What challenges did Tikkurila face before AI implementation?

Outdated IT systems and data silos limited visibility for proactive action (Tikkurila). Unplanned downtime resulted in millions in losses per hour for manufacturing plants. Manual, reactive maintenance processes increased costs and led to unexpected breakdowns. Global equipment fleets required real-time, scalable monitoring (Husky). Difficulty integrating disparate data sources for insights (Dow). High operational costs in maintaining heavy equipment (Komatsu).

What was the impact of the AI solution?

Tikkurila reduced production downtime and improved product quality. Husky saved clients an estimated $4,000-$6,000 per intervention and minimized emergency repairs. Komatsu Australia cut maintenance costs by 49%, improved performance by 25–30%, and reduced downtime by ~30%. 3M and Dow improved data integration and achieved near real-time operational insights. Dow reduced manual processes and enhanced production uptime.

Rank#1,682of 2,931

Source

MicrosoftExpanded

Global manufacturers drive operational efficiency with predictive maintenance

Use case typePredictive maintenanceUpdated Jun 13, 2026

Several leading manufacturing companies—including Tikkurila, Husky Technologies, 3M, Komatsu Australia, and Dow—have transformed their operations through predictive maintenance powered by Microsoft Azure. By leveraging Azure IoT, Machine Learning, SQL Edge, and analytics platforms, these organizations reduced unplanned downtime, optimized maintenance planning, and realized substantial cost savings. Tikkurila centralized data and digitized workflows to modernize maintenance, Husky Technologies deployed IoT-based real-time monitoring that saved clients thousands per intervention, and 3M processed production data at the edge to prevent failures. Komatsu Australia modernized legacy systems to unify company-wide analytics and reduced maintenance costs by nearly half, while Dow established a scalable predictive maintenance platform for improved agility and operational insight. These cases show how predictive maintenance technology directly impacts cost, uptime, performance, and digital transformation in the manufacturing sector across multiple global regions, including the Nordics, Australia, the US, and Poland.

Organization: Tikkurila
Industry: Manufacturing
Location: Finland
Published: November 2024

Reported outcomes

−49%

timeTime & speed

−30%time

Strategic outcomes

Cost efficiencyReduced maintenance costs significantlySpeed & agilityEnhanced production uptimeCustomer experience & trustMinimized emergency repairs

Catalog median for time & speed deployments: −55% across 674 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 4

1Predictive maintenance
2Real-time remote equipment monitoring
3Proactive equipment failure detection

Outdated IT systems and data silos limited visibility for proactive action (Tikkurila).
Unplanned downtime resulted in millions in losses per hour for manufacturing plants.
Manual, reactive maintenance processes increased costs and led to unexpected breakdowns.
Global equipment fleets required real-time, scalable monitoring (Husky).
Difficulty integrating disparate data sources for insights (Dow).
High operational costs in maintaining heavy equipment (Komatsu).

Centralized data platforms and automated workflows using Azure cloud infrastructure (Tikkurila, Dow).
Deployment of Azure IoT Hub and remote monitoring for predictive alerts (Husky).
Edge data processing with Azure SQL Edge for real-time maintenance insights (3M).
Migration to Azure SQL Database Managed Instance and Power BI for holistic analytics and reporting (Komatsu).
IoT sensors and machine learning models analyze equipment health and recommend proactive maintenance (Dow).

Technologies

Azure IoT Azure ML Azure SQL Edge Azure SQL Database Managed Instance Power BI Azure Synapse Analytics Azure Data Explorer

Tikkurila reduced production downtime and improved product quality.
Husky saved clients an estimated $4,000-$6,000 per intervention and minimized emergency repairs.
Komatsu Australia cut maintenance costs by 49%, improved performance by 25–30%, and reduced downtime by ~30%.
3M and Dow improved data integration and achieved near real-time operational insights.
Dow reduced manual processes and enhanced production uptime.

Architecture

Predictive maintenance implementations used Azure IoT Hub and on-premises sensors to collect real-time machine data, transmitted to centralized Azure cloud platforms (including SQL Edge and SQL Database Managed Instance) for ML-driven predictive insights. Local data processing at the edge (3M) minimized latency and provided immediate feedback. Analytics and visualization with Power BI enabled business units to monitor and act on insights. In Komatsu's case, TimeXtender Discovery Hub facilitated integration from Dynamics AX and other ERP systems into Azure SQL, with automated ETL and semantic modeling for reporting. Husky’s platform provided health scores and notifications when anomalies were detected, triggering remote or in-person intervention. Dow consolidated disparate data across multiple facilities into a cloud-hosted data lake on Azure for standardized analytics and real-time dashboarding.

Implementation partners1

TimeXtender

Sources & evidence1

ExpandedExpanded

The same organization appears in newer AI deployment evidence.