Digital Twin Technology: Enhancing Supply Chain Resilience and Visibility

In the rapidly evolving landscape of supply chain management, the adoption of digital twin technology is gaining traction as a tool to bolster resilience and enhance visibility. As businesses face increasing challenges, including labor shortages and disruptions such as those experienced during the COVID-19 pandemic, digital twins offer a promising solution for navigating these complexities.

The Evolution of Supply Chain Management

Supply chain management has undergone significant transformation over the decades. In the 1990s, the emphasis was on cost reduction and efficiency. The 2000s witnessed a shift towards lean operations and just-in-time delivery. The following decade brought about digital transformation and the integration of analytics. Now, in the 2020s, the focus has shifted towards resilience and adaptability, driven by the need to withstand disruptions and ensure continuity.

To meet these demands, the Supply Chain Operations Reference (SCOR) model outlines five key processes: plan, source, make, deliver, and return. These processes are underpinned by critical attributes such as reliability, responsiveness, and agility. Digital twin technology introduces a new layer of resilience to these processes, allowing companies to simulate and analyze thousands of scenarios, thereby improving decision-making and risk management.

Digital Twin Technology: A New Layer of Resilience

Digital twin technology offers a virtual representation of the physical supply chain, enabling companies to model and simulate potential disruptions. By leveraging this technology, organizations can enhance visibility across their supply chains, which often comprise over 1,000 suppliers, each with unique risk profiles. Fragmented information across multiple systems has historically led to visibility issues, a challenge that digital twins aim to address.

Implementing digital twin technology requires a structured approach, divided into two main tracks: operational continuity for risk management and predictive capability building. Following the ISO 31000 Risk Management Process Model, companies can conduct risk analysis using Time-to-Survive (TTS) and Time-to-Recover (TTR) metrics to quantify and prioritize risks. This involves utilizing risk-value matrices and implementing mitigation strategies across various levers.

Technology Integration and Organizational Change

Successful implementation of digital twin technology depends heavily on technology integration and organizational change management. Approximately 40-50% of the effort is dedicated to data integration, as supply chain data is often spread across 15-20 systems. Quantifying risks with a consistent scoring system is essential, as 80% of supply chain risk can be attributed to just 20% of components.

Real-time risk monitoring, enhanced by integration with external data sources, is crucial for improving risk scores. While technology implementation accounts for 30% of digital twin success, the remaining 70% involves managing organizational change. This necessitates developing internal capabilities in data analytics, engaging key stakeholders, and creating clear communication plans for initiatives.

Practical Applications and Future Outlook

Digital twin technology has practical applications in enhancing business continuity planning and improving supplier risk assessment strategies. By integrating with existing Enterprise Risk Management (ERM) frameworks, companies can achieve more accurate recovery time objectives (RTO) and better identify single points of failure.

For organizations looking to adopt digital twin technology, starting with pilot programs is recommended to test feasibility and build cross-functional teams for project success. Establishing governance models and designing flexible systems that can adapt to evolving needs are also critical steps.

As the supply chain landscape continues to evolve, digital twin technology is positioned to play a pivotal role in enhancing supply chain resilience and visibility. Executive leadership and organizational commitment to this transformation are essential, as AI continues to redefine global supply chains, ushering in what is expected to be the age of the AI supply chain by 2026.