In the supply chain, increasing costs have become crippling. Zach Strickland, a market analyst for FreightWaves, commented that "Contract rates for dry van truckload have increased roughly 25% over the past year, or 59 cents per mile, according to FreightWaves invoice data." Strickland also pointed out that "carrier compliance rates for accepting electronic requests for capacity have only improved to 81.9% from 78.5%, according to FreightWaves tender data — basically simple cost inflation for getting the same product." As rising prices continue to impact every leg of the supply chain, shipping companies need to be creative to cut costs and check for business inefficiencies. Machine learning anomaly detection can be a tremendous value-add service in this regard.
Among the multiple unique technologies located within the sphere of artificial intelligence, machine learning is the facet of AI software that enables it to learn based on incoming information continually. Machine learning anomaly detection is software that receives all inputted data streams and interprets them into patterns to reveal anomalies. An anomaly is any deviation from the predicted norms, particularly sudden changes or errors. A logistics intelligence tool will utilize machine learning anomaly detection that continuously runs to locate past and present deviations to improve business flow.
The very nature of machine learning is to continually learn and evolve into more accurate algorithms. This enables a machine learning anomaly detection to repeatedly beat its "personal records" of speed and detailed searches through company data. A business owner may be aware of the setbacks to poor freight visibility, but anomaly detection might be able to locate precisely who and how it's impacting the company.
In addition, a complete business intelligence tool can take this data and offer suggestions for freight network optimization and other avenues of improvement. During a season where supply chain disruption continues to reign, anomaly detection is especially beneficial in isolating the exceptions, which gives business owners specific issues to address instead of vague guidance.
Machine learning anomaly detection has various uses in different sectors of society, including health imaging, data security, bank application fraud checks, and defect detection. In the supply chain, shippers can maximize the value of anomaly detection's ability to:
In a world that is becoming increasingly costly and increasingly virtual, shippers need to have the correct data and people on their side. Machine learning anomaly detection is just one aspect of Intelligent Audit's tools to ensure shipping companies have streamlined systems with procedures to find and address anomalies. By retroactively and proactively identifying these deviations from a company's goal of more innovative, efficient shipping, companies are more equipped to thrive even in a time of inflation and supply chain disruption. Start a conversation with a logistics expert at Intelligent Audit today to gain insight into your company's inner workings and hidden patterns.
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