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https://doi.org/10.62836/jcmea.v4i1.040106
Tian, T., Deng, J., Zheng, B., Wan, X., & Lin, J. (2024). AI-Driven Transformation: Revolutionizing Production Management with Machine Learning and Data Visualization. Journal of Computational Methods in Engineering Applications, 4(1), 1–18. https://doi.org/10.62836/jcmea.v4i1.040106
Volume 4, Issue 1, 2024

AI-Driven Transformation: Revolutionizing Production Management with Machine Learning and Data Visualization

This pioneering research introduces a novel approach for decision-makers in the heavy machinery industry, focusing on production management. The study integrates machine learning techniques like Markov chain analysis and radar charts to optimize North American Crawler Cranes market production processes. Markov chain analysis evaluates risk factors, aiding in informed decision-making and risk management. Radar charts simulate benchmark product designs, enabling datadriven 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.

machine learning; data analysis; risk factors; industrial management; heavy machinery industry

References

  1. Prescott JE, Grant JH. A Manager’s Guide for Evaluating Competitive Analysis Techniques. Interfaces 1988; 18(3): 10–22. DOI: https://doi.org/10.1287/inte.18.3.10
  2. Bloodgood JM, Bauerschmidt A. Competitive Analysis: Do Managers Accurately Compare Their Firms to Competitors?. Journal of Managerial Issues 2002; 418–434.
  3. Barney JB. Looking Inside for Competitive Advantage. Academy of Management Perspectives 1995; 9(4): 49–61. DOI: https://doi.org/10.5465/ame.1995.9512032192
  4. Chinowsky PS, Meredith JE. Strategic Management in Construction. Journal of Construction Engineering and Management 2000; 126(1): 1–9. DOI: https://doi.org/10.1061/(ASCE)0733-9364(2000)126:1(1)
  5. Nyambane JM, Bett S. Competitive Advantage and Performance of Heavy Construction Equipment Suppliers in Kenya: Case of Nairobi County. International Academic Journal of Human Resource and Business Administration 2018; 3(1): 476–502.
  6. Lee S-H, Jeon R-K, Kim J-H, Kim J-J. Strategies for Developing Countries to Expand Their Shares in the Global Construction Market: Phase-Based SWOT and AAA Analyses of Korea. Journal of Construction Engineering and Management 2011; 137(6): 460–470. DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000316
  7. Lee K-W, Han SH, Park H, Jeong HD. Empirical Analysis of Host-Country Effects in the International Construction Market: An Industry-Level Approach. Journal of Construction Engineering and Management 2016; 142(3): 04015092. DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001083
  8. Ronald M Harstad. Alternative Common-Value Auction Procedures: Revenue Comparisons with Free Entry. Journal of Political Economy 1990; 98(2): 421–429. DOI: https://doi.org/10.1086/261684
  9. Netessine S, Shumsky RA. Revenue Management Games: Horizontal and Vertical Competition. Management Science 2005; 51(5): 813–831. DOI: https://doi.org/10.1287/mnsc.1040.0356
  10. Hartmann GC. Linking R&D Spending to Revenue Growth. Research-Technology Management 2003; 46(1): 39–46. DOI: https://doi.org/10.1080/08956308.2003.11671543
  11. Chen Y-S, Cheng C-H. Forecasting Revenue Growth Rate Using Fundamental Analysis: a Feature Selection Based Rough Sets Approach. In Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), Haikou, China, 24–27 August 2007. DOI: https://doi.org/10.1109/FSKD.2007.295
  12. Lévesque M, Joglekar N, Davies J. A Comparison of Revenue Growth at Recent-IPO and Established Firms: The Influence of SG&A, R&D and COGS. Journal of Business Venturing 2012; 27(1): 47–61. DOI: https://doi.org/10.1016/j.jbusvent.2010.08.001
  13. Adner R. When Are Technologies Disruptive? A Demand-Based View of the Emergence of Competition. Strategic Management Journal 2002; 23(8): 667–688. DOI: https://doi.org/10.1002/smj.246
  14. Elahi E. Risk Management: the Next Source of Competitive Advantage. Foresight 2013; 15(2): 117–131. DOI: https://doi.org/10.1108/14636681311321121
  15. Bengtsson M, Kock S. Cooperation and Competition in Relationships Between Competitors in Business Networks. Journal of Business & Industrial Marketing 1999; 14(3): 178–194. DOI: https://doi.org/10.1108/08858629910272184
  16. Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, García Marquéz JR, et al. Collinearity: a Review of Methods to Deal with It and a Simulation Study Evaluating Their Performance. Ecography 2013; 36(1): 27–46. DOI: https://doi.org/10.1111/j.1600-0587.2012.07348.x
  17. Putka DJ, Beatty AS, Reeder MC. Modern Prediction Methods: New Perspectives on a Common Problem. Organizational Research Methods 2018; 21(3): 689–732. DOI: https://doi.org/10.1177/1094428117697041
  18. Ayers KL, Cordell HJ. SNP Selection in Genome-Wide and Candidate Gene Studies via Penalized Logistic Regression. Genetic Epidemiology 2010; 34(8): 879–891. DOI: https://doi.org/10.1002/gepi.20543
  19. Lin F-H, Tsai S-B, Lee Y-C, Hsiao C-F, Zhou J, Wang J, Shang Z. Empirical Research on Kano’S Model and Customer Satisfaction. PLOS ONE 2017; 12(9): e0183888. DOI: https://doi.org/10.1371/journal.pone.0183888
  20. Ahn JJ, Byun HW, Oh KJ, Kim TY. Using Ridge Regression with Genetic Algorithm to Enhance Real Estate Appraisal Forecasting. Expert Systems with Applications 2012; 39(9): 8369–8379. DOI: https://doi.org/10.1016/j.eswa.2012.01.183
  21. Ranjan J. Business Intelligence: Concepts, Components, Techniques and Benefits. Journal of Theoretical and Applied Information Technology 2009; 9(1): 60–70.
  22. Porter ME, Heppelmann JE, et al. How Smart, Connected Products Are Transforming Companies. Harvard Business Review 2015; 93(10): 96–114.
  23. Tsai C-W, Lai C-F, Chao H-C, Vasilakos AV. Big Data Analytics: a Survey. Journal of Big Data 2015; 2(1): 1–32. DOI: https://doi.org/10.1186/s40537-015-0030-3
  24. Sornette D. Physics and Financial Economics (1776–2014): Puzzles, Ising and Agentbased Models. Reports on Progress in Physics 2014; 77(6): 062001. DOI: https://doi.org/10.1088/0034-4885/77/6/062001
  25. Koller D, Friedman N. Probabilistic Graphical Models: Principles and Techniques; MIT Press: Cambridge, MA, USA, 2009.
  26. Asavathiratham C. The Influence Model: A Tractable Representation for the Dynamics of Networked Markov Chains. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA, USA, 24 October 2000.
  27. Chan SL, Ip WH. A Dynamic Decision Support System to Predict the Value of Customer for New Product Development. Decision Support Systems 2011; 52(1): 178–188. DOI: https://doi.org/10.1016/j.dss.2011.07.002
  28. Liu W-Y, Wang B-W, Yu J-X, Li F, Wang S-X, Hong W-X. Visualization Classification Method of Multi-Dimensional Data Based on Radar Chart Mapping. In Proceedings of the 2008 International Conference on Machine Learning and Cybernetics, Kunming, China, 12–15 July 2008.
  29. Liu Z, Crouser RJ, Ottley A. Survey on Individual Differences in Visualization. Computer Graphics Forum 2020; 39: 693–712. DOI: https://doi.org/10.1111/cgf.14033
  30. Zhang Yu, Hao Zhixiu, Wang Rencheng, Jin Dewen. A New Method for the Evaluation of Gait Pathology: A Radar Chart Approach. In Proceedings of the 1st International Convention on Rehabilitation Engineering & Assistive Technology: in Conjunction with 1st Tan Tock Seng Hospital Neurorehabilitation Meeting, Singapore, April 23–26 2007. DOI: https://doi.org/10.1145/1328491.1328525
  31. Bautista-Rodriguez C, Sanchez-de Toledo J, Clark BC, Herberg J, Bajolle F, Randanne PC, Salas-Mera D, et al. Multisystem Inflammatory Syndrome in Children: an International Survey. Pediatrics 2021; 147(2). DOI: https://doi.org/10.1542/peds.2020-024554
  32. Chang Y-C, Chang C-J, Chen K-T, Lei C-L. Radar Chart: Scanning for Satisfactory QoE in QoS Dimensions. IEEE Network 2012; 26(4): 25–31. DOI: https://doi.org/10.1109/MNET.2012.6246749
  33. Duan R, Tong J, Sutton AJ, Asch DA, Chu H, Schmid CH, Chen Y. Origami Plot: a Novel Multivariate Data Visualization Tool That Improves Radar Chart. Journal of Clinical Epidemiology 2023; 156: 85–94. DOI: https://doi.org/10.1016/j.jclinepi.2023.02.020
  34. Jayaprakash SM, Lauría EJM, Gandhi P, Mendhe D. Benchmarking Student Performance and Engagement in an Early Alert Predictive System Using Interactive Radar Charts. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, Edinburgh, UK, 25–29 April 2016. DOI: https://doi.org/10.1145/2883851.2883940
  35. Peng Weishi. Improved Radar Chart for Lighting System Scheme Selection. Applied Optics 2022; 61(19): 5619–5625. DOI: https://doi.org/10.1364/AO.455779
  36. Chang Y-C, Chang C-J, Chen K-T, Lei C-L. 2010. Radar Chart: Scanning for High QoE in QoS Dimensions. In Proceedings of the 2010 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR 2010), Vancouver, Canada, 8–10 June 2010. DOI: https://doi.org/10.1109/CQR.2010.5619915
  37. Zhang Y, Liang R, Fu X, Zheng Y. Visualizing Cognitive Learning Outcomes of Undergraduates with Cloud-Based Assessment System. In Proceedings of the 2nd International Conference on Internet, Education and Information Technology (IEIT 2022), 15–17 April 2022; Zhangjiajie, China. DOI: https://doi.org/10.2991/978-94-6463-058-9_137
  38. Studds RFA. Radar Charting. The International Hydrographic Review. Retrieved from https://journals.lib.unb.ca/index.php/ihr/article/view/27004
  39. Satriadi KA, Ens B, Goodwin S, Dwyer T. Active Proxy Dashboard: Binding Physical Referents and Abstract Data Representations in Situated Visualization through Tangible Interaction. In Proceedings of the Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, Hamburg, Germany, 23–28 April 2023. DOI: https://doi.org/10.1145/3544549.3585797
  40. Tsao Y-P, Yeh W-Y, Hsu T-F, Chow L-H, Chen W-C, Yang Y-Y, Shulruf B, Chen C-H, Cheng H-M. Implementing a Flipped Classroom Model in an Evidence-Based Medicine Curriculum for Pre-Clinical Medical Students: Evaluating Learning Effectiveness Through Prospective Propensity Score-Matched Cohorts. BMC Medical Education 2022; 22(1): 1–11. DOI: https://doi.org/10.1186/s12909-022-03230-z
  41. McGahan AM. The Performance of US Corporations: 1981–1994. The Journal of Industrial Economics 1999; 47 (4): 373–398. DOI: https://doi.org/10.1111/1467-6451.00105
  42. Davies RL. Evaluation of Retail Store Attributes and Sales Performance. European Journal of Marketing 1973; 7(2): 89–102. DOI: https://doi.org/10.1108/EUM0000000005104
  43. Jahan S, Ahmad Khan KI, Thaheem MJ, Ullah F, Alqurashi M, Alsulami BT. Modeling Profitability-Influencing Risk Factors for Construction Projects: A System Dynamics Approach. Buildings 2022; 12(6): 701. DOI: https://doi.org/10.3390/buildings12060701
  44. Jugend D, da Silva SL. Product-Portfolio Management: A Framework Based on Methods, Organization, and Strategy. Concurrent Engineering 2014; 22(1): 17–28. DOI: https://doi.org/10.1177/1063293X13508660
  45. Birger W. The Relation Between Market Share and Profitability. Journal of Business Strategy 1986; 6(4): 67–74. DOI: https://doi.org/10.1108/eb039133
  46. Trivedi M. Distribution Channels: An Extension of Exclusive Retailership. Management Science 1998; 44(7): 896–909. DOI: https://doi.org/10.1287/mnsc.44.7.896
  47. Smith KT, Smith M, Wang K. Does Brand Management of Corporate Reputation Translate into Higher Market Value?. Journal of Strategic Marketing 2010; 18(3): 201–221. DOI: https://doi.org/10.1080/09652540903537030
  48. Keast S. Pricing Strategy: Setting Price Levels, Managing Price Discounts and Establishing Price Structures. Journal of Revenue and Pricing Management 2013; 12(5): 470–471. DOI: https://doi.org/10.1057/rpm.2013.11
  49. Liang Y, Chen C, Tian T, Shu K. Fair Classification Via Domain Adaptation: A Dual Adversarial Learning Approach. Frontiers in Big Data 2023; 5: 129. DOI: https://doi.org/10.3389/fdata.2022.1049565
  50. Deng J, Brown ET. RISSAD: Rule-Based Interactive Semi-Supervised Anomaly Detection. In Eurographics Conference on Visualization; The Eurographics Association: Eindhoven, The Netherlands.