What challenges did ALTEN face before AI implementation?

Manufacturer struggled to maintain consistent product quality. Unplanned downtime increased operational costs. Sustainability targets required reductions in waste and energy usage. Lack of real-time monitoring limited proactive intervention.

What was the impact of the AI solution?

Reduced defective products substantially (quantitative details not disclosed). Minimized manufacturing waste, supporting sustainability goals. Extended the operational life of manufacturing machinery. Improved overall operational efficiency and productivity.

Source

MicrosoftProductionEvidence: Low40/100

ALTEN enables predictive maintenance to improve manufacturing efficiency

Use case typeManufacturing quality monitoringUpdated Jun 13, 2026

ALTEN, a global engineering and technology consulting firm, partnered with a high-volume manufacturer to implement predictive maintenance powered by Microsoft technologies. The solution utilized AI and machine learning models, hosted on Azure, to monitor and assess the quality of ball bearings in real time. By enabling operators to anticipate quality issues up to an hour before production completes, the system empowered real-time operational adjustments. This approach reduced defective products, minimized manufacturing waste, and extended machinery lifespan, delivering measurable sustainability benefits. Real-time dashboards equipped operators with actionable insights, fostering quick, data-driven decisions and more streamlined workflows. The implementation demonstrates how data-driven insight and AI can transform manufacturing operations, sustainability, and productivity. The article also references a second ALTEN Group case (with VMO and a Taiwanese firm leveraging AWS and GCP), but this instance is centered on the Azure-based predictive maintenance deployment for industrial manufacturing.

Organization: ALTEN
Industry: Manufacturing
Location: Global
Published: April 2025

Reported outcomes

Strategic outcomes

New product / capabilityDeployed AI-driven predictive maintenanceNew product / capabilityEnabled real-time quality predictionSpeed & agilityEnabled earlier operator interventionBetter decisions & insightProvided actionable real-time dashboards

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: ALTEN
Provider: Microsoft
Maturity: Production
Linked source: linkedin.com

By enabling operators to anticipate quality issues up to an hour before production completes, the system empowered real-time operational adjustments

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 2 of 2

1Predictive Maintenance for Industrial Manufacturing
2Real-time Quality Prediction in Production Processes

Manufacturer struggled to maintain consistent product quality.
Unplanned downtime increased operational costs.
Sustainability targets required reductions in waste and energy usage.
Lack of real-time monitoring limited proactive intervention.

Implemented AI-driven predictive maintenance using Azure and machine learning.
Deployed machine learning models for real-time quality prediction of ball bearings.
Enabled real-time monitoring and operator intervention up to an hour before production end.
Introduced real-time dashboards to provide actionable, data-driven insights for decision-making.

Technologies

Azure Machine learning

Reduced defective products substantially (quantitative details not disclosed).
Minimized manufacturing waste, supporting sustainability goals.
Extended the operational life of manufacturing machinery.
Improved overall operational efficiency and productivity.

Architecture

ALTEN integrated machine learning models hosted on Azure that continuously monitored production processes in real time. The models predicted quality deviations of manufactured ball bearings, allowing operators to adjust parameters up to an hour before production completion. Real-time dashboards sourced data from production systems to deliver actionable insight. This end-to-end solution reduced waste, minimized defects, and enhanced sustainability.

Sources & evidence1

Evidence: Low40/100Evidence strength