Service Job Value Prediction for Technician Allocation
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Solution Overview
Problem
Service technicians often arrive at job locations without knowing the value of the service job, leading to inefficiencies and financial losses, as they may dispatch inappropriate technicians and fail to provide accurate quotes.
Innovation Solution
The system employs two trained machine learning models to generate value predictions for service jobs, considering historical data and technician-specific conversion rates, and schedules the most appropriate technician based on these predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If service technicians are dispatched to service jobs without prior value assessment, then technician availability and skill matching can be quickly determined, but the service provider cannot accurately assess job value before deployment leading to financial losses
Solution Approach 1:
The system performs preliminary action by using machine learning models to predict service job values and generate comprehensive value predictions before technicians are dispatched. This allows the service provider to assess job value in advance, determine appropriate technician allocation, and provide accurate quotes to customers while still maintaining efficient deployment speeds.
2Adaptability or versatility
If multiple service technicians are kept available to perform service jobs, then service coverage and skill matching improve, but determining the most appropriate technician becomes complex due to varying factors like distance, other service requests, and schedules
Solution Approach 1:
The system applies parameter changes by incorporating multiple variables into the machine learning models, including technician distance from job site, existing service requests, technician schedules, skill sets, and predicted job values. The models process these parameters to automatically determine the most appropriate technician allocation, simplifying the decision-making process while maintaining high service coverage and adaptability.
3Device complexity
If service job value is determined only after technician arrival, then no complex prediction systems are needed, but the service provider loses revenue opportunities and cannot provide accurate quotes in advance
Solution Approach 1:
The system replaces the traditional mechanical approach of determining job value only after technician arrival with an automated information-processing system. Machine learning models analyze historical data, job characteristics, and technician parameters to predict service job values in advance, enabling accurate quoting and revenue optimization without requiring complex manual assessment processes.
Data Source
AI summary
Aspects of the present disclosure involve systems, methods, computer program products, and the like, for analyzing service job metrics and generating job allocations. Examples may receive resource information associated with a request, and determine a list of resources associated with a tenant based at least in part on the resource information. A first trained machine learning model generates a first value prediction associated with the first service job for each resource in the list of resources. A second trained machine learning model generates a second value prediction associated with a second service job for each resource in the list of resources. For each resource in the list of resources, a comprehensive value prediction is generated based on the first value prediction and the second value prediction. A selected resource of the list of resources undertakes the first service job based on the comprehensive value prediction.