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Integration of Computational Mathematics in Industrial Decision-Making

In: Advances in Computational Mathematics for Industrial System Reliability and Maintainability

Author

Listed:
  • Mohammad Yazdi

    (Macquarie University)

Abstract

The chapter talks about the pivotal role of computational mathematics in reshaping industrial decision-making, specifically emphasizing reliability and maintenance metrics. With the increasing influence of technology and data analytics, industries are adopting probabilistic models, life data analysis, fault tree analysis, and survival analysis to optimize maintenance schedules, improve asset longevity, and reduce costs. However, integrating these advanced methods mandates a comprehensive cost–benefit analysis to ensure investments yield tangible returns. Furthermore, while integrating with Asset Management Systems promises real-time analytics and enhanced decision-making, industries face challenges, including data quality issues, skill gaps, and resistance to change. Potential solutions include data pre-processing, skill development initiatives, and employing change management strategies. Looking ahead, the union of computational mathematics with industrial operations is set to be influenced by AI, quantum computing, IoT, and pertinent ethical considerations. This confluence is not merely a trend but an essential evolution for industries striving for data-driven operational excellence.

Suggested Citation

  • Mohammad Yazdi, 2024. "Integration of Computational Mathematics in Industrial Decision-Making," Springer Series in Reliability Engineering, in: Advances in Computational Mathematics for Industrial System Reliability and Maintainability, chapter 0, pages 105-120, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-031-53514-7_6
    DOI: 10.1007/978-3-031-53514-7_6
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