Digital twins historically were associated more with static analysis. The concept is now being operationalized to virtualize the entire supply chain ecosystem—a digitized end-to-end map of assets across operations and business processes.
The global supply chain challenges over the past few years are well-documented and have highlighted the vulnerability and frailties of supply chain ecosystems across many industries.
Running lean supply chains is great when conditions are ‘normal.’ Still, as we continue to contend with the COVID pandemic, geopolitical instability, aggressive economic posturing, labor shortages, and extreme climate events, it’s clear we’re nowhere near normal. The current fragility has exposed a critical lack of supply chain resilience. This, combined with operational inefficiencies, an over-reliance on specific supply chain sources, and lack of viable alternative suppliers, continues to hinder manufacturing in many areas.
The ongoing shortage of microchips and semiconductors, for example, has had a ripple effect on automotive manufacturing and other markets, necessitating the introduction of the bipartisan CHIPS and Science Act. Signed into law in August 2022, the Act seeks to strengthen American manufacturing, supply chains, and national security “to keep the United States the leader in the industries of tomorrow.”
Such Federal level executive action highlights the significance of the supply chain crisis. With shocks occurring more frequently and impactfully, there is a clear and urgent need for enhanced supply chain resilience and rapid recovery. Moreover, the speed and scale of disruption require this resilience and adaptability to happen in real time, wherever possible.
Manufacturers must be able to promptly identify risks and vulnerabilities to ensure rapid course correction. They must pivot away from siloed data and isolated decision-making towards not just greater observability across all elements of the value chain but better end-to-end decision-making through leveraging near-real-time signals and intelligence capabilities.
Digital transformation unlocks supply chain visibility
As enterprises go through digital transformation, advanced technologies can be leveraged to meaningfully address supply chain problems. The Industrial Internet of Things (IIoT) has enabled any number of devices and sensors to be brought online, generating granular data that can provide a snapshot of operational status at any given moment.
By overlaying a digital supply chain operations platform across an enterprise and its connected partners, it’s possible to digitize entire supply chain ecosystems across all constituents, regardless of organization, without barriers. Data can now be drawn from devices and analyzed live in a ‘real-time all the time’ supply chain operating model.
Using real-time visibility to generate and derive actionable value from ground truth data, manufacturers can reliably track and trace assets and inventory throughout the supply chain. Having in-depth knowledge available improves business intelligence, which drives improved supply chain resilience. This enhanced real-time intelligence also informs prompt 360-degree decision-making. Potential supply chain issues can be identified and flagged to the appropriate parties as they happen to be triaged and resolved or escalated further if necessary.
Beyond visibility – digital twins, intelligence, collaboration
Digitizing the entire supply chain also opens up opportunities for enterprises to go further, to look beyond visibility. Advanced technologies such as Digital Twins, Artificial Intelligence (AI), and Machine Learning (ML) can be leveraged to continuously align planning and execution, embed intelligence drawn from data, and collaborate with partners across extended enterprises.
Digital twin modeling
While it sounds modern, the concept of digital twins is not new. Pioneered by NASA and the aviation industry as far back as the late 1960s, digital twin technology enables the creation of a virtual representation of a physical entity. In NASA’s case, digital twins were used to create digitized simulations of space capsules and craft for testing.
Such is the interest in digital twins that ABI Research predicts spending on industrial digital twins will grow from $4.6 billion in 2022 to $33.9 billion in 2030.
Historically digital twins were associated more with static analysis. The concept is now being operationalized to virtualize the entire supply chain ecosystem—a digitized end-to-end map of assets across operations and business processes. This map is drawn from the vast amounts of accessible, real-time, ground truth data flowing across connected systems.
Using digital twins, enterprises can visualize all supply chain dependencies to assess and mitigate risks, model automated workflows, and corrective action, and assess how these improve resilience. Digital twins enable real-time operational modeling and monitoring across all constituents at multiple levels, including attributes, how those attributes are configured, and what metadata they are outputting.
Orchestrated intelligence
Embedding the deep data intelligence generated via digital twins into supply chain operations and models creates a clear baseline for ‘real world’ operations. The end goal is to maximize on-time, in-full delivery, driven by real-time continuous optimization and realignment.
Deep signal and data intelligence can be generated from any entity, system, or device—from containers and pallets to individual unit boxes, bottles, and packets. This data—visualized and shared via interactive dashboards in real-time—produces a thread of connected information that removes data and organizational silos, within the host company and across every connected supply chain constituent, from initial sourcing to the last-mile delivery.
Leveraging the actionable intelligence generated by supply chain data, problems can be anticipated and incorporated into ground truth operations, speeding up responses to potential live issues. When supply chain excursions occur, digital twin models capture significant data from these anomalies. This is fed into AI and ML systems with advanced reasoning algorithms that clarify patterns in the data and deliver actionable insights. Not only does this enable real-time decision-making to mitigate live issues, it also provides valuable data that can drive predictive supply chain intelligence and modeling.
Integrating the vast number of all known variables captured, organizations can conduct virtual modeling and testing of any number of ‘what-if’ supply chain scenarios where disruptions may occur. With potential actions and outcomes outlined, manufacturers are able, based on historic contextual data, to proactively anticipate likely supply chain failures, disruptions, and supply-demand fluctuations. They can then prepare multiple contingency plans and in some cases, avert failures altogether.
Cross-enterprise collaboration
The flexibility offered by digital twins can be extended across any number of digital twin networks covering multiple enterprises. These enterprises can realize greater efficiencies at scale across wider supply chain nodes in terms of automated workflows, continuous realignment, increased collaboration, enhanced communication, and extended predictive intelligence.
Using AI/ML and low-code/no-code scripts, securely sharing live digital twins allows each constituent to construct and embed a digital twin representing its own extended supply chain network.
Adding extensible components to the supply chain infrastructure also has the benefit of highlighting redundancies and dependencies across the network. This allows for streamlining the flow of goods by optimizing the entire network, as against optimizing within silos with unknown consequences outside.
This digital twin network model can be applied to any supply chain dependency, including those that may be several nodes out from the center. For example, there could be transportation hub bottlenecks that are two to three nodes out, or inventory gaps at some nodes and expired products at others. Having better visibility is just the first step. Leveraging the network dependencies to analyze impacts across organizational boundaries or silos allows us to adapt to the disruptions nimbly.
Having visibility into issues and being able to contingency plan is vital. It’s here that the sophistication of the digital twins is key. The ability to model complex supply chains, have shared insight into the status of inventory, assets, and logistics, track wider interdependencies between nodes, and take timely corrective action to react in unison to mitigate potential disruptions, are game changers.
Conclusion
Supply chain disruptions are a fact of life in business. Recent events are prompting more investment in solutions that drive greater resilience and agility to supply chain operations. Better visibility remains a crucial component of supply chain solutions, but solutions that move the needle on agility and resilience need expanded capabilities. Deploying digital twin technology enables companies to digitize their end-to-end supply chains, embed intelligence and automation, optimize operations, and improve delivery.
Digital twins free organizations to innovate faster and on a larger scale while reducing risk by identifying points of weakness and driving strategic initiatives to reduce cycle time. Organizations can use digital twins to increase their competitive edge through efficiencies gained from better insight into sourcing, manufacturing, inventory locations, and logistics tracking — down to the individual item level. Beyond visibility and improving reactive intelligence, digital twins also facilitate predictive modeling, providing manufacturers with the means to model what-if scenarios and confidently contingency plan.
Finally, extensible models of digital twins can be applied across multiple supply chain networks. Digital twins can be shared securely across partner networks and integrated with additional networks to enable predictive intelligence and automated workflows across entire supply chains. This can reduce redundancies and flag second and third-order issues, buying time to take corrective actions before they prove disruptive and potentially costly.