Machine learning-based decision-making approach for predicting defects detection - a case study

Aug 5, 2026

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In today’s highly competitive global market, industries must deliver faultless products to protect profitability and brand reputation. Machine learning enables improved quality control by predicting outcomes of quality inspection processes. This article presents a real fashion-industry case study using ML algorithms to develop a knowledge-based intelligent supervisory system for defect prediction. Defect detection is modeled as binary classification, comparing several algorithms on company data.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Introduction

Machine learning for quality prediction

Defect detection as classification

Algorithms compared and selection criteria

IAES International Journal of Artificial Intelligence (IJ-AI) Vol. 13, No. 3, September 2024, pp. 3052∼3060 ISSN: 2252-8938, DOI: 10.11591/ijai.v13.i3.pp3052-3060 ❒ 3052 | Machine learning-based decision-making approach for predicting defects detection: a case study
Elena Barzizza, Nicol Biasetton, Riccardo Ceccato, Alberto Molena
Department of Management Engineering, Universit degli Studi di Padova, Vicenza, Italy | | | | --- | --- | --- | | Article Info
Article history:
Received May 10, 2023 Revised Nov 10, 2023 Accepted Dec 3, 2023
Keywords:
Defect production Fashion industry Machine learning Predictive quality Quality control | | ABSTRACT
In today’s highly competitive global market, industries must produce faultless products to achieve profitability. Machine learning (ML) algorithms provide a possible method to improve quality standards by enabling the prediction of the outcome of quality control processes. This article presents a real case study based on ML algorithms suggested to develop a knowledge-based intelligent supervisory system to predict defect products in the fashion industry. Defect detection is formulated as a binary classification problem, and several ML algorithms have been compared to determine the most suitable one on the available data. The random forest (RF), LightGBM, and C5.0 algorithms exhibit comparable high-end performances on the pre-processed dataset made available by the company. Nevertheless, since the aim of the analysed industry is to reduce the rate of false negative observations (i.e., the proportion of defected-free products wrongly classified), the best method results is RF, as it minimizes this metric.
This is an open access article under the CC BY-SA licence. | | Corresponding Author: | | | | Biasetton Nicol
Department of Management Engineering, Universit degli Studi di Padova Stradella S. Nicola, 3 Vicenza 36100, Italy
Email: [nicolo.biasetton@unipd.it](nicolo.biasetton@unipd.it) | | | 1. INTRODUCTION Nowadays, quality represents a strategic factor in all industries that contributes to classifying companies as profitable and non-profitable. Quality can be considered as an index of internal performance, as it allows one to develop diversifying product or service differentiation policies. In recent years, the quality of products and services has even increased in importance due to the massive spread of timely written online reviews by customers at no cost who want to share their feelings and opinions after use or after experience. Therefore, the provision of defective or poor quality products can significantly affect both the reputation of the brand and the loyalty of customers to a certain brand; the aforementioned are some of the reasons that have led numerous companies to adopt a zero-defect policy. The definition of a quality management process has changed significantly over the years [1] . The first quality control (QC) process was developed during the mass production period (1900-1940), and it simply consisted of the inspection of the final products. However, increasing pressure from the market led to a major theoretical shift in the first place toward the concept of process quality. The idea underlying this theory was that looking for errors was much less efficient than finding the source of the errors and removing them. In the early 60s, quality assurance theory emerged. The process quality, i.e. a posteriori reaction to QC of either products or processes, was not enough anymore, and industries wanted to prevent risks of failure products or services identifying their major causes before their appearance. With increasing complexity of products and interdependencies with the supply chain, ISO 9000 certification has been introduced to define quality management standards to help industries ensure that they meet the basic requirements of the customer and other stakeholders related to products or services. In the last ten years, the qu

FAQs

How is defect detection formulated in the case study?

Defect detection is formulated as a binary classification problem, distinguishing defected products from defect-free ones.

Which machine learning approaches are compared for defect prediction?

The study compares multiple ML algorithms, including random forest (RF), LightGBM, and C5.0.

Why is random forest considered the best method in this work?

Although several algorithms show high performance, random forest is best because it minimizes false negatives, reducing the share of defect-free products incorrectly classified as defective-free when they are actually defects-free or vice versa according to the study’s metric.

Aug 5, 2026

Upload by Miles