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In Denmark and across the world, Vestas has embodied the principles of intelligent innovation for 125 years. It therefore comes as no surprise that the Vestas Service Analytics team has recently collaborated with Dataiku to optimize their shipment patterns, which is estimated to save Vestas millions of euros in the process.
From its humble beginnings as a small manufacturing company on the west coast of Denmark, the company has grown into 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. To date, 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.
In recent years, the Service Analytics team at Vestas has played 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. But how have they achieved this? We spoke with Mohamed Musthafa Shahul Mohamed, Data Science Lead at Vestas, to learn how his team is bringing data to the core of the business.
Bringing Simplicity to a Complex Industry
If each industry faces a unique set of complex analytics challenges, those facing the sustainable energy sector are among the most complex. In the case of the Service Analytics team at Vestas, they have 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 work together to answer big questions for the company such as how and when to deliver a turbine part from point A to point B.
A year ago, Service Analytics recognized that a more robust data operation could help them simplify and improve logistical challenges like this. They understood, in particular, 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.
Until that point, the data team ran a mighty but more traditional business intelligence (BI) based analytics operation, querying BI-dashboards, deriving insights, and building data products in a less automated manner. The big shift happened when they decided to upgrade the 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 (PoC) 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.
Turning Up the Dial With Dataiku
Over the past year, Dataiku has served as the cornerstone platform for Service Analytics’ CoE. As any data team leader knows, finding the right platform is a careful calculation that balances several data science, business analytics, and IT considerations at the same time. Importantly, Mohamed was looking for an Enterprise AI platform that would help Vestas Service Analytics along its advanced analytics journey, empowering the team to mature and upskill as its capabilities increased.
After conducting an internal study of available data science platforms, Mohamed was drawn to Dataiku for five main reasons:
- It provides a simple tool for data preparation, exploration, model building and model deployment in a single platform.
- It offers support for citizen data scientists through auto-machine-learning features, with augmented functionality at every stage of the data science and machine learning cycle.
- It increased time-to-market, offering targeted business solutions for Vestas’ industry- and function-specific analytic solutions.
- It offers an agnostic toolset allowing existing business users to focus on getting value from their data whilst collaborating with more technical code based users.
- It allows users to access data stored on local machines or from Azure cloud; as well as offering good integration with Snowflake and other cloud services.
Given these benefits, the team decided to onboard the Dataiku instance and begin working on use cases and PoCs that could demonstrate the power of Dataiku and the improved time-to-value it enabled.