As the world’s leading renewable energy provider, our client installs over 40,000 wind turbines each year around the globe. The company’s US division was concerned about inefficiencies in the supply chain and assembly process, and wanted a long-term, scalable solution to reduce costs and increase ROI. We created a custom algorithm that assesses each supply chain and assembly factor in real time, in order to streamline processes, optimize logistics, and shorten project timelines.
The company struggled to efficiently manage the numerous, often unpredictable elements of their supply chain. Factors included weather, fuel costs, road tolls, warehousing taxes, international shipping logistics, traffic, exchange rates, and vendor pricing.
Because of these multiple and rapidly-changing factors, much of the supply chain management process involved last-minute adjustments that increased costs and assembly time. In addition, this largely manual approach was also taking up too much of the team members’ time and resources.
We worked with the client to identify and track the key supply chain factors, using data the company already owned. Then, we created an algorithm that constantly analyzes these complex factors in real time. This allows the company to easily identify the most efficient and cost-effective routes.
For example, the algorithm might suggest shipping out small parts on a Wednesday instead of a Monday, rerouting a turbine tower through a less-expensive toll road, or warehousing a component for two extra days in order to transport it by train instead of a costly container ship.
In addition, the model continues to get smarter with each new project. After each completed assembly, the company compares the model forecast with the real data, and the algorithm adapts for more precise forecasting on each successive project.
As a result of their optimized supply chain, our client saves between $600,000 and $1,000,000 per project, resulting in over $3 million saved in just the first year and a half of implementation.