Digital Supply Chain Twins—Conceptual Clarification, Use Cases and Benefits
Abstract
:1. Introduction
- RO1:
- Describe the distinct characteristics and scopes of DSCT in LSCM literature and derive a unified definition of DSCT.
- RO2:
- Synthesize the application areas of DSCT in LSCM.
- RO3:
- Consolidate specific DSCT use cases and their intended individual benefits.
2. Conceptual Clarification of Digital Supply Chain Twins
2.1. Development of a Definition of DSCT
“The Digital Twin is a set of virtual information constructs that fully describes a potential or actual physical manufactured product from the micro atomic level to the macro geometrical level. At its optimum, any information that could be obtained from inspecting a physical manufactured product can be obtained from its Digital Twin.”
“A Digital Twin is an integrated multiphysics, multiscale, probabilistic simulation of an […] system that uses the best available physical models, sensor updates, […], to mirror the life of its corresponding […] twin. The Digital Twin is ultra-realistic and may consider one or more important and interdependent […] systems […]. The Digital Twin integrates sensor data from the […] system, […] and all available historical […] data obtained using data mining and text mining.”
“A Digital Twin is a digital representation of a physical thing’s data, state, relationships and behavior.”
- Physical and virtual: A DT considers both the physical system and the virtual system.
- Bidirectional data: A DT supports bidirectional data exchange between the physical and virtual system.
- Timely updates: A DT provides timely updates, depending on the requirements of the use case.
- Maintain state: A DT is able to store the last state of the physical system in order to deal with disconnections.
- Modeling and analytics: A DT provides modeling and analytics capabilities.
- Reporting: A DT passes results to people and/or machines.
- The term DSCT is derived from the term DT, which was originally developed for product development. The basic idea of a DT is therefore to be retained in the definition of the DSCT.
- Although DTs have been discussed a lot in literature and are already used extensively in practice [33], the DSCT has only been mentioned in Gartner’s top “supply chain technology trends” since 2019 [4,5]. This suggests that a DSCT does not merely describe a new application field of classic DT, but represents a new, innovative technology concept. The definition of DSCT should therefore be clearly distinguished from that of DT to do justice to the special application field of LSCM [34].
- In DT literature, the automated exchange of data on both sides between the physical and digital system is often emphasized as a characteristic of DT. This is achieved on one side by the use of sensors, and on the other side by the use of actuators and other control elements. In relation to logistics systems, however, this characteristic is questionable, since humans still play a major role in most logistics systems [35]. Data transfer from the logistics system to the DSCT can certainly be implemented with the help of sensors and IoT technology, but automated control of the logistics system by control elements is unrealistic for most use cases. The exchange of data from the DSCT to the logistics system can therefore also take place by a knowledge gain which the actors involved achieve through analyzing the DSCT.
- Many definitions describe the DSCT as a model or simulation model. However, digital simulation models of logistics systems have been used for decision-making in LSCM for years, and are therefore not to be seen as a new, innovative technology concept [36]. The DSCT definition should therefore be clearly distinguished from simple digital models and their use for simulation purposes [37].
- Depending on the literature, different purposes and methods are assigned to DSCT. In this respect there is no consensus among authors. A general definition of DSCT should therefore be independent of the specific purpose.
- Bidirectional: Data are exchanged in both directions. Changes in the state of the logistics system therefore lead to changes in the state of the digital model. Similarly, knowledge gained from the digital model leads to actions or decision-making in the logistics system. A certain degree of automation regarding the data exchange is explicitly not a prerequisite for a DSCT.
- Timely: Data exchange takes place in a timely manner. The specific frequency is determined by the use case. Continuous updates in real-time are explicitly not a prerequisite for a DSCT, unless the use case requires this.
- Long-term: The data exchange and thus the lifetime of the DSCT are designed for continuous, long-term use. Digital simulation models created as part of project activities or for one-time use are thus explicitly not to be considered DSCTs.
2.2. Scopes of DSCT
- Network level: DSCT of a multi-stakeholder value network;
- Site level: DSCT of a logistics site (warehouses, production facilities, …);
- Asset level: DT of a logistics asset (trucks, forklifts, …).
3. Research Design
3.1. Define Research Question
3.2. Determine Required Characteristics of Primary Studies
3.3. Retrieve Sample of Potentially Relevant Literature (“Baseline Sample”)
OR
((“Digital Twin” OR “Digitaler Zwilling”)
AND
(“Logistics” OR “Supply Chain” OR “Supply Network” OR “Supply Chain Management” OR “SCM” OR “Value Chain” OR “Value Network” OR “Supply*”)).
- Web of Science (126 articles, 8 January 2021);
- EBCSO (74 articles, 8 January 2021);
- IEEE (56 articles, 8 January 2021);
- Google Scholar (124 articles, 31 January 2021);
3.4. Select Pertinent Literature (“Synthesis Sample”)
3.5. Synthesize Literature
4. Results
4.1. Descriptive Analysis of Existing Literature
4.2. Application Areas of DSCT
4.3. Use Cases and Benefits of DSCT
5. Implications
6. Conclusions and Final Remarks
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Source | Definition/Understanding |
---|---|
Srai et al. 2019 [28] | Regardless of the specific definition used, the DT concept typically involves the following aspects: (1) a physical object; (2) its “digital” or “virtual” representation; and (3) the nature of the connection between the two, as well as between DTs. |
Ivanov et al. 2019 [29] | A DSCT is a model that can always represent the network state in real-time for any given moment and interacting with other SCM tools provides a control tower for complete end-to-end SC visibility to improve resilience and test contingency plans. |
Marmolejo-Saucedo 2020 [10] | […] we can define a supply chain digital twin as a detailed simulation model of an actual supply chain which predicts the behavior and dynamics of a supply chain to make mid-term/short-term decisions. Consists of six layers: the physical twin, the local data source, local data repositories, IoT gateway interfaces, Cloud-based information repositories and the emulation and simulation platform. |
AnyLogic 2018 [30] | A DT is a special type of simulation model that represents a specific example of something in the present and is achieved by combining current data from the subject with its simulation model. |
DHL 2019 [6] | In logistics, the ultimate DT would be a model of an entire SC network. Key characteristics/attributes that help to differentiate true DT from other types of computer model or simulation: DTs are virtual models of a real “thing”; virtually represent a unique physical asset. DTs simulate both the physical state and behavior of the thing. DTs are unique, associated with a single, specific instance of the thing/physical asset. DTs are connected to the thing, updating themselves in response to known changes to the thing’s state, condition or context; continuously collect data (through sensors) and being connected to the physical asset, updating themselves with any change to the asset’s state, condition or context. DTs provide value through visualization, analysis, prediction or optimization. |
Korth et al. 2018 [31] | DT can therefore be characterized in the following way: it is a linked collection of different types of data (like operation data) as well as different models; it evolves with the real system along its life cycle; it is able to derive solutions relevant for the real systems (e.g., optimize operation and service). |
Ding 2019 [32] | The end-to-end supply chain covers information flow, logistics and capital flow from all suppliers to all customers, covering all product details and time cycles in the supply chain (depending on the specific business of each specific industry). This is not only a physical supply chain network, but also a time cycle (such as a rolling supply chain planning cycle). While most of today’s supply chain processes and software are set up based on a portion of the supply chain, the digital twin supply chain should have the ability to integrate the lowest and highest level of details of all the elements together and provide a real-time level of management decision-making ability. |
Inclusion/Quality Criteria | Rationale |
---|---|
The title and/or abstract must explicitly mention the DT concept as the study focus. | Literature that merely mentions the DT concept as part of a study with a different focus should be excluded. |
The title and/or abstract must demonstrate that the authors conduct research in the area of LSCM. | As DSCTs are applied in the field of LSCM, the research area must be exactly that. |
The scope of the examined twin must be either on the network level or on the site level. | As pointed out in Section 2.2, the research on DSCT must be distinct from asset-focused DT research. |
The study language must be English or German. | English is the dominant research language in this field; still, German is also included to extend the sample size. |
The literatures must be either a scientific article or an extensive trend study. | Other forms of literature do not meet the scientific requirements. However, the articles are not reduced to peer-reviewed papers. |
Use Case | Application Area | Description | Benefits | References |
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Transport Planning | Transportation (Synchromodal) Transport Networks | The DSCT depicts the supply chain nodes, transportation processes and other dynamic processes at ports and terminals. It aims to operationalize and synchronize the system in real time based on data feeds and feedback loops that guide all the stakeholders and decision-makers. The DSCT can simulate the near-future and therefore estimate where assets will be in a few hours based on congestion levels and infrastructural developments. The decision-maker might use this information to re-route freight, assess modal shift or adjust load factors and vessel speeds. In this way she is able to react to disruptions more effectively. | Transparencyof Transportation Processes Increased Fill Rates Transportation Flexibility Service Level Reliability Network Resilience Higher Reaction Speed | Ambra and Macharis (2020) [22]; Carvalho et al. (2020) [43]; Semenov et al. (2020) [44] |
Material Lifecycle Management | Network Management Value Systems | The DSCT represents products and their respective production processes in urban areas. It aims to support the concept of urban mining through identifying recycling potential in urban value chains. Materials at the end of the use phase or byproducts are identified and integrated as the new life cycle starts. The DSCT therefore provides the base for sustainable cascade use of materials across interconnected production networks. | Transparencyof Material Flow Reduced Material Waste Improved Material Efficiency Decreased Energy Consumption Improved Ecological Sustainability | Pehlken and Baumann (2020) [45] |
Multi-Echelon Inventory Management | Network Management Value Systems | The DSCT represents a value system from suppliers to customers. This digital model is used to test various transport and inventory policies by carrying out simulation experiments and measuring their effect on the performance of the logistics systems. | Transparencyof Network-wide Inventory Elimination of Bottlenecks Reduced Inventory Improved Service Level Reduced Lead TimeIncreased Economic Efficiency | Semenov et al. (2020) [44] |
Risk Management | Network Management Value Systems | The DSCT represents a value system from suppliers to customers. Data about customer demand, business processes, inventory policies, productive capacities and facility locations are gathered and fed into the simulation model. The model is permanently being updated, so disruptions are being monitored in real time. Then, what-if scenarios are run to test contingency plans accordingly. The user is therefore able to: (1) know about supply chain disruptions in real time; and (2) react to these disruptions more effectively and efficiently. | Supply Chain Transparency Elimination of Bottlenecks Supply Chain Flexibility Network Resilience Higher Reaction Speed | Ivanov and Dolgui (2019) [46]; Semenov et al. (2020) [44]; Ivanov and Dolgui (2020) [20]; Barykin et al. (2020) [47]; Marmolejo-Saucedo (2020) [48]; Ivanov et al. (2019) [29] |
Multi-Echelon Production Planning | Network Management (Manufacturing) Value Systems | The DSCT depicts a value system, more specifically a multi-level manufacturing system. It reflects the current status of the network in terms of customer demand, product and resource inventory as well as productive capacities. As the model is being updated, production planning and scheduling are synchronized to adjust to dynamic fluctuations like the bullwhip effect or the ripple effect. It therefore allows the user to perform efficient supply chain control. | Transparencyof Network-wide Production Reduced Downtimes Production Flexibility Network Resilience | Park et al. (2020) [49] |
Sustainability Assessment | Network Management Value Systems | The DSCT depicts a value system from suppliers to customers. The digital model is used to evaluate different scenarios in terms of their sustainability. As the system’s components change, for example, production techniques, modes of transportations or amount of demand fulfilled, the model is able to assess the sustainability of these scenarios before they occur. This way, the user is able to adjust her decision-making process accordingly. | Supply Chain Transparency Increased Ecological Sustainability | Barni et al. (2018) [50] |
Use Case | Application Area | Description | Benefits | References |
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Outdoor Vehicle Dispatching | Cargo Handling Industrial Parks Airports Ports | The DSCT depicts an outdoor handling location (e.g., ports, airports or industrial parks) within a value system, including the area, vehicles and processes specific to the site. The DSCT is able to tackle vehicle routing problems by reacting to eventualities in real time and re-routing the vehicles efficiently. In this way, optimal and robust dispatching policies on site can be assured. The dispatcher on site might include the DSCT in her decision process. | Fleet Transparency Reduced Lead Time Service Level Reliability Process Resilience Increased Process Efficiency | Hofmann and Branding (2019) [51]; Pan et al. (2020) [52] |
Cargo Load Planning | Cargo Handling Industrial Parks Airports Ports | The DSCT depicts a cargo terminal within a handling location (e.g., airport cargo terminals), including load carriers, infrastructural elements as well as the cargo itself. It aims to maximize the load carrier volume utilization through positioning the load efficiently while considering specific requirements of the load (e.g., dangerous goods). The load planner might use the DSCT to better adjust load plans through simulation experiments so they are optimally integrated into the overall supply chain processes. Virtual reality applications are conceivable to support the process. | Cargo Transparency Increased Fill Rates Process Resilience Increased Process Efficiency | Wong et al. (2020) [53] |
Warehouse Management | Warehousing DCs Warehouses | The DSCT depicts a warehouse or distribution center, including the site infrastructure as well as the inventory itself. The twin acts as a decision support tool for warehouse management processes. The inventory is monitored almost in real time in synch with the WMS in place. The warehouse manager might use the twin to optimize storage policies and reduce lead time through more efficient retrieval processes. | Inventory Transparency Higher Reaction Speed Storage Flexibility Reduced Lead Time Improved Service Level Site Resilience | Baruffaldi et al. (2018) [54]; Agalianos et al. (2020) [55] |
Material Handling | Warehousing DCs Warehouses | The DSCT depicts a warehouse or distribution center (or parts of it), where material is transported within a site. The model focusses on the transportation of inventory and might therefore refer to the site-specific transportation infrastructure (e.g., conveyor systems). It is used to efficiently plan and control the flow of materials through the system, acting as a decision support tool and even enabling the planning and controlling of throughput in highly automated systems. | Inventory Transparency Process Resilience Increased Process Efficiency Reduced Lead Times Elimination of Bottlenecks | Ashrafian et al. (2019) [56] |
Production Planning | Manufacturing Manufacturing Sites Construction Sites Shipyards | The DSCT depicts a manufacturing site within a value system, which might also be a construction site or a shipyard. The model contains the entire operational data of components, machines and systems needed for production and thus forms a digital ecosystem. By using its simulation capability, it can be used for long- and medium-term planning. A wide variety of scenarios can be run in order to implement the best possible solution in production. This use case is examined frequently in scientific literature. | Production Transparency Production Flexibility Planning Resilience Higher Reaction Speed Reduced Lead Time Elimination of Bottlenecks | Boschert and Rosen (2018) [57]; Rosen et al. (2019) [58]; Lu et al. (2019) [59]; Zhou, et al. (2019) [60]; Hauge et al. (2020) [61]; Park et al. (2020) [62]; Gallego-Garcia et al. (2019) [63]; Makarova et al. (2020) [64]; Dolgov et al. (2020) [65]; Wang, et al. (2020) [66]; Agostino et al. (2020) [67] |
Production Controlling | Manufacturing Manufacturing Sites Construction Sites Shipyards | The DSCT depicts a manufacturing site within a value system, which might also be a construction site or a shipyard. The model contains the entire operational data of components, machines and systems needed for production and thus forms a digital ecosystem. Although it depicts the same scope as the DSCT used for production planning, its tasks differ as planning and controlling of production are different tasks themselves. Production controllers might use this twin as a short- to mid-term decision support tool. Problems within production can be detected in real time, and faults can be solved very flexibly and quickly. | Production Transparency Production Flexibility Higher Reaction Speed Reduced Lead Time Elimination of Bottlenecks Increased Process Efficiency Process Resilience | Jeong et al. (2020) [68]; Zhang et al. (2020) [69]; Min et al. (2019) [70]; Wang et al. (2020b) [21]; Guo et al. (2020) [71]; Herakovic et al. (2019) [72]; Mykoniatis and Harris (2021) [73]; Pang et al. (2021) [74];Makarova et al. (2020) [64]; Dolgov et al. (2020) [65]; Wang, et al. (2020) [66]; Agostino et al. (2020) [67] |
Indoor Vehicle Dispatching | Manufacturing Manufacturing Sites Construction Sites Shipyards | The DSCT represents the transportation processes at an indoor facility. The area of the site (or parts of it) as well as the fleet of vehicles on site are included in the digital model. The DSCT is able to tackle vehicle routing problems by reacting to eventualities in real time and re-routing the vehicles efficiently. The DSCT acts as a decision support tool for production planners. They are therefore able to better plan the providing of material in the production process. With this twin, it is also possible to schedule and control a fleet of AGVs. | Fleet Transparency Reduced Lead Time Service Level Reliability Process Resilience Increased Process Efficiency | Yao et al. (2018) [75]; Eschemann et al. (2020) [76] |
Shopfloor Management | Manufacturing Manufacturing Sites Construction Sites Shipyards | The DSCT depicts a manufacturing site on the floor level, including all relevant parts of the manufacturing process like machines, robots, conveyor systems or means of transportation. This digital representation of the shopfloor enables executives to obtain a picture of the state of the real production processes on a daily basis. Users are therefore able to attend to shopfloor meetings without the need for a physical gathering. | Shopfloor Transparency Increased Operational Efficiency Planning Resilience | Brenner and Hummel (2017) [77] |
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Gerlach, B.; Zarnitz, S.; Nitsche, B.; Straube, F. Digital Supply Chain Twins—Conceptual Clarification, Use Cases and Benefits. Logistics 2021, 5, 86. https://doi.org/10.3390/logistics5040086
Gerlach B, Zarnitz S, Nitsche B, Straube F. Digital Supply Chain Twins—Conceptual Clarification, Use Cases and Benefits. Logistics. 2021; 5(4):86. https://doi.org/10.3390/logistics5040086
Chicago/Turabian StyleGerlach, Benno, Simon Zarnitz, Benjamin Nitsche, and Frank Straube. 2021. "Digital Supply Chain Twins—Conceptual Clarification, Use Cases and Benefits" Logistics 5, no. 4: 86. https://doi.org/10.3390/logistics5040086