Enhancing Industrial Interaction Practices Through AI-Based Parameter Modeling
محورهای موضوعی : Machine learningAshwini Kumar 1 , Rekha Agarwal 2 , Archana Singh 3
1 - Amity Institute of Information Technology, Amity University, Uttar Pradesh, Noida, India
2 - Amity Institute of Information Technology, Amity University, Uttar Pradesh, Noida, India
3 - Ministry of Education, Government of India
کلید واژه: COFI Framework, Random Forest, K-Means Clustering, Industry Interactions, Predictive Modeling, Human-AI Collaboration, Industrial Optimization, Segmentation Techniques, AI System Architecture,
چکیده مقاله :
Industrial systems today depend increasingly on effective communication and coordination among humans and machines. This study proposes an Artificial Intelligence–based approach for modeling and improving industrial interaction practices using the COFI framework—Context, Content, Competency, and Culture. By combining supervised and unsupervised learning techniques, specifically Random Forest (RF) and K-Means clustering, the research models key parameters that influence communication efficiency and organizational alignment. A publicly available behavioral dataset, supplemented with simulated industrial communication records, was used to represent multi-agent interactions within a workplace context. Extensive data preprocessing, feature engineering, and COFI-based variable mapping were performed to ensure interpretability and conceptual coherence. The RF model achieved an improved predictive accuracy of 72.4% following feature optimization, while K-Means clustering produced three distinct communication groups with a Silhouette score of 0.75 and a Davies–Bouldin Index of 0.49, indicating well-separated clusters. Feature-importance and SHAP analyses revealed that contextual and content-based variables contributed most significantly to prediction outcomes, while competency and cultural attributes shaped nuanced interaction patterns. A pilot case simulation demonstrated tangible performance improvements—reducing response time by 12% and improving task resolution by 9% when AI insights were applied to industrial communication workflows. The findings confirm that combining supervised prediction with unsupervised segmentation offers a robust pathway to understanding and optimizing human–machine communication within organizational ecosystems. This research contributes a practical and interpretable framework for AI-enabled industrial interaction modeling, offering both theoretical insight and applied value for adaptive, data-driven management systems.
Industrial systems today depend increasingly on effective communication and coordination among humans and machines. This study proposes an Artificial Intelligence–based approach for modeling and improving industrial interaction practices using the COFI framework—Context, Content, Competency, and Culture. By combining supervised and unsupervised learning techniques, specifically Random Forest (RF) and K-Means clustering, the research models key parameters that influence communication efficiency and organizational alignment. A publicly available behavioral dataset, supplemented with simulated industrial communication records, was used to represent multi-agent interactions within a workplace context. Extensive data preprocessing, feature engineering, and COFI-based variable mapping were performed to ensure interpretability and conceptual coherence. The RF model achieved an improved predictive accuracy of 72.4% following feature optimization, while K-Means clustering produced three distinct communication groups with a Silhouette score of 0.75 and a Davies–Bouldin Index of 0.49, indicating well-separated clusters. Feature-importance and SHAP analyses revealed that contextual and content-based variables contributed most significantly to prediction outcomes, while competency and cultural attributes shaped nuanced interaction patterns. A pilot case simulation demonstrated tangible performance improvements—reducing response time by 12% and improving task resolution by 9% when AI insights were applied to industrial communication workflows. The findings confirm that combining supervised prediction with unsupervised segmentation offers a robust pathway to understanding and optimizing human–machine communication within organizational ecosystems. This research contributes a practical and interpretable framework for AI-enabled industrial interaction modeling, offering both theoretical insight and applied value for adaptive, data-driven management systems.
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