The newspaper of tomorrow costs a million dollars, but yesterday’s newspaper cost less than a dollar. It is always a human endeavor to know or predict the future as accurately as possible, but it remains elusive due to technological limitations, static models, outdated data or gut feeling. In the world, torn by wars, tariffs, political fragmentation, geopolitical risk, rapid AI adaptation and weather catastrophes, uncertainty is just the norm rather than an exception.
During COVID-19, the world was exposed to huge supply chain disruption, essential commodities became scarce, and the world started understanding the challenge of the long supply chain. You will be surprised to know the iPhone travels 240k miles, the distance between Earth and the Moon, before you turn it on. Apple depends on suppliers from 50 countries to manufacture the iPhone; any small disruption has huge implications for the iPhone supply chain.
As a supply chain expert, I have seen how C-suite executives of every company struggled during the tariff war introduced last year by the US. Today, most supply chains are global, meaning raw materials are produced in one country, manufacturing in another and products are used in another. To increase efficiency and cost reduction, the chain of the supply chain has only been elongated. With technological advancement and communication improvement in land, sea and water, dependencies of one country on another have only increased. It is non-negotiable that the critical supply chain must continue in the face of any uncertainty. No company ever considered the tariff in the landed cost (total cost to bring in an item from outside), which increased the price of the final product that consumers pay. Companies are planning to move their manufacturing to the Friend Shore rather than onshore or offshore.
Launching a first-of-its-kind product is a forecasting nightmare. Without a historical roadmap, executives often fly blind, making it nearly impossible to scale production or manage suppliers accurately. The stakes are high: One wrong guess can lead to a mountain of unsold stock or — just as bad — turning away eager customers.
In manufacturing, the failure of a single critical machine can stall operations for weeks. When repairs are delayed by long lead times for parts or specialized expertise, the resulting production bottlenecks lead to significant revenue loss and damaged customer relationships.
In a traditional product design approach, designers and engineers wait for customer feedback to be incorporated in the next iteration. Frequent changes in product safety regulations could derail the entire design.
In an era where decision speed is the ultimate currency, volatility has become the new norm. Moving from annual to monthly product development cycles provides a vital competitive edge. By shifting from reactive to proactive strategies, AI-powered digital twins act as a “crystal ball,” providing the accurate forecasts needed to unlock new operational possibilities.
Digital twins in the supply chain
What is a digital twin in the context of the supply chain? Generally speaking, a digital twin is a digital replica of a physical object. It could be anything from an electric iron to a multistory building. Having a digital twin helps organizations simulate real-life applications and user-friendliness. Similarly, organizations using digital twins in their supply chain are better suited to deploy AI technology to drive and extract the best possible operational outcomes.
Digital twins in the supply chain allow an organization to extract its own captive data from manufacturing, inventory, suppliers, ESG, compliance and more into a single point of truth so proactive decisions can be made from real-time visibility. In this way, digital twins can speed decision-making by up to 90%, according to a McKinsey study.
Real-life use cases
According to a study by SPARQ360, a supply chain optimization, sustainability and compliance firm, some of the most effective digital twins in supply chain are:
- Unilever: Uses digital twin technology to model factory operations, optimize production schedules and enhance sustainability initiatives, leading to greater efficiency and cost savings.
- Boeing: Employs digital twins to improve supply chain transparency, reducing delays in aircraft production by modeling real-time supply constraints.
- DHL: Leverages digital twins in logistics operations to simulate and optimize warehouse efficiency, improving order fulfillment speed and reducing downtime. (
- Siemens: Uses digital twin simulations to design and manage supply chains for its industrial manufacturing clients, ensuring optimized production and distribution. (https://www.siemens.com/global/en/company/stories/industry/2025/digital-twin-for-industry.html)
From personal experience, the Kion Group partnered with Accenture and NVIDIA to leverage this innovation to address inefficiencies in warehouse operations. We enhanced supply chain efficiency through AI-driven solutions and digital twins, which were created to simulate warehouse dynamics. The digital twins facilitated real-time testing and optimization of layouts, robotic fleet management and operational processes.
This collaboration led to more autonomous and efficient warehouse operations, reducing manual interventions and allowing quicker adaptations to operational changes, thereby streamlining warehouse management and increasing overall efficiency.
Overcoming implementation challenges
Digital twins can be a part of the overall digital transformation strategies of an organization’s supply chain. However, as in all digital transformations, the following challenges will need to be considered:
Data integration and accuracy issues
One of the biggest challenges of digital supply chain twins is the mismatch of data. Often, inaccurate or conflicting data is sourced from logistics management software, ERP systems, inventory management systems and IoT sensors. This makes coordination with multiple stakeholders extremely difficult.
One approach we’ve often implemented to fix this issue is to first understand the data maturity model of the architecture you’re implementing the digital twin in, which includes mapping all the data sources and calling out the potential data silos. Secondly, we look at employing a myriad of different additional data strategies, like a data mesh or data fabric, to essentially call some of the data, as opposed to simply leveraging data lakes and data warehouses.
When building a data lake or migrating data to a centralized source, leveraging a solution like Snowflake or AWS can be effective. With regards to data mesh and data fabrics, there are some commercial off-the-shelf (COTS) software solutions that install within existing topologies to map all data points into a meshed architecture. Conversely, we have also developed custom mesh solutions for digital twins in the supply chain by partnering with firms like Palantir or Amazon to achieve similar results.
High upfront and maintenance costs
Digital Transformation initiatives are seldom easy or without high costs. Integrating supply chain digital twins requires investing in hardware, software, AI-driven analytics tools and cloud infrastructures, along with ongoing maintenance costs. Here are some tips as platitudes we have tried in the past that seem to help:
- Don’t boil the ocean. Digital transformation in the supply chain is about showing value as quickly as possible. Nothing gets to value quicker than starting with a small pilot project the scale up. The team needs to focus on the most critical constraint, deliver value quickly and build momentum.
- Don’t reinvent the wheel. Utilize existing data sources and integrate with current infrastructure to reduce the need for new data collection and technology and collaborate with industry partners. Collaborative models can spread financial risk and leverage shared expertise, leading to cost savings and enhanced capabilities.
By employing these strategies, organizations can effectively manage the financial burdens associated with building and maintaining Digital Twins by minimizing upfront investment while continuously improving the system’s value, allowing organizations to allocate resources based on proven benefits.
These steps also allow organizations to test the Digital Twin concept, refine strategies and demonstrate value without significant investment.
Resistance to change
In many ways, we have noticed that the best approach towards removing resistance to change starts by implementing a culture of change. Employee resistance is very common when it comes to the implementation of digital twins, especially in supply chain management. This often stems from inadequate training in the Internet of Things (IoT), data analytics, artificial intelligence (AI) and working with the digital twins themselves.
From our own experiences, the best approach is to implement such changes in a phased manner and then scale up. This includes the following steps:
- Ensuring leadership buy-in. It starts at the top with strategies where leadership champions experimentation, setting the tone and providing necessary resources.
- Creating a safe space for failure. Communicate and champion a message that resonates with how users are in a safe environment where failures are seen as learning opportunities.
- Measuring success beyond traditional metrics. Leverage alternative metrics to promote experimentation and innovation.
By taking these steps, integration and adoption of digital twins become seamless and focused on value, not mandates.
The future of digital twins in the supply chain
Once you’ve implemented digital twins in your supply chain and successfully garnered employee confidence, the story doesn’t just end there. Scaling up your supply chain digital twins is the next logical step forward. And this is where AI integration with digital twin applications comes into the picture.
Imagine the possibilities a well-integrated supply chain digital twin system with AI can bring to the table. With AI enabling the processing, analyzing and interpreting of massive multidimensional datasets, your supply chain can remain agile and resilient while navigating the turbulence of geopolitical or macroeconomic disruptions.
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