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Innovation

2016 Trends: Industrial Adoption of the Internet of Things

Price, speed, and insight will drive demand to bring more IoT projects online.

by Harshad Khatri

January 2016

Looking at 2016 and beyond, industrial organizations will continue to invest heavily in the Internet of Things (IoT). A 2014 market report predicts the industrial IoT market will grow 27% by 2019. As IoT technology—as well as the supporting analytics—continue to improve, organizations will also increasingly succeed in capturing value from well-managed IoT projects. Companies will derive value mainly in three areas: cost, business insight, and speed.

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Cost Savings

Technology enhancements such as the increased capability and rapid cost reduction of sensors have driven the adoption of IoT. The price of a single-axis accelerometer has gone from about $7 in 2007 to as little as 50 cents today. IoT devices now typically have six to nine sensors that have capabilities in one or more areas, such as light, motion, temperature, humidity, proximity, pressure, and sound.

As sensors have evolved at a rapid pace, so have the technologies that form the backbone of the IoT infrastructure—data networking, storage, and analytics. This has accelerated the development of platform capabilities to connect, secure, and manage large-scale IoT deployments. Additionally, the use of open standards such as Java allows companies to better manage large-scale deployments.

Leading companies are using IoT to define their current operations state, and through continual monitoring, IoT analytics, and ongoing optimization, they are capturing value by improving on this normal state. For example, a leading manufacturer of transportation equipment is in the process of fitting its fleet with sensors that monitor the operating conditions—temperatures, acceleration, loads, utilization and fuel consumption. Based on these readings, they are able to determine the baseline performance and identity equipment that deviates from the norm, as well as root causes for this deviation. Over time, they will incorporate the insights from this data analysis into the design of future equipment.

To capture the value of IoT, companies should keep in mind that IoT data analytics is not big data analytics.”

The key to this trend of increased value capture is the transformation that is happening within industrial companies. As GE Chief Executive Officer Jeff Immelt said last year, “If you went to bed last night as an industrial company, you're going to wake up this morning as a software and analytics company.” The potential IoT-driven value in the industrial sector is unprecedented—it has been estimated that a one-percent reduction in fuel consumption in aviation could result in a savings of US$30 billion annually. The potential benefits from the enormous volume of data that is being generated include cost savings, improved operational efficiencies, revenue generation, and better customer stickiness.

Business Insight

The industrial IoT stages of maturity range from descriptive (identifying what happened) to predictive (identifying what will happen) to prescriptive (translating data into action without human intervention).

This trend is increasingly being observed in corporations high on the maturity continuum. A U.S.-based oil and gas company has been monitoring the performance of oil wells. Initially, the goal is to monitor production. In the near term, the company plans to analyze additional data in real time to support predictive maintenance. This will ideally minimize the downtime of the oil wells and optimize their production levels.

To capture the value of IoT, companies should keep in mind that IoT data analytics is not big data analytics. IoT data is persistently streaming (often in real time), requiring different techniques for its capture, storage, and processing. Additionally, the high data volume requires the rapid setup and management of large, scalable data environments and analytics at scale. To add to this complexity, this data is most often non-standard (images, voice, videos) and semi-structured. Given the high volume, frequency of collection, and redundancy of IoT data streams, it is far more effective and valuable to focus on the deviations of the data points rather than their absolute values.

Increased Speed
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Harshad Khatri, Oracle Insight.

A manufacturer of trucks has sensors built into its vehicles to monitor performance data in real time. The analytics monitor performance variations across the fleet, alert the company of potential failures, and route vehicles to service centers as needed. The IoT system has drastically reduced the amount of time it takes to do all of this. Additionally, the analytics serve as critical input for the design of future vehicles and the pricing of vehicle service contracts. This IoT setup allows them to stay one step ahead of the competition by increasing the speed of service, and continually defining and optimizing their operations.

Industrial companies starting their IoT transformation should begin small. Develop an IoT environment for an operational area, create alerts, and initiate actions when shifts from optimal behavior occur. This becomes the basis of a feedback loop where errors and faults are identified and corrective actions are taken. Over time, companies can create a continuously improving operational environment and drive further enhancements. These improvements will soon lead to real business value.

Photography by Shutterstock