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<p>Accurate and rapid cost estimation of injection moulds is a critical challenge for firms operating in the thermoplastics industry. Moulds represent a significant share of production costs—up to 45% in automotive applications—yet quotations must often be prepared under strict time constraints and with limited feasibility analyses. Existing estimation methods either lack accuracy or are too time-consuming for competitive bidding contexts. This paper proposes a parametric cost estimation approach that balances precision with computational simplicity. Using a dataset of 93 moulds produced by a mid-sized company between 2017 and 2024, we identify and evaluate a comprehensive set of cost drivers related to both mould and component characteristics. Stepwise regression techniques are applied to construct cost estimating relationships (CERs) for the overall mould cost and eleven key manufacturing activities. The results show strong predictive performance for overall mould costs (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(R^2 = 0.838\)</EquationSource> </InlineEquation> and adjusted <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(R^2 = 0.827\)</EquationSource> </InlineEquation>). Among the remaining models, the strongest explanatory performance was observed for <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(Y_8\)</EquationSource> </InlineEquation> (Adj. <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(R^2=0.688\)</EquationSource> </InlineEquation>), <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(Y_{10}\)</EquationSource> </InlineEquation> (Adj. <InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(R^2=0.649\)</EquationSource> </InlineEquation>), and <InlineEquation ID="IEq7"> <EquationSource Format="TEX">\(Y_7\)</EquationSource> </InlineEquation> (Adj. <InlineEquation ID="IEq8"> <EquationSource Format="TEX">\(R^2=0.599\)</EquationSource> </InlineEquation>). Intermediate explanatory power was found for <InlineEquation ID="IEq9"> <EquationSource Format="TEX">\(Y_4\)</EquationSource> </InlineEquation>, <InlineEquation ID="IEq10"> <EquationSource Format="TEX">\(Y_5\)</EquationSource> </InlineEquation>, <InlineEquation ID="IEq11"> <EquationSource Format="TEX">\(Y_6\)</EquationSource> </InlineEquation>, and <InlineEquation ID="IEq12"> <EquationSource Format="TEX">\(Y_{11}\)</EquationSource> </InlineEquation>, with adjusted <InlineEquation ID="IEq13"> <EquationSource Format="TEX">\(R^2\)</EquationSource> </InlineEquation> values ranging from 0.488 to 0.546. From a managerial perspective, the proposed model enables mould manufacturers to prepare competitive and reliable quotations quickly, reducing the risk of pricing errors and supporting profitability in highly competitive markets. More broadly, this study contributes to the production economics literature by demonstrating how parametric models can provide explainable, efficient, and decision-relevant cost information in capital-intensive manufacturing contexts.</p>

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Cost estimation of injection moulds for thermoplastic materials through a parametric approach

  • 期刊论文

  • 发表日期：

    2026年6月23日

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  • Roberto Cracogna,
  • Marta Flamini,
  • Andrea Fronzetti Colladon,
  • Maurizio Naldi

摘要

Accurate and rapid cost estimation of injection moulds is a critical challenge for firms operating in the thermoplastics industry. Moulds represent a significant share of production costs—up to 45% in automotive applications—yet quotations must often be prepared under strict time constraints and with limited feasibility analyses. Existing estimation methods either lack accuracy or are too time-consuming for competitive bidding contexts. This paper proposes a parametric cost estimation approach that balances precision with computational simplicity. Using a dataset of 93 moulds produced by a mid-sized company between 2017 and 2024, we identify and evaluate a comprehensive set of cost drivers related to both mould and component characteristics. Stepwise regression techniques are applied to construct cost estimating relationships (CERs) for the overall mould cost and eleven key manufacturing activities. The results show strong predictive performance for overall mould costs ( \(R^2 = 0.838\) and adjusted \(R^2 = 0.827\) ). Among the remaining models, the strongest explanatory performance was observed for \(Y_8\) (Adj. \(R^2=0.688\) ), \(Y_{10}\) (Adj. \(R^2=0.649\) ), and \(Y_7\) (Adj. \(R^2=0.599\) ). Intermediate explanatory power was found for \(Y_4\) , \(Y_5\) , \(Y_6\) , and \(Y_{11}\) , with adjusted \(R^2\) values ranging from 0.488 to 0.546. From a managerial perspective, the proposed model enables mould manufacturers to prepare competitive and reliable quotations quickly, reducing the risk of pricing errors and supporting profitability in highly competitive markets. More broadly, this study contributes to the production economics literature by demonstrating how parametric models can provide explainable, efficient, and decision-relevant cost information in capital-intensive manufacturing contexts.