Work

Improving AI-based modelling for mass customisation strategy: Insights from automotive industry

Year: 2026

Type: article

Abstract: The potential of the mass customisation (MC) strategy is continuously expanding through the implementation of Industry 4.0/5.0technologies.In this study, a novel approach to enhancing the potential of Artificial Intelligencebased techniques for MC level prediction was presented.Firstly, the empirical research was conducted among more than 150 European manufacturing companies in the automotive sector concerning factors influencing the increase in the level of MC, as well as the Industry 4.0 or 5.0 technologies applied.Next, the collected survey data was pre-processed to eliminate infrequent responses and subsequently analysed using the k-means clustering method for labelling purposes.The resulting dataset was then used to fine-tune parameters and train a classifier based on a multilayer perceptron (MLP), aimed at identifying the MC level in automotive enterprises.The developed classifier achieved average accuracies of 0.94 and 0.93 for training and testing, respectively.The proposed approach was validated through real-life case studies from the automotive industry, demonstrating the adaptability of our concept and its potential for diverse industrial applications.

Source: Advances in Science and Technology – Research Journal

DOI: 10.12913/22998624/231080

Authors: Justyna Patalas-Maliszewska, Grzegorz Pająk, Katarzyna Kowalczewska

Institution: University of Zielona Góra

Language: English

Topic: Product Development and Customization

Subfield: Management of Technology and Innovation

Field: Business, Management and Accounting

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $330