Indoor Positioning System for Dynamic Staffing Adjustments
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Solution Overview
Problem
Organizations often fail to optimally staff their customer service locations based on customer needs, as they do not accurately recognize and respond to the specific times and days when certain customer groups visit, leading to inadequate personnel allocation.
Innovation Solution
A computing platform receives data from an indoor positioning system to identify customer presence and attributes at a financial institution's physical location, allowing it to adjust the personnel-staffing schedule accordingly, such as scheduling specific associates for high-wealth customers, retirement-planning needs, or debt-management counseling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If staffing is based on projected customer needs, then personnel allocation follows general expectations, but actual customer presence and specific service needs are not accurately recognized
Solution Approach 1:
The patent introduces an indoor positioning system as an intermediary component that detects customer presence and transmits location data to the computing platform. This mediator bridges the gap between physical customer presence and digital staffing optimization, enabling accurate recognition of which customers are physically present without requiring complex direct monitoring of all organizational processes
Solution Approach 2:
The patent replaces manual or traditional projection-based staffing assessment with automated electronic detection and processing. The computing platform uses algorithmic analysis of positioning data and customer attributes to automatically determine optimal staffing adjustments, substituting mechanical/manual assessment with electronic computation and data-driven decision-making
2Productivity
If staffing schedules are adjusted based on real-time customer data, then personnel allocation optimizes to match actual needs, but data collection and processing requirements increase
Solution Approach 1:
The patent extracts only the essential data elements needed for staffing optimization from the total customer dataset. The computing platform identifies and processes specific attributes relevant to service needs (such as customer type, service requirements, location) while filtering out unnecessary information, thereby reducing the effective data volume that must be processed while maintaining staffing optimization accuracy
Solution Approach 2:
The patent performs preliminary organization and categorization of customer data attributes before the staffing optimization process. Customer profiles and positioning data are pre-processed and structured in advance, allowing the computing platform to quickly retrieve and analyze only the necessary information when making staffing decisions, thereby reducing real-time data processing requirements
3Adaptability or versatility
If specific associates are scheduled for specific customer types, then service quality improves for targeted customer groups, but scheduling complexity increases
Solution Approach 1:
The patent implements dynamic staffing schedules that automatically adapt to real-time customer presence and service needs. Rather than static pre-defined schedules, the system continuously adjusts associate assignments based on current positioning data and customer attributes, enabling flexible response to changing conditions while the computing platform automates the complexity of coordinating multiple dynamic variables
4Reliability
If the right staff are made available at the right time, then customer service quality improves, but real-time monitoring and adjustment capabilities are required
Solution Approach 1:
The patent implements a feedback loop where the computing platform continuously receives positioning data from the indoor positioning system, analyzes current customer presence and service needs, determines optimal staffing adjustments, and communicates these adjustments to associates. This closed-loop feedback system automatically monitors and adjusts staffing in real-time, ensuring reliable customer service through continuous adaptation rather than one-time scheduling decisions
Data Source
AI summary
A computing platform may receive a plurality of messages comprising data indicating physical presence of a plurality of customers of a financial institution at a physical banking center location of the financial institution from an indoor positioning system located at the physical banking center location of the financial institution. The computing platform may identify one or more attributes of at least a portion of the plurality of customers of the financial institution at the physical banking center location of the financial institution, and one or more adjustments to a personnel-staffing schedule for the physical banking center location of the financial institution based on at least a portion of the data indicating the physical presence of the plurality of customers of the financial institution at the physical banking center location of the financial institution and the one or more attributes of the at least a portion of the plurality of customers.