AI in Manufacturing Report

  Enquiry / contact me

The Capgemini Research Institute’s report outlines current AI adoption in the manufacturing sector

Artificial Intelligence

AI in Manufacturing Report
AI in Manufacturing Report

The Capgemini Research Institute released a report titled "Scaling AI in Manufacturing Operations: Practictioners' Perspective", that details the current state-of-the-art of the adoption of Artificial Intelligence (AI) in the manufacturing sector. 300 leading global manufacturers in the automotive, industrial manufacturing, consumer products, and aerospace and defense sectors were analyzed and 30 senior industry executives involved in AI projects were interviewed. The main findings are as follows:

Facts and figures

Europe reasserts its innovativeness and ability to think forward: in fact, 51% of its top manufacturers currently implement AI projects. This rate is even higher in Germany, where 69% of manufacturers utilize AI applications. Japan follows Europe with a 30% AI adoption rate for its leading manufacturers, narrowly followed by the USA (28%), while China reports the lowest rate of AI implementation, ranging around only 11%. 

AI in the value chain

The implementation of AI shows clear added value and benefits throughout the entire value chain.

Out of the 22 use cases analyzed, three were identified as the best use cases and as the possible starting points for other organizations to implement AI projects: intelligent maintenance, product quality inspection, and demand planning

For example, General Motors uses a computer vision system to detect component failure and prevent downtime, Bridgestone performs tire quality control through AI, which resulted in a 15% improvement over traditional methods, while Danone was able to use machine learning to predict demand, reducing the loss of sales by 30%. 

These applications are particularly suitable as they are relatively easy to implement as both relevant data and AI know-how and/or standardized solutions are already available. Furthermore, all the three cases would improve the visibility and the explainability of the processes, which would facilitate AI adoption by the operational teams and foster a systematic mindset shift towards AI in the plant and across the workforce. 

Scaling AI adoption

While successful AI implementation in manufacturing depends on several factors, the most important factor according to the report is the successful deployment of AI prototypes in live engineering environments, including the automation of the collection of real-time data and the prototypes’ integration with legacy IT and IIoT systems.

It is also important for manufacturers to design a data governance framework and establish a central data & AI platform to store and analyze data using AI, making it thus available for issue-specific AI applications. This will also contribute to the development of AI, data science and data engineering expertise directly related to manufacturing applications. After this foundational step, AI applications can be effectively implemented and shared across the entire manufacturing network, including multiple sites and factories. 



Read the full report here

Posted on December 18, 2019 - (382 views)
Related articles
Rugged COM Express Type 6 Module
EuroTech enters IBM Edge Ecosystem
ROS 2 Robot Controller
''Artificial Intelligence Helps Simplify Telecommunications''
Camera-based Spindle Control
Compact Embedded PC with AI Computing Power
Easy-handling Cobot
AnotherBrain Accelerates its Development in 2020 to Deliver New AI Solutions Based on New Approaches
Samsung and Xilinx Team Up for Worldwide 5G Commercial Deployments
Defining a ''Roadmap of Promising Initiatives'' for the Digital Transformation
Visual Inspection with Plug & Inspect™ Technology
Visual Alert System in Case of Fall
Digital Platform for Energy Optimization
Automated ML Platform
Anti-collision Camera
Eurotech Congratulated by Frost & Sullivan for its Everyware Cloud
DataWalk Extends Data Analysis Solution to Help Goverments Fight Coronavirus
Data Extraction with AI-powered Anomaly Detection
"Everyone can become a process miner"
Smart Production in the Injection Molding Sector