Digital Twin Applications in Manufacturing Industry: Comparison
Please note this is a comparison between Version 3 by Peter Tang and Version 2 by Peter Tang.

The existing literature notes a range of potential benefits that digital twin (DT) projects can deliver, including efficiency gains, waste reduction, and improved decision-making, and DTs are also considered integral to smart manufacturing environments and Industry 4.0. Research on three DT projects in a German multi-national concludes that digital twin projects are likely to involve incremental rather than disruptive change, and that successful implementation is usually underpinned by ensuring technology, people and process change factors are progressed in a balanced and integrated fashion. Building upon existing frameworks, three “properties” are identified as being of particular value in digital twin projects - workforce adaptability, technology manageability and process agility–and a related set of steps and actions is put forward as a template and point of reference for future digital twin implementations.

  • digital twin
  • data analytics
  • digital thread
  • manufacturing industry
  • technology manageability

1. Introduction

One of the first documented examples of a DT was as a computer-aided design (CAD) object, which was an element in a product lifecycle management (PLM) process and its support systems [1]. This illustrates the fact that DT is a concept and not a specific technology. Parrott and Warshaw [2] (p. 3) assert that “a digital twin can be defined, fundamentally, as an evolving digital profile of the historical and current behavior of a physical object or process that helps optimise business performance”, and Grieves and Vickers [1] (p. 92) suggest that a DT “is based on the idea that a digital informational construct about a physical system could be created as an entity on its own”. VanDerHorn and Mahadevan [3] reviewed 46 digital twin definitions evident in the extant literature and suggested that DT can be defined as “a virtual representation of a physical system (and its associated environment and processes) that is updated through the exchange of information between the physical and virtual systems” (p. 2). Johnson [4] (para. 2) observes “digital twinning is the process of creating a highly realistic model of a device, system, process or product to use for development, testing and validation”.
The two-way exchange of information between the virtual and physical systems is a key aspect of a DT that differentiates it from most other technology concepts. A decade ago, Grieves [5] set out the three main components of a DT: products in the physical space, products in the virtual space, and the connections of data and information that unify both spaces. More recently, Trauer et al. [6] (p. 761) similarly noted that “a digital twin is a virtual dynamic representation of a physical system, which is connected to it over the entire lifecycle for bidirectional data exchange”. A DT can take several forms and usually utilises a combination of existing technologies, which differ from project to project (Figure 1).
Figure 1. Digital Twin technology components and application areas.

2. Relevant Theory and Models

There are a number of implementation frameworks in the existing literature, some of which are specific to implementing certain types of DTs, or DTs for specific purposes. For instance, Zhang et al. [7] and Guo et al. [8] proposed frameworks to optimise factory layout designs. Friederich et al. [9] focused on developing a framework to improve the simulation functionality of a DT using machine learning and process mining techniques. Loaiza et al. [10] (p. 12) developed a “small-scale digital twin implementation framework”, in which they identified Resources (“labour, capital and materials”), Technology, and Digital Transformation (“enabling digital processes such as simulation, diagnostics, prognostics”) as the “high-level requirements” for DT projects. Schweigert-Recksiek et al. [11], in their case study of a DT in technical product development, suggest five key dimensions for DT projects, based on earlier work by Kreimeyer et al. [12]. These are people, process, data, product, and tools. The International Organization for Standardization (ISO) detailed requirements for a DT framework [13] in which it partitions a DT into four layers defined by standards (Figure 2). The lowest layer describes the observable manufacturing elements—the items that need to be modeled in the DT. The second layer is the device communication entity which “collates all the state changes of the observable manufacturing elements, and sends control programs to those elements when adjustments become necessary” (para. 2). The third layer is the digital twin entity, which reads the data collated by the device communication entity and uses the information to update its models. The fourth layer contains user entities. These are applications that use the digital twins to enhance manufacturing efficiency. They are “legacy applications such as ERP and PLM, and new applications that make processes work more quickly” (para. 3). The ISO 23247 framework is based on the Internet of Things and also includes a “cross system entity”, linking shop floor devices, the digital twin, and main applications in the fourth “user entity” layer.
Figure 2. ISO digital twin framework for manufacturing. Based on [13] (Figure 1).
The three change dimensions of technology, process, and people have been used as a framework to analyse IT projects [14][15] and develop IT strategy [16] in the digital era. This is somewhat similar to the approach adopted by Loaiza et al. [10] (p. 12) and, in particular, the framework put forward by Schweigert-Recksiek et al. [11] for implementing DT projects. Their framework includes process and people change dimensions, plus data and tools (which can be viewed as part of the technology dimension), and the product itself—the focus of the DT simulation. The significance of the people change dimension is emphasised by Schweigert-Recksiek et al. [11] (p. 384), who note “current barriers between departments have to be considered as the usage of a twin also changes the significance of certain tasks or even departments of the product development process”. In terms of process change, the authors note “many of the processes in a company will change with the introduction of a twin, as for example some simulations will be conducted automatically instead of a simulation assignment being handed over from one department to the other”. They also note that “the generation and usage of data, especially from the use phase of products, has to be altered in many places to enable the implementation of a Digital Twin” (p. 385).
More recently, Trauer et al. [17] put forward a “business modelling approach” for digital twin projects, which is a 10-step guide to implementing a DT and transitioning to new processes (Figure 3). In a similar vein, VanDerHorn and Mahadevan [3], in their analysis of DT implementation, identify a number of key challenges, including terminology, standardization, organizational culture, technology maturity, and automation. As regards automation, the authors note “a prominent targeted outcome in many digital twin implementations is to reduce the manual effort through automation of data exchange and analysis”, but that “there is still a strong reliance on human-in-the-loop as part of current solutions” (p. 9).
Figure 3. Digital Twin Business Modelling Framework. Source: based on Trauer et al. [17], Figure 2, p. 125.

3. Research Insights

The study of three DT projects in a German multi-national provides some new insights regarding DT theory and practice. 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 has parallels with the development of the data warehouse, data marts and corporate data models in the 1990s [18]. For example, Dontha [19] (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, Dontha [19] (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, 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, Essex [20] (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 by the manager of one of the studied projects, which focused in the main on advanced use of data analytics “we are not reinventing the wheel, but connecting the dots”. A second project 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. Tonder et al. [21] (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. One of the project management teams, for example, adopted a bottom up perspective in developing the retrospective DT application. Data was collected from a range of corporate information systems, linking and aggregating it via digital thread and data warehouse technologies, which was then made available for the DT simulation. The output data from the DT was then used for further analysis using a range of analytics tools, including Power BI. The ISO model (Figure 2), however, takes a different approach, focusing on shop floor production processes (level one), collecting data from them (level 2), building the simulation (level three), the outputs from which are then used in a range of existing corporate systems (level four). The business modelling approach of Trauer et al. (Figure 3) takes a different approach again, but is essentially a progression of technology, process and people factors (steps 5-8, 10 in Figure 3) with added actions to establish and verify a business case for the project (steps 1-4, 9). It reinforces conventional wisdom about the significance and interdependency of technology, people and process issues in IT projects. In this context, the findings highlight the value of three interdependent digital “properties” - required for digitalization projects in general - which are of particular relevance to DT projects: workforce adaptability, technology manageability, and process agility. More specifically, the projects studied here suggest a series of related smaller steps or actions that are needed to progress DT projects successfully (Figure 4).

Figure 4. A template for digital twin project implementation

There are several aspects to workforce adaptability that impact DT projects. All three projects studied evidenced the value of strong sponsorship and leadership from an appropriate senior manager – the CFO, the CEO and the Vice-President of Operations in these projects. Co-ordinated cross-departmental involvement is also necessary, either as a project team or working party or via a more informal arrangement based around the use-case testing. Either way, committed and coordinated input from a range of functions will be required. A range of skillsets, some of them possibly new to the company (particularly in the IT area) will also be required, which may involve using third-party contract support in the short-term.

Technology manageability implies that IT issues are adequately managed by the resources available and there is a sound and stable technology platform upon which to build. If the IT department is continually fire-fighting maintenance and other problems, it is unlikely to be successful in building a DT. Technology integration issues need to be addressed to provide the linkages and interfaces for sound and consistent data to populate the DT. This may involve the use of APIs and webhooks, the development of a digital thread and the use of a data warehouse as a staging post between feeder systems and the DT itself. The IT function, albeit possibly with third-party support, also need the capabilities for use-case testing of the technology combinations involved in the DT project, and then to provide on-going support and maintenance.

Process agility assumes the use of agile, flexible project management methods in the conduct of the use-cases, which will probably involve a multi-functional team drawn from several departments (IT and users). The company must have the agility and political will to embed process change in existing working practices, in which the commitment of the project sponsor will be a key influencing factor. Process change will be a major contributor to driving through overall project benefits, typically from overheads reduction, better information and improved customer service (internal or external).

VanDerHorn and Mahadevan [22] (p.10) have observed that “the digital twin concept is clearly still evolving, as seen in the diversity of new industries and use cases that digital twins are being applied to. This continued concept evolution is also apparent in the lack of concrete examples demonstrating the clear benefits of digital twins in practice.” This research helps to address this gap in the literature by providing some insights into an area that is only now starting to be researched in depth.


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