This is where standardizing the data format is key to this commonality and where using the Sparkplug B (the Eclipse Tahu open source project) data payload format is ideal. Having solutions using a common, open standard approach is the goal. IIoT or Industry 4.0 solutions are frequently used for a narrow solution, limiting its effectiveness for use across the entire enterprise.
It must work from a sensor, to a device (such as a PLC), to an Edge gateway, and up to the SCADA/MES system on the factory floor. This includes gaining access to the vast brownfield environment of equipment and systems that are currently in place today. The solution must be cost-effective and access to data must be holistically driven across the enterprise for all business needs. It provides the standardization with an ease of implementation that is scalable across the business enterprise. Using the Ignition Platform from Inductive Automation in conjunction with MQTT Modules by Cirrus Link, provides the answer to these Digital Transformation requirements. The SCADA host becomes the only application that has access to the device and its data, making any real Digital Transformation virtually impossible.įor Digital Transformation to be successful we must decouple the data and provide business with tools on platforms mitigating customization and offer an enterprise wide solution architecture. This means that the data producers are tied to one proprietary application, such as the SCADA/DCS host. This is what we call “tightly coupled device”. This makes overall system response slow and deters operations from being able to retrieve other data in the field that business units within the organization may see value in, thus stifling innovation. Lastly with legacy poll/response proprietary protocols, the host asks over and over for the same information that most of the time hasn’t changed by a significant amount or even at all. Also, the business application may require data that OT is not currently polling for, requiring additional investment to gain access to the stranded data. For example, if a range is changed, or a new I/O point added to the PLC. In the diagram above, an operation of data exchange is shown, where any changes in the I/O mapping becomes a logistical challenge. Completing this exchange of data is often called bridging the OT/IT gap. Any other consumers of the data within the Enterprise are constrained by what operations will give them and use complex API’s to extract the data, making the SCADA system into a bad messaging middleware service that it was not originally designed to do. There are hundreds of these complex industrial protocols across various hardware manufacturers each with their esoteric language which creates barriers to the information. This information is then usually manually configured to enter contextual items to each tag for the tag name, engineering range, units and scaling. Data is sent from PLCs or devices in raw values using cryptic register mappings to an MES and or SCADA/DCS host. It has primarily used proprietary poll/response protocols from PLCs and sensors, as seen in the drawing below. Factory automation and telemetry technology has mostly been unchanged for 40 years.
This could be on the Factory Floor or at the Edge of a SCADA solution. Introductionĭigital Transformation starts where the data is produced.
By easing access to information, coupled with being able to use best in class AI applications, companies will be empowered to achieve their goals to change the culture in their business by extracting value from process data previously unavailable. This white paper will describe how global organizations are utilizing MQTT and the Inductive Automation Ignition platform to implement an open standard architecture to achieve these goals. In order to take advantage of these new technologies companies must bridge the OT/IT gap and feed the machine with data in a secure, easily consumable, and cost effective way. This leads to increased revenue, market share, and ultimately increased profit. Digital Transformation will lead to increased performance, increased efficiency, and reduced maintenance-and-down time. Data scientists are now tasked with connecting to the factory floor in order to utilize Big Data Analytics, Machine Learning and Artificial Intelligence. The Industrial Internet of Things (IIoT) has gained attention by progressive leaders and new trends are driving a need for change.