Manufacturers have invested heavily in digital technologies over the past two decades. Systems for product design, lifecycle management, and enterprise planning now play a central role in how companies define products and coordinate production. Yet a persistent challenge remains on the factory floor. While much of manufacturing has been digitized, the actual execution of work often still relies on manual interpretation.
Much of the digital infrastructure in manufacturing focuses on managing information rather than guiding the work itself. Tools such as computer-aided design (CAD), product lifecycle management (PLM), enterprise resource planning (ERP), and manufacturing execution systems (MES) are widely used to define products, track configurations, and manage transactions across organizations. These systems are effective at organizing complex engineering and operational data.
However, they were not originally designed to communicate detailed engineering intent directly to the people performing assembly, maintenance, or inspection tasks. As a result, the information that reaches frontline workers is often a simplified translation of complex engineering knowledge.
In many factories, this translation appears as static documentation. Workers may rely on PDF work instructions, slide presentations used in training, or checklists distributed on the production line. While these materials provide guidance, they can become outdated as designs change or production processes evolve. The gap between digital engineering systems and physical work on the factory floor can create inconsistencies in how tasks are carried out.
According to Garth Coleman, CEO of Canvas Envision, this gap represents a critical limitation in current digital transformation efforts. “Most digital investments in manufacturing focus on managing data, not executing work,” he has said. In other words, companies have built sophisticated systems for defining products and tracking information, but the final step of turning that information into consistent action is often still manual.
When frontline workers must interpret complex engineering documentation without visual or contextual guidance, variability can emerge in how tasks are performed. Even small differences in interpretation can lead to errors, rework, or quality issues, particularly in environments where products evolve frequently.
This challenge is sometimes described as the “last mile” of the digital thread. The digital thread refers to the flow of information that connects product design, engineering, manufacturing, and operations. While many organizations have built strong digital connections between design and planning systems, the final link between those systems and the workforce performing the work can remain incomplete.
Emerging technologies are beginning to address this gap. Model-based and AI-driven execution systems aim to translate engineering data directly into interactive instructions for workers. Instead of relying on static documentation, these systems can present step-by-step guidance derived from the underlying engineering models.
In practice, this may involve visual instructions, interactive workflows, or context-aware guidance that adapts to a worker’s task or location in the process. The goal is not simply to store engineering data but to make it actionable at the moment work is performed.
Advocates of this approach argue that it can reduce ambiguity in production processes. When engineering intent is communicated clearly and directly through digital systems, workers spend less time interpreting documentation and more time executing tasks consistently.
For manufacturers pursuing digital transformation, this perspective highlights an important shift in focus. Digitizing product definitions, supply chains, and production planning has been a major step forward. The next stage may involve ensuring that the same digital continuity reaches the factory floor, where products are physically built.
As manufacturers continue investing in digital infrastructure, the question is no longer only how to manage data across the enterprise. It is also how to ensure that the knowledge embedded in those systems effectively guides the people responsible for turning designs into finished products.