Service Parts Dynamic Pooling for Inventory Optimization
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Solution Overview
Problem
Service parts departments face challenges in efficiently planning and stocking parts across a large network of warehouses, as predicting demand and optimizing inventory placement is complex due to high costs and numerous possible pooling plans, especially for rare parts with low failure probabilities.
Innovation Solution
A system evaluates pooling plans by calculating an efficiency score using a model that considers local demand, transportation costs, and substitution maps, allowing for strategic placement of parts in fewer warehouses and expedited shipments from these locations to unplanned ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If parts are stocked at more warehouses, then parts availability is improved, but inventory costs increase
Solution Approach 1:
The patent merges inventory management across multiple warehouses by establishing a centralized pooling plan that consolidates part stocking decisions. Instead of independently managing inventory at each warehouse, the system combines demand forecasts and pooling strategies across the entire network to identify optimal central locations for part storage, thereby reducing total inventory costs while maintaining availability through coordinated distribution.
Solution Approach 2:
The patent introduces a pooling warehouse as an intermediary between centralized inventory and individual customer locations. This intermediary structure allows parts to be stocked at fewer, strategically located warehouses and then rapidly distributed to unplanned locations where failures occur, balancing the trade-off between inventory holding costs and parts availability.
2Quantity of substance
If a pooling plan is created for rare parts, then inventory costs are reduced, but the complexity of planning increases
Solution Approach 1:
The patent applies parameter changes by adjusting the pooling strategy based on part characteristics, particularly failure probability. For rare parts with low failure probabilities, the system implements more aggressive pooling (fewer warehouses, higher consolidation) while for common parts with high failure probabilities, the system maintains more distributed inventory. This parameter-driven approach simplifies planning by using objective failure probability data to automatically determine optimal pooling levels.
Solution Approach 2:
The patent performs preliminary action by pre-calculating pooling plans and efficiency scores before actual failures occur. The system evaluates multiple pooling scenarios in advance using historical failure data and demand forecasts, then locks in optimal decisions that can be implemented when needed, avoiding the complexity of real-time decision-making during actual failures.
3Quantity of substance
If parts are stocked at fewer warehouses, then inventory costs are reduced, but transportation costs may increase
Solution Approach 1:
The patent introduces dynamics by making the pooling strategy adaptable rather than static. The system continuously monitors actual failure locations and adjusts pooling decisions in real-time, allowing the network to dynamically respond to where parts are actually needed. This dynamic approach enables the system to optimize the balance between inventory holding and transportation costs based on actual demand patterns rather than fixed assumptions.
Solution Approach 2:
The patent implements feedback mechanisms where actual failure data and part usage patterns are fed back into the pooling plan evaluation process. The system calculates efficiency scores that compare actual performance against pooling plan predictions, allowing continuous refinement of pooling strategies to minimize the total cost of inventory holding and transportation based on real-world performance.
Data Source
AI summary
A method for use in a computing device, comprising: obtaining a pooling plan, the pooling plan identifying a respective pooling warehouse for at least a first article; receiving a first data set that identifies one or more second articles that can be substituted with the first article; receiving a second data set that identifies: (i) local demand for the first article at the pooling warehouse, (ii) local demand for the first article at one or more unplanned warehouses for the first article, and (iii) local demand for the second articles at one or more unplanned warehouses for the second articles; calculating an efficiency score for the pooling plan by evaluating a model for gauging an efficiency of the pooling plan, the model being evaluated based on the pooling plan, the first data set, and the second data set.