Assessment of product quality risks by qualimetric methods using functionally dependent statistics

Trishch Roman (), Petraškevičius Vladislavas (), Šimelytė Agnė (), Cherniak Olena () and Lomanov Kostiantyn ()
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Trishch Roman: National Aerospace University “Kharkiv, Aviation Institute”, 17 Vadym Manko st., 61000 Kharkiv, Ukraine, Mykolas Romeris University, 20 Ateities st., Vilnius, Lithuania.
Petraškevičius Vladislavas: Vilnius Gediminas Technical University, 11 Saulėtekio al., 10223 Vilnius, Lithuania
Šimelytė Agnė: Vilnius Gediminas Technical University, 11 Saulėtekio al., 10223 Vilnius, Lithuania
Cherniak Olena: National Aerospace University “Kharkiv, Aviation Institute”, 17 Vadym Manko st., 61000 Kharkiv, Ukraine
Lomanov Kostiantyn: V. N. Karazin Kharkiv National University, 4 Svobody sq., 61022 Kharkiv, Ukraine

Engineering Management in Production and Services, 2025, vol. 17, issue 3, 65-82

Abstract: In modern production systems, ensuring high product quality while minimising risk is a critical challenge. Traditional quality assessment methods often rely on expert judgment or complex models, which may introduce subjectivity or require large datasets. This study aims to develop a universal methodology for assessing product quality risks using a mathematically grounded approach that eliminates the need for expert-based evaluations and can be easily implemented in various industrial contexts. A qualimetric method based on nonlinear mathematical dependence using the error function “erf” is proposed. The method transforms measured quality indicators into a dimensionless scale and derives functionally dependent statistics under the assumption of a uniform distribution. The model is validated through analytical derivations and numerical experiments on piston components in precision mechanical engineering. A new mathematical model was established to calculate the probability density function of transformed quality indicators. The methodology enables the estimation of the probability that a quality indicator will fall within a risky range near tolerance limits. Numerical experiments confirmed the validity of the model, demonstrating its applicability to real-world production scenarios and its alignment with known principles of qualimetry. The proposed method provides a universal, objective, and practical tool for risk-based quality assessment. It can be applied across different industries, integrated into existing quality management systems, and used to support decision-making in production control. Future research should expand the model to accommodate nonuniform distributions and explore its integration with real-time quality monitoring systems.

Keywords: qualimetry; quality of life; risk; risk assessment; quality risk; sustainability criteria; error function; functionally dependent statistics; multicriteria quality assessment (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:ecoman:v:17:y:2025:i:3:p:65-82:n:1004

DOI: 10.2478/emj-2025-0020

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