  This pioneering research introduces a novel approach for decision-makers in
the heavy machinery industry, specifically focusing on production management.
The study integrates machine learning techniques like Ridge Regression, Markov
chain analysis, and radar charts to optimize North American Crawler Cranes
market production processes. Ridge Regression enables growth pattern
identification and performance assessment, facilitating comparisons and
addressing industry challenges. Markov chain analysis evaluates risk factors,
aiding in informed decision-making and risk management. Radar charts simulate
benchmark product designs, enabling data-driven decisions for production
optimization. This interdisciplinary approach equips decision-makers with
transformative insights, enhancing competitiveness in the heavy machinery
industry and beyond. By leveraging these techniques, companies can
revolutionize their production management strategies, driving success in
diverse markets.
