Conceptualisation of a 7-element digital twin framework in supply chain and operations management

Abstract

Digital twins became of greater interest to researchers and practitioners in supply chain and operations management (SCOM). Literature has addressed the need to understand digital twins in SCOM, mostly focusing on fragmented technological solutions and use cases. We start with an integrative literature review to determine which elements belong to research on digital twins in SCOM. We define the seven major elements of a digital twin in SCOM: technology, people, management, organisation, scope, task, and modelling. We also distinguish five major types of digital twins in SCOM: product, process, organisation, supply chain and network-of-networks. Illustration of a SCOM digital twin is provided using an anyLogistix example. We conclude that digital twins in SCOM are not merely a simulation-based replica of a real object but a complex socio-technical phenomenon involved in continuous human-artificial intelligence interactions. This leads to an understanding of the role of digital twins through the lens of Industry 5.0, reconfigurable and viable supply chains. Researchers and practitioners alike can use our framework to structure the knowledge on SCOM digital twins and consider all seven elements when designing and using digital twins.

KEYWORDS:

• Supply chain dynamics
• digital twin
• digital supply chain
• artificial intelligence
• anyLogistix

1. Introduction

A digital twin of a supply chain is a virtual system comprised of (i) a digital visualisation of a physical supply chain and its elements (e.g. firms, flows, and products) in a computer model, (ii) digital technologies providing data about the physical object (e.g. sensors, blockchain, clouds), and (iii) descriptive, predictive and prescriptive analytics for decision-making support (Figure 1).

Figure 1. Concept and design of a digital supply chain twin.

Elements constituting digital supply chain twins.

The definition and realisation of a digital twin depend on the context. Researchers in supply chain and operations management (SCOM) understand a digital twin as ‘a solution for better visualization and understanding of supply chains and an opportunity for further analysis, simulation, and optimization’ (van der Valk et al. 2022), ‘a combination of multiple enabling technologies, such as sensors, cloud computing, AI [artificial intelligence] and advanced analytics, simulation, visualization, and augmented and virtual reality’ (Tozanli and Saénz 2022), and ‘computerized models that represent the network state for any given moment in time’ (Ivanov and Dolgui 2021). Digital twins of single objects (e.g. a product) and centrally-controlled processes (e.g. an in-house manufacturing process) can take a form of a stand-alone software package. Digital twins of decentralised systems such as supply chains are rather understood as a combination of different software, which – in their integrity – constitute a digital represenation of physical networks and advanced analytics algorithms.

Digital twins become more and more important through a digital transformation in SCOM. Organisations in manufacturing and logistics transform their operations by adopting new digital technology such as Internet of Things (IoT), sensors, Blockchain, 5G, AI and data analytics, augmented and virtual reality (AR/VR), cobots, and supplier collaboration portals (e.g. SupplyOn), to name a few (MacCarthy and Ivanov 2022). As a result, digital twins of products, manufacturing and logistics processes, organisations, supply chain networks, and infrastructures (e.g. railway and port networks) arise. While supply chain and operations managers understand how to use digital twins for disruption detection and resilience management, sustainability analysis (e.g. fair trade regulations), collaborative demand planning, and shipment/inventory control, there is some ambiguity on how to design and adopt digital twins in terms of technologies, process integration, decision-making principles (AI algorithm vs human), and data usage (Cui, Li, and Zhang 2022; Saghafian, Tomlin, and Biller 2022).

A digital twin is linked to a real object. Through continuous data exchange, the digital twin always behaves synchronously with its real counterpart, and they influence each other. It mirrors both the state and the performance of the real system and can also send control signals back to the real system. Digital twins can be used for descriptive (e.g. performance visualisation), predictive (e.g. demand forecasting), and prescriptive (e.g. supply chain recovery simulation and optimisation) decision-making support (Burgos and Ivanov 2021; Boyes and Watson 2022; Elmachtoub and Grigas 2022; Rolf et al. 2022).

The digital representation is created by collecting real-time data on current conditions through digital technology and processing this data using simulation, AI, and machine learning tools to facilitate the evaluation of decision alternatives and enable real-time decision-making (Marmolejo-Saucedo, Hurtado-Hernandez, and Suarez-Valdes 2020). Digital twins can help improve process visibility and integration capabilities among supply chain partners enhancing performance, sustainability, and resilience analysis (Chabanet et al. 2022; Kamble et al. 2022; MacCarthy, Ahmed, and Demirel 2022).

While digital twin-related literature is growing quite rapidly, published research is rather engineering-oriented and limited to particular elements of digital twins (e.g. visibility, human-machine interface, automation, particular technologies, and fragmented operational processes) while an integrated notion of a digital twin from an SCOM perspective is missing.

The ultimate objective of our paper is to synthesise a taxonomy of socio-technological components framing the research on the creation and adoption of digital twins in SCOM and to develop a framework that could be applied to the analysis, design and usage of digital twins. Since the SCOM literature on digital twins is in its infancy, we perform our analysis by addressing literature which deals with both digital twins themselves and underlined technologies, decision-making principles (e.g. human–machine interface) and operational capabilities enhanced by digital twins (e.g. visibility and collaboration) (Frazzon, Freitag, and Ivanov 2021; Ivanov and Dolgui 2021; Zhang, MacCarthy, et al. 2022). Our objective is to collate these fragments into an integrated view of a digital twin in SCOM.

In particular, we aim to understand how the following questions have been or should be answered in existing or future research:

Question 1: What are the major digital technologies to build and adopt digital twins in SCOM?

Question 2: What types of digital twins exist in SCOM?

Question 3: What are SCOM capabilities where digital twins can contribute, and how?

Question 4: What are the changes in decision-making principles when using digital twins, and how can these changes affect SCOM decision-making practices in future?

Question 5: What are new opportunities in SCOM stemming from digital twins?

The methodology of this study is as follows. First, we define seven major dimensions of digital twins in SCOM based on integration of three existing frameworks, i.e. the Leavitt’s diamond model, 3D framework of Industry 4.0 (Ivanov et al. 2021), and the digital twin framework proposed by Freese and Ludwig (2021). These frameworks have seven common elements, which frame a digital twin in SCOM: technology, people, management, organisation, scope, task and modelling. Following these dimensions, we perform an integrative literature review to develop an SCOM digital twin framework through an analysis of the existing literature reviews. We depart from there and conduct an expert keyword analysis based on a structured SCOPUS search to validate our framework and supplement the elements identified through literature review analysis. As a side effect of the validation analysis, we deduce five major types of digital twins in SCOM: product, process, organisation, supply chain, and network-of-networks digital twins.

The rest of this paper is organised as follows. Section 2 develops the 7-element SCOM digital twin framework. Section 3 frames the research landscape on digital twins in SCOM in the 7-element structure. Section 4 illustrates an SCOM digital twin design and its use based on an anyLogistix example. We conclude in section 5 by summarising major research outcomes and outlining some future perspectives.

2. Development and validation of SCOM digital twin framework

2.1. Integrative literature analysis

The objective of our integrative literature analysis is to understand the major elements that frame a digital twin framework. For this reason, in the integrative literature analysis, we mainly focus on previous literature reviews, which proposed several frameworks for digital twins in SCOM and supplement them with relevant research papers. To structure our analysis, we rely on a framework from organisation theory, namely Leavitt’s diamond model, which is based on four elements: structure, task, people and technology. In addition, we consider the 3D framework of Industry 4.0 (Ivanov et al. 2021), which classifies technology, management, and organisation elements, and the digital twin framework proposed by Freese and Ludwig (2021), which suggests scope, actor, asset, flow reference object, performance measurement, and supply chain process as major SCOM digital twin elements.

The Leavitt’s diamond model, the 3D framework of Industry 4.0 (Ivanov et al. 2021), and the digital twin framework proposed by Freese and Ludwig (2021) have in total 13 elements. The Leavitt’s diamond model and the 3D framework of Industry 4.0 have one common element (i.e. technology), and Leavitt’s diamond model and the Freese-Ludwig framework also have one common element (people/actor) so there are 11 distinct elements in total: technology, people, management, organisation, structure, scope, task, actor, asset, flow reference object, performance measurement, and supply chain process. Following analysis of digital twin definitions in Section 1, one important capability of digital twins is modeling. We propose to combine elements ‘flow reference object’ and ‘performance measurement’ from the Freese-Ludwig framework and the element ‘structure’ from the Leavitt’s diamond model into an integrated element ‘modeling’. Finally, the element ‘task’ from the Leavitt’s diamond model and elements ‘asset’ and ‘supply chain process’ from the Freese-Ludwig framework are merged into a common element ‘task’.

Accordingly, we propose the following seven elements to be included in the SCOM digital twin framework: technology, people, management, organisation, scope, task, and modelling (Figure 2).

Figure 2. Elements of SCOM digital twin framework.

Elements included in digital twins in SCOM.

We organise our literature analysis according to the seven elements shown in Figure 1.

Technology

Bhandal et al. (2022) present an overview of digital twin-related technologies. They point to IoT, Blockchain, AI and data analytics, AR/VR, and Industry 4.0 as key technological enablers of digital twins in SCOM. Sharma et al. (2022) elaborate on the technological components of a digital twin. They distinquish elementary components (a physical asset (e.g. a product), a digital asset (the virtual component), and information flows between the physical and digital asset) and imperative components (IoT devices, data, machine learning, security of data and information flow among various components, and performance evaluation). Liu, Jiang, and Jiang (2020) focus on the data integration perspective of digital twins. Li et al. (2021) demonstrate a blockchain-enabled digital twin collaboration platform for heterogeneous socialised manufacturing resource management. Wuttke et al. (2022) examined the potential of ramping-up production using AR technology.

Organisation

Digital twins enable new business and operational models (e.g. collaborative platforms, deep tier financing, cloud manufacturing, and cloud supply chains) (Ivanov et al. 2022; Zhang, Guan, et al. 2022). The existing supply chains and operations are being transformed based on the principles of end-to-end visibility and connectivity, human-machine interface, and real-time data-driven decision-making support (e.g. digital companions) (MacCarthy, Ahmed, and Demirel 2022; Mourtzis 2022). Rahmanzadeh, Pishvaee, and Govindan (2022) elaborate on open supply chain management as a new paradigm emerging through digital twins. Cloud supply chains as a new form of ‘supply-chain-as-a-service’ are heavily based on the usage of digital twins and collaborative digital platforms (Ivanov et al. 2022; Zhang, Guan, et al. 2022).

People

van der Valk et al. (2021) note data accuracy and human-machine/man machine interfaces as key features of digital twins. Rožanec et al. (2022) develop the notion of actionable cognitive twins considering them as the ‘next generation digital twins enhanced with cognitive capabilities through a knowledge graph and artificial intelligence models that provide insights and decision-making options to the users.’ Ivanov (2021a) provides two case-studies of digital twins in supply chains which show how digital twins will change future decision-making of supply chain and operations managers towards real-time data-driven decisions and a continuous interaction between human and artificial intelligence. However, despite the key role of people in use of digital twins, the human-related aspects of digital twin-based decision-making remain rather underexamined in literature. Berti and Finco (2022) point to the importance of digital twins for ergonomics, analysis and modelling of mental or physical workload, posture feedback, and warnings to workers aiming to improve their safety conditions.

Task / Process

Nguyen et al. (2022) identify several tasks/processes related to supply chain digital twins, i.e. job shop scheduling, supply chain management, information management, sustainability, manufacturing operations management, and assembly process planning. Badakhshan and Ball (2022) stress the role of digital twins in inventory and cash management. Applications of digital twins to supply chain resilience have been shown in Ivanov et al. (2019), Ivanov and Dolgui (2021), and Lv et al. (2022). Chabanet et al. (2022) focused on production planning and control as an application area for digital twins echoed by Huang, Wang, and Yan (2022) and Wang et al. (2023) who underline the crucial role of real-time data for shop-floor level control and reconfigurable machine tools.

Scope

Literature reviews considers different scopes of digital twins which are related to different SCOM structures, e.g. manufacturing and supply chains. Kritzinger et al. (2018), Negri, Fumagalli, and Macchi (2017), Melesse, Di Pasquale, and Riemma (2020), Psarommatis and May (2022) and Singh et al. (2020) focus on manufacturing-oriented digital twins. Kamble et al. (2022), van der Valk et al. (2022), Freese and Ludwig (2021), and Ivanov (2021) describe supply chain digital twins. Digital twins of products and manufacturing/logistics processes have been extensively considered in literature, especially from an engineering point of view (Negri, Fumagalli, and Macchi 2017; Panetto et al. 2019; Melesse, Di Pasquale, and Riemma 2020; Psarommatis and May 2022). Digital twins of organisations and supply chains cover the inter- and intra-organisational views beyond fragmented consideration (e.g. only production or warehouse) within the company and extending towards supply chain integration and collaboration (Park, Son, and Noh 2020; Frazzon, Freitag, and Ivanov 2021; Holzwarth, Staib, and Ivanov 2022; Ivanov 2022; MacCarthy and Ivanov 2022). Most recently, the network-of-networks perspective of digital twins has been proposed, which covers intertwined supply networks (Ivanov and Dolgui 2020), physical Internet (Pan et al. 2017), cloud supply chains (Huang, Wang, and Yan 2022), and ecosystem viability (Ivanov and Dolgui 2022). Nguyen et al. (2022) point to business ecosystem, sustainability development, supply chain downstream management, cognitive thinking in Industry 5.0, citizen twin in digital society, and supply chain resilience as future-oriented scope of SCOM digital twins. Reim, Andersson, and Eckerwall (2022) discussed the collaboration of digital platforms using digital twins. Practical digital twin implementations of the network-of-networks perspective are SupplyOn supplier collaboration platform (Holzwarth, Staib, and Ivanov 2022) and the Catena-X data ecosystem in automotive industry, which allows for creating digital product passports and improving sustainability and resilience of supply chains from the ecosystem perspective (Catena 2022) echoing the works by Ivanov and Dolgui (2020) and Ivanov and Dolgui (2022).

Management

Digital twins facilitate several management capabilities. Tozanli and Saénz (2022) point to real-time data and end-to-end visibility as key categories associated with digital twins in SCOM. van der Valk et al. (2022) highlight visibility, monitoring, optimisation, simulation and prediction as digital twin purposes. Dubey et al. (2021) and MacCarthy, Ahmed, and Demirel (2022) stress the role of visibility in supply chain mapping echoed by Ivanov (2021), who elaborates on the role of visibility in managing supply chain resilience. Freese and Ludwig (2021), Ivanov and Dolgui (2021) and Kamble et al. (2022) stress the importance of real-time data and information for digital twin case decision-making. Digital twins open new opportunities for end-to-end supply chain visibility and digital collaboration (Holzwarth, Staib, and Ivanov 2022).

Modelling

Simulation and optimisation are the central modelling methods used in digital twins in SCOM (Burgos and Ivanov 2021, Zhang, Guan, et al. 2022). Digital twins support all three types of data analytics: descriptive (e.g. performance visualisation), predictive (e.g. predicting impact of a disruption on production system) and prescriptive (e.g. supply chain recovery simulation and optimisation) decision-making support. Yan, Wang, and Wu (2022) used a double-layer Q-learning algorithm for a digital twin–enabled dynamic scheduling with preventive maintenance. Kusiak (2022) elaborates on predictive models in digital manufacturing, stressing the role of digital twins and real-time data. Burgos and Ivanov (2021) used anyLogistix-based digital twin to examine the COVID-19 pandemic’s impacts on a food retail supply chain. Sharma et al. (2022) stress the importance of machine learning in SCOM digital twins. Rolf et al. (2022) elaborate on the reinforcement learning and its combination with simulation models.

2.2. Framework validation and extension

Through an integrative literature review, we analysed the existing digital twin frameworks and identified seven major elements that comprehensively conceptualise the notion of a digital twin from an SCOM perspective. To validate this framework, we run a SCOPUS search to understand major keywords for each element and compare them with our framework with the objective to confirm/refute/extend the elements of the framework and their content.

The SCOPUS search was organised as follows:

(TITLE-ABS-KEY (visibility) OR TITLE-ABS-KEY (real-time) OR TITLE-ABS-KEY (‘digital twin’) OR TITLE-ABS-KEY (human-machine) AND TITLE-ABS-KEY (‘management’) AND TITLE-ABS-KEY (‘supply chain’) OR TITLE-ABS-KEY (logistic*) OR TITLE-ABS-KEY (manufactur*) OR TITLE-ABS-KEY (‘production’) AND TITLE-ABS-KEY (‘augmented reality’) OR TITLE-ABS-KEY (‘virtual reality’) OR TITLE-ABS-KEY (blockchain) OR TITLE-ABS-KEY (5G) OR TITLE-ABS-KEY (cloud) OR TITLE-ABS-KEY (‘industry 4.0’) OR TITLE-ABS-KEY (‘simulation’) OR TITLE-ABS-KEY (‘artificial intelligence’) OR TITLE-ABS-KEY (‘data analytics’) OR TITLE-ABS-KEY (internet-of-things) OR TITLE-ABS-KEY (cobot)) AND (LIMIT-TO (DOCTYPE, ‘ar’)) AND (LIMIT-TO (SUBJAREA, ‘ENGI’) OR LIMIT-TO (SUBJAREA, ‘DECI’) OR LIMIT-TO (SUBJAREA, ‘BUSI’))

Since the notion of a digital twin is relatively new but its underlying principles and methods have been studied for a relatively long time, we search not only for ‘digital twins’ but also for ‘visibility’, ‘real-time’ and ‘human-machine’ in relation to ‘management’ of ‘supply chain’, ‘logistic*’, ‘manufactur*’ and ‘production’. Next, we add major technologies (see the search stream above), which facilitate the digital twins in SCOM and which we identified through the literature review in section 2.1. With this three-level search procedure, we create a comprehensive notion of digital twin–related literature covering management, organisation and technology. We restricted the search to journal articles only and the areas of management, decision sciences and engineering.

The search yielded 1343 results from 1977 to 2023. We carefully analysed the keywords identified by SCOPUS and then clustered these keywords according to the seven elements of the SCOM digital twin framework. The results are shown in Table 1.

Table 1. Clusters of keywords in the seven-element digital twin framework.

Organisation	Technology	Management	People	Scope	Modelling	Task
Smart Manufacturing Intelligent Manufacturing Smart Factory Computer Integrated Manufacturing (CIM) Flexible Manufacturing Systems (FMS) Digital Manufacturing Cloud supply chain Digital production	Internet Of Things Industry 4.0 Artificial Intelligence Virtual Reailty / Augmented Reality / Computer Vision Big Data Analytics Blockchain Cyber Physical System Automation / Intelligent robots Cloud / edge / fog computing Digital storage Additive Manufacturing RFID 5G Interoperability Sensors Wireless Sensor Networks Computer Aided Manufacturing / Design (CAM/CAD) ERP	Real Time Systems Information Management Visibility Real Time Monitoring Data Visualisation Collaboration Transparency Accuracy Traceability Data privacy Integration	Data Acquisition Human Computer Interaction Real-time Information Real Time Control Man Machine Systems Knowledge Based Systems Human Machine Interface Adaptive control Human-robot collaboration Behavioral research	Product design Production System Supply chain Warehouse	Machine Learning Optimisation Deep learning Neural Networks Predictive Analytics Computer simulation Data Mining Reinforcement Learning Support Vector Machines Multi Agent Systems Neural networks Petri Nets Adaptive control systems System dynamics	Forecasting Scheduling Supply chain management Production control Quality Control Assembly Risk management Sustainability management Manufacturing control Logistics Planning Distribution Fleet management Lifecycle management Resilience Layout planning Predictive maintenance

As shown in Table 1, the SCOPUS search results strongly validate the seven elements of the proposed SCOM digital twin framework. Each of the elements is self-consistent, and there are no intersections across the elements – each keyword is used only in one of the elements.

We now combine Figure 2 and Table 1 and present the SCOM digital twin framework displaying its elements and content within each of the elements (Figure 3).

Figure 3. Seven-element SCOM digital twin framework.

Seven elements included in digital twins in SCOM.

Using the framework developed, we continue with framing the research landscape by discussing each element individually and their combinations.

3. Framing the research landscape

In this section, we provide answers to the five research questions formulated in the Introduction through a structured discussion of how each of the seven elements contributes to the SCOM digital twin notion.

Technology

Digital twins are enabled by data which are generated and processed using different information technologies and systems. Boyes and Watson (2022) and Nguyen et al. (2022) find that several information systems need to be used to enable digital twins. At the product level, CAD/CAM systems are applied. At the manufacturing process level, the MES system delivers data for digital twins. ERP systems enable the building of digital twins of organisations. At the supply chain level, special software such as anyLogistix in combination with external data sources (e.g. data from logistics service providers, weather data, financial market data) are used to build supply chain digital twins (Ivanov and Dolgui 2021). Bhandal et al. (2022) point to IoT, Blockchain, AI and data analytics, AR/VR and Industry 4.0 as digital twin enablers. Future research areas highlight both a technical understanding of system integration and interoperability and management conceptualisation of needs and limits for data-driven decision-making support. Technologies allow for the integration of models with external data sources and ensure interactions with other digital twins.

People

This element is about human–machine interfaces and how digital twins affect the decision-support and decision-making practices in SCOM. Digital twins are developed and used by people, and at the same time, they change the way supply chain and operations managers make decisions. Decisions in SCOM depend on the expertise of the manager, what knowledge and skills they exhibit and their access to real-time data and information. Digital twins can become ‘digital companions’ for managers providing decision-making support by acquiring real-time data and simulating potential outcomes of certain decisions (e.g. alternative recovery policies after a disruption or changes in an environmental footprint due to a supply chain redesign). Digital twins can also consider the competence of making decisions (e.g. placing orders in an inventory control system). Most centrally, digital twins offer real-time, data-driven decision-making support. Further research is needed to examine to which extent a continuous access to real-time data helps managers in decision-making. In addition, behavioural aspects of data-driven decision-making and cognitive biases in human–artificial intelligence interactions belong to the novel topics when digital twins can be explored in SCOM research (Fahimnia et al. 2019; Fu et al. 2022; Sun et al. 2022). At the manufacturing system level, human–robot collaboration is one of the central digital twin–related future research topics (Sheu and Choi 2023).

Organisation

Technology determines organisation. Digital twins mirror SCOM organisations and enable new business and operational models. Through digital twins, novel organisational constructs such as digital manufacturing, cloud supply chain, and collaborative platforms arise. Examination of digital twin–driven transformations in the organisation of production, logistics and supply chains can be conducted in future research areas where impactful and substantial contributions can be made. In addition, digital twins can lead to new organisational structures and a redistribution of decision-making competencies across departments.

Task

Digital twins can support decision-making in several areas such as collaborative demand planning and forecasting, inventory management, collaborative sourcing planning, quality control, shipment control, smart contracting, production control, predictive maintenance, resilience management and sustainable development. While digital twins in manufacturing have received broader attention, the supply chain level of digital twins belongs to future research areas.

Scope

The analysis of this literature allows for a classification of five major types of digital twins – product, process, company, supply chain and network-of-networks – with regard to their scope. Notably, scope, technology and task integration, modelling and decision-making complexity and organisational and management complexity differ across different digital twin types with a tendency to increase with higher physical object complexity and uncertainty as shown in Figure 4.

Figure 4. Types of digital twins in SCOM.

Different digital twins in SCOM.

The digital twins of products and processes received much attention in literature. Less is known about the digital twins of organisations, supply chains and intertwined supply networks. These areas can be considered more detailed in future research. Interestingly, different types of digital twins are covered by different disciplines. For example, product digital twins are usually considered in mechanical engineering, while process digital twins are frequently studied in industrial engineering. Supply chain researchers focus on network digital twins. This calls for multidisciplinary collaboration in exploring digital twins in SCOM, which is in line with recent findings about Industry 4.0 research perspectives (Ivanov et al. 2021).

Management

Digital twins enhance several management capabilities such as visibility, transparency, collaboration and traceability. The use of these capabilities is broad and ranges from simple process monitoring (e.g. containers and their physical locations) to the development of new management principles based on end-to-end visibility and digital collaboration. Understanding the value of digital twin-driven enhanced management capabilities is a future research area. For example, in contrast to some standalone physical objects (e.g. a product or a port) for which digital twins are built to copy the physical object based on its full observability, multi-echelon supply chains are hardly observable and companies frequently do not know their suppliers of suppliers and last-mile logistics structures. In this setting, supply chain digital twin can even help to recognise the physical supply chain providing additional value for decision-makers (e.g. through analysis of cloud data via Google BigQuery, it can be possible to deduce a structure of a physical supply chain network).

Modelling

Analytics capabilities are the backbone of digital twins. Modelling can presume decision-making support (final decisions with humans) or decision-making by AI. Optimisation, discrete-event simulation, neural networks, machine and reinforcement learning, agent-based modelling and system dynamics allow for the implementation of the full variety of descriptive, predictive and prescriptive algorithms in SCOM. While real-time data-driven models constitute a narrow view of digital twins (i.e. a digital twin as a stand-alone software package), in a broader sense, digital twins can be considered as a combination of different information systems and models. Future research can shed more light on the transition from offline to real-time data-driven modelling, revealing its value and barriers.

4. Example: anyLogistix digital twin and its use for supply chain resilience management

In this section, we illustrate the design and use of digital supply chain twins using the example of anyLogistix. We first describe the digital twin design process. Subsequently, some use examples are presented.

4.1. Digital twin design

anyLogistix is a supply chain simulation and optimisation software. It describes the supply chain with all its objects (e.g. factories, warehouses and suppliers) and transportation links (Figure 5).

Figure 5. anyLogistix-based SCOM digital twin.

Example of a digital twin in SCOM.

After defining the network structure, products, customers, and their demand, the associated supply chain processes (e.g. production, distribution, and sources) are defined (Burgos and Ivanov 2021; Ivanov 2022). Depending on the granularity and customisation degree, standard anyLogistix processes can be used or modified according to some supply chain specifics using AnyLogic. Subsequently, control policies for each process (e.g. periodic, continuous, and just-in-time production-inventory control) are set up using standard anyLogistix functionality or individual design in AnyLogic.

anyLogistix supports descriptive, predictive and prescriptive analytics. Through dashboards, performance of supply chains can be visualised. Simulation and optimisation modelling helps predict the impacts of different networks designs and operational policies on supply chain dynamics and performance and prescribe courses of action through the evaluation of alternatives. Moreover, an integration with AI is possible. Different SCOM tasks such as supply chain design, inventory management, sourcing planning, quality control, shipment control, resilience management, sustainability management and production control are in the scope of anyLogistix.

Through the pre- and postprocessing of data, anyLogistix models can be connected to real-time data, and modelling results can be transferred for an extended performance analysis in business intelligence tools (Ivanov and Dolgui 2021). anyLogistix can be considered as an advanced decision-making support tool allowing for knowledge-aware decision-making and creating human–artificial intelligence interface for data-driven decision-making through a digital companion. It develops several important management capabilities such as visibility, real-time decision-making, data visualisation, data accuracy and traceability.

4.2. Modelling examples

4.2.1 Descriptive analytics

Ivanov (2017) used anyLogistix to uncover the ripple effect and its dynamics in supply chains. Using simulation, the impacts of the ripple effect on supply chain performance have been visualised. Ivanov (2018) applied anyLogistix to reveal interfaces of resilience and sustainability in supply chains. Ivanov (2019) uncovered the effect of disruption tails in supply chains describing its performance impact. Dolgui, Ivanov, and Rozhkov (2020) used anyLogistix to reveal interrelations of the ripple effect and bullwhip effects in supply chains and visualised their dynamics and performance impact. This study was extended in Ivanov (2020b). Burgos and Ivanov (2021) used anyLogistix to describe the impact of the COVID-19 pandemic on a food retail supply chain in Europe. Monostori (2021) examined the interrelationships of supply chain robustness, complexity and efficiency in the setting of the ripple effect and using anyLogistix and a distribution network case study.

4.2.2. Predictive analytics

Gianesello, Ivanov, and Battini (2017) applied anyLogistix to study a closed-loop supply chain in the automotive industry to predict the disruption impact of reverse logistics on performance. Ivanov (2020a), Ivanov and Das (2020) and Singh et al. (2021) used anyLogistix to predict the effects of the COVID-19 pandemic on supply chain dynamics and performance in the setting of a global value-creation network. They uncovered specific features of a pandemic as a new type of supply chain risks distinctively characterised by long-term duration, unpredictable scaling and recovery in the presence of a disruption. Moosavi and Hosseini (2021) assessed supply chain resilience during the COVID-19 pandemic considering recovery strategies.

4.2.3. Prescriptive analytics

Stewart and Ivanov (2022) optimised a network design in humanitarian logistics setting using network optimisation in anyLogistix. Ivanov (2021b) predicted the effects which can adversely affect supply chains in the exit period of the pandemic. He pointed to the risks of product shortage and inflation, which were later confirmed in real life. Based on simulation results, some recommendations on how to avoid the disruption tails at the structural and operational levels have been proposed. Timperio et al. (2022) used anyLogistix to develop a decision-support framework for humanitarian logistics both optimising the network structure and improving some operational control policies.

Analysis of the above literature shows that anyLogistix-based digital twins help visualise performance and uncover dynamic effects in supply chain operations, e.g. ripple effect and disruption tails. Through simulation and optimisation, prediction of supply chain performance and behaviours can be done. Moreover, alternative strategies and policies can be tested, and the most promising ones can be recommended for implementation. However, despite significant progress in the conceptualisation of anyLogistix as a digital twin, the literature still lacks research addressing real-time data integration in anyLogistix and its communication with other external systems of supply chain partners. This can be considered as a future research avenue.

5. Conclusion

Digital twins represent products, processes, companies, supply chain and network-of-network infrastructures in a digital form, mirroring and replicating the real objects. Understanding the design, value and application of digital twins belongs to a promising research area in SCOM. The existing literature has addressed the need to understand digital twins in SCOM mostly focusing on fragmented technological solutions and use cases. In this setting of quite an unorganised research landscape, there is a need for a conceptualisation of a digital twin framework in SCOM integrating different elements.

In this study, we defined seven major elements of a digital twin in SCOM: technology, people, management, organisation, scope, task and modelling. Moreover, we distinguished five major types of digital twins in SCOM: product, process, organisation, supply chain and network-of-networks. The seven elements show that digital twins in SCOM are not merely a simulation-based replica of a real object but a complex sociotechnical phenomenon involving continuous human–artificial intelligence interactions. This leads to an understanding of the role of digital twins through the lens of Industry 5.0 and reconfigurable and viable supply chains (Ivanov 2023; Ivanov and Dolgui 2020; Ivanov et al. 2023;  Dolgui, Ivanov, and Sokolov 2020).

Researchers and practitioners alike can use our framework to structure knowledge on SCOM digital twins and consider all seven elements when designing and using digital twins. The framework proposed can be used for further descriptive analyses on this topic using content-analysis based, structured literature reviews (Seuring and Gold 2012). For example, evaluating the manuscripts that would be obtained from a narrower search for papers that necessarily contain the term ‘digital twin’ might reveal additional interesting insights.

We further stress the multidisciplinary nature of research in SCOM digital twins. Mechanical and industrial engineering deals with product and process digital twins; operations research is concerned with modelling; empirical management researchers might be interested in the examination of management capabilities and organisational studies related to digital twins and data and computer science researchers direct their attention towards data and system integration and software development.

In future, novel research areas can be expected such as the networking effects of multiple digital twins and how digital twins evolve and learn from interactions with other digital twins. The understanding of complexity and uncertainty issues in design and the use of digital twins is another promising research stream. Finally, we point to a bidirectional learning process between humans and digital twins – digital twins are designed by humans and learn humans at the same time, changing decision-making roles and principles. How will a future supply chain and operations manager make decisions when supported by digital twins? This and other questions can lead to novel and insightful research in the next years.

Acknowledgements

The authors thanks two anonymous reviewers whose invaluable comments helped to improve the manuscript immensely.

Disclosure statement

No potential conflict of interest was reported by the author.

Data availability statement

Data supporting the findings of this study are available on a reasonable request from the authors.

Additional information

Notes on contributors

Dmitry Ivanov

Dmitry Ivanov is a professor of supply chain and operations management at Berlin School of Economics and Law. He serves at the school as an Academic director of M.A. Global Supply Chain and Operations Management and B.Sc. International Sustainability Management as well as a Deputy Director of Institute for Logistics. His publication list includes around 400 publications, including over 130 papers in international academic journals and leading textbooks Global Supply Chain and Operations Management and Introduction to Supply Chain Resilience. His main research interests and results span resilience, viability and ripple effect in supply chains, risk analytics, and digital twins. Author of the Viable Supply Chain Model and founder of the ripple effect research in supply chains. Recipient of IISE Transactions Best Paper Award 2021, Best Paper and Most Cited Paper Awards of IJPR (2018, 2019, 2020, 2021), OMEGA Best Paper Award 2022, Clarivate Highly Cited Researcher Award (2021, 2022). He co-edits IJISM and is an associate editor of the IJPR and OMEGA. He is Chairman of IFAC TC 5.2 ‘Manufacturing Modelling for Management and Control’.