1,720,972 research outputs found
Optimization of drugs delivery routes through location routing problem (Lrp): A case study
No full textThe location-routing problem (LRP) plays a critical role in logistics system optimization where companies need to set up a proper design of operation strategies to fulfil the demand for product delivery. This paper analysed an efficient products distribution of a delivery drugs Italian company through the development of LRP analysis. Thus, due to a reduction of company sales volume, a delivery routes re-planning was investigated moving from a logistics plan that involved the use of the main warehouse with replenishment at intermediate one to a new configuration with only the main warehouse. The analysis is based on the identification of the ideal location of the main warehouse by optimizing the delivery routes of goods to meet customers’ demands. Optimal solutions are found according to the integration of the facility location and the vehicle-routing problems (VRP) aimed at minimizing the total logistic system cost
Resilience 4.0 in beer production line: IoT, Edge Computing, Fog Computing, and Cloud
No full textThe purpose of this research is to analyze the impact of Industry 4.0 (I4.0) on the Resilience of a beverage production line, particularly a beer one. As this characteristic is fundamental in any field, it must be addressed also in the field of Industry 4.0. The new technologies made available by the Fourth Industrial Revolution can bring huge benefits on the production plants on different aspects, like: predictive and prescriptive maintenance, increased OEE, productivity, efficiency, and effectiveness; better quality of output and customer satisfaction. In this paper, the chosen 4.0 technologies are IoT, Edge Computing, Fog Computing, and Cloud. This choice has been made to follow on of the pillars of Industry 4.0: starting small. This research focuses on field level, with IoT, and the management of the collected data by the integration of Edge Computing and Fog Computing, before sending the final data to Cloud. The methodology used to write this paper is divided into clear steps, logically connected to each other. The first activity has been a deep analysis and study of the existing literature, to understand the features of each technology and their strengths and weaknesses. Moreover, it has also been searched for how to integrate these technologies and their impact on the Resilience of systems embedded with them. After this deep literature review, the next phase was to evaluate the requirements and needs of the beer production line, to establish how the studied technologies can bring value. Finally, the focus on Resilience has been set. The key findings of this research are the characteristics of the analyzed technologies and the impact on Resilience. The value of this innovative paper is the deep study of the field technologies and the design of a framework to bring Resilience 4.0 into production companies, especially to beer production lines
Application of Total Cost of Ownership Driven Methodology for Predictive Maintenance Implementation in the Food Industry
No full textThe Industry 4.0 has boosted technological advancements leading to the development of predictive maintenance solutions in the manufacturing sector. In this scenario, companies are dealing with complex decision-making problems involving investments in technological solutions and data analytics modelling implementation. Therefore, there is a need for strategic guidance for defining the best investments options through a technical-economic approach based on system modelling and lifecycle perspective. This paper presents the implementation within a relevant Italian food company of a methodology developed to evaluate predictive maintenance implementation scenarios based on alternative condition monitoring solutions, under the lenses of Total Cost of Ownership. Technical systemic performances are evaluated through Monte Carlo simulation based on the Reliability Block Diagram (RBD) model of the system. The results provide concrete evidence of effective applicability of the methodology guiding decision-makers toward a solution for improving technical system performances and reducing lifecycle costs
Ageing management and monitoring of critical equipment in petrochemical applications: an Italian case study
No full textOver the years, environmental health and safety issues have seen an ever-growing interest developed becoming a crucial and strategic aspect of today’s business operations. Companies have focused efforts and resources on tasks, techniques, and rules to guarantee high protection level of health and safety at work according to national and international laws. In 2015, Legislative Decree No. 105, was issued in Italy, implementing the Directive 2012/18/EU on the control of major accident hazards involving dangerous substances. This Directive made clear the obligation to adopt a risk monitoring and control plan associated with the aging and corrosion of plants and equipment by operators of industrial plants. In this context, this work proposes a methodology, consisting of a structured approach, aimed at identifying the factors that have an impact on ageing process of industrial devices. In the first step, the root causes that can accelerate or retard the deterioration and damage process associated with time in service have been defined through an ageing fishbone (AFB) approach. In the second step, the effects of these factors have been assessed through a specific tool index-based by considering a rewards and penalties model. This methodology is applied to the critical equipment operating in the Mild Hydrocracking unit of a refinery located in southern Sardinia (Italy). The results show the practical applicability of an effective and easy to implement aging management tool aimed at ensuring safety and reliability of the equipment in a real use-case. Simplicity and relatively easy physical interpretation of the criticalities allow managing all potential aging effects resulting in key aspects to support the risk-based analysis
Proposta di un modello fuzzy logic per un processo multicriterio di localizzazione di un impianto industriale
No full textLow-enthalpy geothermal systems for air conditioning: A case study in the Mediterranean climate
No full textThis paper presents a preliminary evaluation of the technical and economic feasibility of a low-enthalpy geothermal system for air conditioning and its integration with other systems, including a photovoltaic plant and an electrical storage system. The pilot building is a research center located in the southern side of the Mediterranean basin (Sardinia, Italy). Preliminarily, the main geological, hydrogeological and geothermal characteristics of the area were analyzed. Then, an energetic assessment of the building and its plants was performed. The hourly production of a photovoltaic plant already designed for the building was assessed. To improve the energy efficiency and the thermal energy self-consumption, an alternative thermal generation plant was proposed to replace the existing air conditioning system: a water-water heat pump coupled with a low-enthalpy geothermal probe (vertical configuration), to be embedded into the ground or placed into an existing groundwater well. The feasibility of electric storage was evaluated by considering a system capacity of 100 kWh to temporarily store and self-consume the electricity overproduced by the photovoltaic plant. A preliminary economic assessment showed the viability of the photovoltaic system. The 100 kWh-capacity electric storage will increase the self-production percentage, but it is not economically affordable. The replacement of the current air-water heat pumps with one water-water heat pump will be economically convenient if coupled with a groundwater geothermal probe, but the solution of a vertical probe embedded into the ground is unsustainable, due to high drilling costs
A bibliometric analysis of energy efficiency assessment and management practices in ports
No full textEnergy management and efficiency in port terminals play a fundamental role in facing the environmental challenges of the maritime industry. The search for novel and efficient solutions to change energy consumption patterns is increasingly becoming one of the main concerns of researchers and practitioners to ensure sustainable operation and reduce CO2 emissions. This paper presents a bibliometric analysis of the extant literature aiming at providing a comprehensive overview of the current state of knowledge on energy efficiency assessment and management practices in ports and harbors. The main objective is to determine the most influential research streams that have been conducted in this field and identify the emerging trends and areas of interest. The findings show that energy optimization techniques and sustainable practices are receiving more and more attention in the port and maritime sectors, highlighting a consistent body of research focused on energy efficiency measures, renewable energy integration, and advanced technologies adoption. The study's achieved findings can be used to clarify the potential and challenges related to energy efficiency assessment and management practices in port terminals by providing both research and practice perspectives
Industry 4.0 assessment in agri-food and dairy sector: a literature review of Maturity Models
No full textThe agri-food industry and the dairy industry are undergoing a profound transformation due to the spread of new digital technologies. This digital transition is pushing for the use of enabling technologies indicated by the new Industry 4.0 paradigms, impacting on how companies typically provide their goods and services. Although companies are aware of the potential benefits of this transition, they have tackled the required task of adapting or reshaping their existing processes to the new era of digitalization. To do that, proper and accurate digital maturity models (MM) should be developed aiming at defining roadmaps to assist organizations in Industry 4.0 adoption. These models are necessary to both verify the technological level and to define the starting point of a path that can lead to an effective digital transition of manufacturing. The concept of MM has been widely investigated in literature, especially for medium and large companies while limited studies have been focused on its implementation in Small and Medium Enterprises (SMEs) context. Indeed, SMEs exhibit substantial constraints in terms of limited financial and human resources which may hinder the roadmaps adoption developed for large organizations making the definition of the Design Principles a complex and tailored task. This work proposes a critical review of the extant literature on MMs for the implementation of Industry 4.0 technologies in the SMEs context with emphasis on the food processing sector. Having realised the absence of specific MMs for the dairy industry, the main purpose is to identify, among the MMs currently available in the literature, the ones that can more effectively be adapted for the digital level assessment of companies operating in that sector. Results highlight how employing the MMs typically applied for manufacturing organizations may be a valuable tool for addressing the specific needs of the dairy industry
A novel decision support system for managing predictive maintenance strategies based on machine learning approaches
No full textNowadays, the industrial environment is characterised by growing competitiveness, short response times, cost reduction and reliability of production to meet customer needs. Thus, the new industrial paradigm of Industry 4.0 has gained interest worldwide, leading many manufacturers to a significant digital transformation. Digital technologies have enabled a novel approach to decision-making processes based on data-driven strategies, where knowledge extraction relies on the analysis of a large amount of data from sensor-equipped factories. In this context, Predictive Maintenance (PdM) based on Machine Learning (ML) is one of the most prominent data-driven analytical approaches for monitoring industrial systems aiming to maximise reliability and efficiency. In fact, PdM aims not only to reduce equipment failure rates but also to minimise operating costs by maximising equipment life. When considering industrial applications, industries deal with different issues and constraints relating to process digitalisation. The main purpose of this study is to develop a new decision support system based on decision trees (DTs) that guides the decision-making process of PdM implementation, considering context-aware information, quality and maturity of collected data, severity, occurrence and detectability of potential failures (identified through FMECA analysis) and direct and indirect maintenance costs. The decision trees allow the study of different scenarios to identify the conditions under which a PdM policy, based on the ML algorithm, is economically profitable compared to corrective maintenance, considered to be the current scenario. The results show that the proposed methodology is a simple and easy way to implement tool to support the decision process by assessing the different levels of occurrence and severity of failures. For each level, savings and the potential costs have been evaluated at leaf nodes of the trees aimed at defining the most suitable maintenance strategy implementation. Finally, the proposed DTs are applied to a real industrial case to illustrate their applicability and robustness
- …
