凯捷（Capgemini）：从实践者角度看AI在制造运营中的扩展(英文版)(36页).pdf

1、Scaling AI in Manufacturing Operations: A Practitioners Perspective Executive Summary AI in manufacturing is a game-changer. 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

2、 be realized if manufacturers really focus their efforts on where AI can add most value and then drive the solutions to scale. To understand whether organizations are focusing on the most promising use cases, and then achieving scale with the solution, we have undertaken significant research and ana

3、lysis. We analyzed 300 leading global manufacturers from four key segments automotive, industrial manufacturing, consumer products, and aerospace that it can be prototyped, i.e. its development and implementation process are made standardized and repeatable; and the prototype is now ready to be depl

4、oyed at scale. Figure 10: Recommendations to scale AI in manufacturing operations Source: Capgemini Research Institute analysis. I. Deploy successful AI prototypes in live engineering environments a. Implement the AI application to process real-time data from the shop floor So far, the implementatio

5、n of the use case as a pilot/POC has happened in a sandbox or controlled environment. As a result, the system has been trained and tested on a limited set of data. Before the AI application can begin to handle many possible scenarios, it needs to be trained to a level where its accuracy is sufficien

6、tly high for a production environment. Siddharth Verma global head and VP, IoT Services, Siemens pointed out that organizations need to be ready for a few false starts (a lesson they learned during the initial days of an intelligent maintenance implementation). “We used AI to predict failures in fan