… by Rahul Mehra, Co-founder of Roadcast
There was a time when manufacturing was largely about control. Machines followed fixed processes, supervisors monitored production floors, and problems were addressed once they became visible. The same approach extended beyond the factory gate. Vehicles carrying raw materials or finished goods were tracked, drivers were supervised and safety checks were conducted, but much of the decision-making still depended on people observing events and responding to them.
That model was necessary for its time. But manufacturing today operates at a very different speed. The factory is no longer an isolated environment. Raw materials move between facilities, components travel across routes, finished goods leave plants for distribution, and the people and vehicles involved in that movement can directly influence production continuity. Manufacturing intelligence, therefore, can no longer stop at the factory gate. It has to extend to the vehicles, drivers, routes and movement of goods that keep the operation running.
The change is not simply about replacing manual processes with digital ones. It is about changing what technology can do with the information it collects. We have moved from systems that monitor what is happening to systems that help us understand why it is happening, predict what may happen next and intervene before a risk becomes an incident. In that sense, the progression is from monitoring to understanding, understanding to predicting and predicting to intervening.
From Monitoring to Predicting: The New Manufacturing Intelligence
The scale of this shift is already visible in India. Rockwell Automation’s 2026 State of Smart Manufacturing findings show that 97% of Indian manufacturers consider digital transformation essential to staying competitive. At the same time, 88% are already using AI and machine learning in their operations, while 41% of operations are currently AI-augmented. That figure is expected to reach 47% by 2027 and 61% by 2030.
But adoption is only one part of the transformation. The same research found that 60% of Indian respondents identify capturing, interpreting and using data to improve business as their top internal obstacle, compared with 37% globally. The challenge, therefore, is no longer simply collecting more information. It is turning growing volumes of operational data into intelligence that can support a decision at the right moment.
When the Factory Gate Is No Longer the Operational Boundary
This becomes particularly important beyond the traditional factory environment. Historically, location data, visual information, driver behaviour and safety inputs existed as separate signals. A GPS could show where a vehicle was. A camera could record what happened on the road. A safety check could establish whether a driver was fit to operate a vehicle. Each system answered a specific question, but the information often remained disconnected from the wider operational context.
Today, those signals can be connected.
An AI-enabled dashcam is no longer simply a device that records an incident for someone to review later. It can become a layer of visual and driver-risk intelligence, identifying indicators such as fatigue, distraction, phone usage and other unsafe driving behaviour in real time. When these signals are considered alongside vehicle location, route information and trip behaviour, they provide a richer picture of what is happening on the road. Roadcast’s video telematics platform combines AI-based driver monitoring with vehicle and GPS data to provide real-time safety alerts and operational visibility.
From Safety Checks to Preventive Intervention
The same principle applies to alcohol detection. The larger opportunity is not testing alone, but connected preventive safety. When driver identity, fitness-to-drive checks, vehicle access and automated alerts become part of one workflow, a safety policy can become an active intervention rather than a manual checkpoint. Roadcast’s breath analyser, for example, can verify driver identity through facial recognition, use the alcohol-test result as a condition for ignition and alert administrators when a test fails.
Why Fleet Intelligence Is Becoming Manufacturing Intelligence
Manufacturing is useful for understanding this transition because its operational perimeter is moving. It includes raw-material movement into a facility, logistics between plants, warehousing, outbound distribution and the fleets connecting these stages. A delayed component shipment can affect a production schedule. Unsafe driving can put people and cargo at risk. A route deviation can signal a security or efficiency concern. Vehicle downtime can disrupt dispatch planning.
These are not isolated fleet problems. They can become manufacturing problems.
Consider a company transporting components between two plants. Earlier, the primary question might have been whether the vehicle had reached its destination. Today, vehicle location can be considered alongside route deviation, unexpected stoppages, driver behaviour and estimated arrival time. A single signal may not say much. Several signals changing together can indicate that a delivery is beginning to move off course before the delay becomes a production problem.
That is the difference between tracking an asset and understanding an operation.
From Data Collection to Predictive Action
It is also why digital transformation cannot be reduced to buying more technology. Standalone devices create data; connected intelligence creates decisions. The objective is to connect relevant signals, add context to them and reduce the time between identifying an exception and taking corrective action.
That is where predictive intelligence becomes important. A single fatigue alert tells us about one moment. Repeated fatigue events on particular routes or at particular times can reveal a broader risk pattern. Recurring unsafe driving, route anomalies or unexpected stoppages can similarly expose trends that individual alerts may not. Over time, this allows organisations to move from responding to incidents towards understanding the conditions that repeatedly produce them.
The factories of the future will certainly have smarter machines. But they will also need smarter connections between the factory floor and everything moving around it. The future factory will not be judged only by how intelligently it produces, but by how intelligently it manages the people, vehicles, routes, materials and processes that keep production moving.
The next phase of manufacturing intelligence will be built at the intersection of the factory floor and the road.
