Leveraging AI for Predictive Product Costing in Manufacturing

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@article{1709043,
      author = {Abhishek P. Sanakal},
      title = {Leveraging AI for Predictive Product Costing in Manufacturing},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {4},
      number = {6},
      pages = {198-207},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709043.pdf},
      abstract = {Accurate product costing is of crucial importance for the manufacturers wishing to claw out profits, smartly adjust to volatile input markets, and compete on an international scale. In such intricate manufacturing environments, traditional costing methods such as standard costing go far behind in efficacy, with cumbersome assumptions, restrictions in detail, and inclination to historic averages. This article also explains how AI, especially ML algorithms, are potentially providing more accurate and dynamic forecasting of costs for such elements as materials, labor, and overheads. With cases and use cases from the manufacturing domain, we evaluate how predictive algorithms go against all traditional techniques. It elaborates on the practical aspects needed for the installation of an AI-powered costing system, including data integration, model interpretability, and organizational readiness. Although, challenges remain, the transitions to predictive costing provide an acceptable entry into digital transformation and strategic decision-making in manufacturing.},
      keywords = {Artificial Intelligence, Machine Learning, Predictive Analytics, Product Costing, Manufacturing, Material Costs, Labor Forecasting, Overhead Estimation, ERP Integration, Industry 4.0},
      month = {December},
  }