Modern manufacturing challenges demand new tools. Explore how you can achieve IT/OT convergence and advanced analytics with platforms like Snowflake’s Manufacturing Cloud.
The manufacturing industry is experiencing a transformative shift driven by data and artificial intelligence. New technologies promise to unlock the value of advanced analytics and the convergence of Information Technology (IT) and Operational Technology (OT) can unify data to deliver a single source of truth. Now, manufacturers need platforms to help them integrate and scale this data — while controlling costs and ensuring flexibility and scalability.
But what exactly does the right platform look like? And how can manufacturing leaders make sure they’re able to access the tools they need to take advantage of the latest innovations?
The IT/OT integration conundrum
One of the most significant challenges in manufacturing has been the persistent gap between IT and OT systems. As data platforms have grown in capabilities and scale over the last few decades, we’re finally reaching a meaningful point of IT/OT convergence.
Modern data ecosystems can now combine business data from conventional IT systems with operational technology systems that control physical production equipment and processes. The use cases are extensive, with potential to optimize maintenance windows, automate ordering processes and empower decision makers with a single source of truth.
However, the sheer breadth and variety of data coming from OT systems mean these benefits are seldom accessible through a single solutions provider. This means manufacturers should ensure their vendors of choice have a strong partner ecosystem to ingest a broad variety of OT data from shopfloor systems, with integrations that ensure smooth data exchange between digital and physical systems.
Advanced analytics capabilities drive manufacturing excellence
The question remains: what can manufacturers do with all this IT/OT data? Advanced analytics capabilities are helping manufacturers extract meaningful insights from their data to make more informed decisions. The rise of machine learning and AI only opens the door to more analytics opportunities. Machine learning helps manufacturers predict quality deviations during production, and AI and conversational assistants empower teams to manage complex data sets and identify new ways to reduce costs and energy consumption.
However, unlocking this value requires a data ecosystem that provides robust support for advanced analytics, machine learning and data engineering. It also demands flexibility in how developers and data engineers can work with data. After all, a platform that only supports specific large language and data models risks locking you into a narrow way of working that might not support your use cases as they evolve.
Finding the ideal data platform for manufacturing
There are plenty of technology vendors that are ready to support the manufacturing industry. And there are plenty of data platforms that deliver advanced analytics and IT/OT convergence. Manufacturers must find a solution that delivers both and can unify data, enhance operational efficiency and drive innovation. Forward-thinking manufacturers will want to find a data platform that offers:
A single source of truth — even as your organisation grows
The ideal data platform bridges the divide between IT and OT systems to identify upstream risks (forecasting demand shifts and adapting production accordingly) and downstream risks (across the supply chain, including material shortages). It should also support cost management while increasing production quality. For example, a manufacturer of industrial equipment could use live weather data to get a clearer view of delivery dates and potential impacts during extreme weather events.
Crucially, this should also be paired with the ability to scale and adapt to new use cases and operating conditions. That means you’ll want a platform that can handle structured, semi-structured, and unstructured data — including high-volume IoT data from sensors and equipment on the shop floor — while providing comprehensive data integration capabilities. Ideally, this integration should also pair with seamless data sharing and collaboration capabilities so you can work seamlessly with suppliers, partners, and customers while maintaining robust security protocols.
A flexible, cost-effective route to innovation
Another key differentiator to look for is a consumption-based pricing model, so you can scale resources up or down based on actual needs without overprovisioning. And ideally, this would also pair with split compute and storage, so you aren’t paying over the odds for one or the other. These features are essential for operations that may experience seasonal or cyclical demand patterns.
Flexibility is also key. The ideal platform for manufacturers is one that supports diverse skillsets, whether your people prefer working in Python, Java or Scala. This empowers teams to collaborate on the same industrial and enterprise datasets to build AI and ML models with confidence.
Of course, this flexibility should also apply to the way you use Generative AI and LLMs alongside your data. It’s worth finding a platform that reduces the barrier to AI entry, while adhering to your organization’s security and data governance policies. A platform with solid GenAI support can unlock exciting use cases, like summarizing technical documentation and RFPs for faster, more timely responses.
Data-driven manufacturing in action
Snowflake’s Manufacturing Data Cloud is an option that many manufacturers are finding delivers the right balance of flexibility, cost controls, seamless IT/OT integration and support for new innovations like AI and machine learning..
What does this look like in practice? Truck and bus manufacturer Scania uses Snowflake to continuously stream data from 600,000 connected vehicles, supporting machine learning initiatives for vehicle performance monitoring and enabling optimized maintenance schedules based on actual vehicle operation and workshop availability.
This is just one example of a vehicle manufacturer making the most of new data technologies. How could you use new data technologies and AI innovations across your operations?
Dr. Michael Gerstlauer, Manufacturing Field CTO, Snowflake
With experience working with organizations like Siemens, IBM and Amazon Web Services, Dr. Michael Gerstlauer supports manufacturers in leveraging Data and AI to optimize their operations, aftersales and supply chains.
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