Anomaly detection in logistics has become imperative in the supply chain industry, especially when gathering and analyzing data generated in real time. When deviations in data occur, it’s crucial to find them, figure out the reason for their occurrence, and then determine the necessary actions to mitigate them in the future.
Supply chain data contains its fair share of anomalies. But luckily, advanced technologies such as artificial intelligence (AI) and machine learning (ML) make anomaly detection in logistics easier. This is a welcome sign of progress, especially in today’s environment where errors in logistic management have the potential to increase.
Common Challenges in Logistics That Are Subject to Error
Shippers are inevitably faced with uncertainty and there are many things that can go wrong. With companies receiving data at such a rapid pace and across so many systems, it creates much noise. This results in a skewed display of information that can slow down or even bring production to a screeching halt.
Another anomaly can come in the form of data corruption in routing systems, such as the absence of the road, outdated maps, and unpredicted ascents or descents—any of which could lead to delivery delays. Consequently, it could cause a decrease in revenue for the company. Still, the inability to find and address such anomalies quickly is tantamount to overcoming and preventing disruptions in your transportation network. After all, it is simply impossible to try to analyze and watch for anomalies manually.
An additional anomaly possibly found in datasets is data missing vital information for logistics, deliveries, and other essential functions for supply chain management. All these potential issues amount to one outcome, errors in shipping execution, ranging from inaccurate labels to incorrectly tendering shipments. Many companies rely on AI and ML technologies to combat these and other supply chain anomalies and reduce risk and costs in day-to-day business. But how?
Automated Anomaly Detection in Logistics Reduces Risk and Costs
Advanced technology processes such as AI and ML help automate data gathering processes in real time while simultaneously filtering out anomalies from said data. Once singled out, machine learning algorithms are employed to go into the anomalous datasets and correct them, notify you, and present clean and accurate information.
That level of automation makes the jobs of data analysts easier because they aren’t spending countless hours sorting out the data to try and find the proverbial needle in a haystack. Subsequently, it becomes easy to distribute this newly curated data to the delivery managers, who can plan accordingly and ensure all deliveries are accurate and on time.
Of course, the most significant value in anomaly detection in logistics comes from fewer delays resulting from errors. Errors in logistics management may evolve from improper labeling of goods through incorrect routing details. Regardless, automated anomaly detection helps shippers identify which problems are occurring, correct them and effectively move more freight closer to customers or end-users. That also has a natural implication for crisis management during tumultuous periods, and by working together with a dedicated account manager, shippers can finally make sense of errors, why they’re happening and drive them into retreat.
Additional Benefits of Anomaly Detection
As mentioned above, that is only the tip of the iceberg for automated anomaly detection. As the supply chain industry utilizes AI and ML to detect supply chain anomalies, analysts continue to find more benefits of this practice. In turn, they provide their companies with accurate logistics which makes the entire supply chain process smoother. Here are a few more benefits of automated anomaly detections.
- Finding the root cause of the error – Along with detecting the anomaly, AI and ML technologies will also find out what caused the error and enable companies to improve their practices.
- Routing and rerouting – Anomaly detection software can find the best routes for quick and accurate deliveries, and it will continue to work in real time to find more efficient paths on the road.
- Eliminating manual corrections on addresses – Since anomaly detection will automatically ide tify address errors, shippers can intervene, and there’s a significantly lower risk of delays, creating better customer experiences along the way.
More benefits will continuously be discovered as companies continue utilizing anomaly detection to manage shipments and deliveries.
Reap the Reward of Automated Anomaly Detection WIth Intelligent Audit
Automated anomaly detection is evolving, and supply chain companies have only scratched the surface. Luckily, companies like Intelligent Audit are making it easier to utilize this emerging process to do more with less, avoid issues, and streamline freight management and shipping execution.. Find out how your team can put this exciting capability to work by contacting Intelligent Audit today.