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Vestas: Leveraging Dataiku for Sustainable Energy Solutions and Cost Reduction
Technology Category
- Analytics & Modeling - Machine Learning
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Equipment & Machinery
- Transportation
Applicable Functions
- Logistics & Transportation
- Maintenance
Use Cases
- Building Automation & Control
- Inventory Management
Services
- Data Science Services
- System Integration
The Challenge
Vestas, a global leader in sustainable energy solutions, faced a complex challenge in optimizing their shipment patterns to save costs. The Service Analytics team at Vestas had to consider not only external, customer-facing products, but also internal stakeholders across the Operations, Finance, Supply Chain, and Commercial teams. All of these teams worked together to answer big questions for the company such as how and when to deliver a turbine part from point A to point B. The team recognized that a more robust data operation could help them simplify and improve logistical challenges. They understood that data science-based solutions in predictive asset maintenance, field capacity planning, inventory management, demand and supply forecasting, and price planning would provide critical support to the internal customers of Vestas. However, until that point, the data team ran a traditional business intelligence (BI) based analytics operation, querying BI-dashboards, deriving insights, and building data products in a less automated manner.
About The Customer
Vestas is a global leader in sustainable energy solutions, with 29,000 employees working to design, manufacture, install, develop, and service wind energy and hybrid projects all over the world. With over 160 GW of wind turbines installed in 88 countries, Vestas has already prevented 1.5 billion tons of CO₂ being emitted into the atmosphere. The Service Analytics team at Vestas plays a key role in keeping the company at the forefront of a sustainable future, enabling business decisions and processes with data products and insights across the entire value chain.
The Solution
The Service Analytics team at Vestas decided to upgrade their team’s maturity, with an eye toward building solutions that used machine learning and advanced analytics. As part of this transition, a Center of Excellence (CoE) for advanced analytics was put together with the aim of identifying transitional areas within Service Analytics. This involved building proof of concepts (PoCs) to showcase their machine-learning capabilities, upskilling the team, and identifying tools and a technology ecosystem that would support their journey over the long run. Dataiku served as the cornerstone platform for Service Analytics’ CoE. After conducting an internal study of available data science platforms, the team was drawn to Dataiku for its simplicity, support for citizen data scientists, increased time-to-market, agnostic toolset, and good integration with cloud services. The team onboarded the Dataiku instance and began working on use cases and PoCs that could demonstrate the power of Dataiku and the improved time-to-value it enabled.
Operational Impact
Quantitative Benefit
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