In today’s society, project work gains traction across many industries. As projects are characterised by novelty and uncertainty, emerging digital technologies promise solutions that improve performance and help deliver full benefits. Amidst this digitisation, digital technologies such as data analytics and Artificial Intelligence (AI) streamline the large amount of data generated. However, this critical information is only partially leveraged during and after projects to date. Project data are generated, collected, and analysed across all stages of project management and delivery. Eleni Papadonikolaki and Carlos Galera-Zarco conceptualise the relation between projects, information, and data, and discuss key application areas of data analytics in projects, for instance in scheduling and costing. Next, they present the state-of-the-art applications, means, and tools of data analytics in key areas of project management. Finally, they set out challenges and future opportunities around project data analytics especially with regard to leadership, teamwork, and talent management.

We are witnessing an increased interest and advanced developments of Digital Twins (DT) across different industries; manufacturing, construction, oil and gas, aerospace, energy or healthcare among others. Some actors are already realising that the power of DTs goes beyond efficiency gains and cost savings for internal processes, and recognise the potential of DT to enable the design of new service offerings. Uncovering the potential of DT in the development of new value propositions may lead firms to business model transformations, particularly to the expansion of servitization strategies. Thus, there is a need for more research exploring DT as a service enabler, and shedding light on the typologies and characteristics of these innovative digital services. To address the need for a more comprehensive understanding of DT-based services, we develop a taxonomy able to classify and characterise services enabled by DT. To develop this novel DT-based services taxonomy, we drew upon literature review and use cases. In our ongoing research, four main dimensions emerged to configure a taxonomy of digital twin-based services: service recipient, target operand resource, service content and service pricing model. The proposed taxonomy is intended to be useful in the understanding of the current DT-based service offerings and design principles. In addition, the taxonomy has potential to be used as a practical tool for service providers in the development of new DT-based services.

We are witnessing an increased interest and advanced developments of Digital Twins (DT) across different industries; manufacturing, construction, oil and gas, aerospace, energy or healthcare among others. Some actors are already realising that the power of DTs goes beyond efficiency gains and cost savings for internal processes, and recognise the potential of DT to enable the design of new service offerings. Uncovering the potential of DT in the development of new value propositions may lead firms to business model transformations, particularly to the expansion of servitization strategies. Thus, there is a need for more research exploring DT as a service enabler, and shedding light on the typologies and characteristics of these innovative digital services. To address the need for a more comprehensive understanding of DT-based services, we develop a taxonomy able to classify and characterise services enabled by DT. To develop this novel DT-based services taxonomy, we drew upon literature review and use cases. In our ongoing research, four main dimensions emerged to configure a taxonomy of digital twin-based services: service recipient, target operand resource, service content and service pricing model. The proposed taxonomy is intended to be useful in the understanding of the current DT-based service offerings and design principles. In addition, the taxonomy has potential to be used as a practical tool for service providers in the development of new DT-based services.