One of our partners, Mariner, implements Internet of Things (IoT) solutions for their manufacturing and distribution customers. Data from devices and sensors is collected, aggregated, and enhanced using the Microsoft Azure suite of technologies and passed to a SMARTS decision service.
In this post, we’ll explore how Mariner uses the SMARTS decision management platform, to make real-time decisions based on sensor data.
Customer Example
Let’s look at an actual customer story. ABT Power Management is an innovative power management company based in North Carolina. ABT has a joint product/service offering called GuaranteedPOWER® where they use an IoT solution (developed by Mariner) to proactively manage and maintain the batteries that power lift trucks and other material handling vehicles. It turns out that if the batteries aren’t properly maintained, over time they can actually cost more then the trucks they power.
As the high-level architecture diagram illustrates, sensor data from batteries and chargers is collected and enhanced prior to being passed to a SMARTS decision service. The SMARTS decision service alerts ABT to dispatch field engineers to the site when equipment maintenance or repair is needed.
The knowledge on how to properly maintain the batteries and chargers is based on the manufacturers’ recommendations and the expertise of ABT’s field engineers. The engineers have a deep understanding of the technology and years of real-world experience. Mariner worked with the engineers to translate their knowledge into concrete SMARTS decisions that define how to proactively manage and service the batteries and chargers. These decisions are made up of business rules that define when to perform specific maintenance services such as charging, watering, and rotating the batteries at a customer’s site.
Capturing the Knowledge
Let’s look at a simple example. One condition the sensors monitor is the water level in the batteries. When the water level is low, the battery needs water. You could write a simple rule to capture this:
IF battery water level is low
THEN create an alert to water the battery
But in the real world, this rule is not quite as simple as it seems at first glance! Battery sensors are very sensitive instruments and when a battery is being physically moved it could register a low water condition. So the rules need to take this knowledge into account and perhaps detect if the battery water level registers low for two or more consecutive days. Also the rules need to consider additional factors in deciding whether or not to water the battery. For example, how many other maintenance actions are required at a site? How much time does it take and how much does it cost to dispatch a field engineer to the site? And, can the maintenance visit be scheduled so that it coincides with maintenance and repair required by other customers who are in the same geographical area?
All of this knowledge is captured in the decision making rules that analyze the sensor data. In addition, predictive analytics is used to detect patterns in the data that could lead to future equipment failures. Preventative maintenance is scheduled to prevent these failures.
Mariner’s business analysts captured and tested these rules and decisions in the SMARTS Analyst Workbench.
Mariner also used SMARTS to run simulations using historical data in order to ensure that the decisions (and resulting alerts) were consistent with the recommended actions specified by ABT’s field engineers.
Deploying and Continuously Improving
Once tested and validated, the rules were deployed to the SMARTS decision service, where they make proactive maintenance and servicing decisions in real time. Over time, new rules have been added and existing rules have been improved and refined so that more conditions can be automatically detected and acted upon.
In summary, SMARTS Decision Manager provides an ideal platform for automating and deploying IoT real-time decisions. SMARTS helps organizations make sense of vast amounts of sensor data and translate it into concrete actions.
Learn more about Decision Management and Sparkling Logic’s SMARTS™ Data-Powered Decision Manager