Nowadays, the adoption of integrated product-service solutions has become imperative for companies dealing with ever-increasing, sophisticated customer requests. Advanced informatics tools can support companies in collecting and interpreting a large amount of information to provide more customer-tailored solutions. However, few studies have investigated the use of Artificial Intelligence (AI) tools in multiple criteria decision-making (MCDM) for the development of integrated product-service offerings. Using a case study from the green energy sector, this study explores the integration of AI tools with Quality Function Deployment for Product-Service Systems (QFDforPSS) to refine customer-centric PSS development by leveraging data from customer care services and customer relationship management systems. Results obtained by AI tools were compared with those of experts. Research findings underscore AI's capability to extract detailed insights from customer data, enabling a more holistic and concurrent development of PSS characteristics. This research introduces a novel data-driven framework for PSS design, demonstrating AI's potential to transform customer service data into actionable specifications and providing a more thorough analysis of PSS features. The study not only suggests several implications that can assist management in improving business offerings in the photovoltaic industry but also augments knowledge on the capabilities of QFD powered by AI tools.

The transition toward circular business models, particularly in the electrical and electronic equipment (EEE) sector, has increasingly embraced Product-Service Systems (PSS) solutions. While numerous studies support this shift and various tools and methodologies have been developed to facilitate the design and implementation of PSS, much of the existing research remains focused on defining overarching characteristics of these offerings. This emphasis often overlooks the detailed selection of PSS concepts, neglecting the companies’ capabilities and posing potential risks to effective value delivery.This study addresses this gap by proposing a novel method, named Extended Quality Function Deployment for Product Service System (EQFDforPSS). This tool advances the benefits of conventional QFD-based approaches by integrating the analysis of PSS elements (i.e. the units that contribute to making up a product or service component) and providing a procedure for the assessment of PSS conceptual solutions. In practice, the method builds on the metrics of four different Houses of Quality (HoQs) to align customer requirements with the characteristics, components and elements of a PSS solution, offering greater granularity and specificity in identifying and structuring PSS features compared to extant tools.Beyond its theoretical innovation, the effectiveness of the EQFDforPSS method was tested through a real case study involving the development of a PSS business offering for a photovoltaic (PV) system to be used in an urban context. These first results highlighted the relevance of PSS attributes related to improving the system's life cycle management, especially those related to customer care, maintenance, and end-of-life activities.While acknowledging the inherent limitations of novel tools, the study confirms the viability of the proposed method for developing PSS concept models, and its practical application shows a feasible procedure to develop more circular solutions in the EEE sector.

Digitization makes knowledge and information a centerpiece since these connect all dimensions affected by innovation, i.e., people, processes, organization, business, and technology. Maturity Models (MMs) support managers in this evolution. The paper provides a theoretical formalization of MMs to give a practical knowledge contribution to increasing the intensiveness of information in enterprises, through digitization evolution. The paper reviews the main MMs, and presents their state of the art. Then, it defines a backbone structure common to MMs to abstract and describe their features by a meta-model. The new meta-model-driven approach guides companies in the selection of MMs, and in designing a new MM, where needed. The meta-model formalizes the two-levels inputs, the process, and the output to align the company's motivations with the MM features, resulting in the definition of an appropriate MM for organizations. A qualitative exploratory case study shows the approach and its results, providing guidelines for future actions.

In recent years, the need to reduce fossil fuel consumption has triggered a push towards alternative solutions for more sustainable vehicles. This trend is stimulated by increasingly stringent regulations aimed at reducing greenhouse gas (GHG) emissions in the transportation sector. However, in agriculture, the adoption of zero-emission vehicles (ZEVs) is not aligned with the trends seen in other sectors due to technological barriers. Currently, only a few prototypal solutions for hybrid tractors have emerged due to their advantages in battery size, weight, and cost compared to fully electric solutions. In the literature, few studies have examined the environmental performance of hybrid electric tractors (HETs), mainly comparing them to conventional models in terms of work performance. Conversely, the user perspective is scarcely addressed although users' needs vary significantly depending on farm dimensions and cultivation types. The study proposes an analysis of the environmental impact of a hybrid solution by comparing it with a diesel engine Stage V and one produced under the Stage IIIb requirements. The novelty of this paper results from combining the life cycle assessment (LCA) outputs with a scenario modelling framework to uncover the potential environmental impact of the three models considering different use scenarios. Such an approach goes beyond traditional LCA analyses by providing more detailed information on the practical environmental implications related to these propulsion units. The results show that depending on the user scenario, the hybrid solution is not always the least pollutant, thus requiring further research in the HET sector to better tailor technical solutions based on the target customer.

In the recent literature, numerous tools have been found that have been used to evaluate and improve the resilience of socio-technical systems such as hospitals. The Functional Resonance Analysis Method (FRAM) is certainly one of the most diffused, as it can provide information on the system structure and its components through a systemic analysis approach. FRAM has been successfully applied in different contexts. However, in the healthcare sector, only a few studies propose practical analyses that can support practitioners in systematically observing and analyzing events, both when things go right and when they go wrong. To reduce such a research gap, the current study focuses on the application of FRAM to two different case studies: (1) an accident that occurred in a hyperbaric oxygen therapy unit, and (2) the risk assessment of a magnetic resonance imaging unit. The results show the effectiveness of FRAM in detecting discrepancies and vulnerabilities in the practical management of these devices, providing valuable insights not only regarding the analysis of adverse events (i.e., retrospectively) but also concerning the improvement of safety procedures (i.e., prospectively).

In today’s complex engineered systems, comprising a multitude of interacting components, preserving system performance is of utmost importance. The challenge often lies in effectively prioritizing components with the highest potential to compromise system reliability, mainly when human interaction with technical artefacts is not negligible. This study proposes a systemic methodology for pragmatic reliability management within human–machine systems. The proposed approach combines a rule-based adaptation of the well-established Failure Mode, Effects, and Criticality Analysis (FMECA) with a probabilistic Fault Tree Analysis (FTA). Furthermore, the technical considerations are seamlessly integrated into a human-centric analysis, utilizing the Standardized Plant Analysis Risk – Human Reliability Analysis (SPAR-H). The proposed decision-support methodology is instantiated through Monte Carlo simulations to account for stochastic phenomena and uncertain operating conditions. The effectiveness and practicality of the proposed approach are elucidated through a case study involving a high-reliability system, specifically a high-mobility multi-wheeled vehicle. This study demonstrates the step-by-step application of the proposed approach and its implications in challenging operating scenarios, reaffirming its potential to enhance reliability management within human–machine systems.

Today, to properly address circular economy practices, strategic decisions encompassing all the various life cycle stages of products or services have become critically important in the market. However, companies still have difficulties in balancing the technical and environmental requirements of their offerings, and numerous studies outline the need for more research on ecodesign tools to support them in decision-making. To reduce such a research gap, a decision-making framework based on the integrated use of the quality function deployment for the environment (QFDE), analytic hierarchy process (AHP), strengths, weaknesses, opportunities, and threats (SWOT), and TOWS matrix methods was developed through a case study related to the provision of photovoltaic solar systems for domestic use. The results achieved show that to better enhance the company’s offering of ensuring customer satisfaction and green compliance, a shift towards a product–service system (PSS) approach is required, and practical implementation strategies are suggested. Overall, this study contributes to the environmental research literature by streamlining marketing strategy planning decision-making through a novel QFD-based approach that aligns customer requirements with environmental concerns and improvement options. Thus, it provides both academics and practitioners with a useful framework to better address the implementation of circular economy practices.

Increasing industrial resilience is a big challenge for manufacturing enterprises that are continuously facing severe accidents causing injuries, casualties, and economic losses. Assessing industrial resilience requires the analysis of production processes in order to find possible safety flaws. Sociotechnical process management suffers often from misalignments of process descriptions according to formal organization documents or manager views (Work-As-Imagined) and actual work practices as performed by sharp-end operators (Work-As-Done). Furthermore, existing modelling approaches leveraging on techniques such as process mining from digital traces cannot be used to solve such misalignments as these traces are often hardly available. In this context, we propose a computational creativity approach for a semantics-driven transition from Work-As-Imagined to Work-As-Done process models based on the functional resonance analysis method (FRAM). In particular, through formalized semantics, it will be possible to use automatic reasoning for identification of criticalities and prioritization of normal work analyses. To this aim, we introduce some examples of rule patterns, inspired by typical data quality issues, which can be automatically applied to guide such a transition. An explorative case study on chemical cleaning for industrial process is presented to clarify the proposed approach.

Fifth-generation communication also known as internet of things is rolling out worldwide.It has provided higher network capacity and speed for mobile broadband communications; however, 5G network focuses only on terrestrial coverage.6G and Cognitive Radio (CR) is expected to be the future solution to all heavy data traffic and globe coverage.Satellite communication is anticipated to cover rural areas, sea, spanning air, and space in what is known as Internet of Space Things (IoST).Low Earth Orbit (LEO) CubeSat orbits earth provide real time measurements with low transmission power and high data rate.Cognitive radio will focus on providing efficient spectrum use and resources allocations.In this paper, a multi CubeSat cognitive radio network is proposed to improve time delay, increase data exchange, and increase the Signal to Noise Ratio (SNR) of the communication system.simulation results demonstrate the convergence of the multi CubeSat system improving the signal to noise ratio for different number of CubeSats structure.

The optimization of a product’s whole life cycle has become a mandatory task for manufacturers seeking to deal with circular economy requirements while gaining competitiveness in the market. In order to achieve such a sustainability goal, alignment of production, distribution, and field service activities is needed. In the literature, numerous studies indicate the product–service system (PSS) approach as one of the most promising business models to combine the needs of manufacturers and customers in an efficient and effective manner. However, PSS solutions aimed at practically optimizing supply chain management have scarcely been addressed. In order to reduce this gap, the current study proposes a procedure based on the PSS Functional Matrix, the Screening Life Cycle Modelling (SLCM) method, and stock management theory to optimize aftermarket services based on market demand. A case study in the medical equipment sector, where market demand can fluctuate during the contract period, is presented. The analytical results show beneficial effects in terms of both costs and environmental impact, suggesting the need for further research to augment knowledge on PSS and supply chain management. In particular, the PSS allowed the company to customize the manufacturer’s business model, adapting the supply of aftermarket services to varying customer needs.

Economies based on product-centric models have considerably shifted towards service-oriented solutions such as Product-Service Systems (PSSs).Facilities and properties are no exception where maintenance operations and activities play an essential role in their management.In this context, several tools are used to enhance the management performance of those facilities.Nevertheless, only a few studies have investigated these topics while contemplating a shift from product-centered approaches to service-based models.The following study adopts a Building-Information Modeling (BIM) approach in a PSS environment to enhance the maintenance operations of facility-related equipment.In detail, a maintenance organization and management framework is constructed through the implementation of PSS-BIM integration.The study concerns the elevators of an existing structure where the framework is applied bringing forth the benefits that can be achieved notably in terms of reduced downtime, lower maintenance costs, and higher customer satisfaction.Despite the limitations of the intervention, the approach can be deemed as a reliable first step towards a more thorough PSS implementation in this sector.

Traditional safety risk analysis methods are rooted in event chain modeling and looking for individual points of failure. This approach allowed tremendous improvement in safety management but starts to be difficult to apply when dealing with large-scale systems constituted by a wide number of interactions among technical and social elements. Therefore, systemic safety management poses new challenges, demanding approaches capable of complementing techno-centric investigations with social-oriented analyses. For this purpose, this study adopts the Systems-Theoretic Accident Model and Processes (STAMP) as a new accident causation model based on systems theory. Such a model is the first element to gain a complete understanding of the system at hand, and subsequently to create a set of safety recommendations. STAMP can lead to both the development or evaluation of safety management systems and the identification of leading indicators related to hazards, in order to improve decision-making domains and strengthen accidents/loss analyses. The present research incorporates three basic components of systems theory for STAMP models: constraints, hierarchical control structure, and process loops. These items are meant to allow recognizing causes and preventing potential system failures as well as undesired events. In the proposed model, accidents are examined in terms of the ways controls fail and how they may not allow prevention or detection of hazards. This study proposes a hierarchical safety control structure on a demonstrative use case referred to an industrial plant for gas and oil production, The model consists of system-level safety constraints, and a preliminary investigation of system’s components with the purpose of supporting physical and organizational safety requirements elicitation.

In the field of industrial process monitoring, scholars and practitioners are increasing interest in time-varying processes, where different phases are implemented within an unknown time frame. The measurement of process parameters could inform about the health state of the production assets, or products, but only if the measured parameters are coupled with the specific phase identification. A combination of values could be common for one phase and uncommon for another phase; thus, the same combination of values shows a high or low probability depending on the specific phase. The automatic identification of the production phase usually relies on clustering techniques. This is largely due to the difficulty of finding training fault data for supervised models. With these two considerations in mind, this contribution proposes the Latent Dirichlet Allocation as a natural language-processing technique for reviewing the topic of clustering applied in time-varying contexts, in the maintenance field. Thus, the paper presents this innovative methodology to analyze this specific research fields, presenting the step-by-step application and its results, with an overview of the theme.

Despite its large popularity, the Quality Function Deployment (QFD) method has been the object of numerous studies addressing the problem of the assessment and prioritization of customer requirements. Nevertheless, a comparative analysis of these approaches to investigate their practical usability is scarcely discussed. This paper aims at filling this gap by means of a practical case study at a manufacturer in the food sector, where five of the most common approaches used to augment the House of Quality (HoQ) were analyzed and the results were compared. To achieve such a goal, semi-structured questionnaires were developed to capture consumers' preferences and expectations. The outputs of this study contribute to a better understanding of the potential and limitations of the examined approaches in order to address practitioners and companies in decision-making processes and resources allocation. Moreover, the article can serve as a reference for further investigations in the development of food products, where both intrinsic and extrinsic qualities need to be addressed.

Safety of enterprises, as well as socio-technical systems in general, is due to several factors and can be considered an emergent property, depending on non-linear and symbiotic interactions between humans and technical systems taking places in collaborative business processes. The safety-II perspective promotes assessing and gathering meaningful knowledge about normal work, and its effect on safety and productivity. We aim at providing a support to this activity, proposing a three-phases framework for the definition of indicators on enterprise safety performance. This framework includes collection of knowledge on work-asimagined (WAI) business processes, ontology-based modelling of work-as-done (WAD) business processes and some guidelines to define indicators taking into account the actual needs and implicit knowledge of sharp-end operators by means of the Functional Resonance Analysis Method (FRAM).

In recent years, the optimization in the use of resources has a key role in achieving a bigger marginality, reducing the operative costs. Due to the advances in the data science field, even the maintenance context is living important changes. The predictive maintenance and the condition-based maintenance can overcome the classic traditional maintenance methods, like the time-based maintenance or the corrective maintenance, with respect to the first intervention, reducing the costs for unscheduled maintenance, manpower, or loss of production and extending the useful life of the components. Based on these presuppositions, the paper proposes the development of a predictive model for the degradation state of the components of a complex hydraulic system, with some tests and some suggestions about the dimensionality reduction. The system has four known types of breakdown, with different degrees of severity; moreover, a fifth parameter represents whether the cycle has reached stable conditions or not.

This paper presents an exploratory research on the application of bibliometric methods to the field of Resilience Engineering. Starting from the Kuhnian idea on the structure of scientific revolutions, the aim of this research is to define centres of research and explore how their focus changes over time. The research developments have been traced by studying the science footprints revealed by scholarly publications. Such publications constitute a dynamic and self-organizing knowledge repository which is often difficult to understand systemically. By means of bibliometric indicators, this paper aims to identify who are the major authors in the field, what are the “invisible colleges”, which sources publish main documents, and the respective significant changes over years. A further analysis has been based on the usage of Pennant diagrams for scientometrics, with the purpose of exploring the relevance of an author within a certain literature database, and with respect to other authors.Through this multi-method exploratory research, we aim to provide an interpretative summary on the research of past and current community of resilience engineers. The analysis aims to define the structure of the scientific field and of the scientific community, proving the usability of meta-analytic tools coming from scientometrics for the analysis of an inter-disciplinary scientific domain such as Resilience Engineering.