Operational Technology (OT) and Information Technology (IT) have had a long history of isolation from each other in many industrial organizations. However, they have been slowly converging on the scene as companies make advances towards Smart Manufacturing methods. Companies that master this convergence ahead of their peers will have an advantage towards realizing the Smart Manufacturing vision and optimizing their manufacturing operations management practices.
IT, OT, and IT-OT convergence
OT has traditionally been associated with industrial environments and includes the hardware and software systems that control and execute processes on the shop floor including data acquisition, supervisory control systems (SCADA), programmable logic controllers (PLC), and computerized numerical control (CNC) machining systems.
IT has traditionally been associated with the office environment and includes the information systems and communication infrastructure used to run the business functions. IT resources include computers, data storage, networking devices, and processes to create, process, store, secure and exchange all forms of electronic data.
Each one of these technology areas have been traditionally managed by separate departments in many manufacturing companies and staffed by personnel with a different set of skills, training, and career path.
However, Smart Manufacturing has a highly connected vision for the factory of the future with information flowing in near real-time between production and enterprise systems to achieve highly orchestrated business, physical and digital processes within plants, factories and across the entire value chain. [1] For this future to become a reality, the convergence of IT and OT systems is a must-have requirement, even with the technical, cultural and security challenges that comes with it.
IT-OT convergence is the end state sought by manufacturing organizations whereas instead of separation between IT and OT as different technical areas of authority and responsibility, there is an integrated approach to process optimization and information flow between production automation and enterprise information systems.
The evolution of IT-OT convergence
In the last two decades, the distance between OT and IT has become thinner. In part, this can be attributed to the ubiquitous use of internet and wireless connectivity for PCs and IIoT devices at the production floor.
While IT inherently covers communications as a part of its scope, OT has traditionally not been a networked technology, and especially not to the internet. Many industrial devices for monitoring or control didn’t have embedded computing capabilities. Those few devices with computing resources generally used closed proprietary protocols and PLCs rather than technologies that provide control through IT software on IT servers. The production control systems often relied on “air gapping” for security.
However, advances such as machine-to-machine communication, as well as the introduction of IIoT devices fitted to legacy industrial equipment are requiring that OT and IT departments start working closer together.
IIoT devices include a wide assortment of sensors for gathering conditions such as temperature, pressure, vibration, and chemical compositions. IIoT devices also include actuators that translate digital commands and instructions into physical actions, such as controlling valves and moving mechanisms.
IIoT devices can employ wireless communication over standardized networking protocols to communicate the relevant data from each physical system back to IT systems for monitoring and analysis purposes–IT systems that include applications, servers and storage which might be running on-premises or on the cloud. The results of that analysis can then be passed back to the physical system to allow more autonomous operation, enhance accuracy, benefit maintenance, and improve uptime.
Figure 1. IT-OT convergence has been evolving over the last few decades
The diagram in Figure 1 shows a timeline of how OT and IT systems have been evolving independently over the last few decades with a converging trajectory. OT systems like SCADA and PLCs have been adopting support for IT networks, data exchange standards and web interfaces. IT systems have been adopting a more modular approach to apps and support for APIs that help connect IIoT devices, machines and production systems.
Back in the 1970’s, the ISA95 standard (as depicted on the left side of Figure 1) prescribed a four-layer approach to the design of connected production and business systems with PLC, SCADA, MES, MOM, and ERP at different levels. In the last few decades, we have seen that the advent of Smart Manufacturing/IIoT platforms, IIoT devices, APIs, big data analytics and data lakes has challenged the layered approach in favor of a flatter approach with more connectivity options (as depicted on the right side of Figure 1). [2]
Integrated IT-OT systems enable new levels of process integration across production and business functions. For example, an IIoT sensor can collect operational data on a machine at the factory and send it over a wireless network to an IT application that performs predictive maintenance analysis. That application can trigger a maintenance order in the maintenance system to dispatch a mechanic and perform maintenance on the machine to avoid the potential of downtime due to a machine malfunction.
Consider an IIoT-enabled wind turbine as another example of IT-OT systems working together. By itself, a wind turbine would be classified as industrial equipment along with all the equipment and electronics necessary to generate power and connect that power to the grid. However, IIoT sensors can be added to detect wind direction and intensity while communicating its output, condition (temperature, vibration, pressure), and status to a centralized controlling location. The central system analyzes the data, provides commands needed to autonomously configure the wind turbine for optimum performance under current weather, and can trigger workers to take action when unusual conditions are sensed.
Leveraging cloud and edge computing
The addition of edge computing capabilities to IIoT devices enables real-time data processing closer to the source. Instead of sending the data over a network to a centralized location for processing, the IIoT devices can analyze time-sensitive production process data and return insights quickly for direct monitoring of industrial conditions before the data becomes obsolete.
Edge capabilities are important because IIoT and OT devices are often part of a distributed network architecture, making transmission to a central processing location difficult or impossible in some cases. Edge devices can also maintain critical industrial systems running when a connection is down or interrupted, which would otherwise incur costly consequences.
Figure 2. An example leveraging cloud and edge computing for data analytics and storage
Figure 2 depicts an example of how IT and OT systems could be integrated leveraging cloud services and edge computing for enhanced analytical and data storage capabilities. Concerns about connection latency and bandwidth are some of the reasons for deploying workloads to the edge. However, moving processing and storage closer to users and machines can also address concerns about data protection while enabling decentralized autonomous decisions for machine and process adjustments on routine situations and expected changes of conditions. Gartner discusses more edge computing use cases in their “2021 Strategic Roadmap for Edge Computing”. [3]
Figure 3. An example of gradually transitioning in IIoT and big data platforms into the IT-OT landscape
The reality for many industrial organizations is that they have implemented multiple generations of equipment and systems that need to somehow be part of the convergence story. Figure 3 shows an example of how a company can transition gradually to a new architecture by implementing cloud services and an IIoT backbone. In this example, the company first moved their ERP system to the cloud. They also implemented an IIoT backbone in parallel to their SCADA and MES system and started connecting new equipment to it. The IIoT backbone is publishing data to a data lake in the cloud where predictive analytical models are analyzing the data. Similar transition scenarios have been documented by LNS Research in the article “The Holy Grail and the Puzzle in Discrete and Batch Manufacturing Applications”. [4]
Culture and security challenges
IT-OT convergence is not just about technology, it is also an organizational convergence dealing with the structure of the internal business. IT and OT departments must reform their processes to accommodate each other, changes must be well communicated throughout the process, and employees need to be cross-trained. For example, a business might follow specific processes for storing and protecting IT data, but this process might have to be adapted or extended for converging OT systems.
With this new level of integrated systems, workers can be empowered with more insights to help them in their daily jobs. However, workers will need to be educated on new technology, methods, and insights for the organization to fully leverage the benefits of the new capabilities.
As IT reaches more OT systems, air gaps can't provide adequate security for network communication and OT data. Organizations driving IT-OT convergence must educate and train staff to understand and implement adequate security. When implemented properly, IT-OT convergence can merge business processes, insights and controls through secure systems and a uniform governance model.
As an example, Georgia-Pacific implemented several procedural and organizational enhancements when implementing centralized operations data and integrating IT and OT departments including a central collaboration support center to share data and best practices. [5]
Overcoming culture and governance issues with a wider set of stakeholders across the business is not a trivial task and it could make the difference between a smooth or rough ride along the convergence journey. A recommended strategy is to use multi-disciplinary teams to help guide the effort and establish common terminology, drivers and goals among IT and OT teams. Cross-discipline collaboration is key to a successful initiative. [6] Early convergence on security practices will also be essential.
Benefits of IT-OT convergence
The following are some of the benefits of IT-OT convergence over separate IT and OT:
- Improved automation and visibility because integrated OT systems can transmit real-time production data to enterprise systems
- Faster time to implement Smart Manufacturing solutions into an IT-OT integrated environment
- More decentralized autonomous decisions at the edge near the work cell for routine situations and semi-autonomous triggering of alerts for non-routine situations
- Improved IT and OT governance, compliance, and security methods with shared auditing staff
- Effective device management because all IT and OT systems are seen and managed through a common methodology
- Efficient energy and resource usage, as OT systems can be integrated to IT analytics and AI for better data for performance optimization
- Readiness to integrate production data directly into the supply chain, both up and downstream from the manufacturing plant. Intelligent, automated processes based on supply chain and logistics integration can help minimize inventory and fine tune time-to-market delivery
Successful IT-OT integration is an essential step in the journey towards creating a fully connected, dynamic and flexible Smart Manufacturing enterprise. It’s not a small step to take, but without it, the ability of a manufacturer to compete and participate in a highly connected manufacturing ecosystem will be limited. The manufacturers that make early progress on IT-OT convergence will gain significant operational and market advantage.
References
[1] A Refined Smart Manufacturing Definition for 2021, C. Leiva, AutomationWorld, 2021
[2] The Impact of the Internet of Things on MOM Solutions, A. Hughes, LNS Research, 2016
[3] 2021 Strategic Roadmap for Edge Computing, B. Gill, Gartner, 2020
https://www.equinix.com/resources/analyst-reports/edge-computing-strategies-gartner-2021
[4] The “Holy Grail” and the “Puzzle” in Discrete and Batch Manufacturing Applications, T. Comstock, LNS Research, 2021
[5] Georgia-Pacific’s Approach to IT/OT Convergence, L. Rodriguez, AutomationWorld, 2020
[6] Improving Production – How IT, OT and Quality Can Collaborate, W. Goetz/R. Rossbach, Pharmaceutical Technology Europe, 2018
https://www.pharmtech.com/view/improving-production-how-it-ot-and-quality-can-collaborate
Comments