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Operations: How to scale AI in Manufacturing

To understand whether organizations are focusing on the most promising AI use cases, and then achieving scale with the solution, Cap Gemini have undertaken significant research and analysis.

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 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, Cap Gemini have undertaken significant research and analysis. They analyzed 300 leading global manufacturers from four key segments – automotive, industrial manufacturing, consumer products, and aerospace & defense – to understand the focus of their AI initiatives. They also spoke with 30 senior industry executives, all of whom are involved in their organization’s AI initiatives. Finally, they analyzed 22 AI use cases in manufacturing operations. These use cases were spread across seven broad functional areas, from inventory management through to production and quality control.

Key findings operations

The key findings that emerge from this analysis include:

• Europe is leading the way, with more than half of its top manufacturers implementing at least one AI use case in manufacturing operations (within Europe, Germany leads the pack, with 69% of its manufacturers implementing AI). Europe is then followed by Japan (30% implementing) and the US (28%).

• Three use cases stand out in terms of their suitability for kickstarting a manufacturer’s AI journey:
– Intelligent maintenance
– Product quality control
– Demand planning

• These use cases have an optimal combination of several characteristics, that make them an ideal place to start:
– Clear business value/benefits
– Relative ease of implementation
– Availability of data e.g., performance data from machines and equipment for intelligent maintenance, pictures and videos capturing finished products for quality, etc.
– Availability of AI know-how and/or existing standardized solutions
– The opportunity to add features that aid visibility and explainability, allowing employees to understand how decisions are reached and easing adoption by operational teams.

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Critical success factors

In the final section of this report, we look at the critical success factors for scaling these use cases in operations:

• Deploy successful AI prototypes in live engineering environments The first step in achieving scale involves bringing the AI prototype up to speed with processing data in real time from the shop floor/production/operations environment. To automate the collection of real-time, live data, the prototype needs to be integrated with legacy IT (such as MES and ERP) and industrial internet of things (IIoT) systems.

• Put down solid foundations of data governance and AI/ data talent To create a robust foundation for scale, and to encourage new implementations, manufacturers should design a data governance framework that defines critical processes related to the generation, management, and analysis of data. In addition, they need to deploy a data & AI platform – a central platform to store and analyze data using AI and to make it available to issue-specific AI applications. Alongside governance and platform, talent will also be a key building block, including manufacturing-specific expertise in AI, data science, and data engineering.

• Scale the AI solution across the manufacturing network Once the AI platform is ready, AI applications can be deployed and made available across multiple sites/factories. Performance needs to be continuously monitored for value generated, output quality and reliability.

Source: paper


Download the paper now.

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