AI in Manufacturing: How to Turn Data from Factory Operations into Measurable Benefits

Executive Summary

AI in Manufacturing - IDC WP
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Many conversations with stakeholders in factory operations revolve around how the vast amount of data generated in the factory can be used better. Ideally, data should be used to increase transparency and then to achieve measurable benefits from process improvements.

The key to achieving measurable benefits lies in the ability of manufacturing organisations to turn data into relevant and actionable insights. This will entail applying meaningful analytics to data. In this context, there is growing interest in applying artificial intelligence (AI), as this draws even more insights from large sets of structured and unstructured data by detecting dependencies and correlations much faster than if attempted manually.

This IDC White Paper, based on the AI in Manufacturing Survey conducted by IDC and sponsored by InterSystems, shows that manufacturers’ investment priorities in factory operations are significantly impacted by their overall digital maturity and the maturity of their approach to data management. Results also show that the higher a manufacturer’s digital maturity or approach to data management, the more data-driven and AI-enabled its use cases.

The survey also shows that investing in smart factory use cases brings measurable benefits and that the more manufacturers address data-driven and AI-enabled smart factory use cases, the greater the improvements in overall equipment effectiveness (OEE) and the greater the cost savings.

Despite the potential benefits of having implemented smart factory use cases, there are challenges. They relate to the still limited share of connected production equipment and concerns around closer integration of IT and operations technology (OT). In addition, organisations are often unable to analyse data due to a lack of expertise or appropriate tools and platforms for data management.

The results also show that for less digitally mature manufacturers in particular, the primary external providers of support around smart factory use  cases are often providers of production equipment. This could limit the potential to scale the digital transformation (DX) of factory operations or to adopt/implement AI strategies.

To address the challenges of implementing data-driven and AI-enabled smart factory use cases, manufacturers should consider leveraging solutions that help them to retrofit and connect their legacy production equipment, integrate, transform, and harmonise IT/OT data, and provide embedded AI/ML capabilities to simplify and even automate the development of AI/ML models, speeding up the development of smart factory use cases.

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