Digital Twins Take on the Supply Chain

Recent news and events have publicized digital twins supply chain solutions. NVIDIA revealed ReOpt using Isaac Sim, via their Omniverse platform; Microsoft unveiled their Cloud for Manufacturing including the Dynamics 365 Supply Chain Insights digital twin application; HERE Technologies led a webinar on location-enabled digital twins; and Google debuted Supply Chain Twin in September.

Digital twins are digital representations of real-world entities (sensors, devices, equipment, processes, complex systems, or  facilities) implemented to achieve business outcomes. They provide connectivity, metadata management, data management, advanced analytics, and integration with business applications and process systems. Sensors capture data like location and position and feed into the digital twin through some form of Internet of Things (IoT) connection. Manual business processes are transformed into a digital identity to simulate the physical via technologies like Artificial Intelligence (AI) and Machine Learning (ML).

This enables participants to develop a feedback loop to optimize their supply chain operations. Cognitive analytics are able to discover patterns and detect variations. They can optimize inventory, reduce, or increase capacity plans, monitor supply chain risks, and test alternates to supply, transportation modes and locations. Digital twins can evaluate purchase behaviors and even consumer behavior patterns. They can provide predictive outcomes of campaigns to influence demand and evaluate potential changes to the supply chain such as locations for manufacturing, fulfillment, transportation, and retail sites and their impact on success metrics. Both transportation and logistics benefit from digital twins’ ability to address on time delivery, maintenance, and cargo conditions as well as fulfillment optimization from layout to robotics utilization/health to picking.

 A supply chain provider’s ability to obtain critical insights from simulation offer value for a variety of objects from trucks to tools to robotics to forklifts and more to diagnose issues to predict maintenance in advance to enhance process and equipment. Decisions can be made with a view of unbiased data and the impact already assessed of business process or product alternatives.  

The myriad of continuing issues and unforeseen obstacles for supply chains is a rallying cry for a new approach. Industries from manufacturing to industrial to healthcare and energy have already been adopting digital twins and seeing positive Return on Investment (ROI) and competitive advantages. Although supply chain providers are only just recently adopting, ABI Research sees the retail/consumer segment alone being worth at least US$2 billion in revenues by 2026. Further analysis including digital twins will be found in the upcoming Global Supply Chain Challenges and Solutions research paper. Growing real-time visibility of complex supply chain operations and anticipating changes is critical to future viability and success. Those that hesitate in adoption risk a competitive divide that will be hard to overcome.

The technology itself will not solve all supply chain issues, as some will need additional technologies like automation in fulfillment centers, last mile delivery, and Over the Road trucking. Longer-term strategies like reshoring, circular economies, and trusted partnerships will need to be considered sooner rather than later. Still, assessing the impact of these strategies and scenarios via digital twins can determine impact prior to implementation and offer viable alternatives, with AI continuing to learn from each attempt including those previously unknown. The time is now to investigate and trial for quick wins and long-term learning.