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.

The bullwhip effect is defined as the distortion of demand information as one moves upstream in the supply chain, causing severe inefficiencies in the whole supply chain. Although extensive research has been conducted to study the causes of the bullwhip effect and seek mitigation solutions with respect to several demand processes, less attention has been devoted to the impact of seasonal demand in multi-echelon supply chains. This paper considers a simulation approach to study the effect of demand seasonality on the bullwhip effect and inventory stability in a four-echelon supply chain that adopts a base stock ordering policy with a moving average method. The results show that high seasonality levels reduce the bullwhip effect ratio, inventory variance ratio, and average fill rate to a great extent; especially when the demand noise is low. In contrast, all the performance measures become less sensitive to the seasonality level when the noise is high. This performance indicates that using the ratios to measure seasonal supply chain dynamics is misleading, and that it is better to directly use the variance (without dividing by the demand variance) as the estimates for the bullwhip effect and inventory performance. The results also show that the supply chain performances are highly sensitive to forecasting and safety stock parameters, regardless of the seasonality level. Furthermore, the impact of information sharing quantification shows that all the performance measures are improved regardless of demand seasonality. With information sharing, the bullwhip effect and inventory variance ratios are consistent with average fill rate results.

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.