Most manufacturing businesses already have access to a huge amount of production data. The machines on the factory floor are constantly generating information about production, equipment performance, process conditions, energy use and downtime. The challenge is that this information is often spread across different systems, machines, spreadsheets and manual reports, making it difficult to see the full picture.
A plant manager might want to know how a line performed on the previous shift, or why production was below target last month. The information is probably there, but finding it can mean pulling data from several different systems, asking operators for additional information and working through spreadsheets before anyone has a clear answer. By the time the picture is complete, the opportunity to act on it may already have passed.
This is where effective production data collection can make a real difference. The objective isn’t to introduce another dashboard or collect more information simply because the technology allows it. It is about creating a reliable connection between what is happening on the production line and the people responsible for making decisions about the operation.
Turning Production Data Into Something Useful
The machines on a production line are already telling you a lot. They can tell you when they are running, when they have stopped, how quickly they are operating and, depending on the equipment, provide information about temperatures, pressures, flow, output and other process conditions.
But raw data doesn’t necessarily tell you what is happening from an operational perspective. Knowing that a machine stopped at 10.42am is useful, but knowing that it stopped because of a recurring material feed issue is much more valuable. Similarly, knowing that a line produced 5,000 units doesn’t tell you much until you understand what the expected output was and whether the line was operating efficiently.
The real value comes from adding context to the information being collected. A production stop can be assigned a reason. Output can be compared against a target. Equipment measurements can be tracked over time so that changes in performance become visible. Once that context is added, data becomes something that production, engineering and management teams can actually use.
One Set of Data, Different People, Different Needs
Not everyone in a manufacturing business needs to look at production in the same way. An operator standing beside a production line needs immediate information that can help them respond to what is happening right now. They may need to know whether the line is running, whether it is achieving its target rate and, if it has stopped, what has caused the problem.
A production manager or engineering manager is likely to be looking at a much broader picture. They may want to understand where downtime is occurring across a number of weeks, which machines are affecting output, whether performance is improving and where maintenance attention is being concentrated. Senior management may be looking at the same underlying information from a different perspective, using production trends to support decisions around investment, resources, capacity and continuous improvement.
The important point is that these don’t need to be separate versions of the truth. A well-designed production data collection system can use the same underlying information and present it in a way that is relevant to each person using it.
That means the operator gets the information needed to run the process, while management gets the information needed to understand the performance of the wider operation.
How Production Data Is Collected
A connected production data system creates a route from the equipment on the factory floor to the people who need to make decisions from that information. Data can be collected directly from machines, control systems and instrumentation using appropriate communication standards. Using open standards where possible can also make the system easier to maintain and extend as the manufacturing environment changes.
Once the data has been collected, it needs to be interpreted within the context of the process. This is where the system can start turning individual machine signals into useful operational information. A stop becomes a recognised downtime event. A production count can be compared against the expected rate. A change in motor current can be tracked as part of a wider trend that may indicate changing equipment performance.
The information can then be timestamped and stored in a historian, creating a record of what has happened across the production process over time. This historical information can be particularly valuable when investigating recurring downtime, comparing performance or trying to understand whether a change made to the process has actually delivered an improvement.
The final part is making that information accessible. Live information can be displayed on the production floor, while supervisors and engineers can use dashboards to monitor performance and management can access reports showing longer-term trends. The technology should adapt to the people using it, rather than expecting everyone in the organisation to work from the same screen.
Production Data Can Help Explain Why Performance Changes
One of the biggest advantages of connected production data is that it can help move the conversation beyond simply measuring performance.Take OEE, for example. Overall Equipment Effectiveness can tell a manufacturer how effectively a piece of equipment or a production line is operating by looking at availability, performance and quality. That is useful, but the percentage itself doesn’t explain what is causing the losses.
The next question is the important one: why?
Why was availability lower last month? Which downtime reasons accounted for the biggest losses? Is the same issue happening repeatedly? Is one particular machine affecting the performance of the wider line?
When the production data behind the OEE calculation is reliable and detailed enough, teams can start investigating those questions rather than simply recording the score.
That is where production data becomes a tool for improvement rather than another number on a dashboard.
Making Better Decisions With the Information You Already Have
When production data is trusted, it can influence much more than the daily production meeting. It can support maintenance planning, help identify bottlenecks and provide evidence when decisions need to be made about equipment upgrades or investment.
It can also improve visibility of energy consumption. By looking at energy use alongside production output, manufacturers can start to understand how much energy is being used to produce a particular quantity of product and where there may be opportunities to improve efficiency.
Perhaps most importantly, reliable data gives teams something consistent to work from. Instead of one figure coming from the production spreadsheet and another coming from the machine, everyone can work from the same underlying information and spend more time addressing the issue rather than debating which number is correct.
Security Needs to Be Part of the Design
Connecting production equipment to reporting and management systems also means thinking carefully about operational technology security. Bringing more systems together can create additional connections between the factory floor and other parts of the business, so the architecture needs to be designed with security in mind from the beginning.
This can include separating control systems from business networks, controlling how information moves between different environments and ensuring that people only have access to the information and systems relevant to their role. The reporting layer should provide visibility of the process without creating unnecessary access back into the systems controlling the process itself.
For manufacturers, this means production data collection should not be treated as a standalone reporting exercise. The way the system is designed, connected and secured is just as important as the information it ultimately displays. Standards such as IEC 62443 provide an established framework for considering cybersecurity within industrial automation and control systems.
You Don’t Need to Start With the Whole Plant
A manufacturer doesn’t necessarily need to undertake a major plant-wide project to start getting more value from its production data. In many cases, it makes more sense to start with one important production line or one particular operational challenge.
The first step is understanding what the team actually needs to know. From there, the available data can be reviewed, the relevant equipment connected and the accuracy of the information verified. Once the data is trusted, reporting and visualisation can be built around the questions the team needs to answer.
That might mean starting with downtime on one line, understanding production performance against target or monitoring energy consumption in a particular area. Once the approach has demonstrated value, it can be expanded across other areas of the plant using the same underlying principles.
This is often a more practical approach than trying to connect everything at once without first establishing what information will actually make a difference to the operation.
From the Machine to the Boardroom
The goal of production data collection isn’t to give manufacturers more data. Most factories already have plenty of it. The goal is to make that data useful.
When information from machines, instrumentation and production systems can be collected, given context and presented to the right people, it creates a much clearer view of what is happening across the operation. Operators can respond to issues as they happen, engineering teams can investigate trends and management can make decisions based on evidence rather than incomplete reports.
For manufacturers, that connection between the machine and the boardroom can be incredibly valuable. It turns production data from something that is simply recorded into something that can actively support operational performance.
BONNER helps manufacturers connect automation, instrumentation and operational data to improve production visibility and make better operational decisions.