Artificial Intelligence and Machine Learning in Product Cost Planning for Manufacturing Industries

Managing and controlling product costs is crucial in the early stages of value creation, as 70-80% of final product costs are determined during product development and design. Traditionally, businesses focus on the accounting unit that records costs once they arise, but by this point in the product lifecycle, the ability to influence costs is very limited. Therefore, accurately estimating costs during the initial design phase is essential for making informed strategic decisions in product engineering. This study presents a machine learning-based approach to aid in cost management during the early design stage. Using real-world data from a major original equipment manufacturer (OEM), examined the effectiveness of a machine learning framework, applying a sequential forward feature selection method for dimensionality reduction and grid search hyperparameter tuning across six regression algorithms. The findings indicate that it is possible to predict product component costs in the early design phase with an R² of 0.960 using just seven design and development features. A cluster analysis and scatter plots identified the causes of cost estimation discrepancies, highlighting the potential of machine learning models as a valuable tool for product managers and cost engineers.

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