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In this work, electrodes based on laser-scribed reduced graphene oxide were fabricated using filter paper as the substrate. To fabricate the electrodes, a water suspension of graphene oxide was filtered to produce a continuous and uniform film of graphene oxide on the filter paper surface. Subsequently, a CO2 laser was used to "write" the working, counter and reference eelctroes by reducing graphene oxide in specific areas to define complete sensors. Referecnce electrodes were then coated with a commercial Ag/AgCl conductive paste to produce a quasi Ag/AgCl reference. As fabricated devices were employed as electrochemical sensors for detection of phosphate ions in water by employing the molybdenum blue method. This method exploits the reaction between molybdate and phosphate ions in acidic media leading to a Keggin-type complex (PMo12O3 40) which, being electrochemically active, enables the indiret detection of phosphate ions. Sensors exhibited high selectivity and sensitive detection of phosphate ions in a wide linear range, from 1 to 20 & mu;M; with a limit of detection of 0.4 & mu;M. To demonstrate that sensors could be utilized for in-situ phosphate ion detection, paper substrate was first pre-loaded with sul-phuric acid and molybdate ions. During analysis, these chemicals were then desorbed directly into the test sample eliminating the need for any kind of external manipulation or reagent addition. Thus, this paper presents the fabrication of a portable, easy-to-use, biodegradable and fast phosphate ions sensor for in situ and real-time monitoring of water quality.

One of the key sectors for the green transition of European countries is construction, that is more and more asked to evolve towards innovative ecological binders and green cost-effective processes. The construction sector is highly energy intensive, and the cement production is one of the main sources of environmental pollution in the world. In this regard, GeoPolymers (GP) seem to be promising for a sustainable replacement of cementitious materials. Therefore, the Life Cycle Assessment (LCA) of the industrial production of different formulations of Geopolymer Concrete (GC) was performed in this study after scaling up the Life Cycle Inventory (LCI) from a laboratory scale to an industrial one. Based on LCA results, the Global Warming Potential (GWP) indicator demonstrated a lower greenhouse gas emission of the proposed GC production in respect to the CC manufacturing process, while no significant difference was observed in the GWP scores of the considered GC formulations when referring to the functional unit. Nevertheless, the usage of the innovative GC formulations, on an industrial scale, would avoid a significant reduction of sand and kaolin extracted, with a consequent decrease on the environmental impact. Finally, the economic assessment showed that the combined production of thermal and electrical energy by a cogeneration system could provide a significant cost reduction when the percentage of electricity fed into the public network is higher than 39%.

Nell'era digitale, la tecnologia è in continua evoluzione, con enormi progressi nell'automazione che consentono una gestione della manutenzione più efficiente ed economica. Le tecnologie digitali stanno convergendo e avanzando insieme alle industrie, determinando progressi significativi nella gestione della manutenzione. La tradizionale strategia di manutenzione preventiva gestita dall'uomo lascia progressivamente spazio alla manutenzione predittiva, che rappresenta un’ottima opportunità per migliorare significativamente la pianificazione della manutenzione del sistema, in particolare per i sistemi più complessi e dal significativo valore monetario. Tuttavia, l’implementazione di tecniche di manutenzione predittiva si trova ad affrontare una serie di sfide sostanziali, essendo richiesti l’utilizzo di tecnologie di tracciamento moderne, lo sviluppo di solidi sistemi di raccolta dati e l'esecuzione di una varietà di procedure complesse. Considerando il ruolo chiave della gestione della manutenzione nelle industrie, la motivazione principale di questo lavoro di ricerca consiste nell’indagare le pratiche esistenti e proporre nuove metodologie in grado di fornire implicazioni pratiche che possono essere utili nel contribuire a questo campo di studio in termini di previsione dei guasti, efficienza e ottimizzazione dei costi. Il presente lavoro di tesi è organizzato in tre capitoli, che rappresentano le principali aree di studio: 1) panoramica sulla gestione della manutenzione, 2) modelli decisionali a supporto della manutenzione predittiva, 3) trasformazione digitale nella gestione della manutenzione. Gli obiettivi di ricerca relativi ai menzionati capitoli sono: 1) studiare le attuali pratiche di manutenzione predittiva e le sue applicazioni nell'industria per identificare la sua capacità di prevedere e controllare i guasti delle apparecchiature di sistemi complessi; 2) studiare vari metodi di decisione multi-criterio (MCDM) e le loro applicazioni in modo da sviluppare una metodologia decisionale di manutenzione predittiva integrata per sistemi complessi nell'industria 4.0; 3) studiare la trasformazione digitale della gestione della manutenzione e i fattori critici della digitalizzazione, nonché l'incertezza nel processo decisionale per la gestione della manutenzione nell'industria 4.0. Questi obiettivi di ricerca vengono perseguiti attraverso una metodologia mista, ovvero sia qualitativa e sia quantitativa, basata su un ampio studio della letteratura. È stata sviluppata una revisione della letteratura sulla manutenzione predittiva e le sue applicazioni industriali insieme ai suoi limiti per identificare le carenze negli approcci esistenti. Sono state inoltre studiate varie metodologie MCDM per analizzarne gli effetti nella gestione della manutenzione ed è stata sviluppata una pletora di casi reali per offrire spunti gestionali pratici. In the digital era, technology is continually evolving, with enormous advancements in automation enabling more efficient and cost-effective maintenance management. Digital technologies are converging and advancing in tandem with industries, resulting in significant progress in maintenance management. The traditionally human-managed preventive maintenance strategy is outclassed with predictive maintenance, something that represents a wonderful opportunity to significantly improve system maintenance planning, particularly for more complex systems with a significant monetary value. However, predictive maintenance methods face numerous substantial challenges in terms of their application, as they necessitate the use of contemporary tracking technologies, the development of robust data-gathering systems, and the execution of a variety of intricate procedures. Considering the significance of maintenance management in industries, the primary motivation for this research work is to investigate existing practices and propose new methodologies capable of providing practical implications that may be useful in contributing to this field of study in terms of predicting failures, efficiency, and cost optimization. The present work is organized through three chapters, representing the main areas of study: 1) overview on maintenance management, 2) decision-making models supporting predictive maintenance, and 3) digital transformation in maintenance management. The objectives of research linked to the defined chapters are; 1) to study current practices of predictive maintenance and its applications in industry to identify its capability to predict and control equipment failures of complex systems; 2) to investigate various Multi-Criteria Decision-Making (MCDM) methods and their applications so as to develop an integrated predictive maintenance decision-making methodology for complex systems in industry 4.0; 3) to study the digital transformation of maintenance management and critical factors of digitalization, as well as uncertainty in the decision-making process for maintenance management in industry 4.0. In achieving the objectives of this research, a mixed methodology, i.e., qualitative and quantitative research, is carried out on the basis of an extensive literature study. A literature review of predictive maintenance, its industrial applications along with its limitations is developed to identify the shortcomings in existing approaches. Various MCDM methodologies have been studied as well to investigate their effects on maintenance management and a plethora of real-world cases have been developed to offer practical managerial insights.

Over the last decade, as a consequence of the global decarbonization process, the interest towards green hydrogen production has drastically increased. In particular a substantial research effort has focused on the efficient and affordable production of carbon-free hydrogen production processes. In this context, the development of more efficient electrolyzers with low-cost electrode/electrocatalyst materials can play a key role. This work, investigates the fabrication of electrodes of nickel-zinc alloys with nanowires morphology cathode for alkaline electrolyzers. Electrodes are obtained by the simple method of template electrosynthesis that is also inexpensive and easily scalable. Through the analysis of the morphological and chemical composition of nanowires, it was found that the nanowires composition is dependent on the concentration of two metals in the deposition solution. Electrocatalytic tests were performed in 30% w/w potassium hydroxide aqueous solution at room temperature. In order to study the electrodes stability, mid-term galvanostatic test was also carried out. All electrochemical tests show that nanowires with about 44.4% of zinc have the best performances. Particularly, at −50 mAcm−2, these electrodes have an overpotential 50 mV lower than pure Ni nanowire. NiZn nanowires show also a good stability over time without noticeable signs of performance decay.

To realize the benefits of a hydrogen economy, hydrogen must be produced cleanly, efficiently and affordably from renewable resources and, preferentially, close to the end-users. The goal is a sustainable cycle of hydrogen production and use: in the first stage of the cycle, hydrogen is produced from renewable resources and then used to feed a fuel cell. This cycle produces no pollution and no greenhouse gases. In this context, the development of electrolyzers producing high-purity hydrogen with a high efficiency and low cost is of great importance. Electrode materials play a fundamental role in influencing electrolyzer performances; consequently, in recent years considerable efforts have been made to obtain highly efficient and inexpensive catalyst materials. To reach both goals, we have developed electrodes based on Pd–Co alloys to be potentially used in the PEMEL electrolyzer. In fact, the Pd–Co alloy is a valid alternative to Pt for hydrogen evolution. The alloys were electrodeposited using two different types of support: carbon paper, to fabricate a porous structure, and anodic alumina membrane, to obtain regular arrays of nanowires. The goal was to obtain electrodes with very large active surface areas and a small amount of material. The research demonstrates that the electrochemical method is an ideal technique to obtain materials with good performances for the hydrogen evolution reaction. The Pd–Co alloy composition can be controlled by adjusting electrodeposition parameters (bath composition, current density and deposition time). The main results concerning the fabrication process and the characterization are presented and the performance in acid conditions is discussed.

The design of Product-Service Systems (PSS) in manufacturing companies has been widely researched over the past three decades, with contributions from various backgrounds. However, the multidisciplinary field led to the development of disparate approaches for PSS design, which furthermore deficiently include sustainability considerations. Such discord hinders PSS uptake in industry due to the unclarity of which process to use and crucial matters to consider. This paper aims to propose a generic process model to describe the early stage of the PSS design, which is the most influential phase for the success of the PSS offering throughout its life cycle, concerning the three dimensions of sustainability. The proposed generic process model addresses early-stage PSS design in three phases and considers seven clusters of entities through five activities. To achieve this aim, existing approaches for PSS design were identified through a systematic literature review, yielding a comprehensive overview of existing approaches distilled with respect to their content, the actions they propose and the sustainability principles they discuss. The systematic review was then followed by in-depth content analysis using widely adopted methodologies in design research and manufacturing companies for process decomposition and consequently synthesis, resulting in the proposed systematic generic process model for the early-stage design of sustainable PSS. The proposed process model was further examined concerning its use implications, limitations, and potential implementation steps.

The combination of servitization and digitalization is increasingly changing the economy and society at the global level towards sustainability goals. Companies are shifting their business models, typically oriented to selling products, towards providing bundles of products and services and integrating them with technologies enabling data collection and analysis, resulting in the so-called smart Product Service Systems (PSS). Different approaches and techniques have been put forth to design PSS and, more recently, smart PSS, but they continue to primarily concentrate on establishing value propositions and ignore the question of what sort of operational data can be gathered and used to deliver the PSS solution. Therefore, manufacturing companies willing to expand their portfolio with new advanced services nowadays still face multiple challenges. To address this gap, this study proposes the Service Engineering Methodology for the engineering of smart PSS (SEEM-Smart), which takes into account the trade-off between customer satisfaction and internal efficiency with a focus on data gathering and information flow. The methodology is then applied in a real-world setting. The case study shows the application of the SEEM-Smart for engineering a new data-driven service offering enabled by a cloud-based platform supporting the service provision.

The fabrication and characterization of nickel-alloy electrodes for alkaline electrolysers is reported. Three different alloys (Ni–Co, Ni–Zn and Ni–W) at different composition were studied in order to determine the optimum condition. Nanostructured electrodes were obtained by template electrodeposition into a nanoporous membrane, starting from aqueous solution containing the two elements of the alloy at different concentrations. Composition of alloys can be tuned by electrolyte composition and also depends on the difference of the redox potential of elements and on the presence of complexing agents in deposition bath. Electrochemical and electrocatalytic tests, aimed at establishing the best alloy composition, were carried out for hydrogen evolution reaction. Then, test conducted at a constant current density in potassium hydroxide (30% w/w) aqueous solution were also performed. For all investigated alloys, very encouraging results were obtained and in particular Ni–Co alloys richer in Co showed the best performance.

Purpose Spent coffee grounds (SCG) are biowastes extensively generated within the coffee supply chain. Nowadays, their disposal represents an increasing environmental concern due to its toxicity and organic nature. With the estimated increase of coffee production and consumption in the upcoming years, there is an imperative need to find a proper reverse option, along with a novel industrial application, which allows for the valorization of this coffee by-product within a circular economy perspective. This study aims at investigating a potential reuse of spent coffee grounds to produce novel construction materials to be used for sustainable buildings. Methods After having illustrated the forward flows within the coffee life cycle and the potential reverse flow options, an evaluation method based on multi-criteria analyses was elaborated to test not only the technical but also the environmental and economic performances of novel materials originating from the incorporation of SCG as an aggregate in natural hydraulic lime and geopolymer-based mortars. Moreover, we focus on the reuse of another waste streams— biomass fly ash—deriving from the paper-pulp industry, rarely investigated in both traditional construction applications and in geopolymer manufacture. The two (geopolymer- and lime-based) mortar typologies are here studied and compared as potential green material for applications in construction, with satisfying engineering performance and high insulation attitude, giving a new life to a common organic waste. Consequently, we compare eight formulations by means of multi-criteria approaches that are nowadays claimed as a useful and effective decision aiding support instrument to assess the development of new sustainable construction materials. They permit to consider simultaneously some controversial and often uncertain aspects like technological (as the usual scientific studies do), environmental, and economic (more difficult to easily approach and evaluate). For this purpose, in this paper, we have analyzed the performance of the novel bio-composite mortars using VIKOR and TOPSIS methods to rank a set of alternatives according to various evaluation criteria that often conflict one with each other. Results Results show that adding spent coffee grounds can efficiently improve the technical and sustainable performances of the novel mortars for different applications in the building sector. The presence of SCG increases water absorption and improves the insulation performance along with an environmental impact reduction. The considered technological properties are highly promising—such as the improvement in thermal insulation. In particular, even the addition of only 5% SCG leads to a significant reduction of the thermal conductivity and consequently to a greater insulating performance. Conclusions To date, most of the available literature on recycling SCG in construction materials do not consider mortar-based applications and, moreover, nor multi-criteria approaches. Therefore, our study proposes itself as an innovative track solution to food waste management lowering the employment of non-renewable natural resources and the costs associated to construction material production. At the same time, a novel and innovative way of such waste disposal is suggested, pursuing the sustainability and substantially reducing the environmental impact of construction and building materials. This study is a fundamental step in assessing the applicability of our designed and produced materials and its potentials to be produced at an industrial scale.

Wearable sensors for sweat biomarkers can provide facile analyte capability and monitoring for several diseases. In this work, a green wearable sensor for sweat absorption and chloride sensing is presented. In order to produce a sustainable device, polylactic acid (PLA) was used for both the substrate and the sweat absorption pad fabrication. The sensor material for chloride detection consisted of silver-based reference, working, and counter electrodes obtained from upcycled compact discs. The PLA substrates were prepared by thermal bonding of PLA sheets obtained via a flat die extruder, prototyped in single functional layers via CO2 laser cutting, and bonded via hot-press. The effect of cold plasma treatment on the transparency and bonding strength of PLA sheets was investigated. The PLA membrane, to act as a sweat absorption pad, was directly deposited onto the membrane holder layer by means of an electrolyte-assisted electrospinning technique. The membrane adhesion capacity was investigated by indentation tests in both dry and wet modes. The integrated device made of PLA and silver-based electrodes was used to quantify chloride ions. The calibration tests revealed that the proposed sensor platform could quantify chloride ions in a sensitive and reproducible way. The chloride ions were also quantified in a real sweat sample collected from a healthy volunteer. Therefore, we demonstrated the feasibility of a green and integrated sweat sensor that can be applied directly on human skin to quantify chloride ions.

Nanostructured Pb electrodes consisting of nanowire arrays were obtained by electrodeposition, to be used as negative electrodes for lead–acid batteries. Reduced graphene oxide was added to improve their performances. This was achieved via the electrochemical reduction of graphene oxide directly on the surface of nanowire arrays. The electrodes with and without reduced graphene oxide were tested in a 5 M sulfuric acid solution using a commercial pasted positive plate and an absorbed glass mat separator in a zero-gap configuration. The electrodes were tested in deep cycling conditions with a very low cut-off potential. Charge–discharge tests were performed at 5C. The electrode with reduced graphene oxide outperformed the electrode without reduced graphene oxide, as it was able to work with a very high utilization of active mass and efficiency. A specific capacity of 258 mAhg−1–very close to the theoretical one–was achieved, and the electrode lasted for more than 1000 cycles. On the other hand, the electrode without reduced graphene oxide achieved a capacity close to 230 mAhg−1, which corresponds to a 90% of utilization of active mass.

Quantification of oxidative stress is a challenging task that can help in monitoring chronic inflammatory respiratory airway diseases. Different studies can be found in the literature regarding the development of electrochemical sensors for H2O2 in cell culture medium to quantify oxidative stress. However, there are very limited data regarding the impact of the cell culture medium on the electrochemical quantification of H2O2. In this work, we studied the effect of different media (RPMI, MEM, DMEM, Ham’s F12 and BEGM/DMEM) on the electrochemical quantification of H2O2. The used electrode is based on reduced graphene oxide (rGO) and gold nanoparticles (AuNPs) and was obtained by co-electrodeposition. To reduce the electrode fouling by the medium, the effect of dilution was investigated using diluted (50% v/v in PBS) and undiluted media. With the same aim, two electrochemical techniques were employed, chronoamperometry (CH) and linear scan voltammetry (LSV). The influence of different interfering species and the effect of the operating temperature of 37 °C were also studied in order to simulate the operation of the sensor in the culture plate. The LSV technique made the sensor adaptable to undiluted media because the test time is short, compared with the CH technique, reducing the electrode fouling. The long-term stability of the sensors was also evaluated by testing different storage conditions. By storing the electrode at 4 °C, the sensor performance was not reduced for up to 21 days. The sensors were validated measuring H2O2 released by two different human bronchial epithelial cell lines (A549, 16HBE) and human primary bronchial epithelial cells (PBEC) grown in RPMI, MEM and BEGM/DMEM media. To confirm the results obtained with the sensor, the release of reactive oxygen species was also evaluated with a standard flow cytometry technique. The results obtained with the two techniques were very similar. Thus, the LSV technique permits using the proposed sensor for an effective oxidative stress quantification in different culture media and without dilution.

Collaboration in green product design and development is becoming more significant to ensure a brighter future for eco industries, and research into such innovation has increased in recent years. So, it has been emphasized by practitioners that green thinking should be adopted from the design stage through the development stage and into the disposal stage of a product. However, it is challenging to identify the severity of strategies that mainly hampers the growth of green product design and development (GPDD). The current research aimed to identify and rank various strategies based on their significant impact on the development of green product design. The study contains three segments: (1) Multiple strategies were identified based on the published literature, project reports, and interactions with academics and industry experts. Then, 22 strategies were selected for GPDD that could be divided into five clusters: cooperation and commitment from top management commitment (TMC), design for environment (DFE), utilization of green technologies (UGT), green external supply chain management (GESCM), and green internal supply chain management (GISCM). (2) The Kano model was used to identify user requirements and satisfaction levels. (3) The priority weight and rank among selected strategies were determined using the fuzzy AHP approach. The results show that the “DFE” ranks highest among the main categories, and “design for disassembly” has been highlighted as a necessary sub-criteria in the emerging eco industries. Various recommendations are suggested to adopt these techniques, which augment the growth of green product design and development for eco industries.

Digital transformation is changing the textile machinery industry. The growing digital development is breaking down the traditional barriers of the industry, leading to advantages for both the textile machinery enterprises and their customers. Accordingly, many academics and practitioners believe that, in the next years, rethinking the future of this sector in the light of digitalization will be crucial. However, scientific research about digital transformation in the textile machinery industry is lacking. This paper tries to bridge this gap by carrying out a mixed quantitative and qualitative approach through 17 in-depth interviews with a sample of Italian textile machinery companies. The aim of this paper is to provide insights on the level of awareness within the industry and a holistic overview about the main drivers, challenges and opportunities which have to be taken into account towards leveraging of new technologies. Finally, the article suggests some possible developments for further research.

This preliminary work shows a new and innovative way to produce laser scribed reduced graphene oxide (LSGO) electrodes using different porous substrates (ranging from paper to plastic and fabric). The obtained electrodes were also tested as electrochemical sensors towards the detection of phosphate ions in water. To obtain the electrodes, a water suspension of GO was filtered on top of substrate (such as Whatman® filter paper) and a complete sensor was obtained from its reduction using a CO2 laser. The electrode is composed of working and counter electrodes made of LSGO and a reference electrode of a Ag/AgCl obtained by using a commercial AgCl conductive paste. Phosphate ions were detected by exploiting the reaction between molybdate and phosphate ions in acidic media (known in literature as molybdenum blue method). This chemical reaction produces the Keggin-type complex (PMo12O40)3-, that can be reduced under applied potential. The obtained results show that phosphate ions can be detected in a wide linear range, from 0.001 mM to 1mM, in presence of 1mM molybdate with a very satisfying selectivity. We also tried to pre-treatment the paper substrate with acidic molybdate ions in order to obtain a ready-made sensor directly usable for the detection of phosphate ions in situ avoiding any kind of real sample manipulation For this aim, the paper substrate was soaked with sulphuric acid and molybdate solution and dried in order to desorb these chemicals directly into the water sample to be analyzed. Preliminary results, shows that the process of absorption and desorption can be carried out by optimizing the volume and concentration of the absorbed solution and thus can be used to obtain a portable, easy to use and fast phosphate sensor for in situ and real time monitoring of water quality.

With the rise of digital technologies, the possibility to retrieve a large amount of data from connected sources can become a key source of value creation in the context of service provision. Never...

Since the 1970s, Human Reliability Analysis (HRA) methods have received a great interest for the quantification of the Human Error Probability (HEP) in Nuclear Power Plants (NPPs). To this purpose, the second-generation HRA methods consider contextual and cognitive factors - named Performance Shaping Factors (PSFs) - that may influence the workers’ performance during tasks execution. Despite the recent extension of HRA methods to different fields, only few studies refer to the manufacturing sector. In addition, the majority of contributions assume the independence among PSFs, which may result in an over or under estimation of HEP. Therefore, the present paper focuses on the manufacturing sector to propose a Fuzzy DEMATEL (FDEMATEL) based method to support the risk analyst in the quantification of PSF interrelationships and importance, when computing HEP. As a result, the most influential human factors on which taking priority actions to improve the overall human reliability may be identified accurately. Based on a selected list of PSFs, the methodological approach is implemented in an Italian textile company, where experience and training factors are demonstrated to be the most central ones to increase the human reliability when performing the weaving process tasks. The designed approach is well structured and effortless as well as it allows at considering the uncertainty and vagueness of input data and a group decision context.

Recent economic transformations have forced companies to redefine their value propositions, increasing traditional product offerings with supplementary services—the so-called Product-Service System (PSS). Among them, the adoption of Industry 4.0 technologies is very common. However, the directions that companies are undertaking to offer new value to their customers in the Industry 4.0 have not yet been investigated in detail. Based on a focus group, this paper contributes to this understanding by identifying the main trajectories that would shape a future scenario in which PSS and Industry 4.0 would merge. In addition, future research directions addressing (a) the transformation of the PSS value chain into a PSS ecosystem, (b) the transformation inside a single company towards becoming a PSS provider, and (c) the digital transformation of the traditional PSS business model are identified.

Sustainability has been always recognised as a critical factor for the future of our society and the recent advancement in the technology domain can help in achieving sustainable operations. In this paper, the benefits related to data gathering, real-time analysis and optimization are specifically emphasised. A discrete-event simulation model has been proposed as a first step towards sustainable production scheduling in a textile manufacturing industry. The model is designed to be feed in the next future with real-time data, gathered form IoT technologies. It has been proved that the simulation supports the company in reducing setup, increase productivity and avoid resource wasting.

The evolution of internet of things, cyber physical system, digital twin and artificial intelligence is stimulating the transformation of the product-centric processes toward smart control digital service-oriented ones. With the implementation of artificial intelligence and machine learning algorithms, IoT has accelerated the movement from connecting devices to the Internet to collecting and analyzing data by using sensors to extract data throughout the lifecycle of the product, in order to create value and knowledge from the huge amount of the collected data, such as the knowledge of the product performance and conditions. The importance of internet of things technology in manufacturing comes from its ability to collect real time data and extract valuable knowledge from these huge amount of data which can be supported through the implementation of smart IoT-based servitization framework which was presented in this research together with a 10-steps approach diagram. Moreover, literature review has been carried out to develop the research and deepen the knowledge in the field of IoT, CPS, DT and Artificial Intelligence, and then interviews with experts have been conducted to validate the contents, since DT is a quite new technology, so there are different points of view about certain concepts of this technology. The main scope and objective of this research is to allow organizational processes and companies to benefit form the value added information that can be achieved through the right implementation of advanced technologies such as IoT, DT, CPS, and artificial intelligence which can provide financial benefits to the manufacturing companies and competitive advantages to make them stand among the other competitors in the market. The effectiveness of such technologies can not only improve the financial benefits of the companies, but the workers? safety and health through the real time monitoring of the work environment. Here in this research the main aim is to present the right frameworks that can be used in the literature through companies and researchers to allow them to implement these technologies correctly in the boundaries of their businesses. In addition to that, the Smart factory concept, as introduced in the context of Industry 4.0, promotes the development of a new interconnected manufacturing environment where human operators cooperate with machines. While the role of the operator in the smart factory is substantially being rediscussed, the industrial approach towards safety and ergonomics still appears frequently outdated and inadequate. This research approaches such topic referring to the vibration risk, a well-known cause of work-related pathologies, and proposes an original methodology for mapping the risk exposure related to the performed activities. A miniaturized wearable device is employed to collect vibration data, and the obtained signals are segmented and processed in order to extract the significant features. An original machine learning classifier is then employed to recognize the worker?s activity and evaluate the related exposure to vibration risks. Finally, the results obtained from the experimental analysis demonstrate feasibility and the effectiveness of the proposed methodology.

At the same time as the digital age is emerging in all areas of engineering, innovative tools for the design and management of industrial production plants are increasingly being developed, with engineers and managers looking for integrated solutions that speed up the conception of the Life Cycle of an industrial plant. Many tools, which are based on a renewed conception of simulation models, have been revisited thanks to the potential of software and computers on the market today, highlighting the concept of interoperability and data sharing. The present research project has as its basic purpose the integration of the concepts of Building Information Modelling and Process Simulation, applied simultaneously to the construction process and to the industrial plants. The analysis showed that high-performance alternative software solutions are already available, each of which is able to give more priority to specific aspects. The final result is the design of an innovative software solution with the aim of bridging the current gap and constituting a platform truly integrated with new technologies. From the Digital Supply Chain to the Internet of Things and from Artificial Intelligence to Factory 4.0, production plants will continue to play a key role in the end-to-end manufacturing supply chain: the present research project has the ambition to add a small piece in the framework of industrial sustainability based on modelling and process control.

This article aims at proposing a method to stochastically analyze values streams taking into consideration the effect of critical uncertainty sources on lead time. The proposed method combines valu...

The spread of the Industry 4.0 paradigm introduced an increasing number of technological innovations into companies to improve logistics processes. During a transition to Logistics 4.0, it is necessary to consider all the issues that affect logistics, among which human factors (HF) are considered to be the most important. On the basis of the existing scientific literature, this article aimed to identify the relationships between 4.0 technologies implementation and HF concerning the evolution of human activities towards different hypothetical future scenarios (i.e., no changes, work replaced, or work assisted), analysing in detail the HF affected by 4.0 technologies. These relationships, which were summarised in a matrix, were also tested through a single longitudinal case study. The research showed that technology tends to replace logistics operators, not only for dangerous physical tasks but also for cognitive tasks that are stressful and repetitive. Moreover, for some tasks in which many different HF are involved, the evolution is towards operator assistance, rather than replacement.

PurposeThe purpose of this paper is to analyse the fit between the strategy of service centres and their business model (BM) and to identify the BM components' characteristics and links that allow it to stand out in terms of service delivery and business performance.Design/methodology/approachThis study applies an inductive qualitative multiple case study approach through the empirical analysis of top-performing Italian service centres operating in the Medium–Heavy Commercial Vehicle sector.FindingsResearch findings underline that the BM components of top performers are consistent amongst each other and with the adopted strategy and make a positive impact on the firm's performance. In particular, top performers are characterised by a solid financial structure based on equity, formalised and flexible organisational structures and processes, clarity in strategic direction and long-term orientation, grounded capabilities, competences and skills, trustful relationships with main service partners and a comprehensive set of managerial mechanisms.Research limitations/implicationsThis paper presents some limitations, typical of qualitative research based on case studies. Future works may include other dimensions of performance for identifying top performers, and extend the empirical analysis to different sectors and national contexts.Originality/valueThis paper supports the relevance of contingency theory – particularly the strategy-structure-performance paradigm – in the analysis of the role of a BM in successful servitization strategies of service centres. It highlights that the BMs of the top-performing companies are characterised by some common elements. From a practical perspective, the authors provide insights that can be useful for designing successful service-based BMs for service networks.

PurposeThis article aims to investigate the organisational implications of adopting Industry 4.0 (I4.0) technologies, giving specific attention to operations. The paper addresses these implications in two directions: organisational prerequisites for, and consequences of, I4.0 technologies.Design/methodology/approachThe research is based on a multiple case study of Italian small and medium enterprises (SMEs) in manufacturing. Ten case studies have been developed through interviews, company visits and secondary data collection.FindingsThe multiple case study results show that: (1) a lean organisational structure supports effective adoption of I4.0 technologies; (2) introducing such technologies is linked to developing a new kind of job profile (i.e. the “Autonomous Operative Job Profile”); and (3) higher levels of technology adoption create a higher need for non-technical competences.Research limitations/implicationsA limitation of this research relates to the highly heterogeneous maturity levels of the sampled companies, due to the relative newness of the I4.0 paradigm. Future research could, therefore, longitudinally analyse the technology integration process within organisations.Practical implicationsThis research provides preliminary evidence about how organisations and technologies co-evolve, thus suggesting that managers should co-design these areas. It also demonstrates the extreme importance of designing a structured process and a clear set of human resource management tools to favour SME organisational development.Originality/valueThe study is built upon a conceptual framework derived from the sociotechnical perspective that analyses the interconnections between technology implementation and organisational change. From the results, three research propositions are derived to be tested on a larger scale.

In recent years, the introduction of Industry 4.0 technologies in the manufacturing landscape promoted the development of smart factories characterised by relevant socio-technical interactions between humans and machines. In this context, understanding and modelling the role of humans turns out to be crucial to develop efficient manufacturing systems of the future. Grounding on previous researches in the field of Human-in-the-Loop and Human Cyber-Physical Systems, the paper aims at contributing to a deep reflection about human-machine interaction in the wider perspective of Social Human-in-the-Loop Cyber-Physical Production Systems, in which more agents collaborate and are socially connected. After presenting an evolution of manufacturing control organisations, an architecture to depict social interactions in smart factories is proposed. The proposed architecture contributes to the representation of different human roles in the smart factory and the exploration of both hierarchical and heterarchical data-driven decision-making processes in manufacturing.

In the wake of industry 4.0, many industries have started to pivot towards digital, collaborative, and smart manufacturing systems by connecting their machinery as part of the Internet of Things (IoT). IoT has the potential to provide visibility and improve manufacturing systems through data collection, analysis, and subsequent actions based on insights generated from large amounts of manufacturing data. Even though comparatively newer equipment come readily equipped with embedded sensors and industrial connectivity necessary to connect to the IoT environment, there are many manufacturers (equipment users) who rely on long standing “legacy systems” that offer no or very limited connectivity. In this context, solutions mostly result in the development of low-cost retrofit or upgrade kits that allow integrating legacy equipment into Industry 4.0 environment and thus enable digital servitization. Servitization is a transformation journey that involves firms developing the capabilities they need to provide technical and data-driven services that supplement traditional product offerings. However, retrofitting solutions of legacy equipment rarely involve Original Equipment Manufacturers (OEMs) who may otherwise leverage the opportunity to create and capture unique value by retrofitting and then provisioning data-driven value-added services for the manufacturers. Hence, the primary objective of this paper is to identify and analyze the available literature on retrofitting and upgrading of the legacy equipment for Industry 4.0 integration. In doing so, this study also investigates the potential opportunities and challenges of OEMs in supporting the Industry 4.0 transition of legacy equipment in a servitization context.