Machine Learning Dispatch Duration Prediction
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Solution Overview
Problem
Existing field service management systems rely on static or heuristic approaches to calculate dispatch duration, often resulting in inaccuracies that lead to operational inefficiencies, increased costs, and customer dissatisfaction due to under or overestimation of service time.
Innovation Solution
A machine learning model, specifically a deep neural network, is trained using historical field service data to predict dispatch duration, considering factors like customer, product, support type, location, and technician expertise, providing a data-driven approach for scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static or heuristic approaches are used to calculate dispatch duration, then the calculation process is simple and fast, but the accuracy of dispatch duration estimation deteriorates
Solution Approach 1:
The patent replaces static or heuristic calculation methods with a machine learning model (deep neural network) that processes historical field service data to predict dispatch duration. This substitution transitions from simple rule-based mechanics to an intelligent system that learns patterns from data, thereby improving accuracy while managing complexity through automated model training and inference.
2Measurement precision
If machine learning model is used to predict dispatch duration, then scheduling accuracy is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent applies preliminary action by training the machine learning model in advance using historical field service data before actual dispatch scheduling occurs. The model learns from past patterns and stores this knowledge, enabling fast and accurate predictions during operational use without requiring complex real-time computations. This pre-processing of information reduces computational burden during actual scheduling operations.
3Productivity
If inaccurate dispatch duration estimates are provided, then scheduling is simpler, but operational efficiency deteriorates due to under or overestimation
Solution Approach 1:
The patent implements feedback by using historical field service data that includes actual dispatch durations and outcomes. The machine learning model learns from this feedback loop, continuously improving its predictions by comparing estimated durations with actual results. This feedback mechanism enables the system to adapt and refine its accuracy over time, thereby improving operational efficiency through progressively better scheduling decisions.
Data Source
AI summary
An example methodology includes, by a computing device, receiving information regarding a field service dispatch from another computing device and determining one or more relevant features from the information regarding the field service dispatch, the one or more relevant features influencing prediction of a dispatch duration. The method also includes, by the computing device, generating, using a machine learning (ML) model, a prediction of a dispatch duration for the field service dispatch based on the determined one or more relevant features, and sending the prediction of the dispatch duration for the field service dispatch to the computing device.