What is the purpose of the demonstration experiment conducted by Polaris.AI with Hitachi Construction Machinery?

The purpose is to codify the "tacit knowledge" of skilled on-site workers using AI and transform it into a state where it can be utilized across the entire organization.

What challenges did Hitachi Construction Machinery face?

Hitachi Construction Machinery faced the challenge that while on-site workers made accurate situational assessments and decisions, their judgment criteria and insights were not recorded in a database, making it difficult to share and utilize them across the entire organization.

AI News NQ Analysis

Polaris.AI Conducts Proof-of-Concept at Hitachi Construction Machinery’s Hitachinaka-Rinko Works to Codify Skilled Workers' "Tacit Knowledge" via AI for Organizational Use

NQ Score 86/100

AI Summary (NQ-processed)

Polaris.AI Inc. conducted a demonstration experiment at Hitachi Construction Machinery's Hitachinaka-Rinko Works, analyzing skilled workers' conversational voice data with AI to accumulate and reuse tacit knowledge. This initiative aims to leverage the judgment criteria, insights, and know-how of experienced on-site workers across the entire organization. Hitachi Construction Machinery, a global manufacturer of hydraulic excavators and mining machinery, faced challenges in sharing on-site workers' decision-making processes. Polaris.AI seeks to establish a new knowledge circulation model in the construction machinery production field through this verification.

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Frequently Asked Questions

Q: What is the purpose of the demonstration experiment conducted by Polaris.AI with Hitachi Construction Machinery?: A: The purpose is to codify the "tacit knowledge" of skilled on-site workers using AI and transform it into a state where it can be utilized across the entire organization.
Q: What challenges did Hitachi Construction Machinery face?: A: Hitachi Construction Machinery faced the challenge that while on-site workers made accurate situational assessments and decisions, their judgment criteria and insights were not recorded in a database, making it difficult to share and utilize them across the entire organization.