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When dealing with Automated Storage and Retrieval Systems (ASRS), the allocation of items to the most convenient storage location depends on the vast amount of data produced internally (e.g., Enterprise Resource Planning, Manufacturing Enterprise Systems) and externally (e.g. Supply Chain Management). Moreover, a proper item allocation in the warehouse has a strong influence on the warehouse saturation levels and picking times. In this perspective, the present work proposes the application of data-driven algorithms for managing items in an Automated Storage and Retrieval System (ASRS) in order to reduce the picking times and storage space. Specifically, a four-layer framework is adopted for collecting data produced by different information sources and analyzing them through a data-driven approach. The analytics layer is performed by combining the Association Rule Mining (ARM) technique, to investigate the network of influences among data collected, and a simulation approach for assessing the feasibility of the proposed implementation. The Association Rule Mining allows company managers to identify the components that should be located on the same tray in the ASRS, defining the couples of items frequently picked together in order to reduce the total picking time. The proposed approach is applied to the case study of a shoe manufacturing company to explain the research approach and show how the implementation of the data-driven methodology can provide valuable support in defining item allocation and picking rules. The proposed Association Rule Mining method is new in this context and it has shown a positive impact in comparison to traditional solutions of warehouse management, providing a complete overview of the items’ interactions and identifying communities of items that define local and global patterns and locate influential entities.
Sara Antomarioni; Laura Lucantoni; Filippo Emanuele Ciarapica; Maurizio Bevilacqua. Data-driven decision support system for managing item allocation in an ASRS: A framework development and a case study. Expert Systems with Applications 2021, 185, 115622 .
AMA StyleSara Antomarioni, Laura Lucantoni, Filippo Emanuele Ciarapica, Maurizio Bevilacqua. Data-driven decision support system for managing item allocation in an ASRS: A framework development and a case study. Expert Systems with Applications. 2021; 185 ():115622.
Chicago/Turabian StyleSara Antomarioni; Laura Lucantoni; Filippo Emanuele Ciarapica; Maurizio Bevilacqua. 2021. "Data-driven decision support system for managing item allocation in an ASRS: A framework development and a case study." Expert Systems with Applications 185, no. : 115622.
An essential step in the implementation of predictive maintenance involves the health state analysis of productive equipment in order to provide company managers with performance and degradation indicators which help to predict component condition. In this paper, a supervised approach for health indicator calculation is provided combining the Grey Wolf Optimisation method, Swarm Intelligence algorithm, and Fuzzy Cognitive Maps. The k-neighbors algorithms is used to predict the Remaining Useful Life of an item, since, in addition to its simplicity, they produce good results in a large number of domains. The approach aims to solve the problem that frequently occurs in interpolation procedures: the approximation of functions belonging to a chosen class of functions of which we have no knowledge. The proposed algorithm allows maintenance managers to distinguish different degradation profiles in depth with a consequently more precise estimate of the Remaining Useful Life of an item and, in addition, an in-depth understanding of the degradation process. Specifically, in order to show its suitability for predictive maintenance, a dataset on NASA aircraft engines has been used and results have been compared to those obtained with a neural network approach. Results highlight how all of the degradation profiles, obtained using the proposed approach, are modelled in a more detailed manner, allowing one to significantly distinguish different situations. Moreover, the physical core speed and the corrected fan speed have been identified as the main critical factors to the engine degradation.
G. Mazzuto; S. Antomarioni; F. E. Ciarapica; M. Bevilacqua. Health Indicator for Predictive Maintenance Based on Fuzzy Cognitive Maps, Grey Wolf, and K-Nearest Neighbors Algorithms. Mathematical Problems in Engineering 2021, 2021, 1 -21.
AMA StyleG. Mazzuto, S. Antomarioni, F. E. Ciarapica, M. Bevilacqua. Health Indicator for Predictive Maintenance Based on Fuzzy Cognitive Maps, Grey Wolf, and K-Nearest Neighbors Algorithms. Mathematical Problems in Engineering. 2021; 2021 ():1-21.
Chicago/Turabian StyleG. Mazzuto; S. Antomarioni; F. E. Ciarapica; M. Bevilacqua. 2021. "Health Indicator for Predictive Maintenance Based on Fuzzy Cognitive Maps, Grey Wolf, and K-Nearest Neighbors Algorithms." Mathematical Problems in Engineering 2021, no. : 1-21.
Industry 4.0 is one of the primary paradigms of the current industrial context. Despite the widespread research on this topic, an analysis of its key technologies' impact on company performance and resilience is not available. Hence, this work proposes a conceptual model for investigating the influences among Industry 4.0 key technologies (IT-related and Operations-related technologies), organizational resilience (in terms of internal and external) and performances in Italian companies. We distinguished company performance, referring to companies' results in the past, from organizational resilience, which investigates future survival chances. Using structural equation modelling, a second-order construct has been used to test the hypothesized relationships. The results show that the implementation level of Industry 4.0 IT-related key technologies positively impacts organizational resilience and perceived performance. These results can assist company managers and decision-makers in increasing organizational resilience by effectively implementing Industry 4.0 technologies.
Giulio Marcucci; Sara Antomarioni; Filippo Emanuele Ciarapica; Maurizio Bevilacqua. The impact of Operations and IT-related Industry 4.0 key technologies on organizational resilience. Production Planning & Control 2021, 1 -15.
AMA StyleGiulio Marcucci, Sara Antomarioni, Filippo Emanuele Ciarapica, Maurizio Bevilacqua. The impact of Operations and IT-related Industry 4.0 key technologies on organizational resilience. Production Planning & Control. 2021; ():1-15.
Chicago/Turabian StyleGiulio Marcucci; Sara Antomarioni; Filippo Emanuele Ciarapica; Maurizio Bevilacqua. 2021. "The impact of Operations and IT-related Industry 4.0 key technologies on organizational resilience." Production Planning & Control , no. : 1-15.
The transformation from traditional industry to Industry 4.0 can bring many benefits in various spheres, from efficiency to safety. However, this transition involves adopting technologically advanced machinery with a high level of digitization and communication. The costs and time to replace obsolete machines could be unsustainable for many companies while retrofitting the old machinery. To make them ready to the Industry 4.0 context, they may represent an alternative to the replacement. Even if there are many studies related to retrofitting applied to machinery, there are very few studies related to the literature process industry sector. In this work, we propose a case study of a two-phase mixing plant that needed to be enhanced in the safety and maintainability conditions with reasonable times and costs. In this regard, the Digital Twin techniques and Deep Learning algorithms will be tested to predict and detect future faults, not only already visible and existing malfunctions. This approach strength is that, with limited investments and reasonable times, it allows the transformation of an old plant into a smart plant capable of communicating quickly with operators to increase its safety and maintainability.
Fabio Di Carlo; Giovanni Mazzuto; Maurizio Bevilacqua; Filippo Ciarapica. Retrofitting a Process Plant in an Industry 4.0 Perspective for Improving Safety and Maintenance Performance. Sustainability 2021, 13, 646 .
AMA StyleFabio Di Carlo, Giovanni Mazzuto, Maurizio Bevilacqua, Filippo Ciarapica. Retrofitting a Process Plant in an Industry 4.0 Perspective for Improving Safety and Maintenance Performance. Sustainability. 2021; 13 (2):646.
Chicago/Turabian StyleFabio Di Carlo; Giovanni Mazzuto; Maurizio Bevilacqua; Filippo Ciarapica. 2021. "Retrofitting a Process Plant in an Industry 4.0 Perspective for Improving Safety and Maintenance Performance." Sustainability 13, no. 2: 646.
Power plants are required to supply the electric demand efficiently, and appropriate failure analysis is necessary for ensuring their reliability. This paper proposes a framework to extend the failure analysis: indeed, the outcomes traditionally carried out through techniques such as the Failure Mode and Effects Analysis (FMEA) are elaborated through data-driven methods. In detail, the Association Rule Mining (ARM) is applied in order to define the relationships among failure modes and related characteristics that are likely to occur concurrently. The Social Network Analysis (SNA) is then used to represent and analyze these relationships. The main novelty of this work is represented by support in the maintenance management process based not only on the traditional failure analysis but also on a data-driven approach. Moreover, the visual representation of the results provides valuable support in terms of comprehension of the context to implement appropriate actions. The proposed approach is applied to the case study of a hydroelectric power plant, using real-life data.
Sara Antomarioni; Marjorie Maria Bellinello; Maurizio Bevilacqua; Filippo Emanuele Ciarapica; Renan Favarão Da Silva; Gilberto Francisco Martha De Souza. A Data-Driven Approach to Extend Failure Analysis: A Framework Development and a Case Study on a Hydroelectric Power Plant. Energies 2020, 13, 6400 .
AMA StyleSara Antomarioni, Marjorie Maria Bellinello, Maurizio Bevilacqua, Filippo Emanuele Ciarapica, Renan Favarão Da Silva, Gilberto Francisco Martha De Souza. A Data-Driven Approach to Extend Failure Analysis: A Framework Development and a Case Study on a Hydroelectric Power Plant. Energies. 2020; 13 (23):6400.
Chicago/Turabian StyleSara Antomarioni; Marjorie Maria Bellinello; Maurizio Bevilacqua; Filippo Emanuele Ciarapica; Renan Favarão Da Silva; Gilberto Francisco Martha De Souza. 2020. "A Data-Driven Approach to Extend Failure Analysis: A Framework Development and a Case Study on a Hydroelectric Power Plant." Energies 13, no. 23: 6400.
Ornella Pisacane; Domenico Potena; Sara Antomarioni; Maurizio Bevilacqua; Filippo Emanuele Ciarapica; Claudia Diamantini. Data-driven predictive maintenance policy based on multi-objective optimization approaches for the component repairing problem. Engineering Optimization 2020, 1 -20.
AMA StyleOrnella Pisacane, Domenico Potena, Sara Antomarioni, Maurizio Bevilacqua, Filippo Emanuele Ciarapica, Claudia Diamantini. Data-driven predictive maintenance policy based on multi-objective optimization approaches for the component repairing problem. Engineering Optimization. 2020; ():1-20.
Chicago/Turabian StyleOrnella Pisacane; Domenico Potena; Sara Antomarioni; Maurizio Bevilacqua; Filippo Emanuele Ciarapica; Claudia Diamantini. 2020. "Data-driven predictive maintenance policy based on multi-objective optimization approaches for the component repairing problem." Engineering Optimization , no. : 1-20.
Warehouse management activities are critical from an organizational point of view since they can cause a sensitive loss of efficiency. When dealing with automated storage and retrieval systems, the allocation of items to a specific storage cell is a challenging issue since it is the unique modifiable variable due to the constructive characteristics of the warehouse. The vast amount of data available in this field allows the development of policies for an efficient allocation of the items through the development of data mining-based approaches. In this perspective, the current work proposes a roadmap for the allocation of items to the storage cells of an automated storage and retrieval system through the association rule mining. The procedure is, at first, generally described, and, then, applied to the case study of a shoe manufacturer.
Sara Antomarioni; Maurizio Bevilacqua; Filippo Emanuele Ciarapica. An Association Rule-Based Approach for Storing Items in an AS/RS. Blockchain Technology and Innovations in Business Processes 2020, 61 -70.
AMA StyleSara Antomarioni, Maurizio Bevilacqua, Filippo Emanuele Ciarapica. An Association Rule-Based Approach for Storing Items in an AS/RS. Blockchain Technology and Innovations in Business Processes. 2020; ():61-70.
Chicago/Turabian StyleSara Antomarioni; Maurizio Bevilacqua; Filippo Emanuele Ciarapica. 2020. "An Association Rule-Based Approach for Storing Items in an AS/RS." Blockchain Technology and Innovations in Business Processes , no. : 61-70.
In the literature, many applications of Digital Twin methodologies in the manufacturing, construction and oil and gas sectors have been proposed, but there is still no reference model specifically developed for risk control and prevention. In this context, this work develops a Digital Twin reference model in order to define conceptual guidelines to support the implementation of Digital Twin for risk prediction and prevention. The reference model proposed in this paper is made up of four main layers (Process industry physical space, Communication system, Digital Twin and User space), while the implementation steps of the reference model have been divided into five phases (Development of the risk assessment plan, Development of the communication and control system, Development of Digital Twin tools, Tools integration in a Digital Twin perspective and models and Platform validation). During the design and implementation phases of a Digital Twin, different criticalities must be taken into consideration concerning the need for deterministic transactions, a large number of pervasive devices, and standardization issues. Practical implications of the proposed reference model regard the possibility to detect, identify and develop corrective actions that can affect the safety of operators, the reduction of maintenance and operating costs, and more general improvements of the company business by intervening both in strictly technological and organizational terms.
Maurizio Bevilacqua; Eleonora Bottani; Filippo Emanuele Ciarapica; Francesco Costantino; Luciano Di Donato; Alessandra Ferraro; Giovanni Mazzuto; Andrea Monteriù; Giorgia Nardini; Marco Ortenzi; Massimo Paroncini; Marco Pirozzi; Mario Prist; Elena Quatrini; Massimo Tronci; Giuseppe Vignali. Digital Twin Reference Model Development to Prevent Operators’ Risk in Process Plants. Sustainability 2020, 12, 1088 .
AMA StyleMaurizio Bevilacqua, Eleonora Bottani, Filippo Emanuele Ciarapica, Francesco Costantino, Luciano Di Donato, Alessandra Ferraro, Giovanni Mazzuto, Andrea Monteriù, Giorgia Nardini, Marco Ortenzi, Massimo Paroncini, Marco Pirozzi, Mario Prist, Elena Quatrini, Massimo Tronci, Giuseppe Vignali. Digital Twin Reference Model Development to Prevent Operators’ Risk in Process Plants. Sustainability. 2020; 12 (3):1088.
Chicago/Turabian StyleMaurizio Bevilacqua; Eleonora Bottani; Filippo Emanuele Ciarapica; Francesco Costantino; Luciano Di Donato; Alessandra Ferraro; Giovanni Mazzuto; Andrea Monteriù; Giorgia Nardini; Marco Ortenzi; Massimo Paroncini; Marco Pirozzi; Mario Prist; Elena Quatrini; Massimo Tronci; Giuseppe Vignali. 2020. "Digital Twin Reference Model Development to Prevent Operators’ Risk in Process Plants." Sustainability 12, no. 3: 1088.
The Green Supply Chain has proposed to innovate industrial production by implementing a radical perspective change: reconcile economy and ecology. This study aims to contribute to the realisation of a new idea of eco-sustainable industrialisation. Anyone making decisions within any dynamic system faces serious difficulties. For this reason, the proposed study analyses this system using Fuzzy Cognitive Maps arriving at the formation of a real map of the causal relationships between the concepts identified, then divided by areas of membership. In doing so, the most relevant factors affecting the Green Supply Chain decision-making process have been identified and analysed.
M. Bevilacqua; F.E. Ciarapica; G. Marcucci; G. Mazzuto. Fuzzy Cognitive Maps analysis of Green Supply Chain Management: a case study approach. IFAC-PapersOnLine 2020, 53, 17481 -17486.
AMA StyleM. Bevilacqua, F.E. Ciarapica, G. Marcucci, G. Mazzuto. Fuzzy Cognitive Maps analysis of Green Supply Chain Management: a case study approach. IFAC-PapersOnLine. 2020; 53 (2):17481-17486.
Chicago/Turabian StyleM. Bevilacqua; F.E. Ciarapica; G. Marcucci; G. Mazzuto. 2020. "Fuzzy Cognitive Maps analysis of Green Supply Chain Management: a case study approach." IFAC-PapersOnLine 53, no. 2: 17481-17486.
Supply Chain Resilience has been broadly studied during the last decades, especially within the academic community. Therefore, the present research article aims to provide a broad view of the scientific literature about Resilience within Supply Chain research. First, a trend analysis of these topics research and publications is presented. Then, a more detailed analysis is shown, in order to produce bibliometric maps and tables summarizing the main scientific trends under these topics: the latter analysis will provide useful insight of the most used keywords on the subject and the connections among them.
M. Bevilacqua; F.E. Ciarapica; G. Marcucci. Supply Chain Resilience research trends: a literature overview. IFAC-PapersOnLine 2019, 52, 2821 -2826.
AMA StyleM. Bevilacqua, F.E. Ciarapica, G. Marcucci. Supply Chain Resilience research trends: a literature overview. IFAC-PapersOnLine. 2019; 52 (13):2821-2826.
Chicago/Turabian StyleM. Bevilacqua; F.E. Ciarapica; G. Marcucci. 2019. "Supply Chain Resilience research trends: a literature overview." IFAC-PapersOnLine 52, no. 13: 2821-2826.
The development of new and valuable products, from conceptual design to production, is to date supported by advanced methodologies based on ICT tools allowing many controls and checks before proceeding to heavy spending investment decisions. The increasing use ICT allow highlighting product design process and solutions able to improve people’s quality of life. Key product development principles based on human-centered approaches and eco-sustainability concepts prove to be the main factors affecting both the products’ users as well as the product manufacturing staff. This paper outlines product’s development approaches state of the art, foreseeing at the same time possible research trajectories to define manufacturing industry future scenario based on more sustainable economical, environmental and social design choices.
Maurizio Bevilacqua; Filippo Emanuele Ciarapica; Michele Germani; Giancarlo Giacchetta; Marco Mandolini; Ferruccio Mandorli; Maura Mengoni; Claudia Paciarotti. Smart, Eco-Sustainable and Human-Centered Product Development Processes: 21st Century Manufacturing Industries. The First Outstanding 50 Years of “Università Politecnica delle Marche” 2019, 161 -175.
AMA StyleMaurizio Bevilacqua, Filippo Emanuele Ciarapica, Michele Germani, Giancarlo Giacchetta, Marco Mandolini, Ferruccio Mandorli, Maura Mengoni, Claudia Paciarotti. Smart, Eco-Sustainable and Human-Centered Product Development Processes: 21st Century Manufacturing Industries. The First Outstanding 50 Years of “Università Politecnica delle Marche”. 2019; ():161-175.
Chicago/Turabian StyleMaurizio Bevilacqua; Filippo Emanuele Ciarapica; Michele Germani; Giancarlo Giacchetta; Marco Mandolini; Ferruccio Mandorli; Maura Mengoni; Claudia Paciarotti. 2019. "Smart, Eco-Sustainable and Human-Centered Product Development Processes: 21st Century Manufacturing Industries." The First Outstanding 50 Years of “Università Politecnica delle Marche” , no. : 161-175.
The domino effect that occurs among the concepts that affect Supply Chain Resilience has only been marginally analysed, and no conceptual models have been proposed in the literature. In this work, a conceptual model for analysing this domino effect is developed. The method aims to identify which supply chain concepts can support the containment of disruptions and how these concepts affect one another. The proposed methodology is based on Fuzzy Cognitive Maps. The Cognitive Maps tool enables us to connect multidimensional and multidisciplinary concepts (e.g. sources of risk, disruption factors, supply chain management practices and other aspects). Moreover, this tool allows company managers to develop a detailed understanding of a system's behaviour and to explicitly consider the mind models of different players in the supply chain. A case study of the fashion industry supply chain is used to illustrate the application of the proposed method in an operating context. The proposed method enables a company to evaluate the hidden chain reaction of causes behind the most important factors that, from a single trigger event, are able to harm the entire Supply Chain. Through analysis of the causal relationships that this methodology highlights, decision makers can examine the domino effect among the concepts that influence Supply Chain Resilience in a step-by-step manner.
Maurizio Bevilacqua; Filippo Emanuele Ciarapica; Giulio Marcucci; Giovanni Mazzuto. Fuzzy cognitive maps approach for analysing the domino effect of factors affecting supply chain resilience: a fashion industry case study. International Journal of Production Research 2019, 58, 6370 -6398.
AMA StyleMaurizio Bevilacqua, Filippo Emanuele Ciarapica, Giulio Marcucci, Giovanni Mazzuto. Fuzzy cognitive maps approach for analysing the domino effect of factors affecting supply chain resilience: a fashion industry case study. International Journal of Production Research. 2019; 58 (20):6370-6398.
Chicago/Turabian StyleMaurizio Bevilacqua; Filippo Emanuele Ciarapica; Giulio Marcucci; Giovanni Mazzuto. 2019. "Fuzzy cognitive maps approach for analysing the domino effect of factors affecting supply chain resilience: a fashion industry case study." International Journal of Production Research 58, no. 20: 6370-6398.
Equipment control represents a critical aspect of asset management because of its impact on asset’s reliability. Monitoring the behavior of the asset and being able to anticipate the occurrence of critical situation like asset’s failures play a vital role in the success of a company. Hence, in this paper, we propose a decision support system based on the development of an Artificial Neural Network and Association Rule Mining. The aim of this work is supporting the Operations and Maintenance managers in defining the best control policy on asset’s behavior, in order to anticipate the occurrence of failing situations and find relations among operating conditions’ values helpful for failures prediction and recognition. Implementing the proposed approach may support the decision maker in defining the best maintenance policy for the asset, knowing in advance the conditions leading to its failure. A brief example-case is provided for the discussion of the practical implications of the proposed approach.
Antonio De-La-Fuente-Carmona; Adolfo Crespo Márquez; S. Antomarioni; Maurizio Bevilacqua. Decision support systems in asset control: an approach based on Artificial Neural Network and Association Rule Mining. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) 2019, 2596 -2601.
AMA StyleAntonio De-La-Fuente-Carmona, Adolfo Crespo Márquez, S. Antomarioni, Maurizio Bevilacqua. Decision support systems in asset control: an approach based on Artificial Neural Network and Association Rule Mining. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). 2019; ():2596-2601.
Chicago/Turabian StyleAntonio De-La-Fuente-Carmona; Adolfo Crespo Márquez; S. Antomarioni; Maurizio Bevilacqua. 2019. "Decision support systems in asset control: an approach based on Artificial Neural Network and Association Rule Mining." 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) , no. : 2596-2601.
Nowadays, although the lithium-ion batteries have been widely applied in the context of electric vehicles for passengers, lead-acid batteries are still prevalent in motive-power applications, such as electric pallet jacks and laser guided vehicles. The battery cost is the main disadvantage that limits the employment of lithium-ion solutions in such applications. Several strategies for reducing the battery life cycle cost have been discussed in the scientific literature. The opportunity charging is one of them, even though it is suitable only for batteries having high lifecycles and high charging/discharging rates, such as the Lithium Titanium Oxide ones. This paper aims at assessing a feasible solution to reduce the life cycle cost of the energy storage units for laser guided vehicles. A tool has been proposed to analyze the Total Cost of Ownership of batteries, under the adoption of an opportunity charging strategy. Simulations of energy consumption have also been included, to predict the battery cycles and the operation costs. The life cycle analysis has investigated the use of a compacted Lithium Titanium Oxide battery in comparison with a traditional lead-acid battery. The results have shown the feasibility of the Lithium Titanium Oxide solution and its economic advantage in an industrial context.
Paolo Cicconi; Leonardo Postacchini; Emanuele Pallotta; Andrea Monteriù; Mariorosario Prist; Maurizio Bevilacqua; Michele Germani. A life cycle costing of compacted lithium titanium oxide batteries for industrial applications. Journal of Power Sources 2019, 436, 226837 .
AMA StylePaolo Cicconi, Leonardo Postacchini, Emanuele Pallotta, Andrea Monteriù, Mariorosario Prist, Maurizio Bevilacqua, Michele Germani. A life cycle costing of compacted lithium titanium oxide batteries for industrial applications. Journal of Power Sources. 2019; 436 ():226837.
Chicago/Turabian StylePaolo Cicconi; Leonardo Postacchini; Emanuele Pallotta; Andrea Monteriù; Mariorosario Prist; Maurizio Bevilacqua; Michele Germani. 2019. "A life cycle costing of compacted lithium titanium oxide batteries for industrial applications." Journal of Power Sources 436, no. : 226837.
One of the most important challenges of high-risk industries regards environmental risk management. In all sectors characterized by high-risk processes, failure can lead to catastrophic environmental events, so it is necessary to have a model capable of extracting useful information from the data collected and able to provide company managers with decision-making tools. In this work, a framework has been developed to manage environmental risk in a process industry. In order to analyse adverse environmental events, data provided by different sources and geographically dispersed repositories have been considered. A conceptual model, based on Association Rules (AR), has been developed to investigate the network of influences among data collected. Moreover, a Social Network Analysis has been used to represent the association rules, providing a complete overview of the factors’ interaction and to identify communities of nodes in order to define local and global patterns and locate influential entities. To test the proposed approach and assess its impact on environmental management strategies, a medium-sized refinery case study has been analysed. The big data analytics approach proposed in this work, taking into consideration a wide set of objective and predictive variables, allowed the refinery managers to show new cause–effect correlations in refinery processes regarding adverse environmental event typology, immediate and root causes, refinery plant area involved in the adverse event, risk index and corrective actions.
Filippo Ciarapica; Maurizio Bevilacqua; Sara Antomarioni. An approach based on association rules and social network analysis for managing environmental risk: A case study from a process industry. Process Safety and Environmental Protection 2019, 128, 50 -64.
AMA StyleFilippo Ciarapica, Maurizio Bevilacqua, Sara Antomarioni. An approach based on association rules and social network analysis for managing environmental risk: A case study from a process industry. Process Safety and Environmental Protection. 2019; 128 ():50-64.
Chicago/Turabian StyleFilippo Ciarapica; Maurizio Bevilacqua; Sara Antomarioni. 2019. "An approach based on association rules and social network analysis for managing environmental risk: A case study from a process industry." Process Safety and Environmental Protection 128, no. : 50-64.
Effective maintenance policies can support companies to deal with process interruptions and consequently, to prevent significant profit losses. Moreover, the proliferation of structured and unstructured data due to production plants validates the application of knowledge discovery in databases techniques to increase processes’ reliability. In this paper, an innovative maintenance policy is proposed. It aims at both predicting components breakages through association rule mining and determining the optimal set of components to repair in order to improve the overall plant’s reliability, under time and budget constraints. An experimental campaign is carried out on a real-life case study concerning an oil refinery plant. Finally, numerical results are discussed considering different blockage categories and number of components and by varying some significant input parameters.
Sara Antomarioni; Ornella Pisacane; Domenico Potena; Maurizio Bevilacqua; Filippo Emanuele Ciarapica; Claudia Diamantini. A predictive association rule-based maintenance policy to minimize the probability of breakages: application to an oil refinery. The International Journal of Advanced Manufacturing Technology 2019, 105, 3661 -3675.
AMA StyleSara Antomarioni, Ornella Pisacane, Domenico Potena, Maurizio Bevilacqua, Filippo Emanuele Ciarapica, Claudia Diamantini. A predictive association rule-based maintenance policy to minimize the probability of breakages: application to an oil refinery. The International Journal of Advanced Manufacturing Technology. 2019; 105 (9):3661-3675.
Chicago/Turabian StyleSara Antomarioni; Ornella Pisacane; Domenico Potena; Maurizio Bevilacqua; Filippo Emanuele Ciarapica; Claudia Diamantini. 2019. "A predictive association rule-based maintenance policy to minimize the probability of breakages: application to an oil refinery." The International Journal of Advanced Manufacturing Technology 105, no. 9: 3661-3675.
The life-cycle assessment methodology was used to evaluate the environmental impact of friction stir welding of AA5754-H114 aluminium alloy sheets. Other works in literature considered the environmental impact of friction stir welding, but in this study the influence of different process parameters on midpoint category impacts were analysed. Friction stir welding was performed under different values of rotational and welding speeds. Moreover, pin tool wear and mechanical properties of joints were also evaluated. The pre- and post-processing stages were also considered. Raw materials, energy and all inputs associated with each stage of product life cycle were collected and evaluated to analyse the environmental impact index. The results showed that, irrespective of the rotational speed, the lowest welding speed investigated leads to the highest energy consumption and, consequently, to the highest values of the midpoint category impact. On the contrary, at the highest welding speed, the environmental impact assumes the lowest values. By concerning the rotational speed, its effect on the midpoint category impact is strongly reduced compared with the one given by the welding speed. A performance index, obtained by considering both the midpoint category impact and ultimate tensile strength of the joints, was also defined. Finally, the environmental sustainability of friction stir welding was compared with two different fusion welding technologies, namely gas tungsten arc welding and laser beam welding. The results showed that friction stir welding was characterized by midpoint category impacts much lower than those of the gas tungsten arc welding, while such discrepancies decreased with the laser beam welding.
Maurizio Bevilacqua; Fe Ciarapica; A Forcellese; M Simoncini. Comparison among the environmental impact of solid state and fusion welding processes in joining an aluminium alloy. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 2019, 234, 140 -156.
AMA StyleMaurizio Bevilacqua, Fe Ciarapica, A Forcellese, M Simoncini. Comparison among the environmental impact of solid state and fusion welding processes in joining an aluminium alloy. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 2019; 234 (1-2):140-156.
Chicago/Turabian StyleMaurizio Bevilacqua; Fe Ciarapica; A Forcellese; M Simoncini. 2019. "Comparison among the environmental impact of solid state and fusion welding processes in joining an aluminium alloy." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 234, no. 1-2: 140-156.
Purpose The purpose of this paper is developing a data-driven maintenance policy through the analysis of vast amount of data and its application to an oil refinery plant. The maintenance policy, analyzing data regarding sub-plant stoppages and components breakdowns within a defined time interval, supports the decision maker in determining whether it is better to perform predictive maintenance or corrective interventions on the basis of probability measurements. Design/methodology/approach The formalism applied to pursue this aim is association rules mining since it allows to discover the existence of relationships between sub-plant stoppages and components breakdowns. Findings The application of the maintenance policy to a three-year case highlighted that the extracted rules depend on both the kind of stoppage and the timeframe considered, hence different maintenance strategies are suggested. Originality/value This paper demonstrates that data mining (DM) tools, like association rules (AR), can provide a valuable support to maintenance processes. In particular, the described policy can be generalized and applied both to other refineries and to other continuous production systems.
Sara Antomarioni; Maurizio Bevilacqua; Domenico Potena; Claudia Diamantini. Defining a data-driven maintenance policy: an application to an oil refinery plant. International Journal of Quality & Reliability Management 2019, 36, 77 -97.
AMA StyleSara Antomarioni, Maurizio Bevilacqua, Domenico Potena, Claudia Diamantini. Defining a data-driven maintenance policy: an application to an oil refinery plant. International Journal of Quality & Reliability Management. 2019; 36 (1):77-97.
Chicago/Turabian StyleSara Antomarioni; Maurizio Bevilacqua; Domenico Potena; Claudia Diamantini. 2019. "Defining a data-driven maintenance policy: an application to an oil refinery plant." International Journal of Quality & Reliability Management 36, no. 1: 77-97.
The specific quality and safety requirements, typical of the food supply chain, force to strong action on implementing distribution networks, reducing transport and delivery costs, improving distribution efficiency and performance, increasing carriers control and flexibility. In this context, the selection and evaluation of third-party logistics has become a crucial aspect in order to realise an efficient food products distribution, with both a high level of service and competitive costs. This paper implements data envelopment analysis theory to analyse the case of an Italian food producer, which distributes its products on the national territory, through several third-party logistic carriers. This study made possible to define the most efficient carrier among those responsible for the distribution process, analysing the retail trade and large-scale retail trade. This paper represents a reference guideline to all the food companies involved in the process of evaluation and improvement of the distribution process.
Claudia Paciarotti; Maurizio Bevilacqua; Filippo Emanuele Ciarapica; Giovanni Mazzuto; Leonardo Postacchini. An efficiency analysis of food distribution system through data envelopment analysis. International Journal of Operational Research 2019, 36, 538 .
AMA StyleClaudia Paciarotti, Maurizio Bevilacqua, Filippo Emanuele Ciarapica, Giovanni Mazzuto, Leonardo Postacchini. An efficiency analysis of food distribution system through data envelopment analysis. International Journal of Operational Research. 2019; 36 (4):538.
Chicago/Turabian StyleClaudia Paciarotti; Maurizio Bevilacqua; Filippo Emanuele Ciarapica; Giovanni Mazzuto; Leonardo Postacchini. 2019. "An efficiency analysis of food distribution system through data envelopment analysis." International Journal of Operational Research 36, no. 4: 538.
This work aims at developing a method for analysing the domino effect among concepts affecting Supply Chain Resilience. The proposed method allows connecting concepts at many different levels (e.g. sources of risk, disruptions, supply chain management practices and other aspects). A case study for an electrical appliance supply chain has been used to illustrate the application of the proposed method in an operating context. The proposed method, based on Fuzzy Cognitive Maps, allows the players of the supply chain to evaluate indirect and total causal effect among different concepts affecting the supply chain resilience. The analysis conducted in this study unveiled several paths which form a sequence of concepts towards the top event (i.e. Supply Chain Resilience). The research approach proposed in this work can be applied to different industrial sectors and company managers can identify the capabilities and strategies that can make a firm more resilient and define supply chain design strategies to increase system resilience.
Maurizio Bevilacqua; F.E. Ciarapica; G. Marcucci; Giovanni Mazzuto. Conceptual model for analysing domino effect among concepts affecting supply chain resilience. Supply Chain Forum: An International Journal 2018, 19, 282 -299.
AMA StyleMaurizio Bevilacqua, F.E. Ciarapica, G. Marcucci, Giovanni Mazzuto. Conceptual model for analysing domino effect among concepts affecting supply chain resilience. Supply Chain Forum: An International Journal. 2018; 19 (4):282-299.
Chicago/Turabian StyleMaurizio Bevilacqua; F.E. Ciarapica; G. Marcucci; Giovanni Mazzuto. 2018. "Conceptual model for analysing domino effect among concepts affecting supply chain resilience." Supply Chain Forum: An International Journal 19, no. 4: 282-299.