Multidimensional Innovation Scoring With Adaptive KPI Weights
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Solution Overview
Problem
Organizations face challenges in measuring innovation velocity due to the lack of a standardized measurement system, leading to siloed teams, redundant efforts, and superficial innovation, with conventional methods failing to provide accurate dynamic recommendations.
Innovation Solution
A method and system for innovation velocity measurement that involves receiving multidimensional data, identifying relevant KPIs with dynamic coefficients, computing raw and scaled innovation scores, and generating insights and recommendations using a self-learning feedback loop.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional statistical methods are used for estimating enterprise innovation, then the measurement process is simple, but the accuracy and reliability of innovation measurement is insufficient
Solution Approach 1:
The patent segments the innovation measurement process into multiple dimensions including idea generation, development, implementation, and impact. Each dimension is measured using specific KPIs, allowing for comprehensive and accurate measurement without requiring a single complex measurement system.
Solution Approach 2:
The patent introduces dynamic coefficients that adjust the weight of different KPIs based on organizational context, industry sector, and innovation type. This allows the measurement system to adapt to different scenarios while maintaining accuracy, resolving the contradiction between precision and complexity.
2Productivity
If siloed teams are used for innovation activities, then each team can focus on narrow innovation goals, but innovation coordination overhead increases and redundant efforts occur
Solution Approach 1:
The patent creates a universal innovation measurement framework that can be applied across all teams and departments. This common framework enables different teams to work independently on their specific innovation goals while being measured against the same standardized metrics, reducing coordination overhead and enabling efficient resource allocation.
3Measurement precision
If dynamic coefficients are updated based on self-learning feedback loop, then the recommendation accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent implements a feedback loop where measurement results are used to update dynamic coefficients for subsequent measurements. This continuous improvement mechanism enhances recommendation accuracy over time while the feedback is systematically processed to manage computational complexity.
Solution Approach 2:
The patent pre-calculates and stores baseline coefficients and normalization rules before the actual measurement process. This preliminary preparation reduces the computational burden during real-time operations, allowing the system to maintain high accuracy without excessive computational complexity during execution.
Data Source
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AI summary
Due to lack of a standardized measurement system, it has become hard for organizations to identify the true reflection of the impact of the innovation. In conventional methods mainly utilize statistical methods for estimating enterprise innovation based on likelihood and are not effective. The present disclosure initially identifies a plurality of potential Key Performance Indicators (KPIs) from multidimensional data. Further, a plurality of relevant KPIs is selected for computing a raw innovation factor and a scaled innovation score is computed for each of the plurality of enterprise accounts by applying a set of pre-defined normalization rules. Further, a plurality of similar enterprise accounts is identified, and a plurality of recommendations are generated. Furthermore, a dynamic coefficient associated with each of the plurality of relevant KPIs are updated. Finally, an innovation score percentile is computed for each of the plurality of enterprise accounts based on an updated dynamic coefficient.