Anomaly Detection

Anomaly detection can be broken down into two categories.

Aspect	Outlier Detection	Exception Reporting
Focus	Identifies statistically anomalous data points	Flags data points that violate predefined business rules
Purpose	Find unusual or unexpected data based on distribution	Ensure adherence to expected operational or performance benchmarks
Methodology	Statistical Methods	Rule-based (thresholds, benchmarks)

Outlier Detection

Identifying extreme data points to detect operational errors, irregularities, or data points that deviate significantly from the majority of the dataset.

Purpose: Identifying these outliers helps organizations understand and action on situations where typically there is a larger more impactful opportunity to reduce costs and increase revenue.

Use Case: Supply chain manager might use Lumi AI to identify unusually high lead times from specific suppliers, which could indicate bottlenecks or quality issues. By identifying these outliers, businesses can take action to prevent larger disruptions and optimize their workflows.

Typical Prompts:

1. "Identify sales orders from the last 12 months that deviate more than 1.5 times the interquartile range. Include relevant details."

2. "Show me any suppliers with delivery times that deviate more than 30% from the average in 2024."

3. "Find inventory items where the stock level is more than 20% above average."

Example Output: What are the items that make up 80% of the total order volume in 2023?

Example Output: What is the days difference count with buckets of 1 day for payment date to actual payment.

Example Output: What are the items in the lowest quartile in terms of gross profit in 2023?

Exception Reporting

Exception reporting refers to identifying data points or events that fail to meet predefined business rules or thresholds. It is more focused on rule-based violations rather than statistical anomalies.

Purpose: Exception reporting highlights events or data points that violate set business rules or fail to meet operational standards. This helps businesses maintain compliance, ensure quality control, and track performance benchmarks.

Use Case: A logistics manager can use exception reporting to flag orders that missed delivery deadlines, helping the team analyze potential delays and improve future performance. Similarly, a quality control manager might use Lumi AI to identify production batches that fall below acceptable quality levels. Exception reporting enables teams to quickly spot deviations from business rules, ensuring operational efficiency and compliance.

Typical Prompts:

1. "Identify all orders from the last 6 months where the shipment was delayed by more than 7 days past the expected delivery date. Include order ID, customer name, and number of days delayed."

2. "What are all products that were sold below the minimum set price over the last quarter."

3. "What are the purchase orders from 2024 where the order quantity exceeds the set limit for any item?"

4. "What are the top 3 warehouses with highest variance between the Actual vs Gaol for Outbound Cases per Hour KPI".

5. What are the top 5 items with the lowest gross profits YTD?

Example Output: What top 5 suppliers with the lowest margins that are below 40%? Output should be: Supplier, Item Count, Avg Margin %