Operational Analytics and IoT: Ready for Prime Time

Operational analytics and efficiency has always been a critical success factor for asset-intensive industries such as utilities and manufacturing. The domains of traditional enterprise IT systems and Operational Technology (OT) have historically had little integration and so the potential value generation has been limited. The past decade has witnessed a number of fundamental developments that have broken down the barriers to tighter integration between OT and IT. The explosion of wireless networks, powerful mobile devices, and Big Data technologies have all driven a wide range of enhanced opportunities to analyze operational processes in real-time to improve efficiency and cut costs.

The advent of IoT offers many new opportunities to exploit operational analytics. Sensor data on equipment and machines integrated with enterprise IT applications gives operational analysts and managers new tools and insights to change entire business processes.

The growing adoption of IoT in utilities and manufacturing offers many examples where operational analytics has been taken to a new level. Businesses of all types are finding use cases in even mundane processes that have not traditionally been the focus of operational analytics.

Manufacturing Examples:

The value of combining newly captured IoT sensor data with operational and process knowledge in industrial settings is covered well by Michael Guilfoyle from ARC Advisory Group in this blog. Examples where these principles apply are:

  • Sensors on assembly line equipment provide early warnings in real-time of equipment performance degradation and possible breakdown. Analytics integrated with work implementation and scheduling systems can initiate work processes to do repairs before any problems arise or get worse.
  • Sensors in logistics containers in a complex supply chain indicate out of band environmental and temperature conditions that will spoil sensitive goods. Shippers can act to either prevent the problem or replace the shipment before any negative customer experience or feedback.
  • Sensors across a factory environment unearth correlations and possible causality along a process that includes a range of equipment. Human analysts use the sensor data to test out alternative configurations for the assembly line to drive higher efficiency and lower production costs.

Utilities Examples:

  • Data from smart meters provides input for determining patterns of usage in various geographic zones to help optimize load distribution and generation requirements.
  • Historical data on storm damage and weather is analyzed with upcoming weather and real-time conditions to recommend load distribution, transformer maintenance, and other equipment maintenance and actions to prevent downtime.
  • Running a successful utility depends on continuous operations of a wide array of decentralized equipment spread throughout a wide geographic area. Ensuring all this equipment is running smoothly and avoiding unexpected breakdowns depends on assessing its operational performance in real-time and taking specific actions in advance of potential problems and breakdowns. Sensors in all types of utilities equipment in the field enable service technicians to upgrade repair, and service the equipment in advance of problems to achieve continuous operations.
  • Advances in analytics for utilities as well as customer-owned IoT meters and devices in the home have also begun feeding on the broader utilities ecosystem – in-depth customer involvement with operational analytics gives utilities more latitude to balance supply and demand on their power grids. This in turn improves their operating efficiency, lowers costs, and enhanced customer loyalty and satisfaction. The advanced of IoT and the growth of a decentralized grid are driving customer focused use cases.Olivia Chen does an excellent review in this piece in GreenTech Media focusing on how these intersecting trends are complementing each other, and will change the way utilities manage operations and customer relationships.

General Examples:

In addition to utilities and manufacturing, there are a wide range of businesses that can benefit from the marriage of operational analytics and IoT. Some of the interesting and powerful use cases we have seen involve energy efficiency. In this article in Forbes, Casey Talon from Navigant Research outline how IoT has enabled businesses of all sizes – including small companies – to achieve major gains in energy efficiency for buildings of sizes.

The growing use of sensors for basic operational efficiency is covered well in this article in LEDs Magazine. And the growing use of advanced IoT-based lighting systems is documented in this release from Navigant.

The Importance of Edge Data to Real-Time Operational Analytics

One of the underlying structural trends in real-time operational analytics is the growth of edge processing of IoT sensor data. Our experience is that many IoT use cases require processing quickly at the edge, or the value of the data degrades as more data flows in to change situations and context. The real-time requirements for operational analytics requires balancing Cloud vs. Edge processing. The power and comprehensiveness of central processing must be balanced with the performance advantages of edge processing of operational data for rapid decision-making. Many major industry participants such as Dell are focused on the build-out of this critical piece of infrastructure for operational analytics.

Summary

Operational Analytics and IoT are a natural combination. The maturation of IoT platforms that capture sensor data combined with the increased ability to combine, process, and analyze this data in real-time is creating many new opportunities to improve efficiency and avoid operational issues. These kinds of use cases have always been paramount in industries such as manufacturing and utilities, but they are also now becoming increasingly prevalent in industries of all types. The practice of operational analytics is now in a major growth path, and we expect to see many new and innovative use cases in the year ahead.

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