Dynamic Machinery-as-a-Service Contract Management via Virtual Twins
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Solution Overview
Problem
Managing machinery-as-a-service (MaaS) contracts in a multi-stakeholder environment is complex due to diverse requirements and a lack of trust between stakeholders. Existing digital contract management systems struggle to dynamically adjust contract terms, provide comprehensive insights, and integrate data from various sources.
Innovation Solution
A computer-implemented method and system that utilizes mathematical models and virtual twins to simulate machinery performance, evaluate Key Performance Indicators (KPIs), and generate dynamic business models. This system receives requirements from stakeholders, orchestrates mathematical models, evaluates multiple usage scenarios, monitors operational data, and compares it against defined KPIs to assess performance and adapt contract terms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional manual contract management is used, then contract terms remain static and simple to manage, but the system cannot dynamically adjust to changing requirements and lacks precision in performance assessment
Solution Approach 1:
The patent implements dynamic contract management by enabling real-time adjustment of contract terms based on monitored machinery performance data. The system continuously updates contract conditions, pricing models, and service levels according to actual KPI measurements, transforming static contracts into adaptive agreements that respond to changing operational realities.
Solution Approach 2:
The system establishes a closed-loop feedback mechanism where machinery performance data is continuously collected, analyzed, and used to automatically adjust contract terms. Performance metrics feed back into the contract management system, triggering automated renegotiations of service levels, pricing, and penalties based on predefined performance thresholds and stakeholder requirements.
2Measurement precision
If historical data and subjective judgments are used for performance assessment, then the assessment process is simple, but precision and objectivity are insufficient
Solution Approach 1:
The system performs preliminary actions by pre-defining performance thresholds, KPI targets, and contract adjustment rules before machinery operation begins. These predetermined parameters are configured based on stakeholder requirements and machinery specifications, enabling automated real-time assessment without requiring extensive post-operation analysis or subjective evaluation.
Solution Approach 2:
The patent replaces manual, subjective performance assessment with automated digital systems that objectively measure and evaluate machinery performance against predefined KPIs. Sensors, IoT devices, and data analytics platforms substitute human judgment with precise, real-time measurements, eliminating bias and improving assessment accuracy.
3Loss of information
If comprehensive data integration from multiple sources is implemented, then holistic performance insights are achieved, but data integration complexity and trust issues increase
Solution Approach 1:
The system segments data integration by creating standardized interfaces and protocols for different data sources (machinery sensors, operational systems, stakeholder inputs). Each data source is integrated through modular connection points that maintain data integrity while simplifying the overall integration architecture. This segmented approach allows comprehensive data collection without creating a monolithic complex system.
Solution Approach 2:
The patent introduces intermediary layers including digital twins, data normalization protocols, and trusted execution environments that mediate between diverse data sources and the contract management system. These intermediaries standardize data formats, validate information integrity, and build trust among stakeholders by providing transparent, auditable data transformation processes.
4Reliability
If mathematical models and virtual twins are used to simulate machinery performance, then future performance trends can be anticipated, but the complexity of modeling increases
Solution Approach 1:
The system creates virtual twins as digital copies of physical machinery, replicating key performance characteristics and behavioral patterns through simplified mathematical models. These virtual representations enable simulation of future performance scenarios without requiring complex full-fidelity modeling of every machinery component, balancing predictive accuracy with model simplicity.
Data Source
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AI summary
Disclosed is a system (200) and method (100) for managing a machinery-as-a-service (MaaS) contract involving multiple stakeholders (302-308), including machinery provider(s) and machinery user(s). The method includes steps of receiving machinery-specific requirements from the stakeholders (302-308), and orchestrating mathematical models (316a-316n) of the product, which models its properties and behaviours. A plurality of derived mathematical models (318) is generated based on these specific parameters received from stakeholders. Multiple usage scenarios of the product are then evaluated, simulating the performance of the machinery across the derived mathematical models. The method involves defining a set of Key Performance Indicators (KPIs) based on evaluations of the scenarios, monitoring operational data related to machinery usage, and comparing this data against the KPIs to assess performance of the machinery. The method further involves generating business model(s) for the machinery, based on the performance assessment.