As Factory Digital Twin Suppliers Jockey for Technological Differentiation, Customers Remain Inhibited by Practicalities

While digital twin technology applies to both design and production environments, manufacturers increasingly recognize the primary benefit of digital twins to be in the factory: 51% of manufacturers rank improvements in production efficiency (through process and equipment optimization) and downtime reduction (through predictive maintenance) as the top drivers of digital twin adoption.

As the prospect of maximizing production efficiency excites customers, factory digital twins have become a strategic priority for a range of suppliers including industrial automation players (AVEVA, Honeywell, Rockwell), industrial software suppliers (Autodesk, Dassault Systèmes, Siemens), simulation software providers (AnyLogic, Simio, Simul8), and compute layers (AWS, Microsoft, NVIDIA). All of these suppliers are jockeying for technological differentiation amid a rapidly changing factory digital twin market, but customers remain limited by practical concerns such as data, change management, and, most importantly, cost.

Factory digital twin suppliers recognize that customers’ existing data silos and disparate teams are significant obstacles to successful digital twin implementation, as a digital twin requires the context from connected data and processes to accurately reflect the state of a physical system and effectively coordinate action across domains. However, ABI Research’s 2026 survey of manufacturing decision makers found that the leading inhibitor to adopting or expanding digital twins is that solutions are too expensive—a topic that suppliers are hesitant to acknowledge at risk of undermining factory digital twin projects before they begin.

While the price of a digital twin in the factory can be below 6 figures if its scope is narrow enough, projects can quickly turn into large-scale integration and data infrastructure projects that significantly raise prices. Still, with the rapid change in the factory digital twin market—largely leveraging NVIDIA’s quickly progressing software libraries and compute stack—suppliers are currently more incentivized to layer advanced Artificial Intelligence (AI) and simulation capabilities (such as Dassault Systèmes Virtual Twin Factory and Rockwell Automation AI-orchestrated factory engineering) to demonstrate technological leadership rather than clearly address the practical concerns manufacturers have about factory digital twin projects.

Suppliers must balance the pursuit of technological leadership with the risk of distancing customers whose digital reality remains much further behind advanced factory-level digital twins. The first step is greater transparency around cost. Pricing for digital twin solutions remains opaque, partly because solutions involve underlying software, integration services, customization, and supporting data infrastructure. This lack of visibility can discourage adoption, especially if manufacturers expect projects to eventually scale.

Factory digital twin suppliers must also offer free or low-cost diagnostic sessions that assess specific customer environments before a project begins. These diagnostics help frame projects by providing a clearer view of a customer’s data landscape, system architecture, and organizational readiness. A clear foundation allows suppliers and manufacturers to define a project scope realistically, identify integration burdens early, and limit implementation surprises that often inflate cost.

Lastly, suppliers must encourage phased and use case-led deployments. The vast majority of manufacturers are not looking to develop factory-level digital twins as a first step. Phased, modular approaches targeting specific operational problems further help to tie a project scope to specific outcomes, demonstrate Return on Investment (ROI) for customers, and improve adaptability of the solution by building flexible starting points rather than a single expansive structure.