Found at the core of the Industrial Internet of Things (IIoT) transformation, industrial analytics is the engine that turns machine data into actionable insights, driving intelligent industrial operations and business processes.
Whether applied to discrete manufacturing or process production, an industrial analytics platform can be a solid foundation to build this powerful engine and ease the convergence of operations technology (OT) and information technologies (IT) by adapting requisite information technologies and innovating based on operational requirements.
IIoT seeks to connect machines, equipment, and industrial control systems (ICSs) to enterprise-information systems, business processes, and people. By applying analytics to the large volume of data collected from connected machines, we gain insights into their operations and the ability to use these insights to drive intelligent operations of the machines and business processes. Data, analytics, and applications are key elements in the intelligent lifecycles that turn data into insights, and insights into actions (Figure 1). They are applicable to the control, operations, and business loops. At its core, analytics is the engine that powers each of these intelligent loops and drives value-creation in IIoT.
The value of analytics
Manufacturing equipment in a typical production-industry environment today can be best described as digital-control automation systems-built with microcontrollers (MCU) and programmable logic controllers (PLC). Many of them are connected to supervisory control and data acquisition (SCADA) or distributed control systems (DCS), and are monitored and controlled remotely.
Equipment-operational states are monitored by human operators, who in some cases are aided by simple analytic algorithms such as threshold-based alerts. By and large, most of these systems have not benefited from advanced analytics capabilities developed over the past decade. On the other hand, these industrial-control systems connect to many sensors and have sophisticated data-collection capabilities that provide a wealth of information about their instant operational states. There is substantial value hidden in these data. By connecting to the manufacturing equipment, SCADA and DCS are able to collect data from them and then apply advanced analytics, to gain valuable insights into their operations.
This will enable us to:
By integrating with the enterprise-information systems, operational intelligence from machine-data analytics can be combined with business insights to enhance business processes and planning in supply chain and resource planning, work scheduling, and customer relation management, as well as for engineering design and processes. All these increase productivity and operational efficiency, enhance customer experience, improve worker safety and even facilitate the emergence of new applications, products, and services. They ultimately strengthen competitiveness, create new business value, and potentially bring transformational business outcome.
The use of analytics in the production environment will reduce the reliance and burden on human operators in detecting data patterns and anomalies. Equipped with advanced analytic algorithms and techniques, a solution can monitor and detect patterns in the live streaming data more effectively and often more reliably. This is especially true for complex pattern-recognition requiring correlations of high-dimensional data over long spans of time. These kinds of patterns may not be easily detectable by a human eye.
With the latest machine-learning technologies, analytic models even can improve themselves by learning from their accumulated experience. In fact, analytics can monitor large amounts of equipment around-the-clock with equal effectiveness. Human operators will be informed via alerts only when important patterns are detected, especially those requiring human input or intervention. This would make human operators responsible for mission control and to monitor quality and productivity, freeing them from repetitive tasks.
To meet the needs of the production industries, an industrial-analytics solution should demonstrate a few important capabilities. The first one is to deliver correct results and to "do-no-harm." This requires strong analytics and safeguards in their application. Further, as we just have seen, continuous application of analytics must be possible. However, continuous analysis often requires substantial amounts of data to be transferred from the point of data collection to the point of analysis-the decision point.
Therefore, the analytics solution must support distributed deployment at the edge, whether in IoT gateways next to the equipment, within a server cluster in a facility, or in a remote data center and the Cloud. Different deployment tiers may be required depending on the scope of the data being analyzed. For example, analytics for comparing performance of several factories may be performed better at an enterprise data center. Analytics for local supervisory monitoring may be performed better at the edge, enabling higher reliability, shorter latency, smaller data transfer volumes, and better control over the data.
Another often-overlooked characteristic of an analytics solution is the overall complexity. The analytics solution must be easy to set up, configure, and maintain. Reducing implementation and operating complexity of the system helps to accelerate the success of IIoT by reducing its development cost, risks, and time-to-value.
Industrial analytics platform
An industrial analytics platform, compared to a custom-built solution, can simplify and optimize IIoT deployments, making them effective, reliable and scalable. It can offer the power of machine learning, Big Data, cloud computing and other emergent technologies without having to directly address their complexity and demand for expertise.
To meet the requirements discussed above, an industrial-analytics platform should have the following capabilities:
In the following sections, these key characteristics will be considered in a bit more detail.