Connected Warehouse System for Real-Time Worker Visibility
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current warehouse systems lack real-time worker visibility, making it difficult to track bottlenecks and predict workforce issues, leading to production delays and increased worker attrition due to inadequate tools for reacting to unexpected events and reallocating workers.
Innovation Solution
A connected warehouse system that facilitates real-time data exchange among edge systems, gateway systems, and operations centers, using sensors and IoT devices to collect and analyze data for optimizing worker performance, energy calculations, and detecting performance deviations, enabling dynamic task allocation and corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time data collection and analysis systems are implemented, then worker performance visibility and operational efficiency are improved, but system complexity and implementation cost increase
Solution Approach 1:
The system segments worker performance tracking into discrete task-level units with specific performance parameters. Each task is broken down into measurable components that can be independently monitored and analyzed, allowing complex worker behavior to be understood through aggregated simple data points.
Solution Approach 2:
An intermediary computing system acts as a mediator between workers and management. This system automatically collects data from workers, processes it through analytics engines, and generates actionable insights, eliminating the need for direct complex interactions between management and individual worker performance monitoring.
2Productivity
If real-time monitoring and analytics are deployed, then operational efficiency and bottleneck detection are improved, but data processing requirements and computational resources increase
Solution Approach 1:
Performance parameters and thresholds are pre-configured and established before data collection begins. Analytics rules and bottleneck detection criteria are predetermined, allowing the system to process incoming data through pre-established logic rather than requiring complex real-time computation for every data point.
Solution Approach 2:
The system automatically processes and analyzes its own collected data without requiring external computational resources. The analytics engine self-manages the transformation of raw worker performance data into actionable insights, reducing the need for additional data processing infrastructure.
3Measurement precision
If comprehensive worker performance tracking is implemented, then task completion monitoring and corrective action capabilities are improved, but worker privacy concerns and acceptance issues arise
Solution Approach 1:
The system focuses measurement and monitoring only on specific task-level performance parameters relevant to work output and efficiency. Rather than comprehensive surveillance of all worker activities, it selectively tracks localized performance metrics such as task completion times, quality measures, and bottleneck indicators.
Solution Approach 2:
The system provides feedback primarily to workers themselves about their own performance metrics and task completion status, enabling self-management and self-correction. This self-service feedback approach reduces privacy concerns by empowerr workers with their own data rather than imposing external surveillance.
Data Source
Figure 1
Figure 2
Figure 2
AI summary
Systems and methods are disclosed for operating a warehouse by connecting a gateway device with a data ingestion pipeline, the data ingestion pipeline being in communication with a plurality of worker computing devices and a plurality of sensor devices, the worker computing devices each relating to one or more workers of a plurality of workers; connecting the gateway device with a plurality of process safety suit (PSS) devices in communication with a system integration framework, the PSS devices comprising one or more voice devices, mobility devices, hand-held devices, printers, and/or scanners, the system integration framework comprising a plurality of event manager modules; determining, based on information received from the data ingestion pipeline and the system integration framework, warehouse energy and emission calculations; and determining, based on the warehouse energy and emission calculations, key warehouse performance calculations by aggregating across one or more reporting periods.