DATABERG
Data has long been the precious fuel for manufacturing, driving efficiency improvements, reductions in waste and incremental profit gains. These days, notions like “big data” and “smart data” create new dimensions to the value of research on industrial data bridges.
As a consequence, data is no longer used for reporting past activities only: valid near-real time data allows manufacturers to predict future events and risks, as well as investigate their extended value chain, improving the delivered customer experience. Data now presents the businesses with multidimensional capabilities and broader horizons, guiding them towards numerous exciting ways for manufacturing growth.
Industry 4.0 – industrial data from start to finish
The concept of Industry 4.0 is all about data, or, to be more precise, actionable data. Harnessing actionable data leads to valuable insights, data-driven intelligence and analytics in which also artificial intelligence and cognitive science come into play. The value of such data reaches across the entire value chain and product lifecycle: from ideation, prototyping and development, to maintenance, production and ecosystems of information. It drives innovation, logistics, and a multitude of industrial processes, all the way to disposal and recycling.
So, why does the industrial data bridge present a challenge for the manufacturers?
Bridging Information Technology (IT) and Operational Technology (OT) might feel like learning a new foreign language for both of these domains. It can be challenging for IT and OT to understand each other, which is why your company should focus on this issue.
OT staff are used to various Fieldbus protocols, based on serial communication principles. However, they are often not very familiar with systems in an enterprise environment that use TCP/IP based protocols. These protocols are used to efficiently transfer information over the Internet, store it in data centers and manage the data in a way that enables companies to retrieve extensive information through the visibility of a much larger database - this is the domain of the IT department.
To have this information guide the production process, the path is reversed: the gained insights must be converted to commands that can be used to reprogram the devices on the manufacturing floor. So we are back in the OT domain.
One of the main challenges the manufacturing faces today is to develop a foundation for successful Industrial Internet of Things (IIoT) and Industry 4.0 implementations by enabling connectivity even from legacy devices and making the data available and understandable for people in both of these domains.
Top-4 industrial data challenges and how to overcome them
Industrial IoT (IIoT) is currently struggling with the following obstacles :
Connecting devices between OT and IT
Connecting devices in the field and making the data available in private and public clouds to be utilized by both OT and IT systems. A set of new tools are needed to forward the data acquired in the field on sensor level, translating them so that they are understood by the big number-crunchers up in the cloud. The biggest issue is the lack of standards that can be applied to streamline such access methods.
Protect transferred data
Making sure the acquired and transferred information is protected and is available to the specific user only. This data is often business-critical, and should be shielded from unauthorized access. Creating new data paths opens them up for potential cyber attacks, hence the security of such data channels is of utmost importance, as they can provide pathways to unauthorized access to machines, potentially endangering the complete plant.
Time-Sensitive Networking
The data transfer must happen in a predetermined way and in real-time to enable full control. It is mandatory to ensure data integrity, primarily via the use of Time-Sensitive Networking (TSN). The different TSN standards can be grouped into three basic key component categories: time synchronization, scheduling and traffic shaping. Any critical data also requires provisions for fault tolerance.
TSN will play an essential role in the future, but it still needs to be widely adopted to become effective.
Data silos
In most information management and data environments, existing data silos represent the biggest problem. In Industry 4.0 and industrial data, these silos range from Enterprise Resource Planning (ERP) and industrial control systems to traditional documents, both digital (such as useful old spreadsheets) and traditional paper documents.
Silo issues need to be solved by people who clearly understand the gaps, can formulate a solid strategy, understand the processes and put you on a path of the journey and vision called Industry 4.0.
Conclusion
All in all, manufacturers can harness the power of all of their data. They can find the right balance between constant production upkeep, assurance of just-in-time delivery of goods and high yield management on the one hand, while reducing costs of product quality at the same time.
The priority should be to consider the phenomenon of the so-called “industrial data bridge” seriously and from different angles. When the IT and OT world understand each other and eventually merge, it will be possible to work toward a common goal in a more cost- and time-efficient way. Finally, lots of shared issues, tasks, and requirements will serve as a starting point for a fruitful cooperation between these two domains.
