Warehouse Operations Dashboard for Real-Time Bottleneck Prediction
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Solution Overview
Problem
Current warehouse systems lack real-time visibility into regional productivity, making it difficult to track and predict production degradations and bottlenecks, and there is a need for a system to optimize employee operations using real-time and historical data.
Innovation Solution
A connected and automated warehouse system that facilitates communication among employees, managers, and data centers through edge and gateway systems, utilizing edge devices and gateway systems to collect, analyze, and transmit data in real-time, enabling dynamic data exchange and optimization of processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time data collection and analysis systems are implemented across multiple warehouses, then productivity monitoring and bottleneck identification improve, but device complexity and data processing requirements increase
Solution Approach 1:
The system divides the monitoring infrastructure into distributed edge devices at each warehouse location, which independently collect and pre-process data locally. This segmentation allows each warehouse to maintain its own monitoring capabilities without requiring a centralized complex system, thus improving productivity monitoring while managing device complexity through modular deployment
Solution Approach 2:
The patent introduces a cloud-based data processing platform that acts as an intermediary between edge devices and user interfaces. This intermediary handles the complex tasks of data aggregation, analysis, and visualization, allowing edge devices to remain relatively simple while still achieving comprehensive productivity monitoring across multiple warehouses through the mediating cloud infrastructure
2Loss of information
If real-time data transmission from multiple edge devices is implemented, then operational visibility improves, but data processing time and computational resources increase
Solution Approach 1:
Edge devices perform preliminary data processing and filtering before transmission, pre-aggregating metrics and identifying only relevant changes in operational status. This preliminary action reduces the volume of data requiring further processing and ensures that only meaningful information is transmitted, thereby improving operational visibility while minimizing data processing time and computational resource requirements
Solution Approach 2:
The system implements continuous data streaming with incremental updates rather than periodic batch processing. Edge devices continuously transmit data as it becomes available, and the cloud platform processes incoming data in real-time streams. This continuous action maintains up-to-date operational visibility without requiring periodic processing cycles, reducing overall data processing time while maintaining comprehensive visibility
Data Source
AI summary
A method for monitoring performance of one or more a warehouses, including steps to receive a request to generate a dashboard visualization associated with a portfolio of assets, the request comprising: a location of the portfolio of assets; and at least one KPI descriptor; and in response to the request: obtain, based on the location of the portfolio of assets and the KPI descriptor, aggregated data associated with the portfolio of assets; determine metrics associated with the portfolio of as2sets for the aggregated data; and provide the dashboard visualization to an electronic interface of a computing device, the dashboard visualization comprising the metrics associated with the portfolio of assets, and optimize one or more process conditions for one or more assets associated with the portfolio of assets based on the aggregated data.