Manufacturers are increasingly adopting digital technologies, and Microsoft plays a key role in this transformation through its cloud solutions. Companies are turning to cloud platforms because they offer more than just better performance—they help reduce costs and give businesses the flexibility to scale operations as needed. Cloud solutions also make it easier to manage large volumes of data and share information across teams and locations. Ultimately, these technologies allow manufacturers to streamline processes, improve decision-making, and focus more on innovation and long-term growth. By using cloud-based tools, they can build systems that support everything from day-to-day operations to more strategic initiatives like research and development.
This article examines how Microsoft’s Azure platform is being applied in real-world manufacturing settings. Frou case studies demonstrate the practical benefits: ASMPT’s improved production accuracy, Siemens’ advancements in predictive maintenance, Tikkurila's updates in using IoT and predictive maintanence and Epiroc’s operational optimizations.
ASM Pacific Technology (ASMPT), a leader in the semiconductor and electronics industries, faced the challenge of ensuring seamless communication across its global operations, particularly in a traditionally cloud-averse sector. By evolving its Factory Chat app into a web-based platform powered by Microsoft Azure, ASMPT improved production accuracy and efficiency and also enhanced collaboration across international teams.
ASM Pacific Technology (ASMPT) is a world market leader and technology pioneer in the semiconductor and electronics industries. Headquartered in Singapore, ASMPT supports manufacturers around the globe in establishing integrated smart factories. With a strong commitment to innovation and quality, the company has solidified its position as a key player in the industry. In 2023, ASMPT reported a revenue of US$1.88 billion, reflecting its significant influence and reach in the global market.
ASMPT faced the challenge of ensuring effective knowledge transfer across its global locations in an industry that is traditionally cloud-averse.
The semiconductor industry, which underpins sectors such as automotive, consumer electronics, data centers, LED lighting, and medical technology, is experiencing continuous growth in demand. To meet this demand, electronics manufacturers must constantly optimize their production processes. However, the ideal infrastructure varies from company to company, with complex production lines that require seamless integration of numerous machines.
Given the complexity, any disruption in machine operation can halt the entire production line, leading to significant economic losses and, in the case of medical devices, potential risks to human lives. To address these challenges, ASMPT initially introduced Factory Chat, a shop-floor communication app based on Microsoft Azure.
However, as the Internet of Things (IoT) and Industrial Internet of Things (IIoT) became more integral to operations, the limitations of the on-premises solution became apparent. The existing infrastructure was insufficient for the scalability and availability needed for modern IoT applications, and there was significant industry resistance to adopting cloud-based solutions due to concerns over data security.
ASMPT responded by evolving Factory Chat into a completely web-based application that leverages Microsoft Azure's capabilities.
Key elements of the solution include:
“By deploying Factory Chat on Azure, we’ve taken an important step toward the cloud and created a solid foundation for the future that we’re already benefiting from now—a kind of springboard for digital transformation.” - Olimpiu-Petru Datcu: Project Lead, ASMPT
The implementation of Microsoft Azure Translator and the evolution of Factory Chat had a positive impact on ASMPT’s operations:
“The more concentrated data we have, the more knowledge we have on performance and optimization. The IoT, and the IIoT in particular, are helping to make processes within factories more efficient.” - Olimpiu-Petru Datcu: Project Lead, ASMPT
“ With the help of Microsoft Azure, ASMPT has been able to design a complete high-performance system: from the application to hosting to authorization management. We’ve established an excellent platform that will allow us to build on that knowledge base going forward and add more applications.” - Michael Rominger: Head of Business Development IIoT, ASMPT
The ASMPT's case study is available on the Microsoft website, and you can access it by clicking this link: https://customers.microsoft.com/en-us/story/1565004142676255055-asmpt-elunic-azure-translator-en
Siemens, a global leader in industrial automation and digitalization, revolutionized its maintenance operations by adopting AI-driven predictive maintenance models through Microsoft Azure. This transformation aimed to standardize AI development processes across the company’s global operations, significantly enhancing efficiency and reducing equipment downtime.
Siemens is a global powerhouse in industrial automation and digitalization. With operations in over 200 countries, Siemens leads in engineering and technology, providing cutting-edge solutions for manufacturing, energy, healthcare, and infrastructure sectors. In 2023, Siemens reported revenues of €84.7 billion, reflecting its significant impact on the global market. The company employed approximately 303,000 people worldwide in 2021, further highlighting its vast operational reach and influence.
Siemens faced the challenge of establishing a standardized process for developing AI models to optimize maintenance processes for industrial equipment across its global operations. AI-driven digital solutions offered tremendous potential to make processes more efficient, from reading metadata in PDF documents to detecting product damage through automated image analysis.
However, the development of these AI solutions was hindered by a lack of standardized tools, code, and processes, leading to inefficiencies.
Previously, when departments had ideas for AI solutions to speed up business processes, they faced challenges in communicating these ideas to data analysts who lacked user-friendly tools to implement them.
There was no common language or reusable components, which meant that each new AI model required starting from scratch, often taking several months to develop. This slow turnaround was a significant obstacle in delivering timely, impactful solutions.
Siemens IT recognized the need for a platform that would provide standardized AI services, enabling faster development and broader participation from domain experts across the company. In response, Siemens launched the AI initiative in 2021, a platform fully based on Microsoft Azure, designed to streamline the development of AI models.
To address this challenge, Siemens implemented Microsoft Azure Machine Learning to develop and deploy advanced predictive maintenance models. Key elements of the solution include:
The implementation of Microsoft Azure Machine Learning had a profound impact on Siemens’ maintenance operations:
“We can now deploy compute clusters in Azure in just a few minutes. That wasn’t possible before.” - Dr. Ioannis Petrakis: IT Principal Key Expert Data Analytics & AI, Siemens IT
The Simens's case study is available on the Microsoft website, and you can access it by clicking this link: https://customers.microsoft.com/en-us/story/1637783244393505156-siemens-azure-machine-learning-en
Tikkurila, a renowned Nordic paint company, faced the challenge of modernizing its production line maintenance to reduce downtime, optimize costs, and improve product quality. By leveraging IoT for predictive maintenance, Tikkurila aimed to enhance operational efficiency, reduce product returns, and minimize warranty claims.
Tikkurila, a prominent Nordic paint company with a rich history since 1862, develops premium surface products and services. Operating in 11 countries with over 2,400 employees, Tikkurila became part of PPG in June 2021, reporting EUR 582 million in revenue in 2020.
Tikkurila faced significant challenges in optimizing production line maintenance. The company needed to minimize production downtime and reduce the rate of product returns and warranty claims.
Traditional maintenance approaches were reactive and insufficient for the demands of modern, high-volume manufacturing. The goal was to implement an IoT-based predictive maintenance solution that could monitor equipment in real-time and predict maintenance needs, preventing costly disruptions and improving the overall production process.
Tikkurila deployed an IoT-based predictive maintenance system that utilized sensor data, including temperature, composition, environmental parameters, vibration, electric current, and voltage readings.
These sensors, previously used only by the production line vendors, were integrated into Tikkurila’s global BI system. Predictive maintenance models based on neural networks, decision trees, and AFT models were employed to forecast equipment failures and optimize maintenance schedules.
The full cloud solution architecture ensured seamless integration with Tikkurila's business data analytics systems, allowing for real-time monitoring and adjustment of manufacturing parameters.
By combining sensor data with predictive algorithms, Tikkurila was able to address equipment maintenance needs before significant disruptions occurred.
The implementation of the IoT predictive maintenance solution resulted in reduced production downtime, minimized product warranty claims, and improved overall product quality.
The solution enabled better maintenance scheduling and workload management, ensuring equipment was maintained proactively rather than reactively. Additionally, Tikkurila was able to optimize manufacturing parameters and identify potential quality issues related to insufficient ingredients, further enhancing the quality of their products.
Interested in Learning More? If you'd like to learn more about our collaboration with Tikkurila, you can check the client story here: https://www.itmagination.com/clients/tikkurila
Epiroc, a leader in mining and infrastructure equipment, sought to enhance its manufacturing and operational processes to meet the industry's growing complexity and demands. By implementing Microsoft Azure's suite of tools, including Azure Machine Learning and Azure Databricks, Epiroc developed a robust, scalable data platform that enabled predictive maintenance, improved product quality, and reduced waste.
Epiroc is a leading productivity partner for the mining, infrastructure, and natural resources industries. Headquartered in Stockholm, Sweden, Epiroc develops and produces innovative equipment, machinery, and tools that improve productivity, safety, and sustainability.
The company operates in more than 150 countries, delivering essential solutions for the modern mining and infrastructure sectors. In 2023, Epiroc reported revenues of over SEK 60 billion, supported by approximately 18,200 passionate employees who collaborate closely with customers worldwide.
Epiroc faced the challenge of optimizing its manufacturing and operational processes to meet the growing demands of its customers in the mining and infrastructure industries.
As equipment and machinery became more advanced, the complexity of operations increased, leading to challenges in maintaining efficiency and minimizing downtime.
Epiroc identified the opportunity to leverage data and machine learning to gain deeper insights into their operations, boost predictive maintenance, and optimize production processes.
However, the company needed a robust and scalable platform to handle the vast amounts of data generated and to develop accurate predictive models that could be deployed across its global operations.
To address this challenge, Epiroc implemented a comprehensive solution using Microsoft Azure Machine Learning, Azure Data Factory, and Azure Databricks. Key aspects of the solution include:
The implementation of Microsoft Azure Machine Learning had a significant impact on Epiroc’s operations:
“Now, with Azure Machine Learning and predictive AI we consistently produce material with better quality.” - Peter Malmberg: Vice President Digitalization
The Epiroc's case study is available on the Microsoft website, and you can access it by clicking this link: https://customers.microsoft.com/en-us/story/1653030140221000726-epiroc-manufacturing-azure-machine-learning
At ASMPT, the transition to a cloud-based communication platform using Azure Translator has significantly improved production accuracy and collaboration across global teams. Siemens has transformed its maintenance processes with predictive AI models, reducing downtime and increasing equipment reliability on a global scale. Tikkurila has modernized its production line with IoT-based predictive maintenance, reducing product returns and warranty claims while enhancing product quality and operational efficiency. Epiroc’s integration of Azure Machine Learning and AI has not only optimized their operations but also driven innovation and sustainability within their manufacturing processes.
For more information on how Microsoft Azure's cloud solutions can reshape your business, contact us and let’s discuss how we can support you.
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