Unleashing the Power of AI: Transforming Marketing Decision-Making in Heavy Machinery with Machine Learning, Radar Chart Simulation, and Markov Chain Analysis

Tian Tian and Jiahao Deng

Papers from arXiv.org

Abstract: 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.

Date: 2024-05
New Economics Papers: this item is included in nep-big, nep-cmp and nep-inv
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