last century was carried out to highlight the evolution of the topic before and after Industry 4.0 introduction, from 1999 to now. Eighty-two articles were reviewed and classified. A first interesting result is the greater number of works published by the USA and the increasing interest after the birth of Industry 4.0.
Blockchain is a disruptive technology that is revolutionizing information technology and represents a change of cultural paradigm for the way in which information is shared. Companies are rushing to understand how they can use blockchain distributed ledger technology to innovate processes, products and transactions. In a globalized world where environmental sustainability is a critical success factor, what is the role of the blockchain? By using a systematic review approach and the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) protocol, this study attempts to identify whether and how blockchain technology is considered able to affect environmental sustainability. Findings from 195 studies from 2015 to 2020 were analysed after the search protocol was applied. The results indicate that blockchain technology could contribute to environmentally sustainable development goals (SDGs) from different points of view, such as supporting the realization of a sustainable supply chain, improving energy efficiency and promoting the creation of secure and reliable smart cities. Furthermore, the investigation highlights the sectors where to focus research investments, providing a way to reward sustainable behaviour and increasing environmental sustainability. On the other hand, blockchain has no negligible negative effects on the environment that need to be considered before adoption.
Nowadays the biggest challenge for most organizations is a real and substantial application of sustainability through the measurement and comparability of results in order to satisfy the principles of sustainability of all the stakeholders. Definitively, it is necessary to pursue sustainability through the measurements of specific indicators and control the variables that influence the state of the economic, social and environmental issues. The aim of this paper is to contribute to the development of a comprehensive, yet practical and reliable tool for a systematic sustainability assessment, based on the Life Cycle Assessment (LCA) and the Analytic Hierarchy Process (AHP) to support decision makers in complex decision problems in the field of environmental sustainability. The results are applied to a novel compressed air energy storage system proposed as a suitable technology for the energy storage in a small scale stand-alone renewable energy power plant (photovoltaic power plant) that is designed to satisfy the energy demand of a radio base station for mobile telecommunications. The outcome is a dynamic analysis and iterative integrated sustainability assessment of corporate performance
The bullwhip effect leads to considerable inefficiencies along the food supply chain such as missed production schedules, poor customer service, excessive inventory, and misguided capacity plans. To tackle this problem, it is necessary, apart from other interventions, to continuously monitor the performance of food suppliers so that the demand information flow, order batching, transportation planning, and inventory management can be substantially improved. Therefore, supplier assessment has then become critical decision‐making support for identifying and addressing inefficiencies of food providers, which ends up reducing the variation of several key logistics parameters for upstream members of the food supply chain. In addition, such assessment is of multicriteria nature given the presence of several criteria from different domains and various food suppliers. With these considerations in mind, this paper proposes a hybrid approach integrating the analytic hierarchy process (AHP), decision‐making trial and evaluation laboratory (DEMATEL), and the technique for order of preference by similarity to ideal solution (TOPSIS) for evaluating the performance of pork suppliers. Thereby, the economic and operational burden caused by the bullwhip effect throughout the pork supply chain can be alleviated. AHP was first used to determine the criteria and subcriteria weights. Then DEMATEL was applied to assess the interdependence and feedback between the decision elements. Finally, TOPSIS was implemented to discriminate between high‐performance and low‐performance pork suppliers. A case study from the Colombian pork supply chain is presented to validate the proposed approach. The results of this study evidenced that the most important criterion was the “service level” and the most influencing factor was the “financial profile.” In addition, based on the supplier assessment results, improvement plans, and new negotiation, strategies were established for each supplier in order to diminish the bullwhip effect along the pork supply chain.
In recent years, a series of important emergencies have been taken place worldwide in industrial plants. \ud After the occurrence of a disaster, it is essential to activate the correct emergency procedures. Particularly, it is important to direct people injured in hospitals able to handle emergencies. Thus, nowadays, the emergency services require a “transversal” process that starts from the disaster moment occurred until the involvement of all actors that participate in the process to provide integral, safe and quality attention. The aim of this study is to evaluate the overall performance of emergency departments in the hospital sector. A hybrid model called the “Analytic Decision Making Preference Model - ADT Model” based on AHP, DEMATEL and TOPSIS methods is proposed. AHP was been used to determine the criteria and sub-criteria weights. Then, DEMATEL is used to evaluate interdependence between criteria and sub-criteria. After this, TOPSIS is applied to rank the emergency departments from highest to lowest according to their closeness coefficient. A real case study in Colombia is presented
Selecting a suitable Multi Criteria Decision-Making (MCDM) method is a crucial step in selecting appropriate medical equipment. The aim of the research is to define the most appropriate tomography equipment through the integration of the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. A hybrid model is presented. The AHP is used to define the weights of each criterion and sub-criterion through qualitative comparisons. Then, TOPSIS is used to evaluate the purchase options. This research provides decision makers with a scientific and rigorous decision support system useful in strategic and complex decision. A numerical example is also presented.
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