Original article was published on Artificial Intelligence on Medium
Capgemini: top three use cases of AI in manufacturing
Manufacturing Global takes a look at the top three use cases of AI in manufacturing reported by Capgemini.
When it comes to the adoption of artificial intelligence (AI) in manufacturing Europe’s currently leading the way with over half of its leading manufacturers using at least one form of AI in their operations. Within Europe Germany leads the region with 69% of its manufacturers implementing AI.
In a recent study conducted by Capgemini — AI in manufacturing operations — the organisations looked to determine the best starting point for manufacturers looking to adopt artificial intelligence in their operations, by looking at 22 uses cases of AI in manufacturing.
Capgemini identified three use cases that they believe to be the key areas for manufacturers to start. These included: intelligent maintenance; product quality inspection; and demand planning.
Intelligent machine maintenance
Defined as the ‘low hanging fruit’ by Capgemini, intelligent maintenance of machinery and equipment is the most common use of AI in manufacturing. When applied to operations the ROI can be significant. Not only does it minimise downtime, intelligent maintenance can also reduce maintenance costs and increase productivity. With good quality data and the expertise to analyse the data, this use of the technology is relatively easy to implement.
Leading manufacturers using this technology: General Motors and Volvo
Product quality inspection
By combining AI with analysis capabilities another way organisations are harnessing this technology is to help predict and prevent quality issues, via in-line visual inspections that can capture trends.
With the increased availability of high resolution cameras and powerful image recognition technology the cost of real time in-line inspection has drastically reduced.
This technology allows manufacturers to effectively tackle stringent regulatory environments particularly with regulations relating to product specifications and compliance.
Leading manufacturers using this technology: Audi and BMW
Today, organisations are harnessing machine learning capabilities to predict changes in consumer demand and behaviours. This enables manufacturers to make the necessary adjustments to production schedules and the procurement of raw materials. As a result organisations are benefiting from better forecasting yields several, better client service and inventory reduction.
Leading manufacturers using this technology: Danone Group
What makes these use cases the ideal starting point for manufacturers
Capgemini reports that intelligent maintenance; product quality inspection; and demand planning all have an optimal combination of several characteristics which make them the ideal starting point for manufacturers these characteristics include:
- Clear business value and benefits
- Relatively easy to implement
- Having the availability of data
- The availability of AI know-how and/or existing standardised solutions
- The opportunity to add features to increase visibility and explainability for clearer and better decision making
“AI in manufacturing is a game-changer,” commented Capgemini, “It has the potential to transform performance across the breadth and depth of manufacturing operations. However, the massive potential of this new Industrial 4.0 era will only be realised if manufacturers really focus their efforts on where AI can add most value and then drive the solutions to scale.”
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