When you hear about AI/ML your thoughts most likely go to the uses that have been grabbing headlines of late. You may think of writers using ChatGPT to write something (not this article) or asking DALL-E 2 to create an image, or Netflix using ML to determine what movies to suggest to you.
What you may not hear as much about are the practical uses of Machine Learning to make equipment more useful and easier to maintain. These cases go beyond the trivial use of ML to make a creative act quicker and enable manufacturers the opportunity to solve some real problems with this new technology. These applications feature a tight integration of hardware, electronics, and software and require thoughtful system engineering to make sure that these different modes of development work closely together.
At Source Allies, we’ve helped manufacturing and agricultural companies apply system engineering approaches to help organizations use Machine Learning to increase the value they can provide with their concrete, real world products.
What is Machine Learning
Before sharing some examples of how software can make hardware more valuable, we should probably explain what we mean when we say machine learning and systems engineering. Machine learning is a less glitzy, but quite useful, subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed. It provides machines the ability to perform complex activities such as analyzing images and detecting objects.
These activities come in handy when you want a machine that does a repeated activity that should only be done in certain circumstances. You can now give the machine the ability to determine whether it should do something, such as spray herbicide on a weed, and when it shouldn’t. When designed and configured appropriately, machine learning removes the need for a person to oversee the repeated actions of a machine. This way, those actions are more predictable and the person involved can focus on activities that are not so easily turned over to machine learning.
What is Systems Engineering
According to NASA, systems engineering is a “methodical, multi-disciplinary approach for the design, realization, technical management, operations, and retirement of a system”. A system is the combination of elements (hardware, software, equipment, facilities, personnel, processes, and procedures) that function together to produce a particular outcome. For NASA, a system could be the New Horizons spacecraft. Back here on earth, systems could be a tractor, an agricultural sprayer, valves, or lighting.
One of the keys to successfully designing and delivering a system is making sure that all its elements work together in harmony. That’s where systems engineering comes into play. People most familiar with developing software would love to take the same iterative, incremental approach to developing an entire system. Unfortunately, the laws of physics can get in the way when electronic circuits and metal casings can’t be changed as quickly - or cheaply - as software can.
As a result, if you’re working on a complete system you need to be more intentional in how you design and build your solution. You need an interdisciplinary approach to product development that includes people skilled in all the different technologies involved in the system. You use systems engineering to translate the overall requirements to the requirements specific to each subsystem. You also build traceability between the overall system requirements and the subsystem requirements. From that point, the experts in each subsystem follow the appropriate design and development approach for their technology, all while continuing to coordinate from an overall systems perspective.
Source Allies has contributed several times to these types of efforts, both from an overall systems engineering perspective as well as on the software aspects of applying machine learning.
Solving real problems in the real world
Mobile development to improve global farming
For farmers, planting seed can be a complex process entailing planning and forecasting. Many agriculture companies invest in technology with hopes of improving overall annual yield. One of our Fortune 500 agriscience partners looked to Source Allies to help them improve the way farmers accessed critical planting data in real-time.
The original process from seed sale to planting and an evaluation of the crucial data was clunky. Farmers could not get access to planting recommendations with enough time to make modifications and have a real impact on overall yield.
We built a mobile application, Sync Service, that uses a small hardware component that allows farmers to wirelessly instruct their tractors how to plant our partners’ seed products, so they can achieve the highest yield.
From Foundation to IoT Innovation
We helped one of our large agriculture manufacturing clients develop and enhance their data-driven approach to monitor and improve its farming equipment’s quality and efficiency. We sourced machine data from displays, receivers, and a MTG system to track hours of operation, distinguishing between idle, working, and transport hours.
We incorporated acreage data to shed light on defect locations and causes. We also captured and ranked “cell strength” data on each machine to create a global map that showed signal strength levels.
This groundbreaking approach not only helps pinpoint areas with poor data reporting, such as Australia, but also reveals surprising gaps in supposedly well-covered regions like Wisconsin and Washington. Armed with this insight, our client strategized alternative methods to track machines, and ensure continued participation in software testing and data reporting.
Predicting when lights should be on or off
In-office work has grown more inconsistent due to remote work. We helped one of our clients, a lighting manufacturer, develop a model to determine when lights should be on or off depending on facility usage at a given time and in a given room.
We developed a system to look at the prior day’s events to determine when lights should be on or off in a particular room. We used that data to make predictions which are relayed back to the lights each hour necessary to implement the prediction of turning the lights off. A result of our work is a 20% reduction in energy costs.
Anomaly Detection with IOT Data
One of our trucking clients used odometer data to create preventative maintenance plans. If that data is incorrect, it can cause trucks to miss needed maintenance, leading to lost time and money. Conversely, trucks may get maintenance before they should, leading to unnecessary spending.
We worked with our client to build a solution that showcases how ML can be used to bring awareness to these abnormal odometer readings, allowing our partner to make decisions quicker in order to get preventative maintenance plans back on track.
Our team established a system to detect incorrect odometer data. The system relays those data points to subject matter experts who update the incorrect data, which leads to quicker correction of preventative maintenance plans. The result is hours saved and less unnecessary maintenance on trucks resulting in a cost savings of hundreds of thousands of dollars.
How Source Allies can help
There is a lot of hype about all the things you can do with AI. Many of the common mainstream examples of using AI yield questionable value providing solutions that may be in search of a problem. At the same time, many companies that operate in the physical world are quietly applying machine learning to solve real world problems. If you’re one of those companies, Source Allies can help you engineer your system to achieve your vision. Reach out to find out how.