2024/05/03 by Tian Tian, Tian, Tian, Jiahao Deng +1
Business, Management and Accounting · #Big Data and Business Intelligence #Econometrics (econ.EM) #FOS: Economics and business
paper · pdf · doi:10.48550/arxiv.2405.01913
openalex publication_date 2024/05/03 · openalex created_date 2024/05/11 · openalex updated_date 2026/07/28
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.