Partnering opportunity

Dutch multinational is looking for data solutions to predict coating performance

Summary

The Dutch multinational is a producer of paints and coatings. They are looking for data solutions for predicting performance of their coatings. How can they collect and augment data more innovatively to make predictions for real world surface performance more accurate and help them to speed up the process of bringing new products to the market. The cooperation would be in the frame of a research or technical cooperation agreement. This technology request is part of an open innovation challenge.

Technical Specification or Expertise Sought

The solution should generate, collect or augment data on performance quickly and accurately. It should allow the development of more robust predictable performance models, either in surface monitoring of coating performance in real time, or even predict future behaviour. The solution should include one of the following elements: • New ways of creating and/or collecting real world data • Extracting new insights from (existing) data sources not currently known or accessible to the multinational

Partner sought

Type of partners: The company is especially looking for global innovators, start-ups, scale-ups, early stage tech companies that: • want to grow by jointly developing their products into the companies markets Type of partner collaboration: The Dutch multinational wants to work together as equal partners, under shared-IP conditions, this could be in the form of a research, technical cooperation agreement or any other kind of agreement that suits the purpose.

Description

The Dutch multinational has more than 200 years of experience with the production of painting and coatings. They have developed many ways (often based on empirical methods) to understand how current products will behave over time, for example how long it takes for colours to fade and when surfaces will need a recoat. To ensure that all their coatings are up to the long-term challenges they need effective ways of predicting the real-world performance of their coatings. The multinational is aware of the advances in digital technology and it’s potential to dramatically change and improve the ways they measure and predict how paints and coatings perform in the real world. During development, they often rely on accelerated testing of their products under laboratory conditions, but they also test them under extreme external conditions by leaving coated surfaces out in the elements to see how they survive over time. These are tried and true methods, appreciated and often specified by their customers. However, they are always looking to do it better and faster, allowing them to accelerate the development and introduction of warranted products. What are they looking for: • The data collection or data augmentation concept has been fully developed and is ready to apply • The data collection or data augmentation solution is scalable, cost effective and easy to implement around the world Cooperation with partners such as institutes, universities and companies would be in frame of a research or a technical cooperation agreement. IMPORTANT NOTE: This technology request is an innovation challenge and is published on an open innovation platform until the1st of March 2019. If an organization does express interest in cooperation with this firm before closing date, it will be guided towards this open innovation platform on which one can get in touch with the company experts. Please be aware that posts on this platform are not confidential. Beside open discussions on the platform, sharing of confidential information will be made possible on demand within a private channel. After that, the firm will select the SME's with whom they would like to cooperate in the development of a solution. Once the challenge is closed, EOI's for this technology request will be treated in the traditional way.

Advantages and innovations

Not available

Development stage

Available for demonstration

Intellectual Property Rights (IPR)

Not available


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