AI Industrial Analytics
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About the project
The project will develop, test and launch two innovations to the market. These products will empower shop floor workers to identify and solve complex problems without requiring them to master statistical modelling, computer programming and report generation.
It aims to accomplish the following objectives:
(1) improve manufacturing processes through the creation of a digital twin of the production line and manufacturing processes,
(2) foster human and machine collaboration by actively engaging shop floor workers in artificial intelligence (AI) processes and
(3) make manufacturing attractive to young people and innovative technologies accessible to workers of all ages and skill sets.
Challenges
Industry 4.0 has changed the way manufacturers do business, yet the lag time from data collection to insight is too long and the cost is too high for all but the largest European manufacturers. In fact, McKinsey & Company estimates that the lack of insights costs manufacturers between € 1.2 – € 3.7 trillion and that applications in operations have the potential to create value of € 633B billion- € 1.8 trillion per year.
The proposed solution empowers shop floor workers of all skill sets at manufacturers of all sizes to make informed, smart decisions at speed by reducing the time and cost of insights and transforming the way manufacturers consume analytics.
Solutions
The proposed solution employs AI to rapidly and efficiently deliver actionable, explainable, cross-functional insights in real time and without the need for a specialised in-house team.
It utilises human-in-the-loop AI to create human-computer collaboration and bring domain knowledge into machine learning models. The end users have a shared interest in utilising data to drive decision-making and enhance competitiveness.
The project aims to increase the competitiveness and resilience of European manufacturers by reducing the time-to-insights, reducing scrap rates and increasing production efficiency.
Expected results
The project aims to launch two scalable, replicable innovations across manufacturing companies and industrial sectors. The success of the project will be measured against a number of key performance indicators (KPIs), as well as the time to onboarding of each successive end user.
The outcome will be measured according to its ability to enhance overall factory efficiency, reduce waste of resources and environmental burden through optimised production processes and increase the competitiveness of manufacturing companies in the global economy.
About the solution: AI Automated Industrial Analytics
The solution integrates cutting-edge artificial intelligence (AI) technologies and user-centric features to address critical challenges in manufacturing operations by:
- Breaking down data silos and automating the entire data science workflow.
- Creating a digital twin of the production line and manufacturing processes.
- Putting human knowledge and experience at the centre of machine learning processes.
- Modelling insights into easy-to-understand interactive stories.
- Ranking insights by impact.
Target market
Designed specifically for the manufacturing sector, the solution targets discrete and process manufacturers with complex production, such as semiconductor, food processing, consumer chemicals, auto components, tire production, white goods and consumer goods. Early adopters are data driven. Target end users are shop floor operators, technicians, engineers and their managers.
Societal impact
By reducing waste and downtime, the product advances responsible consumption (SDG 12), improves working conditions and skill levels (SDG 8) and supports industrial innovation (SDG 9).
The dissemination efforts and the learning path created fostered awareness and supported workforce skill development. Inclusivity measures and a human-centric approach to AI adoption also expanded workforce diversity, strengthening the possibility for manufacturers to attract young talent while contributing to shaping a forward-looking, equitable manufacturing ecosystem.
