Hierarchical Demand Estimation for Remote Equipment Dispatch
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Solution Overview
Problem
Existing methods for estimating consumer demand at remotely located equipment, such as vending machines, are inaccurate due to reliance on product sales rates that do not account for inventory issues or equipment malfunctions, leading to suboptimal inventory management and maintenance scheduling.
Innovation Solution
A system and method using a predictive algorithm to process data hierarchically, considering the reliability and quality of inventory data and equipment operating status to estimate consumer demand, predict future sales and inventory, and optimize dispatch schedules for maintenance and refills, similar to a traditional retail outlet's efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sales rate methods are used to estimate consumer demand, then the estimation process is simple, but the accuracy of demand estimates deteriorates due to not accounting for inventory issues and equipment malfunctions
Solution Approach 1:
The patent segments consumer demand estimation into multiple hierarchical levels (Level 1 through Level 9), where each level represents a different data quality and reliability tier. This segmentation allows the system to process complex multi-source data while maintaining structured organization, resolving the contradiction by breaking down the complex estimation task into manageable segments that collectively improve accuracy without overwhelming system complexity.
Solution Approach 2:
The patent changes the parameters used for demand estimation from simple sales rates to a multi-parameter hierarchy including data reliability scores, inventory status indicators, equipment operational status, and consumer demand signals. This parameter transformation enables more accurate estimates by incorporating previously ignored factors, while the hierarchical structure manages the increased complexity through systematic parameter organization.
2Reliability
If simple inventory measurement methods are used, then the data collection process is straightforward, but the reliability of sales data deteriorates due to equipment malfunctions and inventory issues
Solution Approach 1:
The patent implements feedback mechanisms where equipment operational status and inventory levels continuously inform the demand estimation process. By incorporating real-time feedback from equipment sensors and inventory monitors into the hierarchical data structure, the system compensates for malfunctions and inventory issues, improving data reliability while the automated feedback collection manages the complexity of continuous monitoring.
Solution Approach 2:
The patent performs preliminary classification and validation of data quality before processing sales information. By pre-assessing data reliability and filtering out compromised data points from equipment malfunctions or inventory issues, the system ensures higher reliability in final estimates while the preliminary sorting step organizes the complex data collection task into manageable preprocessing phases.
3Productivity
If traditional dispatch scheduling is used based on simple sales rates, then the scheduling process is efficient, but operational efficiency deteriorates due to suboptimal inventory management and maintenance timing
Solution Approach 1:
The patent performs preliminary prediction of future consumer demand and equipment needs using the hierarchical data analysis before actual inventory depletion or equipment failure occurs. By predicting future states and scheduling maintenance and restocking in advance based on these predictions, the system improves operational efficiency by preventing stockouts and failures, while the advance planning reduces reactive time loss through proactive resource allocation.
Data Source
AI summary
A method and system are provided for estimating consumer demand at remotely located equipment and establishing dispatch schedules for servicing the remotely located equipment. Data from the remotely located equipment may be classified into a hierarchy or various levels of reliability for use in calculating a consumer demand estimate for each product and/or each service available at the remotely located equipment. A full set of sales data for each product or service over multiple time intervals with no equipment problems, no out of stock conditions and no other operating problems may be classified as the highest level of reliability and predictability possible for calculating a consumer demand estimate. The lowest level of data used to calculate a consumer demand estimate may be a historical average of daily sales for all products or services sold over a long period of time at the remotely located equipment.