Sales Order Anomaly Detection

Sales order anomaly detection helps you identify sales orders that differ significantly from usual patterns.

It uses data analysis and machine learning, which is a type of technology that learns from data, to detect unusual activity that may indicate fraud, pricing errors, operational issues, or other business problems.

Historical and real-time sales data are analyzed to understand typical sales order behavior. Unexpected patterns, such as unusual increases or decreases in sales, pricing inconsistencies, or unexpected purchasing behavior, are highlighted so that you can investigate them promptly.

The results are available in a Birst report, and you can also use an Infor AI endpoint.

Benefits

Examples of benefits include:

  • Reduced financial losses by helping you identify fraudulent transactions and pricing errors before they have a significant impact.
  • Improved data quality by helping you find and correct inaccuracies in sales data, resulting in more reliable reporting and analysis.
  • Proactive issue resolution by showing potential problems, such as website issues or supply chain disruptions, as they occur.
  • Enhanced business insight by helping you understand usual sales patterns and make more informed business decisions.
  • Better customer experience by enabling you to address technical issues and suspicious activity faster, helping maintain customer trust.

Scenarios

Order value anomaly

You want to know when the total value of a sales order is significantly higher or lower than the customer's typical order value. Early visibility helps you identify pricing issues, missed sales opportunities, or customer entry errors before revenue is affected.

Price anomaly

You want to identify unusual price variations on a sales order. Detecting price deviations helps you find data or configuration issues in M3 and resolve problems before wider business impact occurs.

Revenue loss anomaly

Some customers purchase the same items in a predictable pattern. When purchasing behavior changes, such as when items are no longer ordered or order quantities decrease significantly, you can investigate the cause. Possible reasons include stock shortages, missing items, or declining demand. Early awareness helps you reduce revenue loss and support customers proactively.