Aggreko embeds machine learning across global operations with Azure Databricks
Aggreko, a supplier of temporary power generation equipment across 265+ locations worldwide, used Azure Databricks and Delta Lake to unify data silos and analyze real-time IoT data from tens of thousands of field assets, embedding machine learning for predictive maintenance, saving over 30,000 hours of productivity, reducing service material costs by 10%, and achieving 140% of its cost-reduction target in the first half of 2020.
Overview
Aggreko, a supplier of temporary power generation equipment across 265+ locations worldwide, used Azure Databricks and Delta Lake to unify data silos and analyze real-time IoT data from tens of thousands of field assets, embedding machine learning for predictive maintenance, saving over 30,000 hours of productivity, reducing service material costs by 10%, and achieving 140% of its cost-reduction target in the first half of 2020.
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Inspect the highlighted sourceThe challenge
Aggreko's data across 265+ global locations was siloed, making it difficult to empower the organization with consistent, trusted, data-driven insights; access to up-to-date information was limited, hindering the ability to identify underperforming assets or anticipate maintenance risk in real time.
The solution
Aggreko selected Azure Databricks as its centralized data and AI platform, using Delta Lake to unify data into a modern lakehouse with automated cluster management, and embedded machine learning to analyze real-time IoT data streaming from tens of thousands of field assets, predicting maintenance issues to prevent catastrophic failures and augmenting human decision-making.
Reported business value
Aggreko saved over 30,000 hours of productivity through data automation such as automated manufacturing reporting, reduced service material costs by 10%, and achieved 140% of its cost-reduction target in the first half of 2020.
Sources
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