Companies across industries have recently found themselves operating with supply chains designed for a world that no longer exists. Today’s growing complexity and volatility means that companies need to evolve their supply chains to deliver transformational outcomes that help them become more resilient, agile and intelligent to stay ahead of disruption and seize new opportunities.

We often hear about supply chain control towers and digital twins as solutions that combine a range of technologies, near-real-time data and analytics to provide visibility and speed up decision making across the supply chain ecosystem. But what is the difference between these two concepts? Do supply chain leaders need to deploy both a control tower and a digital twin?

There is growing confusion in the market about what ‘supply chain digital twin’ and ‘control tower’ actually mean, and what they offer to organizations. These terms are not interchangeable, and it is important to understand the key differences between the concepts for leaders to better communicate, manage expectations, and implement the right capabilities for visibility and decision intelligence across their business.

Let’s take a look at defining a supply chain control tower vs a digital twin and explore what each term entails to help shed some light on the matter.

What is a Supply Chain Control Tower?

Control towers are static and functional models which run without a digital supply chain twin. According to Gartner: “Control tower is an operational framework to capture and use a variety of data, leveraging a functional model for providing enhanced visibility, predictions and suggestions for predominantly domain-specific, short-term and midterm decision making.”

Control towers are usually deployed by companies at early stages of supply chain maturity to improve domain-specific control and decision making. These models provide visual dashboards that typically focus on a single node or a function, versus an end-to end view.

Supply Chain Insights’ Founder, Lora Cecere highlights the issue with control tower implementations often lacking a clear definition of what is being controlled and what can be achieved from a business perspective, resulting in “projects without clarity on how to tie improved visibility to enterprise processes.”

What is a Digital Supply Chain Twin (DSCT)?

As defined by IDC, a digital twin is "a virtual representation of a physical product, component, asset, or even process. Digital twins visualize data flows and provide collaboration across engineering,

operations, supply chains, and servicing.”1 Similarly, Gartner notes, a “digital supply chain twin is a transformational concept that creates a near-real time digital representation of the physical supply chain — a comprehensive single model — as a basis for visibility and decision intelligence across all horizons.”

Unlike control towers, a DSCT is a state-of-the-art model usually adopted by organizations at more advanced levels of supply chain maturity – it covers the entire supply chain network, providing end-to-end visibility for augmented decision making.

How Digital Twins Work for the Supply Chain

A digital supply chain twin is a dynamic and real-time representation of the various associations between the data objects that compose how the physical supply chain operates. It is the basis for local and end-to-end decision making for the supply chain, ensuring that decision making is aligned horizontally and vertically throughout the ecosystem. As such, digital twins work closely alongside other advanced technology such as artificial intelligence, machine learning and IoT (Internet of Things) to keep the supply chain network fully interconnected.

A physical supply chain is defined by the relationships between the different entities that make up the supply chain – customers, products, orders, lead-times, resources, capacities, plants, suppliers, facilities, attributes, etc. The changes in these relationships have an impact in the supply chain. A digital supply chain twin truly mirrors the various relationships and interdependencies of the supply chain, and it models scenarios to recommend a course of action to support decision-making.

The DSCT is fed very granular, low latency data including streaming real-time data from various sources – such as IoT sensors and signals from GPS devices and other data sources such as manufacturing execution systems, and supplier risk, 3PLs, and more – to build a comprehensive model of processes across time horizons from strategic to tactical, and operational through multiple tiers of the supply chain.

The DSCT may, for instance, support decisions as to possible outcomes from changing relationships, such as pushing an order through an alternative route in the supply chain, using a different production line, or sourcing from a different supplier. Some decisions (typically short-term ones) can be fully automated, and the DSCT can interact directly with the execution systems (e.g., ERP) to alter the flow path of an order, for example. Consequently, the DSCT and the decisions that are being made with it are directly influencing the course of the physical supply chain based on changes to the relationships.

Why Digital Twins are the Cornerstone of Digital Supply Chain Strategy

Today’s supply chains are highly complex, interconnected, and interdependent, so businesses require more dynamic and responsive solutions that help them make better, faster, and more confident decisions. This is why digital twins are so important – to enable data-driven decisions using near-real-time data, improving agility in both sensing and responding to challenging events and disruptions. The DSCT is the foundation on which both predictive and prescriptive analytics can run.

With a comprehensive virtual replica of the physical end-to-end supply chain, and the power to help predict future events, digital twins play a crucial role in modern organizations across manufacturing and other industries, replicating a physical environment for business experiments, supporting the prediction of performance before decision making and execution.

As companies embark on a journey to transform their supply chains, they need to consider digital supply chain twins as part of a strategic framework to drive ongoing digital transformation. With these models, companies can support not just one but multiple processes across the network, creating true end-to-end visibility to drive better decisions and superior business outcomes.

Ready to Create a Digital Twin of Your Supply Chain?

Supply chain management has become hugely important in these exceptionally uncertain times. Digital twin technology is here for organizations to gain true visibility for maximizing opportunity and reducing risk. Businesses can better understand how various aspects of the supply chain interact, while detecting and planning against issues and disruptions, to improve decisions, performance, and efficiency.

We at John Galt Solutions work closely with companies across all industries and maturity levels to help transform their supply chains and continuously drive value. Our Atlas Planning Platform offers an advanced end-to-end supply chain planning solution that helps leading organizations master the interconnectedness of their multi-enterprise supply chain ecosystems. Let’s take a look at how you can model your multi-enterprise supply chain network, create a digital twin of your supply chain and realize your business goals through intelligent orchestration.


1 IDC FutureScape: Worldwide Manufacturing 2023 Predictions, Reid Paquin, Jan Burian, Simon Ellis, Jeffrey Hojlo, Stephanie Krishnan, Stefanie Naujoks, Aly Pinder, John Snow, Lorenzo Veronesi, Sampath Kumar Venkataswamy, October 27, 2022