Collaborative Human-Machine Learning (CHML) ID: 2023-032

A groundbreaking invention integrating machine learning with human judgment to revolutionize demand planning in supply chains.

Technology Overview

Collaborative Human-Machine Learning (CHML) is an innovative technology that combines the systematic processing capabilities of machine learning algorithms with the nuanced sensing and flexibility of human decision-making. Designed to improve demand planning processes within the supply chain, CHML corrects for human biases, addresses errors in sequential judgments, and enhances forecast accuracy by incorporating actual demand outcomes.

Key Advantages

• Integrates algorithmic precision with human insight for superior demand planning
• Corrects human biases and sequential judgment errors
• Improves forecast accuracy by learning from real demand outcomes
• Demonstrated efficacy over traditional machine learning systems in proof of concept

Problems Addressed

• Overcomes limitations of machine learning tools that lack autonomous anomaly recognition
• Addresses the issue of human intervention biases in demand planning
• Enhances overall forecast accuracy and efficiency in supply chain management

Market Applications

• Demand planning and forecasting in supply chains
• Scheduling and routing optimizations
• Potential to extend applications beyond traditional supply chain tasks

Additional Information

Technology ID: 2023-032
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Date Published: 28 March, 2025