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Industrial AI Challenge – selected companies 2023

   

2023

Adige BLM AI23

Adige BLM AI23

Adige BLM AI23

Adige BLM AI23

Adige S.p.a – BLM Group, is an industrial company, devoted to the design, construction, and digitalization of tube processing plants.

The company is interested in obtaining an algorithm that supports production planning by suggesting both material requirements planning actions and deferring purchasing actions when consumption is reduced.

Bonfiglioli AI23

Bonfiglioli AI23

Bonfiglioli AI23

Bonfiglioli AI23

Bonfiglioli is a family-run Italian company with a global presence that designs, manufactures, and distributes effective, tailored solutions for all types of applications in industrial automation, mobile machinery, and wind energy.

To date, the company has already developed ML algorithms that can prevent possible critical situations or plant malfunctions. With the state of the art in mind, the request is to integrate the capabilities of understanding unstructured data and generating Large Language Models (LLM) code into the development of the algorithms.

Dolomiti energia AI23

Dolomiti energia AI23

Dolomiti energia AI23

Dolomiti energia AI23

Dolomiti Energia is the trading company of Dolomiti Energia Group that provides energy and gas solutions to households and businesses.

The Challenge proposed is to analyze Dolomiti Energia’s inbound multichannel call center traffic using NLP – Natural Language Processing to identify types of requests and needs, and suggest improvements in the current way they are handled.

Pirelli AI23

Pirelli AI23

Pirelli AI23

Pirelli AI23

Founded in 1872, Pirelli is a company with deep Italian roots, a brand recognized worldwide for its cutting-edge technologies, its capacity for innovation and the quality of its products.

The company’s proposed project is developed in the area of tire vulcanization and aims to build a system that can anticipate press malfunctions from the early stages to reduce waste and production inefficiencies, through a Predictive Failure Maintenance logic.

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