Hybrid Human-Machine Demand Forecasting System
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Solution Overview
Problem
Existing machine learning algorithms for demand planning in retail industries face challenges in accuracy due to the inability to perceive changes and anomalies that human managers can sense, leading to biases in human overrides.
Innovation Solution
A collaborative human-machine learning system that integrates algorithm-based machine learning with human judgment, allowing the system to evaluate and systematically incorporate human judgment capabilities and algorithmic processing to adjust demand planning forecasts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning algorithms are used for demand planning, then processing speed and consistency are improved, but accuracy deteriorates due to inability to perceive changes and anomalies that human managers can sense
Solution Approach 1:
The patent combines machine learning algorithms with human judgment capabilities into a hybrid system. The machine learning component processes data quickly and consistently, while human managers contribute their ability to perceive changes and anomalies. The system integrates both approaches to achieve forecasts that maintain the processing speed of algorithms while incorporating the perceptual accuracy of human managers.
Solution Approach 2:
The patent introduces a hybrid modeling framework that acts as an intermediary between machine learning algorithms and human judgment. This framework allows human managers to sense and interpret changes and anomalies, then translates these perceptions into adjustments that refine the machine learning forecasts, thereby improving accuracy without sacrificing processing speed.
2Measurement precision
If human judgment is used to override machine learning forecasts, then accuracy may improve by sensing changes and anomalies, but biases are introduced due to subjective perception
Solution Approach 1:
The patent implements a feedback mechanism where human judgments are systematically incorporated into the machine learning model. Human managers' perceptions of changes and anomalies provide feedback that refines the algorithm's understanding, allowing the system to learn from human expertise while maintaining consistency through structured integration rather than arbitrary overrides.
Solution Approach 2:
The patent creates a dynamic hybrid system where the balance between machine learning and human judgment adjusts based on the situation. The system dynamically determines when human perception adds value and when algorithmic processing suffices, allowing flexibility to capture anomalies while maintaining overall consistency through adaptive integration.
3Measurement precision
If a hybrid human-machine system is implemented, then forecast accuracy is improved by leveraging both strengths, but system complexity increases
Solution Approach 1:
The patent segments the forecasting system into distinct functional components: machine learning algorithms for data processing and pattern recognition, and human judgment for sensing changes and anomalies. This segmentation allows each component to operate independently in its strength area while being integrated through a structured framework, managing complexity through functional decomposition.
Solution Approach 2:
The patent designs a universal hybrid framework that can accommodate different machine learning algorithms and human expertise areas. The integration mechanism is general-purpose, allowing the system to handle various forecasting scenarios without requiring separate complex structures for each case, thereby managing complexity through multi-functionality.
Data Source
AI summary
The present disclosure relates to systems and methods for collaborative human-machine learning for demand planning. The method includes receiving a forecast for the demand planning from a machine, receiving an indication of a particular event using private information from a user, estimating an effect of the particular event, receiving lagged demand, and adjusting the forecast for the demand planning using the estimated effect of the particular event and the lagged demand.