Managing Disruptions: The Digital Twin For Supply Chain
Business leaders talk about disruptive technologies and digital transformation for value creation. The digitalisation of industry is gaining momentum in supply chain amid the COVID-19 environment. Digitalisation has facilitated supply chains to access, store, and process massive amounts of internal and external data. This big data, in turn, has enhanced the predictive and prescriptive accuracy of solutions. The focus of this article is on digital twin technology, which is driving sensor-enabled real-time data interaction and bringing the physical and digital worlds together. Advanced analytics and machine learning algorithms are very useful in the digital twin to create complex what-if scenarios. These scenarios are then mapped out to the supply chain to attain best operational conditions to manage disruptions.
Supply chains have become more dynamic and complex because of globalisation, interdependence and the associated outsourcing activities amongst companies. As a result, these supply chains lack information asymmetry. Information asymmetry results in the form of disruption with no access to real-time data both from within the company and across the supply chains. Existing simulation models provide decision-making support, but amid the COVID-19 pandemic, these models need real-time data to construct disruption scenarios to timely identify bottlenecks in the supply chain redesign. Industry 4.0 constitutes a technological framework, which focuses on digitalisation and analytical capabilities to identify events on real-time basis. Although IT vendors, consultants and researchers have proposed many technologies for real-time data, the digital twin technology trend has recently started gaining attention as it brings the physical and digital world closer together. Recent research projects estimate that the digital twin market will reach US $35.8 billion by 2025.
Digital twin technology has been defined differently in industry and academia. Some define it as an integrated model with built-in features relevant to the type of application. Others define it as a sensor-enabled digital model of a physical product or system, which is simulated on real-time data. The widely circulated definition by Deloitte is a digital twin can be defined, fundamentally, as an evolving digital profile of the historical and current behaviour of a physical object or process that helps optimise business performance. A digital twin is not just the computer-aided design (CAD) or the sensor-enabled Internet of Things (IoT). It is a smart sensor-enabled data interaction, active collaboration, advanced analytics with AI or machine learning, coupled with the application of it to the physical world across the full life cycle of processes and objects.
The digital twin trend is gaining more attention in industry because of improvements in technical and computational capabilities with operations technology. As managers investigate, evaluate, or even implement digital twins to manage disruptions within their companies or across the supply chains, they may find it useful to:
Understand the critical success factors in the deployment of digital twin technology
Understand the application of a digital twin in supply chains by learning lessons from 4 companies
Critical Success Factors
In light of the continuous growth of companies’ capabilities in analytics, it is important that all stakeholders within the organisation and across the supply chain operations are technically and technologically capable of deploying digital twin technology. All stakeholders must be willing to integrate and cooperate to align with the mission. Stakeholders must share the large volumes of data to predict the events and manage disruptions. The factors critical to the successful deployment of digital twin technology to manage disruption are:
IoT Sensors and Data: Internet of Things (IoT) sensors embedded in assets throughout the supply chain can directly feed operational data into digital twin simulations. This functionality will enable continuous monitoring of events.
Simulation Capability: The focal company should have experts of simulation who can design complex what-if scenarios based on real-time data. The data is simulated with machine learning algorithms to gain deeper insights about disruptions.
Interoperability: Interoperability of supply chain data amongst the stakeholders is very important for digital twin technology. Industry standardisation and open systems have somehow facilitated effective communication across different platforms. The collected data from stakeholders is crucial for running simulation and proposing disruptive solutions.
Supply chain practices: Real-time data collection from supply chain operations is a prerequisite in digital twin technology. However, data structures vary across the upstream and downstream supply chains because of interoperability and heterogeneous platforms. The solution is to have standardised processes within the company.
Advanced analytics and visualisation:Advanced analytic techniques are used to analyse huge amounts of data in digital twin simulations. Advanced data visualisation and algorithmic simulations can help to filter, dashboard and visualise the information in real time. Some of the techniques used for visualisation are virtual reality (VR), augmented reality (AR) and artificial intelligence (AI) based on real-time data.
Platform: Platforms, whetherinternally within the firm or externally with the stakeholders are essential for digital twin technology. Most of them are cloud-based platforms and provide powerful computing infrastructure with analytics capabilities to create a digital twin. These platforms provide capabilities to process effective simulation and scenario evaluation based on real-time data to avoid disruption.
Application of Digital Twin Technology
Unilever: Consumer-goods giant Unilever PLC is using real-time sensor data and data streaming via digital twin technology to optimise its supply chain operations. Their main aim is to make the production more efficient and flexible. Unilever launched a digital twinstrategy to create virtual models of its factories. At each location, the IoT sensors feed real-time information related to performance such as temperature, motor speed and other production variables into the enterprise cloud. Advanced analytics and machine learning algorithms are used to create complex what-if scenarios in digital twin simulations to map out the best operational conditions. The analysed operational information then leads to timely identification of predictive maintenance and reduction of disruption that may occur in the form of poor product quality and eventually wastage. This project has already saved its Valinhos, Brazil factory about $2.8 million.
Royal Dutch Shell: Theworld’s largestOil and Gas company has launched a two-year digital twin initiative to improve its operational excellence capability. Mainly, the digital twin technology is tested on Finite Element Analysis (FEA), which is used to design large assets or structures. The holistic structural assessments are now possible because it incorporates real-time sensor and big data into digital twin simulations. Application of AI and machine learning algorithms on real-time simulations reflect the true counterpart of real conditions. This not only helps in reducing the maintenance cost, but also in reducing the disruption that may occur because of downtime throughout the lifecycle of assets.
Bridgestone: Leading tire manufacturer Bridgestone is transforming its mobility solutions by incorporating digital twin capabilities. The main reason of this digital initiative is to extend mobility by enhancing safety and efficiency. Presently, it is difficult to know what is happening to a given tire or to road surfaces. This lack of information is one of the biggest barriers in attaining safety and reducing disruptions. Bridgestone is addressing this barrier by incorporating the tire sensor data on a real-time basis. This tire sensor data is simulated with actionable predictions and helps in enhancing the precision of safety systems.
Boeing: The largest commercial and military airplanes manufacturer Boeing is transforming its asset management by using digital twin technology. Boeing uses the virtual three-dimensional (3D) replication model of complex systems and assets. The model is simulated with real-time data that the asset undergoes throughout its lifecycle including the environmental conditions of the physical world. The simulation helps to manage potential disruptions that may occur because of malfunctioning and eventually can increase production efficiency. As a result of this digital twin drive, Boeing has already achieved 40% improvement in its first-time quality of its parts and assets.
In the coming years, digital twin technology will present the potential to manage supply chain disruptions into sectors such as retail, manufacturing, pharmaceutical and aerospace. The technology ecosystem is developing with the cost of sensors dropping, more connectivity of devices, computing power increasing with more data storage from cloud and cheaper bandwidth to process the massive amount of data involved in creating a digital twin. Digital twin technology deployment will therefore allow companies to manage their complex supply chains by:
Optimising processes
Making data-driven decisions based on real-time simulations
Helping to design large assets, products and services based on real conditions
Companies aiming to deploy digital twin pilot projects into their operations and supply chains will do well to observe the critical success factors listed above and learn lessons from the industry front-runners before venturing into digital twin technology.
About the author
Dr Imran Ali is a Senior Lecturer of Logistics and Supply Chain Management at Huddersfield Business School, University of Huddersfield. Imran engages widely with the industry and has research interests in digitalisation of supply chains, supply chain data analytics, industry 4.0 applications (AI, IoT, Blockchain, Digital Twin) and innovation. He is currently conducting research in the usage of technologies broadly in the logistics and supply chain to make the data-driven decisions with more accuracy and efficiency. He has published in academic and managerial journals including International Journal of Production Economics, International Journal of Production Research and International Business Review. https://pure.hud.ac.uk/en/persons/imran-ali
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