Why manufacturing companies should focus on data platforms

In today’s dynamic manufacturing environment, the ability to respond efficiently and flexibly to change is crucial. This is where openpack comes in, a company that specializes in the standardization and digitization of manufacturing data, bridging the gap between tradition and innovation.

Why successful digitization starts with reliable data

In industry, the question is no longer whether to digitize, but how. But while buzzwords such as smart factory, Industry 4.0, and AI in production are being bandied about, one crucial aspect is often overlooked: without a uniform and reliable database, digital technologies cannot reach their full potential. If data is not structured, comparable, and available across systems, many digitization initiatives fail due to their own complexity.

In the corrugated board industry in particular, which is characterized by complex manufacturing processes, heterogeneous machine interfaces, and increasing pressure to improve efficiency, there is enormous potential in the intelligent use of data:

Production lines could be controlled more precisely, maintenance cycles optimized, and quality deviations detected at an early stage. Studies show that companies can increase their productivity by up to 30% and significantly reduce maintenance costs through data-driven production approaches, for example, through predictive maintenance or advanced analytics (McKinsey: “Manufacturing’s next act”).

However, those who work with incomplete, unstructured, or non-integrated data risk making wrong decisions with direct implications for productivity, quality, and costs. In an environment that demands fast and flexible responses, a clean database is increasingly decisive for competitiveness and future security.

The importance of data standardization

Imagine you have a huge jigsaw puzzle where each piece comes from a different manufacturer. Without uniform standards, putting this puzzle together would be almost impossible. A data management platform solves precisely this problem by providing uniform data formats that enable the smooth exchange of information between different systems and machines. The result? More efficient production that both reduces costs and improves quality.

The data jungle on the shop floor

There is a lot of production data: machine data, order data, quality parameters, energy consumption, maintenance information – and it is often stored in silos. One system communicates via OPC UA, the next uses a proprietary protocol. Some data is collected automatically, other data is entered manually. The result? A patchwork of information that raises more questions than it answers. In theory, such large amounts of data offer great insights, but in practice, this creates a massive problem: without standardization, there is no comparability. KPIs cannot be calculated reliably, deviations cannot be clearly identified, and optimization potential remains hidden.

What exactly does data standardization mean?

Data standardization means bringing data formats, names, timestamps, units of measurement, and interfaces to a common denominator. The goal is to create a “common language” for machines, systems, and employees, across departments, plants, and locations. This begins with structured data collection, for example, through standardized sensor technology or MES systems and extends to the definition of uniform data models and communication interfaces. A central data platform (e.g., a manufacturing data lake) then serves as a single source of truth. Only with clearly defined standards can these relationships be mapped transparently.

Why standardized data improves decision quality

Decisions are made every day in manufacturing plants: Should the line be stopped? When is the best time for maintenance? Where are the bottlenecks in the supply chain?

These decisions are only as good as the data on which they are based. Standardized data ensures that information is consistent, up-to-date, and comparable. This not only increases the speed of decision-making, but also its quality.

• Real-time monitoring enables early intervention in the event of deviations.
• Predictive maintenance reduces downtime through data-based forecasts.
• Production optimization through key figure comparisons between lines or locations becomes possible in the first place.

Digitalization is not purely an IT project, it is a business project. It affects not only technology, but also processes, organization, and culture. Those who recognize data standardization as a strategic lever now are laying the foundation for a more resilient, agile company.

If clarity is desired, standards are required

The question is no longer whether companies should use data, but how they should do so. Too many companies are stuck with fragmented systems, disjointed processes, and data silos that slow them down. The digitalization of production offers enormous opportunities here, but only on the basis of reliable, uniform data.

Standardization is not a chore, but a decisive factor for success. Investing in data competence, data infrastructure, and standardization today lays the foundation for agile, efficient, and future-proof production.