Artificial Intelligence utilizing true unsupervised machine learning to identify anomalous patterns
Data that has been ingested and normalized paves the way to apply machine learning models for anomaly detection that uncovers bad behaviors, human errors, and data glitches in a TMS, WMS, OMS, or another system. Armed with this knowledge, you can:
Intelligent Audit’s proprietary machine learning algorithms are an ensemble of auto-encoders, boosted decision trees, and LSTM (long short-term memory) deep learning frameworks, which can give invoice-level results in real-time.
Training the algorithms includes a combination of self-supervised learning and supervised learning, which uses fully labeled datasets and leverages important insights on historical data made by account managers and other company experts.
The input data includes dozens of variables over hundreds of millions of entries and leverages both known KPIs (for example, the amount charged or the total weight) and the more abstract, auto-generated features.
Due to the relative rarity of anomalous events, the algorithms are tuned to deal with heavily imbalanced datasets. The end result is a suite of detection algorithms with high sensitivity and specificity in the face of enormous amounts of data.
The final step to understanding machine learning is bringing actionable intelligence into the loop so the recipient understands what the data is saying and what actions to consider next as a result. In other words, anomalous events are presented with contextualized information to our customers to explain why something anomalous happened, and what they can do to mitigate any further exposure.
Once you clearly understand that something has changed, you can begin to assess whether it truly is a problem and determine the impact it is having on your business. The visibility Intelligent Audit provides makes this possible.
The problem could be as simple as a misplaced comma in data entry, transforming a 10-pound shipment into 1,000 pounds.
Most shippers have metrics in place to monitor the standard Key Performance Indicators (KPIs) - spend, volumes, cost/shipment, cost/kg, cost/mile, etc. For a shipper spending millions on transportation annually, it is unlikely for an anomalous pattern to occur that rises to the degree of changing any of those KPIs at a macro level. When they do figure it out, it's after they have spent irrevocable money that could have been an easy fix had they been made aware of it quickly.
This is where true unsupervised machine learning is impactful. Artificial intelligence is able to detect the slightest new pattern in your transportation—data across any type of KPI—and makes you aware quickly before you end up spending millions of dollars on a technical glitch or human error.
Here are seven ways machine learning has worked in real time for our customers in the real world.
As supply chains become more complex and nuanced, it’s crucial to have a powerful tool that uses true unsupervised machine learning to uncover instances where changes are happening that will impact your transportation spend or your end customer experience. Intelligent Audit’s proprietary machine learning algorithms can do what no person or team of people could ever accomplish at a significant scale and speed. Backed by our team of experts who can explain what the data is showing you paves the way for a more efficient, cost-effective supply chain.