Quick Overview
Epiroc used Microsoft Azure Machine Learning and AI governance software to automate and optimize steel manufacturing processes, ensuring consistent quality and reducing customer rejections and product returns.
Epiroc
Company Size
~14,000 employees (estimated)
Revenue Range
$4B-$5B annual revenue (estimated)
Primary Challenge
Fragmented data and inconsistent manufacturing processes causing quality issues and product returns
Key Metrics
\- 30% reduction in product returns and customer rejections\- Created an “AI Factory” in 60 hours\- Developed ML models for heat treatment process in 6 weeks\- 11 analytical teams using AI models globally
The Problem
Manufacturing facilities worldwide had siloed data and lacked a unified system to share best practices and optimize complex steel heat treatment processes, impacting product quality and efficiency.
The Solution
Implemented Azure Machine Learning, Azure Data Factory, Azure Databricks, and AI governance software from Sogeti to create a scalable “AI Factory” platform for data sharing, process automation, and predictive modeling.
Results
\- Consistent steel quality with better control over density, hardness, and flexibility\- Reduced waste and improved operational efficiency\- Faster innovation cycles and model deployment\- Enhanced compliance with regulations
“Now, with Azure Machine Learning and predictive AI, we consistently produce material with better quality.”
Peter Malmberg, Vice President Digitalization, Epiroc
Details
Industry
Manufacturing
Departments
Product Development & Innovation
Use Cases
Project Planning
Project Reporting
Project Tracking
Tags
Predictive Modeling
Machine Learning
AI Tools Used
No items found.
Sources
https://www.microsoft.com/en/customers/story/1653030140221000726-epiroc-manufacturing-azure-machine-learninghttps://www.vktr.com/ai-disruption/5-ai-case-studies-in-manufacturing/
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