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.

Although many firms report large benefits from lean implementation, a lot of scepticism still remains regarding attainable results and the possibility to apply Lean approach outside high-volume manufacturing and stable context. In this work, combining field interviews with literature review, theoretical connections have been developed among Lean manufacturing techniques, operational responsiveness and company growth performances. A conceptual model has been proposed for investigating the network of influences among lean practices (supplier management, human resource management, just-in-time and total quality management practices), operational responsiveness (Product mix variety, Product innovation and Time effectiveness) and company growth performances in Italian companies. Using structural equation modelling, a second-order confirmatory factor analysis has been used to test the hypothesised relationships in the structural models. This study highlights that the operational responsiveness is only partially connected to a Lean strategy of a company. Indeed, the lean practices implementations are negatively influenced by product mix variety and innovation, while positively influenced by time effectiveness variables. Moreover, product mix variety and time effectiveness are the main characteristics of the operational responsiveness that positively influences company growth performances. So time effectiveness could be considered as a mediator between Lean best practices and firm growth. Moreover, no direct relationship has been found between lean bundles and firm’s performances. Lack of resources and mainly poor communication and managers’ commitment and support seem to be the main obstacles of lean implementation and success.

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.

This study discusses a methodology for integrating Design for Environment (DfE) and life cycle assessment (LCA) techniques both into new product development and into the process of redesigning a set of existing products. The article explains the reasons for developing DfE in general, and pays particular attention to a specific, chosen product, a class of electrical distribution boards, to illustrate the concept. The main process steps in the development of the DfE are outlined, and the development of a LCA that satisfies the requirements of the ISO 14040 standard is illustrated. A major benefit of the DfE methodology proposed in this work is the possibility to use LCA data both during new product development and when modifying old products, with the aim of continuously reducing the overall environmental impact of products during their life cycle. This improvement cycle begins with the attempt to find new design solutions (for assembly and set-up in the case of electrical distribution boards), continues with the calculation of the enviromental break even point (BEP) and with the assessment of the BEP for the expenses incurred by the client. On the basis of these calculations and bearing in mind the technical specifications required by the clients and the work environment in which the product will be used, the designers will be able to make the most efficient choices from both the environmental and the economic point of view.

This research work focuses on the application of life-cycle assessment methodology to determine the carbon footprint of different players involved in a supply chain of the textile sector. A case study of a product by a textile leader company was carried out. This study demonstrates that, in the textile chain, the main contribution to the greenhouse effect is provided by the electrical and thermal energy used and by the transportation (since different production phases are delocalised in a wide range that goes from South Africa, Italy, Romania and all around the world, from the distribution centre to the stores). The Monte Carlo analysis has been used in order to obtain, for each calculated impact, not only the average value but also the distribution curve of the results characterised by uncertainty parameters. Moreover, a sensitivity analysis was carried out to evaluate the impact of management choices such as: • a change in the transportation modality, from aeroplane to boat; • a combination of road and rail transportation; and • a selection among suppliers that allows the firm to cut environmental impacts.