ManuKnowVis: How to Support Different User Groups in Contextualizing and Leveraging Knowledge Repositories

Area of Science:

• Manufacturing Process Analysis
• Data Visualization
• Knowledge Management

Background:

• Discrepancy exists between domain experts and data scientists in manufacturing data analysis.
• Knowledge providers lack data analysis skills, while consumers lack domain expertise.
• Bridging this gap is crucial for effective data-driven decision-making in manufacturing.

Purpose of the Study:

• To present ManuKnowVis, a tool designed to contextualize data from multiple knowledge repositories in electric vehicle battery module manufacturing.
• To address the challenge of integrating domain knowledge with data-driven analysis in serial manufacturing processes.
• To facilitate the creation and completion of manufacturing knowledge by connecting knowledge providers and consumers.

Main Methods:

• A multi-stakeholder design study involving iterative development of ManuKnowVis with knowledge providers and consumers from an automotive company.
• Development of a multiple linked view tool enabling providers to describe and connect manufacturing entities.
• Leveraging enhanced data by consumers for improved understanding and efficient data analysis.

Main Results:

• ManuKnowVis successfully bridges the gap between knowledge providers and consumers in manufacturing.
• The tool enables providers to externalize their domain knowledge effectively.
• Consumers can perform data-driven analyses more efficiently using the enhanced data.

Conclusions:

• ManuKnowVis significantly impacts the success of data-driven analyses from manufacturing data.
• The tool facilitates knowledge externalization and improves data analysis efficiency for different stakeholder groups.
• This approach enhances understanding of complex domain problems in manufacturing settings.