Probabilistic Cancellation Module for Dynamic Inventory Thresholds
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Solution Overview
Problem
Retailers face challenges in determining when to offer items for sale online or assign orders to nodes in retail supply networks due to inventory inaccuracies and dynamic store conditions, leading to increased cancellation and re-sourcing costs.
Innovation Solution
A probabilistic cancellation module uses historical and current data, machine learning techniques, and predictive models to set inventory thresholds and rules for offering and fulfillment, determining the probability of cancellation and identifying suitable nodes for order fulfillment, thereby reducing cancellations and re-sourcing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If inventory thresholds are set low to reduce fulfillment costs, then more nodes can be used for fulfillment, but inventory inaccuracies and dynamic store conditions lead to increased cancellation and re-sourcing
Solution Approach 1:
The system dynamically adjusts inventory thresholds based on real-time conditions. The probabilistic cancellation module continuously evaluates current inventory levels, node capacity, and historical data to determine optimal thresholds at different moments, making the threshold static value adaptive to changing store conditions and demand patterns
Solution Approach 2:
The system performs preliminary actions by pre-calculating probabilistic cancellation rates and establishing inventory thresholds before fulfillment decisions are made. The probabilistic cancellation module proactively identifies high-risk items and nodes, allowing the system to prevent cancellations by making informed offering decisions in advance rather than reacting after problems occur
Solution Approach 3:
The system implements feedback mechanisms by using historical fulfillment data, cancellation patterns, and current node status to continuously refine inventory threshold calculations. The probabilistic cancellation module learns from past performance and adjusts thresholds based on actual outcomes, creating a closed-loop system that improves reliability while managing costs
2Reliability
If inventory thresholds are set high to reduce cancellation risk, then order fulfillment reliability improves, but fulfillment costs increase due to restricted node usage
Solution Approach 1:
The system applies different inventory threshold levels to different nodes and items based on their specific characteristics. Rather than using a uniform high threshold across all nodes, the probabilistic cancellation module calculates customized thresholds considering each node's capacity, historical performance, and item-specific demand patterns, allowing high reliability where needed while enabling cost-effective fulfillment elsewhere
Solution Approach 2:
The system changes the parameter of inventory thresholds from fixed high values to dynamically adjusted values based on probabilistic cancellation rates. By modifying the threshold parameter according to real-time conditions and historical data, the system achieves high reliability when necessary while reducing thresholds to optimize costs when risk is low
3Reliability
If probabilistic cancellation modeling is implemented to optimize offering decisions, then cancellation risk is reduced, but computational resources and system complexity increase
Solution Approach 1:
The probabilistic cancellation module operates autonomously, automatically collecting data, calculating cancellation probabilities, and determining inventory thresholds without requiring complex manual configuration or intervention. The system self-adjusts parameters based on historical data and current conditions, reducing the operational complexity despite the sophisticated modeling underlying the calculations
Data Source
AI summary
A method and system determining an inventory threshold for offering for online sale or an inventory threshold for sourcing in node order assignment. The method includes receiving by a computer processor of a probabilistic cancellation module an electronic record of a current order or item. The program instructions executed by the processor of the probabilistic cancellation module allows the module to retrieve historical and current data of each node from a plurality of nodes. The method then includes automatically converting the retrieved historical data into a probability of cancellation of an item comprising the one or more items from the plurality of items. Further, the method includes identifying an inventory threshold for offering of an item or an inventory threshold for sourcing of one or more items of the current order, where the probability of item cancellation is lower than a predetermined order cancelation rate of the one or more items from the plurality of items.