The data from the now six-year-old study entitled "Smartening up with Artificial Intelligence (AI) - What's in it for Germany and its Industrial Sector?" on the use of AI and ML in manufacturing is certainly no longer up to date, but still speaks for itself:

1. 20 percent increase in plant productivity and effectiveness with a simultaneous 10 percent reduction in maintenance costs
2. 30 percent less yield loss due to a reduction in reject rates and testing costs
3. 50 percent more productivity thanks to automated quality checks and a defect detection rate that is up to 90 percent higher than human inspection
4. Up to 20% increase in productivity thanks to AI-supported improved human-machine interaction and context-aware robots
5. Research and development costs reduced by 10 to 15 percent and time-to-market improved by 10 percent at the same time

In addition to the benefits mentioned by McKinsey, there are further advantages:

• Better planning reliability: AI can also help with warehouse management, for example, in order to predict bottlenecks in supply chains and respond to them quickly.
• More flexibility to be able to adapt production processes more quickly and precisely to various new requirements and customer needs
• Greater IT & OT security, because AI-supported better monitoring of work processes and detection of hazards and risks is possible, for example to prevent accidents
• AI-supported data and trend analyses for better and faster decision-making by managers

Finally, in addition to predictive maintenance, AI also supports prescriptive maintenance to further optimize maintenance and its intervals, save costs and ensure smooth production with 100% availability.

Let your production systems think for you - for improved productivity, quality assurance & planning reliability. Find out more now:

The problem with all the advantages that AI and ML offer, however, is that only around 20 percent of existing application examples are used and most projects do not make it past the proof-of-concept (PoC) or test phase.

It is therefore important to have a strong partner like Hewlett Packard Enterprise at your side for such industrial projects. 90 percent of the Fortune 500 are HPE customers. The company has more than 17,000 experts and an ecosystem of over 100 partners worldwide.

HPE therefore has the necessary head start in terms of concentrated experience and know-how to help industrial customers "go the last mile" from the PoC phase to profitable operations. Strategies, initiatives and digital transformation activities for B2B customers follow HPE's proven approach and are interlinked. The three major trends mentioned at the beginning - individualization, flexibilization and sustainability in production - are incorporated into the initiative and planning before the various building blocks are then transferred into the corresponding solutions, for example for the standardization of IT and OT or for AI- and ML-supported quality analyses and control processes.

From the IoT connection and networking from the edge to the cloud to the intelligent evaluation and use of the data obtained, HPE also has the necessary technological requirements in its portfolio - all from a single source.

Conclusion: AI in industrial production offers many advantages, but in most cases these are not sufficiently utilized and projects hardly get beyond the PoC or test phase because companies lack the necessary experience and expertise. But it doesn't have to stay that way, because a strong partner like HPE can help to exploit the full potential of AI and ML in the manufacturing industry.

Find out how to implement your AI project in this illustrative example.

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