## 5 Discussion  
The results outlined above raise some issues worthy of further discussion. Firstly, DT projects require consistent and accurate data, upon which simulations can be developed and operationalised. For this reason, DT projects will often go hand-in-hand with data analytics initiatives. The development of a digital thread and the use of webhooks and APIs to provide the necessary connectivity have parallels with the development of the data warehouse, data marts, and corporate data models in the 1990s [48]. For example, [PERSON] [15] (para. 15) notes \"digital thread enables accessing, transforming, integrating, and analyzing data from various different systems [and] helps in delivering the right information at the right time and at the right place\", which resonates with the objectives of data warehouse projects of the past. Similarly, [PERSON] [15] (para. 12) adds \"the very first step in digital thread as it relates to data management is identifying data sources, accessing the data, and organizing it in a way that various functions can harness that data\", which again is very similar to the objectives set out for developing corporate data models in a previous era. This highlights the lack of integration of core information systems, which bedevils many businesses. Perhaps this is inevitable in an advanced manufacturing environment when the main suppliers of company ERP systems do not contain the functionality for shop floor and MES requirements, and thus a series of add-on, supplementary systems and technologies are acquired. The vision of having one integrated package for everything, as put forward by the ERP vendors since the 1990s, has been put aside for a \"best of breed\" approach, using APIs, AASs, webhooks, and other technologies to create a data thread as a means of locating and extracting the data from various systems required for the DT. In this context, [PERSON] [13] (para. 11) notes that \"deriving value from digital twin technology...... requires mature data management processes and sophisticated systems integration, making digital twin development a complex, multi-year effort\". Secondly, the nature of innovation introduced by the DT projects constitutes incremental rather than disruptive change. As noted in Project 1, which focused on advanced use of data analytics \"we are not reinventing the wheel, but connecting the dots\". Project 2 could be viewed as an advanced planning tool, applied in product development, having its origins in MRP, MRP2, and the load planning and forecasting modules evident in many ERP systems. The research findings provide some perspectives on existing frameworks and guidelines, but perhaps the most significant factor is that DT projects have very little that is new in terms of technology or business change. There were a number of key issues for project success that emerged from the project studies--a committed high level project sponsor, cross-functional teams, use of outside expertise, appropriate technology mix, the need for a sound data platform, the use of agile methodologies, and the value of a process change expert--but all these factors are of similar relevance in many other IT projects, big and small, digital era and pre-digital era. These projects harness and combine technologies to produce virtual simulations of physical reality, but project management issues and methodological considerations remain as in many other IT projects. Thirdly, the cases highlight the absence of any common approach to DT projects, which limits the potential for developing models and frameworks that are applicable to a range of DT projects. [PERSON] et al. [49] (p. 112) recently concluded that \"there is no universally accepted, robust conceptual framework that can assist businesses, practitioners and academics to understand the constructs of digitalisation, digital transformation and business model innovation\", and this would seem to apply to DT projects as much as to other digital technology implementations.