A production stoppage may be resolved on the shop floor long before the planning department is even aware that it has occurred. The maintenance department may never receive this information. During his presentation at our “ Plantvision ” Technology Insight 2026 event, Claes Engman described how a cohesive system can tie together the factory’s operations. This article summarizes his thoughts on MES as an operational backbone, shared data, and why every role needs actionable information.
The same stop can have completely different consequences
Claes began by presenting two ways of handling the same event, which clearly demonstrated the benefits of using an integrated system.
In the first scenario, a production line comes to a halt; the operator resolves the problem and writes a note. At the end of the shift, the information is entered into an Excel logbook under the category “other.” The next morning, possible causes are discussed at the pulse board. It is only later that the planning department learns that the stoppage may have affected a high-priority customer order. The maintenance department may not be informed at all. By the time the information reaches the right people, nearly a full day has passed, and the opportunity to take timely action may already have been lost.
In the second, illustrative scenario, the stoppage is recorded and categorized in an integrated system. The shift supervisor can immediately see that production is behind schedule, and the planning department receives information about the loss of capacity. Recurring stoppages can trigger a maintenance order. The event becomes available to more parts of the organization while there is still an opportunity to take action.The machine and the people are the same in both examples. The difference lies in how the information flows and what support it provides. This was the starting point for the lecture’s central statement: “Data is not the goal—decisions are the goal,” by making the information available to the right people at the right time.
There is often a gap between business systems and automation
Many businesses have good control over their business systems—including orders, inventory, and finance—as well as over automation on the production floor. In between, however, there may still be manual logbooks, scraps of paper, and Excel files that hold the day-to-day operations together. Claes described this “middle ground” as a recurring gap, even in factories that have made significant progress in digitalization.
Using ISA 95 as a framework, he positioned production, quality, inventory and materials management, and maintenance at the operational level between business systems and automation. MES was presented as a tool to link these areas together. Its function goes beyond measuring OEE and showing how well the equipment is being utilized. This can involve controlling which order is to be run, providing access to electronic work instructions, recording quality checks, and tracking materials through production. Energy consumption can also be linked to orders or batches. A common thread is the need for traceability and a context that shows which machine, which material, and which activity the information pertains to.
A common data model provides context for the signals
A signal with a value of seven means very little without an explanation. Which machine did it come from? Which production order was in progress, and what material was used? Claes used this simple example to show why data collection must be followed by putting the information into context. A common data model makes it possible to build multiple functions around the same information. This reduces the need to transfer numbers between different lists and systems, where the meaning can ultimately become unclear. Daily management and follow-up need to be based on the same data, so that meetings can focus on what the business should do about the results.
Claes also described how MES solutions have evolved toward more modular implementations, with the option to start on a limited scale and build out from there. TrakSYS from Parsec—a partner of Plantvision —was used as an example. Even when implementation occurs in stages, the information model must remain cohesive so that the next feature can connect to what already exists.
The operator's daily routine determines the quality of the information
Different roles use the same data for different purposes. The operator needs instructions and feedback in the here and now. The shift supervisor needs to see what requires action during the shift, while maintenance and planning also need to be able to assess long-term consequences. A shared data source therefore does not mean that everyone should see the same view. Claes warned against key performance indicators that become so abstract that those who can influence the outcome fail to see the connection to their own work. The same problem arises when causes of downtime have so many subcategories that the operator selects “other” just to move on. Guided instructions, digital checklists, and simple data entry need to be designed for the people who will be using the support.
One customer example involved complaints about products with missing components. Guided workflows and camera support made it possible to detect errors and correct them before the product moved on to the next stage. Claes described a significant reduction in the number of errors and complaints, without providing a specific figure. The point was to give the operator the opportunity to do things right and check quality close to the source, before a minor defect leads to major consequences for the customer.
AI needs the structure on which the business is built
Once defects and events have been categorized and a reliable context is established, AI can be used to analyze patterns. Claes emphasized that the model needs to be trained using information that makes it possible to understand what has happened. Similarly, camera-based inspection needs to be adapted to the specific product and the expected production process.
The analysis must also be able to feed back into the work on the shop floor. If an incorrect instruction or method is identified, that insight should be used to correct the work. At the same time, an MES does not automatically fix a flawed process. Claes therefore urged that the process be improved first, that operators be involved, and that IT and OT collaborate on the information needed from both areas.
Five Steps to Get Started
Claes concluded by outlining a concrete approach: choose a direction, assess the current situation, identify the roles and their information needs, define the data model before selecting a tool, and launch a limited pilot. The pilot should provide an opportunity to identify mistakes and learn from them. At the same time, there needs to be a goal for continued development so that the work can be expanded rather than remaining in the pilot phase.
Summary of the Lecture
The operational backbone of a factory involves linking events, information, and actions across the various roles in production. MES can support this when its functions are based on a common data model and a clear context. The benefit arises when information reaches the right person on time and can be used in their work. Start small, keep data entry simple, and build upon a process that works and lessons learned that can be put into practice.
Where are you currently losing information between production, planning, and maintenance? We’d be happy to discuss further which of these issues resonate with you and which decisions you’d like to be able to make sooner. Get in touch, and we’ll work together to figure out where it might be best to start.
