Efficient Production Management System with a Packaging Quality Control Module

Zaiets, Nataliia; Lutska, Nataliia; Junge, Lidiia

doi:10.25673/123060

Proceedings of International Conference on Applied Innovation in IT · 2025/12/22 · Vol. 13 · Issue 5 · pp. 411–416

Efficient Production Management System with a Packaging Quality Control Module

Nataliia Zaiets, Nataliia Lutska, Lidiia Vlasenko and Stefan Junge

📄 Download PDF DOI: 10.25673/123060

Abstract

The paper presents a comprehensive study aimed at improving the efficiency of the mayonnaise manufacturing and packaging process by analyzing the key performance indicators of the mayonnaise making machine, developing the structure of a packaging quality recognition system, and modeling a convolutional neural network to identify packaging defects. The article analyzes key performance indicators for productivity, energy efficiency, product quality and equipment downtime. A system has been developed for recognizing the quality of packaging, which is part of the indicator of the level of product defects, based on the use of modern machine vision algorithms. The final part of the study presents the results of modeling and training a convolutional neural network for automatic detection of packaging defects. Experimental results demonstrate a high level of accuracy and reliability of the proposed system, which makes it possible not only to timely identify defective products, but also problems in the operation of the machine in the early stages of its operation. This in turn helps reduce waste, improve production process efficiency and reduce maintenance costs.

Keywords

KPI Automation Defect Recognition Machine Learning Convolutional Neural Network Packaging.

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