What challenges did Sandvik face before AI implementation?

Lack of granular, real-time visibility into equipment status for mining operators. Historical reliance on local server solutions with limited analytics capabilities. Frequent unplanned equipment failures and costly downtime. Difficulty in making data-driven maintenance decisions. Sustainability and efficiency pressures in the mining industry.

What was the impact of the AI solution?

Significantly improved operational visibility and efficiency for customers. Reduced downtime and prevented equipment failures. Extended machine lifespan and reliability. Supported sustainability by optimizing operational energy/resource use.

Source

MicrosoftWent dark

Sandvik optimizes predictive maintenance and sustainability in mining operations

Use case typePredictive maintenanceUpdated Jun 13, 2026

Sandvik, a global leader in mining and engineering, developed an advanced predictive maintenance solution using Microsoft Azure's cloud and AI services. The company transitioned from local telemetry data storage to a centralized cloud-based platform, enabling real-time analytics from hundreds of machine sensors. This system grants mining operators granular insights into equipment health and operational efficiency. By leveraging AI and big data, Sandvik supports its clients with better equipment uptime, proactive issue detection, and data-driven decisions. The solution not only optimizes operations but also contributes to sustainability goals by preventing failures, reducing unplanned downtime, and lengthening equipment lifespan. This digital transformation showcases the power of industrial AI to drive efficiency, safety, and environmental performance in the mining sector.

Organization: Sandvik
Industry: Manufacturing
Location: Sweden
Published: May 2025

Reported outcomes

Strategic outcomes

New product / capabilityDeveloped a cloud-based predictive maintenance platformBetter decisions & insightEnabled data-driven maintenance decisionsCustomer experience & trustImproved operational visibility for customersSustainability & ESGOptimized energy and resource use

Primary read

Use case focus

Showing 3 of 3

1predictive maintenance
2equipment monitoring
3failure prevention

Lack of granular, real-time visibility into equipment status for mining operators.
Historical reliance on local server solutions with limited analytics capabilities.
Frequent unplanned equipment failures and costly downtime.
Difficulty in making data-driven maintenance decisions.
Sustainability and efficiency pressures in the mining industry.

Centralized sensor data from all Sandvik machines in Azure Cloud.
Developed a cloud-based AI analytics platform for predictive insight.
Leveraged Azure AI services for advanced telemetry data analysis.
Enabled real-time monitoring and proactive equipment maintenance.

Technologies

Azure AI Azure IoT Azure cloud

Significantly improved operational visibility and efficiency for customers.
Reduced downtime and prevented equipment failures.
Extended machine lifespan and reliability.
Supported sustainability by optimizing operational energy/resource use.

Architecture

Sandvik's solution centralizes telemetry data from sensors on all mining equipment in Microsoft Azure. The system uses Azure analytics services to process this data in real time, applying AI-driven models for predictive maintenance and operational optimization. Customers access actionable insights via cloud dashboards, enabling data-driven interventions to minimize downtime and extend equipment lifespan.

Sources & evidence1

Went darkLost public footprint

The cited source is no longer reachable and the organization has no newer case. Not a claim the system was discontinued.