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AkzoNobel: Revolutionizing Color Prediction with IoT
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Education
- Equipment & Machinery
Applicable Functions
- Product Research & Development
- Quality Assurance
Use Cases
- Predictive Maintenance
- Time Sensitive Networking
Services
- Data Science Services
- Testing & Certification
The Challenge
AkzoNobel, a Dutch paint and coatings company, has been at the forefront of color matching for two centuries. However, the company faced challenges in keeping up with the rapidly evolving color trends in industries like automotive and interior decor. The traditional method of color prediction, which involved complex mathematical models, was no longer efficient or innovative. The paint industry was under immense pressure as new colors emerged daily, and manufacturers constantly sought new finishes to gain a competitive edge. AkzoNobel's color prediction process, which involved deciphering multiple physical elements influencing color, was complex and time-consuming. The company needed to innovate and adapt to meet the modern demands and expectations of its customers.
About The Customer
AkzoNobel is a leading Dutch paint and coatings company that operates in more than 150 countries and employs approximately 34,500 people. The company owns popular brands like Dulux and Sikkens and has a rich heritage of over 200 years. AkzoNobel has always been at the forefront of color trends, with a dedicated team of scientists working to adjust, recalibrate, and tweak colors to meet the latest trends in various industries. The company prides itself on its commitment to innovation and exceeding customer expectations. AkzoNobel's key to success lies in its ability to adapt and innovate, constantly seeking new ways to meet the evolving demands of the market.
The Solution
AkzoNobel turned to machine learning technology and AI, powered by Microsoft Azure, to revolutionize its color prediction process. The introduction of Azure Machine Learning transformed the core process of color prediction, extending it with new technology. Instead of relying solely on physical models, the company could now make calculations based on deep learning models. This technology enabled AkzoNobel to create more color recipes, more accurately, and in less time. The transition to the new technology was seamless, with lab technicians and scientists using the same process and software tools, but with smarter calculations and fewer rounds of tests. The machine learning models were easy to deploy with the help of partner Machine2Learn, and the platform as a service (PaaS) capabilities of Azure increased operational simplicity, allowing AkzoNobel to scale up or down as needed.
Operational Impact
Quantitative Benefit
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