Authenticated Component Tracking for Dynamic Maintenance Incentives
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Solution Overview
Problem
Conventional systems fail to provide personalized and customized maintenance recommendations for equipment, leading to the use of counterfeit parts, increased downtime, and safety risks, as they lack visibility into the usage of authenticated components and do not incentivize customers to purchase genuine parts or services from OEMs.
Innovation Solution
A computing system that detects and tracks authenticated components and services using unique encrypted codes, generates asset scores based on usage data, predicts maintenance needs, and offers dynamic incentives to encourage the use of genuine parts, thereby improving equipment maintenance and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional systems offer extended warranty programs and maintenance contracts, then after-sales revenue is generated, but the system cannot track usage of authenticated components and enforce their use
Solution Approach 1:
The system implements tracking mechanisms that monitor usage of authenticated components and provide feedback to both the OEM and customer. This feedback loop enables the system to verify component usage, generate personalized recommendations, and enforce warranty conditions dynamically, resolving the contradiction between generating revenue and maintaining visibility.
Solution Approach 2:
The system enables self-service through automated tracking and monitoring of authenticated components. The tracking infrastructure automatically captures usage data without manual intervention, and the system autonomously generates maintenance recommendations and enforces warranty conditions, eliminating the need for manual tracking while maintaining comprehensive visibility.
2Quantity of substance
If customers use counterfeit parts to reduce operational costs, then operational costs decrease, but equipment downtime increases and safety risks arise
Solution Approach 1:
The system dynamically adjusts warranty conditions and incentives based on real-time tracking of component usage. By making warranty benefits conditional on using authenticated components and dynamically updating recommendations based on actual usage data, the system creates flexible economic incentives that adapt to customer behavior, making genuine parts more attractive without rigid fixed terms.
Solution Approach 2:
The system changes key parameters of the warranty program dynamically - including warranty status, incentive levels, and recommendation priorities - based on tracked usage data. This allows the system to respond to customer behavior patterns and adjust parameters to maintain reliability while considering operational cost concerns.
3Reliability
If conventional systems provide fixed-term manufacturer's warranty, then customers have protection during warranty period, but OEMs lose aftermarket sales after warranty expiration
Solution Approach 1:
The system performs preliminary actions by tracking component usage and building customer profiles during the warranty period. This accumulated data enables the system to proactively generate personalized maintenance recommendations and offer targeted incentives before warranty expiration, transitioning customers to aftermarket services seamlessly rather than losing them to competitors.
Solution Approach 2:
The system maintains continuous useful action by extending the value proposition beyond warranty expiration. Through ongoing tracking, personalized recommendations, and dynamic incentives, the system keeps engaging customers throughout the equipment lifecycle, transforming the discontinuous warranty model into a continuous value delivery system that sustains aftermarket sales.
Data Source
AI summary
A system and method to provide data-driven dynamic recommendations for equipment maintenance lifecycles is disclosed. The method includes detecting one or more authenticated components and one or more authenticated services of utility equipment and obtaining usage data, utility parameters, events, and timing of the events. Further, the method includes generating a weight profile and an asset score associated with the utility equipment. Furthermore, the method includes predicting a rate of variation of the asset score by using a variation prediction-based AI model, determining a health condition of the utility equipment, updating dynamic incentives, generating notifications corresponding to the dynamic incentives, and outputting the notifications and the rate of variation on a user interface screen of electronic devices associated with the users.