Despite the increasing attention that supply chain risk management is receiving by both researchers and practitioners, companies still lack a risk culture. Moreover, risk management approaches are either too general or require pieces of information not regularly recorded by organisations. This work develops a risk identification and analysis methodology that integrates widely adopted supply chain and risk management tools. In particular, process analysis is performed by means of the standard framework provided by the Supply Chain Operations Reference Model, the risk identification and analysis tasks are accomplished by applying the Risk Breakdown Structure and the Risk Breakdown Matrix, and the effects of risk occurrence on activities are assessed by indicators that are already measured by companies in order to monitor their performances. In such a way, the framework contributes to increase companies’ awareness and communication about risk, which are essential components of the management of modern supply chains. A base case has been developed by applying the proposed approach to a hypothetical manufacturing supply chain. An in-depth validation will be carried out to improve the methodology and further demonstrate its benefits and limitations. Future research will extend the framework to include the understanding of the multiple effects of risky events on different processes.

This paper is aimed at identifying the main patterns related to the application of new Digital Supply Chain Technologies that through the Industry 4.0 paradigm, are redefining supply chain organisations. To this end, a set of DSC initiatives has been analysed via an Analysis of Variance (ANOVA) to understand the influence of several social and economical factors on the implementation of DSC solutions. Results show that the time factor, the Gross Domestic Product per capita, the amount of foreign investment, and the expenditure in Research and Development are significant drivers of DSC technologies. In particular, Big Data are associated with a higher economic effort than Blockchain, although these two technologies, together with Augmented Reality and Artificial Intelligence, usually characterise most recent implementations. The present work might foster research about the contextual factors affecting the DSC diffusion it could assist both practitioners and policymakers in defining appropriate DSC strategies.

Purpose: The purpose of this paper is to investigate the diffusion dynamics of electric and hybrid commercial vans and its enabling factors in the city logistics (CL) contexts. The case of parcel delivery in Torino, Italy, is considered. Attention is paid to the influence on the choice of low impact vehicles of not only public strategies but also operational aspects characterizing urban freight distribution systems. Design/methodology/approach: A System Dynamics model based on the Bass diffusion theory computes the number of adopters of low-emission vehicles together with the quantity of vans required and the associated economic savings. The model includes variables about freight demand, delivery frequency, van carrying capacity, routes, stops, distances traveled, and vehicle charging stations. A sensitivity analysis has been completed to identify the main diffusion levers. The focus is on advertising and other drivers, such as public contributions, taxes traditional polluting vehicles are subjected to, as well as on routing optimization strategies. Findings: Advertising programs, green image, and word-of-mouth drive market saturation, although in a long time period. In fact, low-impact vehicles do not offer any economic advantage over traditional ones requiring higher investment and operating costs. Public incentives to purchase both green vehicles and charging stations, together with carbon taxes and a congestion charge affecting polluting vehicles, are able to shorten the adoption time. In particular, public intervention reveals to be effective only when it unfolds through a number of measures that both facilitate the use of environmentally friendly vehicles and discourage the adoption of traditional commercial vans. Route optimization also hastens the complete market saturation. Research limitations/implications: This work fosters research about the mutual relationships between the diffusion of low-emission commercial vehicles and the operational and contextual CL factors. It provides a structured approach for investigating the feasibility of innovative good vehicles that might be part of assessments of CL measures and requirements. Finally, the model supports studies about the cooperation among stakeholders to identify effective commercial vehicle fleets. Practical implications: This study fosters collaboration among CL players by providing a roadmap to identify the key factors for the diffusion of environmentally friendly freight vehicles. It also enables freight carriers to assess the operational and economic feasibility of adopting low-impact vehicles. Finally, it might assist public authorities in capturing the effects of new urban transportation policies prior to their implementation. Originality/value: Most of the current CL literature defines policies and analyzes their effects. Also, there are several contributions on the diffusion of low emission cars. The present study is one of the first works on the diffusion of low-impact commercial vehicles in urban areas by considering the associated key operational factors. A further value is that the proposed model combines operational variables with economic and environmental issues.

Industry 4.0 technologies, originally developed in the manufacturing sector, can be purposefully implemented to improve City Logistics (CL) processes by automatizing some of their operational tasks and enabling real-time exchange of information, with the ultimate goal of providing better interconnection among the actors involved. This work aims to identify the main social and economic contextual drivers for investing in the application of Industry 4.0 technologies to urban logistics. To this end, a dataset based on the primary collection of 105 CL projects exploiting the main 4.0 technologies has been built. After that, a regression model has been completed including potential economic, strategic, and demographic determinants of investments in CL 4.0. According to the obtained outcomes, Gross Domestic Product, Foreign Direct Investments, Research and Development Expenditure, Employment Rate, and Number of Inhabitants are significant contextual factors for the adoption of Industry 4.0 technologies in last mile logistics. The study might support academicians to investigate novel application fields of Industry 4.0 technologies. Also, it can serve as a roadmap for orienting the investments of private organizations and public entities to promote CL innovation and digitalization. Moreover, Industry 4.0 technology providers might find this study interesting to uncover prospective business sectors and markets. Future research efforts will analyse the impacts of internal business factors on CL 4.0 and the satisfaction levels of urban logistics stakeholders.

Purpose: The purpose of this paper is to study mobile services for supply chain management (SCM) in the electronic grocery (e-grocery) sector. The authors investigate their diffusion and formulate policies in order to stimulate the adoption. Design/methodology/approach: A System Dynamics model is proposed for a short fresh food supply chain (SC). The model predicts how product traceability, mobile payment, and time-based delivery management functionalities contribute to the adoption of a SCM mobile application. Findings: The three services drive the diffusion of the application. A high level of real time information brings decreased inventory levels and more frequent order placing, leading to an increased number of logistics transactions managed by the mobile application and growth in the associated revenue for the service provider company. Research limitations/implications: The proposed study fosters research on overcoming the barriers that prevent integration, collaboration, and better visibility in e-grocery SCs. Practical implications: This work constitutes a roadmap to identify the key enabling factors of e-grocery expansion. Originality/value: This is one of the few contributions focussing on increasing the efficiency of e-grocery SCs by applying management strategies supported by mobile devices.

An empirical analysis is presented to investigate the factors that have significant influence on the share of public financing into the total initial investment required to develop build-operate-transfer (BOT) health-care projects. Based on a model describing the main risks associated with public participation in BOT projects, a linear regression analysis has been conducted on a data set of Italian BOT hospital projects to yield implications. Outcomes reveal that the size of investment, the financial strength of the concessionaire, the duration of the concession period, the number of services, and the level of borrowing of the local health-care granting agency are significant factors of the level of public funding. The study confirms that public funding is provided not only to cover the non-self-financing portion of investment but also as a way to undertake a project in periods of scarce public financial resources. The methodology may be useful to refine the decision criteria for determining the l...