Smarter Solutions
How high quality AI can catalyze the transition to a greener world
The options of analytics to simultaneously contribute to climate change and business are endless.
Be inspired by some examples we have seen and worked on.
Renewable energy procurement
Is time-consuming and complicated, requiring significant expert evaluation. The trade-off between price and ESG impact are not easy to distill. Leveraging artificial intelligence (AI) to enhance energy procurement holds the promise to simultaneously advance both profitability and sustainability objectives. AI-driven insights enable the identification of cost-effective procurement windows, aligning energy consumption with periods of lower prices and reduced demand. This not only bolsters profitability by minimizing expenditure but also promotes energy efficiency and grid stability, contributing to a more sustainable energy landscape.
Cleaner industry
We have seen companies we worked for facing significant inefficiencies in e.g., product production planning, raw material procurement, replenishment and in/outbound logistics. As a result the amount of energy consumed and impact on budget and our planet is much higher than required. Advanced analytics can play a key role in addressing this. The importance of process automation, developing digital twins and advanced analytics models for greener, more profitable industries is underscored by critical benefits like route optimization, lower downtime and more accurate stock levels.
Predictive wind turbine maintenance
These towering structures play a vital role in the energy transition, but their intricate mechanics and exposure to harsh conditions make them susceptible to wear and tear, leading to downtime and reduced output. By leveraging data analytics, sensors, and advanced algorithms, predictive maintenance ensures continuous surveillance of key components. It allows early detection of even subtle performance shifts, preventing potential issues from escalating and enabling timely intervention, preempting unplanned downtime and enhancing reliability, lifespan and ROI.
Sustainable travel
Rail transport stands as a commendable sustainable alternative, although its management poses inherent complexity. For instance, the Netherlands alone recorded a substantial 1.3 million train movements in 2019, underscoring ample room for efficiency improvements. Energy consumption is significantly influenced by variables such as train velocity, route intricacies, and passenger capacity. Furthermore, as rail companies strive to transition from conventional to eco-friendly energy sources, the intricacy deepens. To effectively mitigate environmental repercussions and streamline operational expenses, the implementation of an AI-driven solution represents a judicious course of action.