Digitalization & New Technologies

For industrial operators, the ultimate goal is uptime. To maximize it, well managed, high-quality master data is paramount. It ensures, for instance, predictable production and better maintenance planning with fewer surprises and correct spare parts. It is also the basis of AI-driven predictive maintenance. How to improve master data so that the business runs efficiently?

The success of a company producing complex industrial equipment depends on its ability to provide end customers with smooth services. These days, companies also need the ability to provide digital product passports. These abilities are badly hampered if any irregularities occur in the data value chain, especially in PLM and BOM. Why does digital maturity play a key role in avoiding unreliable or missing product data?

Executive teams in manufacturing companies may assume their core business data is in perfect shape, informing what is in production, what is being sold, and what has been delivered. However, a surprising number of companies have far less control than they believe. Why? Because master data has serious gaps, leading to hidden, but significant operational waste.

Industrial companies are rushing to adopt AI, advanced analytics, and many other digital systems that demand data to function. Yet few grasp what the prerequisite is for any success. It’s well-managed master data, which too often is left in neglect. Start treating master data as the crown jewel in your company and stop losing productivity, making poor investments, and struggling with regulations.

Prevent Fragmented Responsibilities Around Safety and Cybersecurity

Why Restructuring is Now Strategically Necessary

A future-proof data structure is essential to meet new EU regulations, implement scalable AI, and remain competitive.

According to new regulations, technical documentation is entering the path of digitalization. What does this mean? What are the trends? Piotr Garbela, Sales Manager, Technical Communication and Data Solutions at Etteplan, explains.

Requirements engineering is one of the most important phases in product development. It defines what the product does, who it is intended for, and under what conditions it meets technical, regulatory and user-driven requirements. When requirements engineering fails, the entire project can collapse — which is why quality is critical. With AI-based agents, requirements work can be streamlined, enabling projects to progress faster. AI doesn’t replace experts, but it gives them better tools to succeed.