Artificial intelligence in manufacturing: realizing smart factory projects in industrial companies quickly, easily and safely

A joint study by IDC and InterSystems analyzes to what extent the smart factory has already found its way into daily business in the DACH region

Smart factory projects in the trend among industrial companies: While production monitoring and predictive maintenance rely on a minimum of digital maturity in factories, advanced factories in particular are already implementing use cases that rely heavily on artificial intelligence (AI) to increase their efficiency. A very attractive concept for a simple realization of these projects is the Smart Data Fabric. systemsa provider of innovative data technologies, explains what companies should pay attention to when implementing intelligent operations.

Determining the level of digital maturity

Digital maturity is a critical factor in determining the appropriate transformation strategy for each individual company. It is no coincidence that allocate one The current study IDC recently did its own analysis of the aspect. The companies’ current digital maturity levels, investment priorities in relation to smart factory projects and current use cases for AI in production were examined. A five-stage model was developed in which respondents rated themselves. At one end of the spectrum, the company does not have any strategic insights regarding digital transformation, and at the other end this is a fundamental building block of the company’s philosophy. Key finding: the majority of companies in Germany (33 percent) and Switzerland (42 percent) place themselves in the middle, while in Austria a majority of 38 percent put themselves in second place. Striking: None of the respondents from Switzerland see themselves at the top. In an international comparison, the numbers show that digital transformation issues have already hit the local agendas, but there is potential for more measures and growth.

Define data-driven use cases

Looking at the use of AI in production practices in detail, it is noticeable that significant investments have already been made, particularly in continuous monitoring of production systems, expanded production planning and predictive maintenance. Meanwhile, the digital transformation pioneers agenda already includes investments in use cases that have so far only been addressed or implemented sporadically, if at all, across all industries. Usually these are very Data-driven use cases and especially AI-driven use casessuch as defect detection based on video analysis, autonomous transportation vehicles, and the digital twin of the manufacturing process.

This effort is paying off because, according to the study, investments in the smart factory yield measurable benefits. Manufacturers can use it to increase overall equipment efficiency (OEE) by ten percent and reduce production costs by ten percent. The golden rule here: Companies that use their data extensively with the help of AI have the opportunity to realize greater potential for optimization and savings and are more likely to achieve more reliable, flexible and cost-effective production.

Ensure centralized access to all relevant IT and OT data

The study demonstrates once again that company data is the cornerstone of an innovative production process. Central access to all relevant data via a data platform as a single source of truth is an indispensable prerequisite for the success of Smart Factory projects. For companies, it’s about the availability and standardization of their data – for production monitoring, predictive maintenance, analytics, and the use of artificial intelligence. as such study show, but there is usually no comprehensive networking of IT and OT systems. In addition, there is a shortage of technical know-how and corporate IT equipment and concerns about closer integration of IT systems and operating systems. To overcome technical challenges, implementing smart data fabric as a single source of truth provides a solution that has been tried and tested many times in practice.