Optimize Inventory Churn by Identifying Root Causes and Optimizing Supply Chain Parameters

Objective: Reduce inventory by identifying causal factors (like supplier reliability and lead time variability) and optimizing inventory levels.

Data Sources:

1. Supplier Data: Lead time, part defect rates, delivery frequency.
2. Production Data: Downtime events, machine utilization rates, assembly line throughput.
3. Inventory Data: Stock levels, turnover rates, holding costs.

Step-by-Step Process

1. Define the Problem and Objectives

• Target Variable: Inventory churn rate.
• RCA Objective: Identify factors contributing to high inventory churn, specifically focusing on supplier lead times and defect rates.
• Optimization Goal: Minimize inventory while maintaining production efficiency.

2. Collect and Prepare Data

• Example Data (see below): Historical records of inventory levels, supplier lead times, defect rates, and production efficiency.

• Data Cleaning: Handle missing data (e.g., by imputation) and normalize lead times and defect rates.
• Feature Engineering: Create additional features such as lead time variance, average weekly defect rate, etc.

3. Identify Causal Relationships

• Hypothesis: Longer lead times and higher defect rates cause increased inventory churn due to uncertainty in supply chain reliability.
• Causal Discovery: Using techniques like the PC Algorithm, identify relationships:
  • Lead time → Inventory Churn Rate
  • Defect Rate → Inventory Churn Rate
  • Production Downtime → Inventory Churn Rate
• Construct a Causal Graph: Using Pathfinder, visualize this:

[Supplier Lead Time] → [Inventory Churn Rate] ← [Defect Rate]
                              ↑
                    [Production Downtime]

4. Perform Root Cause Analysis (RCA)

• Counterfactual Analysis: Calculate the inventory churn rate if lead times were reduced by 20% and defect rates by 50%.
• Interventional Analysis: Simulate scenarios with changes in supplier lead times to measure impact on churn. For example, reducing Supplier A’s lead time from 15 days to 7 days resulted in a 10% decrease in churn.
• Sensitivity Analysis: Test sensitivity by varying lead times and defect rates to understand their influence on inventory churn.

5. Optimize Based on Causal Insights

• Objective Function: Minimize the Inventory Churn Rate, incorporating lead times and defect rates.
• Constraints: Ensure that inventory levels meet production demand without significant overstocking or understocking.
• Optimization: Use Genetic Algorithms to find optimal levels for lead time and defect rates while considering cost constraints.
• Example Optimization Result:
  • Optimal Average Lead Time: 8 days
  • Defect Rate Target: < 2%
  • Result: Reduced inventory churn by 25% while maintaining minimal stockouts.

6. Validate and Test the Model

• Backtesting: Apply the model on historical data to see if the predicted outcomes match actual results.
• Holdout Validation: Split the data into training and test sets, train the model on the training data, and validate on the test set.
• Metrics:
  • RMSE: 0.02 (indicating a good fit)
  • Accuracy: 85% in predicting churn events.

7. Deploy and Monitor the Model

• Deployment: Containerize the model using Docker for scalable deployment across different manufacturing plants.
• Monitoring: Set up real-time dashboards to track supplier lead times, defect rates, and inventory churn. Continuously monitor and adjust based on observed data.

Example Result and Insights

• By implementing these steps, the company achieved a significant reduction in inventory churn by targeting supplier reliability and reducing variability in lead times and defect rates. Continuous monitoring enabled ongoing adjustments and improvements based on real-time data.

This example shows how causal inference and optimization can be applied in practice, using RCA to address root causes and optimize operational factors in automotive manufacturing.