Order Routing System Optimizing Resource Utilization
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Solution Overview
Problem
In distributed order processing systems, resources at physical locations often remain underutilized due to high demand at certain locations, leading to customer wait times and inefficiencies, as existing systems do not effectively direct orders to less busy locations based on real-time QOS metrics.
Innovation Solution
A system and method that utilize Quality of Service (QOS) statistics to recommend less busy locations for order processing, taking into account proximity, time of day, wait times, and requestor ratings, and dispatch orders to optimize resource usage by pre-processing at remote locations and completing at the recommended interaction location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If orders are processed at the requested location, then customer satisfaction is improved, but resource utilization deteriorates due to uneven distribution of workload
Solution Approach 1:
The system performs preliminary actions by pre-processing orders at the requested location before the customer arrives. This allows the order to be prepared in advance at the preferred location, reducing actual wait time while enabling the system to redistribute subsequent processing steps to less busy locations, thus improving both customer satisfaction and resource utilization.
Solution Approach 2:
The system introduces a new dimension of spatial distribution by recommending alternative locations to customers. Instead of solely processing orders at the requested location, the system considers geographic distribution across multiple locations, using QOS metrics to identify and recommend less busy locations, thereby balancing workload while maintaining service quality.
2Reliability
If customers are served at their requested location, then service quality is improved, but wait time increases due to queues at busy locations
Solution Approach 1:
Orders are pre-processed at the requested location before customer arrival, with preliminary actions completed in advance. This reduces the actual service time at the location, maintaining service quality while minimizing wait time for the customer.
Solution Approach 2:
The system introduces an intermediary recommendation mechanism that mediates between customer preference and actual service delivery. By recommending alternative locations with better QOS metrics, the system reduces wait time while maintaining service quality through the intermediary step of location suggestion and customer acceptance.
3Ease of operation
If all orders are processed at requested locations, then customer convenience is improved, but system efficiency deteriorates due to underutilization of resources at other locations
Solution Approach 1:
The system applies partial redistribution of orders to alternative locations rather than completely changing all order destinations. By partially redirecting orders to less busy locations based on QOS metrics, the system improves resource utilization while maintaining customer convenience for the majority of orders that can still be served at requested locations.
Solution Approach 2:
The system uses feedback from QOS metrics about location busyness and resource utilization to dynamically adjust order routing recommendations. This feedback mechanism enables the system to improve efficiency by directing orders to locations with available capacity while monitoring and responding to changing conditions in real-time.
Data Source
AI summary
There is provided a system and method to direct processing of orders to improve processing resource usage. QOS statistics relative to wait times and/or processing times or other measures of busyness may be maintained relative to physical office or branch locations where customers attend to have orders processed. QOS measures may be defined relative to these statistical measures of busyness that predict future busyness at respective locations. The QOS measures may be provided to a customer, such as in association with an order ahead application, to recommend to the customer to attend at a location that is less busy thereby distributing processing requests (orders) to less busy locations and better utilize resources.