Request Manager Optimizing Facility Assignment via ML
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Enterprises face challenges in efficiently managing customer requests for products, including inconvenient access to physical products, delayed processing, and inefficient resource utilization, leading to poor customer experience and strain on employees.
Innovation Solution
A system that uses a request manager informed by enterprise facility resources and resource utilization to recommend facilities for request fulfillment, employing machine learning to analyze current and historical resource availability and estimate service times, thereby dynamically assigning requests and optimizing resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If enterprises use traditional manual methods to manage customer requests, then employees can handle requests, but service delivery efficiency is low and processing is delayed
Solution Approach 1:
The patent replaces manual mechanical processing of customer requests with an automated computer-based system that uses machine learning models to analyze resource availability and determine optimal facility assignments, thereby eliminating manual delays and improving service delivery efficiency
Solution Approach 2:
The system enables automated self-service by having the computer automatically analyze resource availability, evaluate candidate facilities, and assign requests without requiring manual employee intervention for each request, thus improving efficiency and reducing processing time
2Productivity
If enterprises assign requests without analyzing resource availability, then requests can be processed, but resource utilization is inefficient and employees are strained
Solution Approach 1:
The system implements feedback by continuously monitoring resource availability at various facilities and using this information to dynamically adjust request assignments, ensuring that resources are utilized efficiently and employees are not overburdened with excessive or inappropriate requests
Solution Approach 2:
The patent applies dynamics by making the request assignment system adaptive and flexible, using machine learning models that continuously learn from resource availability data to optimize facility assignments in real-time, thereby improving resource utilization and preventing employee strain
3Ease of operation
If enterprises use simple request assignment methods, then processing is quick, but customer experience is poor due to inconvenient access and delayed service
Solution Approach 1:
The patent segments the request management process into distinct functional components including resource availability analysis, candidate facility identification, machine learning-based evaluation, and automated assignment, allowing the complex system to handle multiple factors systematically while improving customer experience
Solution Approach 2:
The system introduces an intermediary computer-based request management system that acts as a mediator between customer requests and facility resources, automatically analyzing and matching requests to appropriate facilities based on resource availability, thereby improving customer experience without requiring direct complex human-to-human coordination
Data Source
AI summary
Various embodiments are directed to techniques for organizing fulfillment of enterprise products, such as by using a request manager informed by enterprise resources and resource utilization to recommend a facility to fulfill a product request. Some embodiments are directed to identifying a product request and determining the equipment and skills necessary to fulfill the product request. Based on this information and location data, embodiments may determine a set of facilities as candidates to fulfill the request. A machine learning model may be used to analyze current resource utilization of the facilities and predict facility availability and estimated completion times for the request fulfillment. A candidate facility may be recommended for fulfillment of a request based on facility availability and estimated completion times. In some embodiments, historical resource utilization may be used to inform further staffing and equipment service decisions.