ML Parts Provisioning for Hardware Maintenance
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Solution Overview
Problem
Current methods for provisioning replacement parts for hardware maintenance service calls often result in inefficient part allocation, leading to either over-provisioning with additional costs or under-provisioning, which can cause delays and penalties, as they rely on remote agent experience and user-reported symptoms without a data-driven approach.
Innovation Solution
A machine learning-based system processes historical maintenance tickets to generate symptoms vectors and train a decision model that predicts the probability of part usage, enabling the generation of optimized parts provisioning plans for new service calls by analyzing symptoms and usage records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If parts are provisioned based on remote agent experience and user-reported symptoms, then service calls can be responded to, but part allocation becomes inefficient leading to over-provisioning costs or under-provisioning delays
Solution Approach 1:
The patent replaces the manual, experience-based decision-making system with an automated machine learning system. The ML model processes historical maintenance ticket data, symptoms, and parts usage records to automatically predict part usage probabilities, eliminating reliance on remote agent experience and enabling data-driven provisioning decisions that reduce both costs and delays
Solution Approach 2:
The system implements feedback by continuously processing historical maintenance ticket outcomes and parts usage data to train and improve the machine learning model. The model learns from past provisioning decisions and actual part usage patterns, adjusting its predictions to improve accuracy over time and reduce provisioning errors
2Loss of time
If more parts are provisioned to ensure availability, then service delays are reduced, but provisioning costs increase due to over-provisioning
Solution Approach 1:
The system changes the provisioning approach from binary (provision or not) to probabilistic (predicted probability of usage). By calculating specific probability thresholds and adjusting provisioning decisions based on these parameters, the system optimizes the balance between having enough parts to avoid delays and not over-provisioning to control costs
3Loss of energy
If fewer parts are provisioned to reduce costs, then over-provisioning expenses decrease, but service delays and penalties increase due to under-provisioning
Solution Approach 1:
The system applies partial provisioning based on predicted probability thresholds. Instead of always provisioning all possible parts or never provisioning, the ML model identifies the optimal subset of parts to provision for each specific maintenance ticket based on historical patterns, achieving cost efficiency while maintaining sufficient parts availability to meet service level agreements
Data Source
AI summary
The methods, systems, and computer program products described herein provide optimized provisioning of replacement parts for service calls through the use of machine learning. In some aspects, historical hardware maintenance tickets may be processed to generate symptoms vectors identifying sets of symptoms associated with the hardware maintenance tickets. The symptoms vectors and corresponding parts usage records of the historical hardware maintenance tickets may be used train a decision model to predict a probability that a particular part will be used to fulfill the new hardware maintenance ticket. The predicted probability may be used by the system when generating a parts provisioning plan for the new hardware maintenance ticket.