If IoT (Internet of Things) wasn’t enough to figure out, now there’s IIoT (Industrial Internet of Things). Rather than assume, let’s take a step back and ground ourselves in a few definitions.
According to Wikipedia, the Internet of Things (IoT) “is the inter-networking of physical devices, vehicles, buildings, and other items embedded with electronics, software, sensors, actuators, and network connectivity which enable these objects to collect and exchange data. IoT allows objects to be sensed or controlled remotely across an existing network infrastructure, creating opportunities for more direct integration of the physical world into computer-based systems. Experts estimate IoT sensors will exist within approximately 30 billion objects by 2020.
Current market examples include home automation such as the control of lighting, heating, ventilation, air conditioning, and appliances like washer/dryers, robotic vacuums, air purifiers, ovens, or refrigerators/freezers that use Wi-Fi for remote monitoring.”
IoT is consumer-focused; whereas, IIoT is focused on improving industrial applications for business operations (cost reductions, improved efficiency, proactive maintenance, implementation of green practices, etc.).
IIoT on Wikipedia is defined as “the industrial subset of the IoT. IIoT in Manufacturing has the potential to generate so much business value that it could eventually lead to the fourth industrial revolution – the so-called Industry 4.0.
Connectivity and data acquisition are imperative for IIoT, but these should form the foundation and path to something bigger rather than be the main purpose. Among all the technologies that could fall under IIoT, predictive maintenance is probably a relatively ‘easier win’ since it is applicable to existing assets and management systems.”
As an entry point into IIoT, a major hamburger franchiser has begun their journey with predictive maintenance through the incorporation of sensors into their cooking platforms.
Faced with questions like: “at what point do you stop fixing a machine that constantly breaks and simply replace it?” and, “how does an operable machine effect sales and customer satisfaction?”, the retailer took action by adding sensors into their grill stations. Grill downtime for a fast food retailer directly correlates to lost sales, but to what degree and at what point do these losses justify a replacement? A number of variables factor into addressing these questions: time, frequency, temperature, cost, and historical/OEM data.
The embedded sensors record various grill temperature points throughout the day. By logging this information relative to OEM data, temperature baselines and anomalies (fluctuations outside of the baselines) emerge. A high frequency of anomalies or outliers over a short period of time raises questions around the sustainability of the equipment. Is the anomaly being detected across all temperature points or a single area?
Typically, a single area will point to a coil that may be going bad or perhaps an electrical malfunction, but detecting anomalies across various sensor points typically indicates the need for a unit replacement over a part replacement.
The business understands the difference between the cost of a repair and a replacement. With repairs, the time it takes to repair the equipment, the cost of the part(s), and lost sales are contributing data points. With replacements, the cost of the new grill and the lifespan of the asset are key factors.
Using these repair and replacement data points, the business has the ability to infer operational risk with a certain degree of confidence. Once risk is assessed and the indicators have been fully tested, trialed, and proven, predictive models and automation can be implemented to minimize downtime while leveraging historical service record data.
A high-level example of this would be that Grill #1 sensor data has recorded an increase in temperature fluctuation frequencies over shorter periods of time. Current and historical sensor data reflects the amount of service tickets that have been logged for that specific machine since installation. This automated, sensor data-based process can automatically generate a service ticket ahead of time, eliminating the manual process required for submitting a ticket and thereby minimizing downtime. Grill production data can be monitored through a management dashboard accessible via mobile phone. In addition, a decision hierarchy can be added to allow a manager to review data and log a repair or replace decision.
The new John Deere Connect Mobile App allows growers to monitor job quality in real time to better understand their sprayer or planter detail.
To help growers monitor, adjust, and learn from the performance of their sprayer while it moves through the field, a leading farming manufacturing company introduced IIoT technology and an accompanying app to allow growers to better understand nozzle-by-nozzle and row-by-row sprayer details.
Growers can seamlessly monitor one machine to the next and from one production step to another within this app. Using the app, integrated with machine sensor technology, growers can easily compare data layers from previous production steps with what’s seen when scouting fields.
This technology, combining IIoT and mobile accessibility, allows users to monitor data taken from the fields and make decisions all from the comfort of their tractor cab. This would include access to performance data averaged across the entire field or individual planted sections.
Within these sensor-enabled sprayers, growers can view mapping of critical job quality information that includes as-applied rates, rate deviation, spray pressure, and ground speed, ensuring on-target application, mitigating the risk of product drift and avoiding the need for re-application.
All good things come with a price. When it comes to IIoT and IoT, security, privacy of information, and even physical security are of major concern. In particular, cyber-attacks have the potential to evolve from data and identity crimes to life-threatening assaults (shutting down pacemakers, insulin pumps, and/or implantable cardioverter defibrillators to name a few). Household appliances could spy on people in their own homes. Computer-controlled devices in automobiles such as brakes, engine, locks, hood, and trunk releases have shown vulnerability. This all sounds like things out of a sci-fi movie, but they are real. We must make sure the desire to innovate doesn’t outpace common sense.
Tony Streeter is the Chief Marketing Officer, SVP at Y&L Consulting, Inc. in San Antonio. Tony has led new product development, Ecommerce marketing, and integrated platform marketing initiatives for major corporations such as Harland Clarke, Deluxe Corporation and RR Donnelley. Y&L Consulting is a comprehensive IT services & solutions company specializing in IT Development, Information Management/BI, and IT Helpdesk Services.