Through the use of Artificial Intelligence (AI) and Machine Learning (ML), asset tracking technologies are becoming smarter than ever before. Companies are finding that the incorporation of AI and ML allows them to make better and more informed decisions.
Artificial Intelligence technology is human simulation in a machine. This means that AI does the work of a human without the need for manual operation. AI-based devices take raw data and turn it into useful information; think about virtual assistants like Siri and Alexa that answer questions and perform tasks for the end-user in just a matter of seconds.
Machine Learning is a subfield of AI. ML is the process of technology systems learning and recognizing patterns. These data patterns become more granular over the course of weeks, months, or even years. It uses observational data to gather higher volumes of information. A common example of this includes digital retargeting advertisements. How often have you thought or talked about a product for it to all of a sudden appear in your social media feed? This doesn’t mean your smart devices are always listening to you. Instead, they are pulling data from various sources including search history, patterns in similar searches, and even data based on other smart devices in your proximity to target ads accordingly.
The use of these technologies within consumerism is becoming ubiquitous. But how do AI and ML fit into asset tracking systems? With less manual interaction needed with AI and ML, devices and programs are becoming more automated in the commercial industry as well. The goal of using Artificial Intelligence and Machine Learning is to improve data analysis and decrease the room for human error.
How Are AI and ML Shaping Asset Tracking?
The use of AI and ML in asset tracking systems improves the way humans perform their day-to-day jobs by simplifying and streamlining tasks such as the process of searching for equipment, and so much more. By being able to quickly identify and visualize historical data, AI can use predictive and preventative analytics to enhance workplace processes. AI technology can also help with use cases such as product quality inspections and demand planning within facilities. Using AI in an asset tracking system allows users to make more informed decisions within their UI.
Analysts within supply chains, manufacturing, logistics companies, and more are able to gather and more quickly analyze higher volumes of information about their environments. Instead of just providing the location of assets, AI and ML can provide specific data about those assets by trend analysis. This type of information can include the amount of assets over time, such as inventory trends.
Another example of how these technologies can be used in an asset tracking system is inventory management. When a company needs to purchase more inventory they want to be sure they order the correct amount of items needed. Companies don’t want to over-order and waste money, but they also don’t want to under-order and not have enough stock, resulting in disappointed consumers. Using an asset tracking system with machine learning allows users to look at historical data and see inventory purchase history. You can then base your upcoming purchases on patterns.
AI and ML can be applied to nearly any device or program, and within the coming years, adoption of them in commercial and corporate environments is going to skyrocket.
Searching for a Smarter Asset Tracking System?
Investing in an asset tracking system is a great decision for your company, but investing in an asset tracking system that incorporates Artificial Intelligence and Machine Learning is an even better decision.
The Link Labs IoT software platform allows users to view asset location and condition in real-time while visualizing trends and historical data. Through integrations like Tableau, users have the power to take their data even further. Our team is consistently making upgrades to our software platform to enable users to make more informed and smarter decisions. Reach out to our team to learn more.