Retail Process Shaping via Video Action Recognition
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Solution Overview
Problem
Existing business process management systems lack real-time information, making them ineffective in managing and improving corporate performance, especially in environments with unpredictable human behavior.
Innovation Solution
A system that captures real-time image and video data to recognize human actions and track entities, integrating this data with IoT, POS, and ERP systems to generate continuous processes, detect anomalies, and implement nudging messages to optimize processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional business process management systems are used, then process management is possible, but real-time information is lacking making the system blind to daily business activities
Solution Approach 1:
The system implements continuous feedback loops by capturing real-time video data of business activities, processing this data to extract current process states, and feeding this information back to update the process model dynamically. This ensures the system always has current information about actual business activities rather than relying on outdated snapshots.
Solution Approach 2:
The patent replaces traditional mechanical process discovery methods (manual observation, periodic audits) with automated computer vision technology. Video cameras and AI algorithms continuously monitor and capture business activities, substituting human effort and periodic sampling with automated real-time detection systems.
2Productivity
If manual process discovery methods are used, then current state processes can be documented, but the processes become outdated quickly due to unpredictable human behavior
Solution Approach 1:
The system maintains continuous operation by constantly capturing video data and continuously processing this data to update process models. Rather than periodic documentation efforts, the system operates without interruption, ensuring process information is always current and never becomes obsolete due to time delays.
Solution Approach 2:
The system enables processes to document themselves automatically through video capture and AI analysis. Business activities are self-recorded by the monitoring system without requiring manual intervention from process participants, eliminating the time loss associated with manual process documentation while maintaining accuracy.
3Measurement precision
If comprehensive video monitoring is implemented, then real-time action recognition is possible, but system complexity increases
Solution Approach 1:
The system uses a universal video monitoring infrastructure that serves multiple functions: capturing business activities, tracking entities, recognizing actions, and detecting anomalies. This multi-functional approach consolidates what would otherwise require separate specialized systems into a single integrated platform, managing complexity while maintaining high measurement precision.
Solution Approach 2:
The patent introduces an AI-based video processing component as an intermediary between raw video data and business process insights. This intermediary layer handles the complex tasks of action recognition and entity tracking, shielding the rest of the system from dealing with raw video processing complexity while delivering precise action recognition results.
Data Source
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AI summary
A system for process shaping in a retail store environment comprises a video generation and processing component, a data source integration and aggregation component for aggregating and integrate information received from various sources, a process sensing component for generating one or more continuous processes, a process aggregator and weighing component for aggregating the one or more continuous processes into a merged weighted process, a proof of problem and value component for determining one or more process variations, a ripple effect analyser for sending one or more nudging messages to the retail store environment, and a gamified feedback algorithm component for communicating a nudging action corresponding to a nudging message, to one or more entities in the retail store environment.