Dynamic Staffing Using Mobile and Wearable Device Data
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Solution Overview
Problem
Current scheduling methods in call centers fail to dynamically adjust staff resources to match peak service demands and unforeseen changes, leading to inefficiencies and increased costs, as they do not leverage advanced data from mobile and wearable devices for real-time staffing adjustments.
Innovation Solution
A system utilizing mobile and wearable devices to determine staff availability and skillsets, analyzing location and biometric data to dynamically adjust staffing by requesting staff to work during peak periods, ensuring adequate coverage while minimizing unnecessary staff presence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed schedule is assigned to 100% of workers, then staffing coverage is maintained, but staffing flexibility and responsiveness to peak demand deteriorate
Solution Approach 1:
The system transitions from static fixed schedules to dynamic real-time scheduling by continuously monitoring worker location data, biometric data, and service demand to automatically adjust staffing assignments and request intra-period schedule changes based on current conditions
Solution Approach 2:
The system implements closed-loop feedback by monitoring service demand metrics, worker availability status, and schedule adherence, then using this feedback to dynamically adjust staffing requests and communicate schedule changes back to workers in real-time
2Ease of operation
If work schedules are put out to bid with senior staff priority, then staff scheduling preference is improved, but skillset matching with service needs deteriorates
Solution Approach 1:
The system changes the selection parameters from seniority-based priority to real-time parameter matching based on current service demand, required skillsets, and worker capabilities, allowing dynamic optimization of skillset matching while still accommodating worker preferences through the bidding interface
3Loss of energy
If distributed home work model is implemented, then office space costs are reduced, but flexible scheduling capability and peak service coverage deteriorate
Solution Approach 1:
The system enables workers to perform multiple functions by allowing them to work from various locations (home, mobile, or office) while maintaining the same productivity and service coverage through real-time monitoring and dynamic scheduling that adapts to worker location and service demand
4Device complexity
If traditional scheduling methods are used, then system complexity is minimized, but real-time staffing responsiveness to unexpected events deteriorates
Solution Approach 1:
The system enables self-service scheduling where workers autonomously manage their own availability by reporting location and biometric data through their mobile devices, allowing the system to automatically identify and request staffing adjustments without complex centralized management intervention
Data Source
AI summary
Methods and apparatuses are described for efficient resource management using mobile devices and wearable devices. A server determines an event causing unavailability of a center in a distributed network. The server determines an expected demand for service from the distributed network during an upcoming time period and identifies a mobile device and a wearable device associated with each staff member assigned to work at one of the plurality of centers that is available. The server retrieves location data from the mobile device and biometric data from the wearable device. The server identifies staff members capable of working and initiates a communication to the mobile device associated with each staff member capable of working. The server receives a response from the mobile device indicating whether the staff member has accepted and determines whether the expected demand from the distributed network during the upcoming time period is satisfied.