Queue Optimization Model for Retail Resource Allocation
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Solution Overview
Problem
Retailers face inefficiencies in optimizing product assortment, in-store product location, and out-of-stock situations due to error-prone and time-consuming methods that fail to consider interdependencies and changing customer behaviors, leading to suboptimal resource allocation and customer satisfaction.
Innovation Solution
A method involving a queue optimization model that allocates operational resources by applying control signals to determine operational effects, setting target values, and optimizing resource allocation across operational components like out-of-stock, assortment, and in-store product location, using a continuous adaptive clinical trial approach to experimentally determine causal relationships and adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional error-prone and time-consuming methods are used to evaluate product assortment and location, then teams can focus on individual issues based on isolated beliefs, but resource allocation becomes inefficient and interdependencies between issues are overlooked
Solution Approach 1:
The patent combines multiple operational components (product assortment, location, replenishment, temporary displays) into a unified queue optimization model that evaluates them collectively. This merging allows the system to consider interdependencies between issues and allocate resources efficiently across all components simultaneously, rather than evaluating each issue in isolation as done in traditional approaches.
Solution Approach 2:
The queue optimization model serves multiple functions: it evaluates product assortment, determines optimal locations, schedules replenishment, and plans temporary displays all within a single unified framework. This multi-functionality enables the system to address various retail operational challenges simultaneously while considering their interrelationships, improving overall resource allocation efficiency.
2Adaptability or versatility
If static evaluation methods are used for operational decisions, then implementation is simple, but the system cannot adapt to changing customer behaviors and market conditions
Solution Approach 1:
The patent implements a dynamic queue optimization model that continuously adapts to changing conditions by incorporating real-time or near-real-time data on customer behaviors, market trends, and operational performance. The model dynamically adjusts priorities and resource allocation across operational components based on current conditions, enabling the retail system to respond flexibly to changing circumstances rather than relying on static pre-determined plans.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor operational outcomes and customer responses, using this information to adjust and refine the queue optimization model. This feedback loop enables the system to learn from past performance and adapt to changing conditions, improving its ability to make accurate decisions as market and customer behaviors evolve over time.
3Productivity
If comprehensive analysis of interdependencies between operational components is performed, then resource allocation optimizes overall performance, but the analysis becomes computationally intensive and time-consuming
Solution Approach 1:
The patent segments the comprehensive operational analysis into distinct queue optimization models for different operational components (product assortment, location, replenishment, temporary displays). Each segment can be evaluated and optimized independently to some extent, yet the segments are integrated through the unified queue optimization framework that considers their interdependencies. This segmentation reduces computational complexity while maintaining the ability to optimize overall performance.
Solution Approach 2:
The system changes parameters dynamically based on priority levels and resource availability, adjusting the depth and scope of analysis for different operational components. By varying analysis parameters according to current conditions and component priorities, the system achieves comprehensive optimization without requiring equally intensive analysis of all components at all times, thus reducing overall analysis time while maintaining effectiveness.
Data Source
AI summary
A method includes selecting a plurality of operational components to which operational resources are allocated, selecting a plurality of control signals for the plurality of operational components, applying the plurality of control signals to the plurality of operational components to determine an operational effect of the plurality control signals on the operational components, applying a resource requirement for each of the plurality of operational components, determining, using a queue optimization model, a first control signal target value, based on the operational effect of each of the plurality of control signals on each of the plurality of operational components and the resource requirement for each of the plurality of control signals for each of the plurality of operational components, and assigning, based on the first control signal target value, an operational resource allocation to each of the plurality of operational components.