Inventory Redistribution via Hierarchical Clustering and ML Forecasting
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Solution Overview
Problem
In industries with extensive parts distribution networks, such as retail and consumer packaged goods, fluctuations in supply and demand lead to excess inventory in some locations and shortages in others, resulting in increased storage costs and lost sales, as existing methods fail to accurately predict and match product levels across locations.
Innovation Solution
A machine learning model is applied to product inventory data to generate a redistribution plan, prioritizing product transfers within and between clusters of repositories based on historical data, inventory trends, and priority values, optimizing the flow of products to balance supply and demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If companies attempt to predict supply and demand to match products with each location, then product availability should improve, but some locations are still left with excess inventory while others run out of products
Solution Approach 1:
The system performs preliminary actions by predicting future supply and demand conditions before making redistribution decisions. It uses historical data and machine learning models to forecast inventory needs at different locations, enabling proactive redistribution planning that prevents both excess inventory and stockouts before they occur.
Solution Approach 2:
The system implements continuous feedback loops by monitoring actual inventory levels, sales data, and demand patterns at multiple locations. This feedback is fed back into the machine learning models to continuously refine predictions and adjust redistribution strategies, improving the balance between supply and demand over time.
2Reliability
If excess inventory is maintained at locations to prevent stockouts, then product availability improves, but storage costs increase
Solution Approach 1:
The system applies local quality by optimizing inventory levels specifically for each location based on its unique demand characteristics, rather than maintaining uniform excess inventory across all locations. Machine learning models analyze local sales patterns, seasonal variations, and demand volatility to determine the precise inventory needs of each individual location.
Solution Approach 2:
The system performs preliminary redistribution actions by predicting which locations will experience demand increases before they occur. This allows excess inventory to be moved proactively from locations with lower-than-expected demand to locations with higher-than-expected demand, preventing the need to maintain excess inventory at all locations as a safety buffer.
3Quantity of substance
If product redistribution is performed frequently to balance inventory, then inventory balance improves, but operational complexity increases
Solution Approach 1:
The system applies partial action by performing redistribution only when and where it is truly needed, rather than implementing frequent comprehensive redistributions across the entire network. Machine learning models identify specific locations and time periods where redistribution will have the greatest impact, allowing the system to achieve inventory balance with fewer, more targeted interventions.
Solution Approach 2:
The system leverages parameter changes by using machine learning models to dynamically adjust redistribution parameters such as timing, quantity, and destination based on changing demand conditions. This allows the system to respond to inventory imbalances efficiently without requiring complex manual intervention or frequent scheduled redistributions.
Data Source
AI summary
Techniques for generating recommendations for redistributing product inventories are disclosed. A product redistribution system applies hierarchal prioritization of product locations to generate a plan for redistributing products among the product locations. A system identified product repositories organized into clusters. A product redistribution plan first redistributes products among repositories within a cluster. Then the system redistributes products between different clusters. A system predicts whether repositories have excess products or product shortages by applying product excess windows and product shortage windows to inventory data. The system predicts supply and demand of a product over a period of time including the product supply window and the product shortage window. The system generates the product redistribution plan based on the predicted product excess predictions and product shortage predictions.