Predictive Maintenance in Manufacturing with Azure: Real Use Cases From Tikkurila, Husky, 3M, Komatsu, Dow

Summary

• Unplanned downtime costs manufacturers millions annually, with industries like automotive and oil & gas seeing significant financial impacts.
• Over 75% of manufacturers now prioritize predictive maintenance, using advanced tools to reduce unplanned downtime.
• Microsoft Azure's tools, including IoT Hub, Machine Learning, and SQL Edge, enable real-time data processing, predictive insights, and proactive maintenance strategies.
• Real use cases: Tikkurila, Husky, 3M, Komatsu, and Dow transformed operations by reducing downtime, cutting costs, and enhancing efficiency using Azure-powered predictive maintenance.

Predictive maintenance has become a game-changer in the manufacturing industry, allowing companies to reduce downtime, optimize performance, and stay competitive. 

The cost of unplanned downtime has skyrocketed, with the automotive sector experiencing over $2 million in losses per hour, and the Oil & Gas industry facing nearly $500,000 per hour—both significant increases in just two years. 

On average, large industrial plants now lose $129 million annually to unplanned downtime, a 65% surge compared to two years ago. 

These challenges have driven the adoption of predictive maintenance and condition monitoring as mainstream strategies, with more than three-quarters of manufacturers prioritizing predictive maintenance initiatives.

A great advantage when introducing or updating your predictive maintenance initiatives is leveraging Microsoft Azure’s suite of tools and AI capabilities to transform your operations and significantly reduce downtime. 

This article explores how Tikkurila, Husky Technologies, 3M, Komatsu, and Dow have utilized Azure for predictive maintenance.

Tikkurila: Reduced Downtime During Production Cycles and Increased Final Product Quality with Predictive Maintenance

Founded in 1862 in Finland, Tikkurila is a leading paint and coatings manufacturer known for its high-quality, sustainable products across Europe. Through a digital transformation initiative, the company modernized its infrastructure centralized data, and implemented an IoT Predictive Maintenance platform to optimize production costs, enhance line maintenance, and minimize downtime.

Challenge

Tikkurila faced challenges with outdated IT systems, data silos, and inefficient processes. The lack of predictive maintenance led to unexpected equipment downtimes and increased costs:

• Legacy Systems: Outdated IT limited agility and responsiveness to market changes.
• Data Silos: Fragmented data across departments hindered effective decision-making.
• Manual Processes: Inefficient, manual workflows increased operational costs and slowed productivity.
• Reactive Maintenance: Lack of predictive maintenance led to unexpected equipment breakdowns, disrupting production schedules and increasing costs.

Solution

Tikkurila's digital transformation focused on:

• Data Centralization: A unified data platform was implemented to integrate data from multiple departments, enabling real-time insights and better coordination.
• Process Automation: Automation tools were introduced to replace manual workflows, reducing time and effort spent on routine tasks.
• Predictive Maintenance: Tools were deployed to monitor equipment health and proactively schedule maintenance, minimizing unexpected downtimes.
• Modern IT Infrastructure: Legacy systems were replaced with scalable, cloud-based solutions to support current and future needs.

Impact

• Reduced Downtime: Predictive maintenance reduced unexpected equipment breakdowns, ensuring smoother production schedules and reducing maintenance costs.
• Cost Savings: The modernized IT infrastructure led to reduced operational costs and improved productivity.

Lessons Learned

• Data Integration is crucial for gaining actionable insights and improving operational efficiency.
• Predictive Maintenance is a key factor in reducing downtimes and minimizing maintenance-related disruptions.
• Modernizing systems can be challenging, but they provide long-term benefits regarding scalability, efficiency, and cost savings.
• Collaboration between internal teams and technology partners is essential for successful implementation.

Another great example of predictive maintenance being used successfully to improve operational efficiency was made by Husky, who used Azure IoT Hub for their implementation.

Husky Technologies Saves Clients ~$5,000 Per Intervention with Their Predictive Maintenance Solution

Husky Technologies, a leading global industrial technology supplier specializing in manufacturing and engineering services for the plastics industry, leveraged Azure IoT Hub to improve its maintenance processes, which saved customers an estimated $4,000 to $6,000 per intervention. With more than 4,300 employees in 35 locations worldwide and supporting customers in over 140 countries, Husky needed a reliable way to prevent downtime, thus reducing potential revenue loss.

Challenge

Husky Technologies faced challenges in maintaining production efficiency due to unplanned downtimes, which impacted productivity and increased costs. 

As a global leader in injection molding systems, Husky supports equipment producing diverse products, including vaccines, packaging, and automotive parts.

With over 13,000 systems in operation, increasing demand for varied parts, sustainable packaging, and a shortage of skilled workers added complexity. 

Adopting Industry 4.0 technologies, such as IIoT, cloud computing, and AI, also introduced new challenges and opportunities. Previously, reactive maintenance led to delays, as local sensor data often went unnoticed until addressed by on-site technicians.

Solution

Husky recognized the need for a proactive approach and began centralizing knowledge from subject matter experts into the Husky Genius knowledge management system. 

They developed the Advantage+Elite proactive monitoring system, using Azure IoT Hub, Azure Data Explorer, and Azure Synapse Analytics to monitor and analyze real-time data from customer equipment. The solution was designed with several key components:

• Centralized Knowledge Management: Husky developers centralized decades of knowledge from subject matter experts into the Husky Genius knowledge management system.
• Real-time Monitoring with Azure: Advantage+Elite uses Azure IoT Hub to collect real-time data, allowing Husky to predict potential failures and notify customers before they occur.
• Pilot Testing: The solution was pilot-tested with six customers globally, with a 12-month validation period to establish capabilities, prove execution steps, and assess effectiveness in reducing unplanned downtime.
• Scalability and Azure Migration: Husky migrated from its previous cloud platform to meet new functional and scalability requirements. Azure services, including Azure IoT Hub, Azure Data Explorer, and Azure Synapse Analytics, provided end-to-end solutions for secure device provisioning, data integration, and analytics.
• Collaboration of OT and IT: A New Service Model team comprised of subject matter experts and data analysts to develop data algorithms and monitor hundreds of variables on customer machines.

“We estimate that each 'We Call You' intervention saves the customer an average of $4,000 to $6,000, depending on the specific circumstances. “ - Phil Kinson: Director, Service Contracts - Husky Technologies

Impact

Using Azure IoT Hub, Husky significantly reduced downtime and maintenance costs. The implementation of the Advantage+Elite system had several key impacts:

• Round-the-clock Remote Monitoring: Enabled continuous monitoring from six global monitoring centers, allowing Husky technicians to anticipate, identify, and resolve issues before they affect productivity or part quality.
• Proactive Customer Communication: When the system health score falls, Husky initiates a 'We Call You' notification to customers, outlining the problem, potential impact, and next steps for resolution, which saved customers an estimated $4,000 to $6,000 per intervention.
• Reduced Emergency Service and Downtime: Proactive insights minimized emergency service calls and reduced downtime by allowing technicians to visit with the correct parts and quickly resolve issues.
• Optimization of Spare Parts Usage: Allowed customers to replace parts based on actual usage, reducing costs associated with unnecessary replacements.
• Enhanced Use of Recycled Material: Advantage+Elite helped customers manage variability when increasing the use of recycled materials, contributing to more sustainable practices.

“Our dashboards consume this data and provide a health score for every monitored system. When the health score falls, we initiate a ‘We Call You’ notification to the customer champion.” - Phil Kinson: Director, Service Contracts - Husky Technologies.

Lessons Learned

Husky learned that utilizing real-time data and predictive insights helps them make informed maintenance decisions. Key takeaways include:

• Enhanced Customer Skill Development: Insights from weekly meetings helped improve customer maintenance teams' skills, giving them a deeper understanding of equipment use.
• Continuous Improvement: Learnings from Advantage+Elite have informed the design of more serviceable equipment, driving improved performance and lower ownership costs.
• Efficient Maintenance Planning: Azure IoT Hub's integration capabilities allowed seamless connectivity, which, combined with predictive insights, has driven more informed and effective operational choices.

“Azure saves us a lot of time by offering end-to-end monitoring support for applications, infrastructure, and the network,” says Jean-Christophe Witz, Chief Information Officer for Husky

Husky's experience demonstrates the power of predictive maintenance in driving operational improvements. Next, we explore how 3M used Azure SQL Edge to bring data processing closer to their manufacturing facilities.

3M: Moves to Industry 4.0 Enhancing Efficiency with Azure SQL Edge in the Process

3M Company, founded in 1902 and known for its diverse portfolio of over 60,000 products, used Azure SQL Edge to bring data processing closer to their manufacturing facilities, driving real-time insights and enhancing efficiency. 

With approximately 95,000 employees across various industries, including worker safety, healthcare, and consumer goods, 3M leveraged this technology to address equipment performance and productivity challenges.

Challenge

3M needed to enhance data processing at the edge of their manufacturing facilities to quickly analyze and respond to equipment performance, reducing the risks of costly failures and delays.

The existing process of gathering and transferring data from production systems was time-consuming and labor-intensive, limiting efficiency. 

3M aimed to process and analyze big data locally—at the edge—by leveraging Microsoft Azure SQL Edge, which helped them streamline data flow, reduce latency, and improve overall efficiency.

Analyzing sensor data alongside product information enabled 3M to detect potential manufacturing line issues hours before they occurred. 

This proactive approach meant they could address problems early, preventing escalation and resulting in improved productivity and cost savings.

Solution

3M deployed Azure SQL Edge to process and analyze data directly at the edge of their network, closer to their machinery. 

This solution allowed them to gain real-time insights into their equipment's health and detect early signs of wear and tear. Key components of the solution included:

• Easy Interoperability: Azure SQL Edge was selected for its compatibility with 3M's existing stack, allowing seamless integration. The implementation took just one engineer an average of six hours, compared to prior manual steps that could take weeks.
• Efficient Incorporation: The consistent SQL codebase simplified deployment, reduced the need for custom code, and minimized coordination between different modules.
• Scalability for Future Deployment: Azure SQL Edge was efficient in incorporating and can be easily replicated for deployment to other manufacturing sites, supporting 3M's move towards Industry 4.0.

“Azure SQL Edge is a great bridge between traditional processes and the newest AI and compute features in the cloud. This is the type of solution that will drive us towards Industry 4.0.” - Mike Gerlach: Manufacturing Technology Manager - 3M

Impact

With Azure SQL Edge, 3M significantly improved response times to equipment issues, reducing downtime and increasing operational efficiency. Key impacts include:

• Reduced Latency: Moving data processing to the edge reduced latency, allowing for faster issue detection and action.
• Improved Maintenance Efficiency: Real-time insights enabled the maintenance team to prevent minor issues from escalating, which previously required labor-intensive manual steps.
• Increased Uptime: Streamlined data flow reduced the time required to move data from on-premises systems to Azure, boosting operational uptime.
• Cost Savings: The predictive maintenance approach saved significant costs by reducing emergency repairs and unnecessary parts replacements.
• Scalable Edge Solution: To test the capabilities of the solution and its impact on the business, 3M built and implemented a Proof of Concept (PoC) that delivered promising results. Seeing the scalability and overall improvements from the solution, 3M decided to roll it out across their entire organization, supporting the broader adoption of Industry 4.0 solutions.

“We leverage the latest cloud capabilities to accelerate our research & development towards new epic solutions. Efficient edge and cloud data flow capabilities are strategic areas of focus. Cloud capabilities are constantly evolving, so it is critical for us to stay close to or in front of these rapid technology evolutions.”  - Hung Brown Ton: Chief Architect & Lab Manager - 3M

Lessons Learned

3M discovered several valuable lessons from implementing Azure SQL Edge for predictive maintenance:

• Edge Computing's Role in Efficiency: Processing data locally at the edge improved response times and minimized reliance on network connectivity, allowing uninterrupted operation even during network outages.
• Simplified Implementation: Azure SQL Edge's interoperability allowed for smooth integration with existing infrastructure, highlighting the importance of using native services to reduce complexity.
• Scalable Deployment: The success of the edge solution showed that similar approaches could be replicated across multiple sites, reinforcing the importance of scalable, consistent solutions in adopting Industry 4.0 technologies.
• Cost and Time Savings: Reducing manual tasks and faster data processing highlighted the potential for operational cost savings and improved worker efficiency, strengthening the business case for edge computing investments.

Building on the successes at 3M, Komatsu Australia also leveraged Azure tools to enhance maintenance practices, ensuring their heavy machinery stayed operational in demanding environments.

Komatsu Australia: Reduced Costs by 49% and Increased Performance by ~30% by Using Azure for Predictive Maintenance

Komatsu Australia leveraged Microsoft Azure tools to enhance maintenance practices, ensuring their heavy machinery stays operational in demanding environments. 

Founded in 1921, Komatsu employs 65,738 people and is committed to utilizing digital technologies to drive efficiency and sustainability. In the quarter ending June 30, 2024, the company reported revenue of 959.84 billion JPY (approximately 6.41 billion USD), reflecting 6.70% growth. 

Over the last twelve months, revenue reached 3.93 trillion JPY (approximately 26.23 billion USD), up 6.69% year-over-year. For the fiscal year ending March 31, 2024, annual revenue was 3.87 trillion JPY (approximately 25.83 billion USD), with 9.08% growth.

Opportunity

Komatsu Australia saw an opportunity to enhance equipment maintenance for their heavy machinery, especially in the challenging construction and mining sectors, where downtime can be extremely costly. 

They moved their mainframe applications to Microsoft Azure SQL Database Managed Instance, which improved performance, cut costs by 49%, and ensured that employees and customers had access to timely data. With over 30,000 machines streaming productivity and condition data, Komatsu used these insights to help customers boost productivity and maximize their return on investment.

“Azure SQL Database Managed Instance was the best choice for us regarding scalability, cost, and performance.… We’ve seen a 49 percent cost reduction and 25 to 30 percent performance gains. “ - Nipun Sharma: Analytics Architect, Business Technology and Systems - Komatsu Australia

Solution

Komatsu turned to Azure's predictive maintenance tools to monitor equipment health more effectively. 

Using IoT and machine learning, they could collect data through Azure Machine Learning and IoT Hub, allowing them to predict potential issues before they resulted in equipment breakdowns. 

This proactive approach helped them avoid costly disruptions. To further support their digital transformation, Komatsu implemented several key strategies:

• Unified Data Management: Komatsu consolidated multiple mainframe applications into a single system with Azure SQL Database Managed Instance, enabling a holistic data view and advanced analytics.
• Advanced Business Intelligence: Implementing TimeXtender Discovery Hub on Azure allowed Komatsu to extract, transform, and load data efficiently, providing employees access to all relevant data for analytics and improved customer service.
• Data-Driven Predictive Maintenance: With Azure SQL Database Managed Instance as a single source of truth, Komatsu can now effectively utilize data to make predictive maintenance recommendations, providing timely updates and improving decision-making across the organization.
• Holistic Analytics Solution: Komatsu Australia built a comprehensive data management and analytics solution using TimeXtender Discovery Hub, underpinned by Azure SQL Database Managed Instance. TimeXtender’s prebuilt adapter for Dynamics AX facilitates easy data extraction, transforming it into tabular models with Azure Analysis Services. The resulting high-quality data is then served to business users via Power BI, providing actionable insights for improved operations.

Impact

Implementing predictive maintenance has enabled Komatsu to minimize unplanned downtimes, reducing them by approximately 30% and ensuring their machinery operates at peak efficiency. Key impacts include:

• Improved Data Accessibility: The move to Azure SQL Database Managed Instance has provided employees with easy access to the latest logistical data, enhancing decision-making and operational agility.
• Operational Cost Reduction: The shift to Azure resulted in a 49% cost reduction and a 25-30% performance gain, leading to significant savings in operational costs.
• Enhanced Predictive Capabilities: Employees can now access trustworthy, consolidated data, enabling more accurate predictive maintenance and reducing emergency service needs.
• Faster Data Processing: Data processing times improved, allowing for more frequent data loads, which enhanced responsiveness to market and customer needs.

“Now we have a single consolidated source of truth that everyone uses, and we can increasingly automate our analysis for a deeper dive into the intricacies of the data. “ - John Steele: General Manager, Business Technology and Systems , Komatsu Australia.

Lessons Learned

Komatsu learned several key lessons from their digital transformation initiatives:

• Unified Data Systems Improve Efficiency: Consolidating data into Azure SQL Database Managed Instance provided a unified data source, enhancing efficiency and accuracy in data analysis.
• Digital Transformation Drives Cost Savings: Moving to Azure resulted in substantial cost savings and improved system performance, emphasizing the value of cloud migration in reducing operational expenses.
• Enhanced Customer Service Through Data: Providing employees easy access to real-time data allowed for more informed and timely customer interactions, improving overall customer satisfaction and service quality.

Komatsu Australia used Azure tools to improve equipment maintenance and ensure reliable operation in tough environments. Similarly, Dow adopted Azure's predictive maintenance with IoT sensors to monitor equipment in real time, reducing breakdowns and enhancing efficiency.

Dow: Aligns to the Industry 4.0 Standards by Scaling Predictive Maintenance with Azure

Dow, a global leader in chemical manufacturing, implemented Azure's predictive maintenance solutions to achieve consistent maintenance standards across its facilities. With operations worldwide and a workforce of 37,800, Dow reported $11.98 billion in revenue in the third quarter of 2024. 

Challenge

Dow, a global materials science leader, needed to aggregate and integrate siloed data to drive decision-making across all levels of its business. 

The company aimed to improve data accessibility for real-time decision-making, reduce manufacturing and operational costs, and enhance workflows. 

Dow's existing data was siloed and challenging to access, necessitating a comprehensive data ingestion and integration solution. By leveraging Microsoft Azure's tools, Dow sought to enable a more connected and efficient approach to predictive maintenance, aligning with their Industry 4.0 strategy.

Solution

Dow implemented Azure's predictive maintenance solutions, including IoT sensors connected to Azure Machine Learning, to monitor equipment conditions in real-time. This allowed them to predict when maintenance was needed, ensuring all processes ran smoothly. Key components included:

• Building a Scalable Data System: Dow aggregated data from multiple sources across engineering, maintenance, and operations into a cloud-hosted platform on Azure. This created a single data hub for analytics capabilities and integrated data access.
• Visualization and Quick Dashboarding: Using Microsoft Power BI gave Dow employees access to real-time analytics, allowing faster decision-making across various levels of the organization.

Impact

With Azure, Dow achieved several measurable improvements:

• Enhanced Equipment Uptime: Real-time monitoring and analytics led to fewer unexpected breakdowns, improving equipment uptime and production efficiency.
• Reduced Operational Costs: Digital workflows, enhanced by Azure, reduced manual and paper-based processes, leading to lower operational costs and streamlined maintenance activities.
• Increased Flexibility and Agility: Improved digital interconnectivity allowed employees to respond more quickly to disruptions, enhancing operational flexibility.
• Improved Collaboration: Mobile and connected field platforms enabled better employee collaboration through real-time voice, video, and data sharing, enhancing workflow efficiency.

“Building on our Azure data, analytics, and application infrastructure, we’re seeing improvements in equipment uptime, production efficiency, and employee collaboration. The power of this solution is apparent across the business, facilitating everything from better logistics to streamlined work schedule, with a bottom-line impact. “ - Clark Dressen: Senior Director of Information Systems, Dow.

Lessons Learned

Dow gained valuable insights from implementing Azure-based predictive maintenance:

• Value of Executive Buy-in: Gaining support from executive leadership was crucial to the success of the digital acceleration initiative, helping drive rapid implementation and scale-up.
• Importance of Strategic Partnerships: Collaborating with partners like Microsoft helped accelerate deployment and provide critical technical expertise.
• Scalable Data Integration: Creating a unified data hub on Azure enhances data accessibility and reliability, supports informed decision-making, and enables consistent maintenance standards across multiple facilities.

Conclusion

The experiences of Tikkurila, Husky, 3M, Komatsu, and Dow highlight the transformative power of predictive maintenance using Microsoft Azure. 

These companies have successfully optimized operations, improved decision-making, and reduced costs through Azure's cloud-based technologies, driving efficiency and sustainability. The examples demonstrate the value of integrating advanced technology with industry expertise, positioning companies to effectively innovate and meet future challenges.

If you're interested in exploring Microsoft Azure for predictive maintenance, schedule a call with our team of manufacturing technology consulting experts.