Root Cause Analysis
Last updated
Last updated
This category investigates the underlying causes of performance issues, helping teams make data-driven decisions by identifying what drives trends and anomalies.
Hint: Drill-down analysis typically follows anomaly detection. Once an anomaly or outlier is identified, drill-down techniques help determine its root cause.
Purpose: Lumi AI allows users to dig deeper into specific causes behind patterns using historical chats. Users first identify performance issues or inefficiencies and analyze the contributing data points.
Use Case: A supply chain analyst can examine declines in purchase quantities, breaking the issue down by warehouse, region, or supplier to uncover the root causes of stock discrepancies. Leveraging historical data and past inquiries, teams can address recurring challenges and drive continuous improvements.
Typical Prompts:
Isolate Issues: "Identify the top 5 suppliers with the highest number of late deliveries over the past 6 months. Include relevant details like average delay time and regions affected." Follow-up : "What is the number of late deliveries for XYZ supplier over the last 12 months?."
Isolate Issues: "Identify the products with the highest rate of out of stocks in Q2 2023. Include details such as warehouse locations and supplier names."
Follow-up: "Show me the historical out of stocks rates for these ABC product over the past 12 months, broken down by warehouse."
Isolate Issue: What items have experienced the largest decline in gross profit when comparing the past 3 months to the previous 3 months.
Follow Up: For item LR-E0059, what is the gross profit for every month in the last 12 months along with the moving average?
Isolate Issue: Can you show me total sales revenues for every day in May?
Follow Up: Investigate the reasons behind the negative revenues on May 10 and May 24.
