Neural Network Stakeholder Simulation for Design Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The conventional product design process is hindered by miscommunication and inefficiency due to differing opinions and priorities among stakeholders, leading to friction and increased costs in accommodating feedback, as designers typically do not see full stakeholder feedback until the end of the design process.
Innovation Solution
A machine-assisted collaborative product design system that uses neural networks to simulate stakeholder personas, aggregate individual scores, and provide a summary of aggregated feedback, facilitating cross-functional understanding and compromise through machine learning and behavioral science.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If feedback is collected through standard communication means (in-person meetings and email) until the end of the design process, then all stakeholder feedback is eventually gathered, but the design process becomes time-consuming and costly to redesign
Solution Approach 1:
The system performs preliminary actions by simulating stakeholder feedback and providing predictions about stakeholder reactions before the actual design process completes. This allows designers to anticipate and address stakeholder concerns early, avoiding costly redesigns later while ensuring all stakeholder perspectives are considered.
2Manufacturing precision
If designers prioritize innovation and aesthetics, then product design quality is improved, but stakeholder alignment deteriorates due to conflicting priorities
Solution Approach 1:
The system implements feedback by providing designers with predicted stakeholder reactions and feedback on their design choices. This allows designers to understand how their innovation-focused decisions will be received by stakeholders with different priorities, enabling them to adjust designs to maintain both quality and stakeholder alignment.
Solution Approach 2:
The AI system acts as an intermediary between designers and stakeholders, translating stakeholder priorities and concerns into actionable feedback. This mediator helps bridge the gap between designer priorities (innovation/aesthetics) and stakeholder priorities (feasibility, market appeal), facilitating better alignment without compromising design quality.
3Loss of information
If multiple stakeholder perspectives are considered, then comprehensive understanding is improved, but communication complexity increases
Solution Approach 1:
The system provides self-service by automatically gathering, analyzing, and synthesizing feedback from multiple stakeholder perspectives. Instead of requiring complex manual coordination among stakeholders, the AI system independently manages the complexity of multi-perspective analysis and presents integrated insights to designers, reducing communication overhead while maintaining comprehensive understanding.
Data Source
AI summary
A method for machine-assisted collaborative product design is described. The method includes training a neural network to simulate a plurality of stakeholder personas in a product review process to provide a plurality of stakeholder models. The method also includes simulating, using the plurality of stakeholder models, the plurality of stakeholder personas in the product review process of a potential product. The method further includes aggregating individual scores output from the plurality of stakeholder models corresponding to each of the plurality of stakeholder personas regarding the potential product; wherein each of the individual scores corresponds to a stakeholder persona and that stakeholder persona's reaction to the potential product. The method also includes displaying a summary providing an overview of the aggregated individual scores regarding the potential product to a user.