Ceramic Manufacturing Digitalization Without Replacing All Existing Machinery

For our client in the ceramics industry, ceramic manufacturing digitalization began at a large manufacturing plant with aging machinery, multiple controller types, and data stored in several places at once. With a legacy equipment base that had grown over the years, it was time to organize how production data was collected and take the next step toward Industry 4.0.

The plant was not short on data. Quite the opposite: there was plenty of it, generated every day by machines, operators, and local production systems. The problem was that it was scattered across spreadsheets, paper reports, and local systems. In some areas, operators manually entered information as often as every hour, which meant that checking the current production status quickly was not always possible. The client needed a solution that could automatically collect data directly from machines and make it available for visualization and reporting.

The client manufactures ceramic products for both domestic and international markets. At this scale, manual reporting was becoming an increasing burden. Production and machine data existed, but often only after information had been collected from several sources or manually transcribed by operators.

With this model, it was difficult to see quickly what was happening in the plant at any given moment. It also increased the risk of routine errors caused by manual data entry. The client did not want ceramic manufacturing digitalizationto begin with replacing the entire machine fleet. The goal was to use what was already working and create a layer that would connect older equipment with a modern approach to production data.

The equipment base had developed over many years, so there was no single standard. Machines came from different periods, used different controllers, and communicated in different ways. Although equipment from two manufacturers dominated the plants, the infrastructure as a whole included about 15 communication protocols. On top of that, some older control systems were closed, and machine manufacturers were not always able to provide the information we needed.

That meant ceramic manufacturing digitalization could not rely on one ready-made integration method. We had to assess each group of machines, identify available data sources, and determine how those signals could be used. At the same time, the solution had to work technically and remain within the client’s budget.

Our first step was an extended plant audit. We reviewed the machines operating on the production floor, their communication capabilities, and the data that could be retrieved from them. The objective was to understand the actual technical landscape before deciding how the integration should be built. In parallel, we spoke with process technologists to determine which information mattered most in day-to-day operations.

Depending on the process, this included temperatures, pressures, and specific signals indicating machine status. The client’s engineer responsible for digitalization also played an important role by helping identify data sources, providing documentation, and contacting machine manufacturers. As a result, ceramic manufacturing digitalization was carried out together with the people who knew the plant and its equipment best.

We used KEPServerEX for the integration. In a machine fleet this heterogeneous, one of its key advantages was the ability to communicate with equipment using different protocols and then expose the data through the standardized OPC UA protocol.

This allowed information from different machines to flow into a shared data layer instead of remaining in separate, closed environments. It also reduced the need for every downstream application to handle each machine protocol separately. On our side, KEPServerEX was primarily responsible for collecting and initially filtering the data so that only the information actually needed was passed on to downstream systems. We also worked with the company responsible for visualization to make sure the data was interpreted and processed correctly.

The solution was also designed with redundancy. Two servers collected data in parallel, so a failure of one server did not have to interrupt the operation of the entire system.

Where possible, we used standard communication protocols, including Modbus. After connecting to a device, we still had to identify the correct signals and determine which ones corresponded to specific production parameters.

With older machines, the work became more complex. Some equipment did not support direct communication through standard methods, so we had to find alternative routes. In some cases, that meant analyzing signals and doing work similar to reverse engineering. In others, it meant retrieving data directly from electrical signals. This flexibility was essential for ceramic manufacturing digitalization in a plant where replacing functioning equipment was not the objective.

There were also less typical situations in which we had to analyze highly specialized serial protocols and develop custom drivers to handle communication with signals transmitted within the machine. All of this work took place in an operating plant with significant dust levels, so the project required not only IT systems expertise but also a strong understanding of industrial automation and real production conditions.

We started the implementation at one plant. This allowed us to validate the assumptions on real machines and see how the solution performed in day-to-day production.

After that stage, the project was expanded to two additional plants. The client was gradually building a more consistent model for data collection, regardless of equipment age, manufacturer, or communication protocol. Scaling the same approach across sites also made ceramic manufacturing digitalization more structured and easier to develop further.

After implementation, data could be collected directly from machines and viewed in real time. Accuracy improved, while the risk of errors caused by manually re-entering information was significantly reduced.

Employees could also observe changes and trends in machine operation without having to check parameters on the shop floor each time. In a high-dust environment, that delivered additional value. Production information became available when it was needed and could be passed directly to reports and visualizations. For the client, ceramic manufacturing digitalization meant faster access to current information without adding more manual reporting work.

Most importantly, the project created a data layer that can support further automation and additional digital manufacturing solutions. Instead of treating ceramic manufacturing digitalization as a one-time implementation, the client gained a foundation that can be expanded as new business and integration needs emerge.

This project showed that aging machines and multiple communication protocols do not automatically mean that an entire production infrastructure has to be replaced. Successful ceramic manufacturing digitalization starts with a clear understanding of what is already in the plant, what data can be retrieved, and how that data can be connected with IT/OT systems.

Without access to reliable data, it is difficult to build analytics, automation, or AI-based solutions. That is why, when purchasing new machines, it is worth thinking about future integration from the start. And when working with an older equipment base, ceramic manufacturing digitalization should begin with an audit and a realistic assessment of what is technically available.

If your plant operates equipment from different manufacturers and generations, data still ends up in spreadsheets, or some reports are still created manually, ceramic manufacturing digitalization does not necessarily require replacing the entire machine fleet.

Start with an audit. The explitia team will assess what data is available, how it can be retrieved, and how your machines can be connected with IT/OT systems to prepare the plant for further ceramic manufacturing digitalization.