Automated Forecasting Data Substitution Using Facility Similarity Indicators
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Solution Overview
Problem
Retail facilities face challenges in forecasting future staffing and inventory needs due to insufficient historical data, leading to inaccurate forecasts when arbitrary substitution of data from similar facilities is used.
Innovation Solution
An automated system that selects substitute historical data for forecasting by generating similarity indicators based on facility attributes and performance metrics, using a modular scoring mechanism to identify the most suitable candidate facility for data substitution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If historical data from similar facilities is substituted for forecasting, then forecasting can be performed without sufficient local historical data, but the accuracy of forecasts deteriorates due to arbitrary substitution
Solution Approach 1:
The patent creates a structured copy of historical data from candidate facilities by evaluating similarity across multiple dimensions (facility attributes, performance metrics, temporal patterns). Instead of arbitrary copying, the system generates similarity indicators to systematically select and adapt historical data from the most similar facilities, thereby maintaining forecast accuracy while enabling forecasting capability.
Solution Approach 2:
The patent transforms the forecasting approach by changing parameters from single-facility local data to multi-facility comparative data. It introduces similarity evaluation parameters across multiple dimensions (facility attributes, performance metrics, temporal patterns) and uses weighted combinations to select the most appropriate historical data, improving forecast accuracy while maintaining productivity.
2Adaptability or versatility
If operator judgment is used to select substitute data, then flexibility in data selection is improved, but reliability deteriorates due to subjectivity and inconsistency
Solution Approach 1:
The patent implements feedback mechanisms by systematically evaluating candidate facilities against multiple criteria and using similarity indicators to guide data selection. The system provides structured feedback on why certain facilities are selected over others, combining algorithmic consistency with the ability to adapt to different facility types and data availability scenarios.
Solution Approach 2:
The patent segments the data selection process into distinct evaluation dimensions (facility attributes, performance metrics, temporal patterns) with separate weighting. This segmentation allows the system to maintain reliability through systematic evaluation while preserving adaptability by adjusting weights and criteria based on specific forecasting needs and data availability.
3Measurement precision
If a comprehensive similarity evaluation system is implemented, then forecast accuracy is improved through systematic data selection, but system complexity increases
Solution Approach 1:
The patent divides the complex similarity evaluation into segmented, manageable components: facility attribute comparison, performance metric analysis, temporal pattern evaluation, and weighted similarity scoring. Each component handles a specific aspect of the evaluation, making the overall system more tractable and maintainable while achieving comprehensive accuracy.
Solution Approach 2:
The patent creates a universal similarity evaluation framework that can handle multiple facility types, data sources, and forecasting scenarios through a common architecture. The modular design with configurable weights and criteria allows the same system to serve diverse forecasting needs without requiring separate complex systems for each case.
Data Source
AI summary
A method includes: storing, for a plurality of facilities, respective facility datasets including (i) facility attributes, and (ii) historical time series of values for a plurality of performance metrics; obtaining an identifier of a target one of the facilities; selecting a set of candidate facilities from the plurality of the facilities; obtaining a similarity evaluation stack configuration; for each candidate facility, generating a similarity indicator based on (i) the respective facility attributes, (ii) the respective historical time series, and (iii) the similarity evaluation stack configuration; selecting, based on the similarity indicators, one of the candidate facilities; and substituting the historical time series of the selected candidate facility for the historical time series of the target facility in a forecasting mechanism.