In a world of escalating costs and consistent supply chain disruptions, businesses, particularly in shipping, are under mounting pressure. Creativity is called upon as companies look for novel ways to cut costs and eliminate inefficiencies. Emerging as a beacon in this challenging landscape is machine learning, particularly its application in anomaly detection. Offering a new layer of actionable intelligence, it promises transformative potential to those willing to harness its power.

Machine learning, a unique technology within the wider realm of artificial intelligence, enables software to learn based on continuous data influx. Anomaly detection, a subset of machine learning, interprets voluminous data streams into meaningful patterns, unveiling deviations or anomalies from predicted norms. This “smart” technology promises to redefine our understanding of anomalies, putting them in context and suggesting next steps to mitigate further exposure.

From health imaging to banking fraud detection, machine learning anomaly detection finds diverse applications. However, its utility in the supply chain industry cannot be overstated. It's a tool that breathes life into data, transforming abstract numbers into lucid visuals, providing business owners with actionable insights.

Picture a sophisticated logistics intelligence tool tirelessly sifting through your company's historical and current data, identifying anomalies and recommending strategic improvements. It's like an ever-vigilant sentinel, perpetually observing, learning, and evolving. Take, for example, a global retail shipper who embarked on an e-commerce initiative for a recently procured business line. Unbeknownst to them, the packaging for a few of their SKUs fell within the large package and additional handling dimensions, leading to unexpected costs.

The e-commerce launch, initially viewed as a potential revenue stream, suddenly became a financial drain due to these unforeseen charges. Machine learning takes it a step further by pinpointing exactly what the issue is and how it impacts the business financially, generating precise data for corrective action. Anomaly detection in this scenario revealed that the newly initiated shipments were getting burdened with hefty large-package surcharges and additional handling fees. This surge in fees caused the cost per shipment to skyrocket by over 250%.

Having identified the problem, the shipper promptly adjusted their shipping settings for the affected SKUs. They offered customers the choice to pick up in-store or pay an additional, transparent shipping charge if they chose parcel carrier delivery. With machine learning and AI identifying the source of the excessive costs and aiding in quick resolution, the shipper was able to save over $1.5M annually! Now that's the power of smart technology at work.

One of the defining features of machine learning is its ability to perpetually evolve, refining its algorithms for higher accuracy. This constant evolution allows machine learning anomaly detection to surpass its “personal best,” optimizing speed and detail in data analysis. With this tool, business owners can be armed with valuable insights for strategic decisions like freight network optimization.

In the era of supply chain disruption, anomaly detection is a trusted ally. It dissects seemingly overwhelming problems into manageable chunks, isolating exceptions, and presenting specific issues for resolution, rather than a list of vague guidance.

By integrating machine learning anomaly detection into the supply chain systems, shippers can maximize the value derived from their data. The tool can aggregate data from all systems, analyze it in real-time, and identify helpful or detrimental patterns. The benefits of real-time data analysis are manifold. Not only can it provide a rapid response to deviations, but it also enables early detection, prevention, and resolution of potential disruptions.

Furthermore, machine learning anomaly detection brings clarity amidst the chaos. It generates clear, concise reports and Key Performance Indicators (KPIs) that can be easily understood and acted upon. By defining targets and variant thresholds, shippers can manage exceptions effectively, even in intricate processes.

One cannot underestimate the power of real-time alerts, especially in the fast-paced freight industry. Real-time alerts are a tool for rapid intervention when anomalies occur, ensuring the problem does not spiral out of control. Pair this with an expert who handles reporting, tracking, and correction recommendations, and you have a fail-safe system to maintain operational excellence, even amidst disruptions.

The power of anomaly detection has been proven in several instances. For example, a global retail and e-commerce company had mistakenly shipped all shipments at 5,00 pounds instead of 5.00 pounds due to a typographical error. Real-time anomaly detection identified the error, allowing the company to rectify the issue and save thousands of dollars in unnecessary fees.

Machine learning brings a sea of possibilities to the table. Shippers can better anticipate demand, optimize inventory levels, and gain insights into consumer behavior and preferences. With machine learning, they can navigate the complex carrier landscape effectively, quickly detect anomalies, and ensure accurate invoicing.

In today's dynamic supply chain environment, marked by persistent fluctuations and uncertainties, shippers must adopt both proactive and reactive strategies. By harnessing machine learning, businesses can unlock unprecedented agility and adaptability, enabling them to address these evolving challenges promptly and efficiently.

By leveraging machine learning, shippers can gain valuable insights, optimize processes, and make real-time informed decisions. The adoption of machine learning equips shippers with the tools to achieve operational excellence, improve customer satisfaction, and drive business success.

Hannah Testani is CEO of Intelligent Audit.

This article originally appeared in the September/October, 2023 issue of PARCEL.