7 benefits of Factry Historian for multi-site data management
David Dierickx on , updated
In an era in which efficiency is paramount, industrial enterprises with multiple plants have a lot to gain by switching to a single IIoT platform. Here’s 7 benefits of Factry Historian for multi-site data management.
Learning from large industrial data sets is a top priority for process manufacturers. However, with multiple plants generating piles of data 24/7, many struggle to consolidate these loads of information into a unified view, needed to make data-driven decisions at the highest business level.
Here’s a look at 7 key advantages of our open IIoT platform Factry Historian for data management over multiple production sites.
1. Gain a bird’s-eye view over global operations
Factry Historian integrates data from an unlimited number of data sources across the enterprise, such as production equipment, supply chain management systems, and ERP. With fluent access to data from multiple sites, you can gain a much more comprehensive view on group performance.
Set up a cloud control room
By rolling out our open historian on multiple sites, you can easily set up a control room in the cloud, offering a global view on operations. From this global cockpit, you can get high-level insights on how assets are running, which sites or lines are down, or get notified when KPIs are not met.
To set up a cloud control room, there are two options. You can either send the raw data from each site to a centralised system. Or you could store the most part of the process data on-premises and send only the relevant business data to the cloud. As such, the individual sites then become the edges.
What is the best cloud strategy?
Storing process data in the cloud or between the edges: you could make a case for both. What the best option is depends on what you need to achieve with your production data, the architecture and the needs of your enterprise, and the benefits you get from centralising data compared to the risks involved.
2. Improve enterprise-wide data consistency
In Industry 4.0, manufacturing companies need accurate, complete, and consistent data across plants, processes, and systems.
Yet, many companies that consist of multiple sites experience the exact same problem: due to historical reasons or acquisitions, each site has its own data management platform, integrator, and naming structure. As a result, comparing the business performance of each site becomes a nightmare.
Streamline data quality standards between sites
By plugging in multiple production sites on the same IIoT data collection platform, manufacturing data from each site is collected, processed, and maintained in the same consistent manner. This makes it a lot easier to implement consistent data quality standards across the individual sites.
In theory, you could also push relevant data from traditional historians to a central database in the cloud. In reality, this is often a bad idea, as it comes with additional costs for integration, while having to maintain traditional systems locally. Only in cases with strong validation requirements (e.g. in the pharmaceutical industry) this strategy could be a viable option.
3. Enable lean operations with less costs
There are good reasons why some airlines choose to leverage only one type of aeroplane. They can train pilots and maintenance crew for a single device and reduce costs, and improve their buying position when acquiring new flying machines. With an industrial data management platform, it is no different.
From a cost perspective, there’s simply huge value in working with one central data management platform that can be maintained by a limited team, that every site knows to the bone, and enables streamlined reporting – whether it is on at local, operational level, or at the highest corporate business level.
4. Compare OEE performance of individual sites
Why is one production site producing more goods than others? Which products give us the highest margins? And how does the Overall Equipment Efficiency (OEE) compare between production sites? With accurate and consistent data from each site, advanced reporting is only one click away.
For instance, you can investigate production events on a cross-site level, such as unplanned downtime, materials or energy usage, and calculate OEE for each individual plant, product, production line.
To make this happen, raw process data must first be translated to a data format your BI tool can handle. Two strategies can be applied:
Push the raw production data to a cloud service such as AWS or Azure, and then calculate and analyse process events from there.
Calculate production events on the level of the individual production site and then send them to the cloud, ready for business analysis.
For the latter, we developed an event detection and analysis module for Factry Historian that allows you to configure, detect and analyse any process event, without needing expert knowledge. At the same time, it allows for advanced business analysis by simply importing the events in your BI tool.
The open data management platform for Industry 4.0
5. Cost-effective horizontal scalability
The issue with proprietary historians is that they’re notoriously expensive by charging a ridiculous cost per tag, which makes them difficult to scale without losing an arm and a leg. Let’s say that you want to collect data from a million sensors across 10 sites. You could kiss goodbye to this year’s profits.
The technology you choose is therefore crucial. With proprietary technology, you risk getting squeezed by the vendor when adding new sites later on. With Factry Historian, you can collect data from hundreds of thousands, or even millions of data points across multiple sites, with no artificial restrictions.
While old-gen historians can be quite good at what they do – collecting and storing data – scalability is not their strongest suit. Factry offers the best of two worlds: a robust solution to collect and analyse industrial data, yet one that is accessible and scalable, a lot more affordable, and easy to implement.
6. Better cross-site collaboration and knowledge sharing
Set up a cross-site CI-programme
Because Factry Historian enables you to compare the performance of multiple sites, you can easily identify best practices and areas for improvement, such as downtime, waste, or yield. This can help you standardise group-wide operations, pinpoint improvement potential, and take the necessary steps.
Let’s say plant A, B and C manufacture the same product. Yet, plant A produces it more efficiently than plant B and C, either with less energy, waste or materials. Being able to compare sites and lines objectively, allows you to set up an enterprise-wide CI-programme, learn from it, and measure success.
Improve group-wide collaboration
With an aerial view over global operations, and by providing frictionless access to real-time process data and analytics across sites, best practices and lessons learned can be easily shared between production sites, accelerating problem-solving, and improving the group’s core stability
7. Facilitate the use of technologies such as AI and ML
As a manufacturer, you need a variety of tools and systems, each solving one piece of a complex manufacturing puzzle. Through the radical interoperability of the historian, you can create a seamless flow of data across different platforms and systems, allowing for deeper understanding of your business.
By being able to plug into emerging IT technologies, such as machine learning, artificial intelligence, and predictive maintenance, a world of opportunity opens up. You can identify patterns and trends that might be missed by human analysts, enabling an even more efficient workflow.
In summary, choosing a cross-site data collection solution is a highly strategic choice that could make or break your company’s future. The solution should be extremely robust, horizontally scalable at an affordable cost, and be able to bridge the gap between OT and emerging technologies such as AI and ML.
With Factry Historian, you get a future-proof IIoT platform that enables enterprise-wide data integration, visibility and analysis, can be scaled horizontally in a cost-effective way, and drives data-driven decision-making – whether it is on the shop floor, or at the highest corporate echelons.