Management and Production Engineering Review

Zawartość

Management and Production Engineering Review | 2026 | No 1

Abstrakt

Quality assessment of manufactured products is vital to ensure performance, safety, and customer satisfaction across industries. Defects in items such as bottle caps, cables, capsules, leather, and metal components can affect functionality and durability. Traditional inspection methods relying on manual visual checks are time-consuming and error-prone. This study proposes an AI-driven framework using the Probabilistic U-Net integrated with a Conditional Variational Autoencoder (CVAE) for automated defect detection. The model introduces stochastic latent variables to generate multiple plausible segmentation maps, enhancing accuracy under ambiguous or noisy conditions. Using the MVTec Anomaly Detection dataset, which includes defects such as scratches and discoloration, the system applies preprocessing steps including resizing, normalization, and data augmentation to enhance the robustness and consistency of the input data. A hybrid loss combining cross-entropy and Kullback–Leibler divergence improves segmentation precision and latent space alignment. Experimental results confirm robust and reliable defect detection across diverse product categories, demonstrating the model’s potential for automated manufacturing quality assurance.
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Autorzy i Afiliacje

Ajay Kumar BOYAT
Vinit GUPTA

Abstrakt

The study presents an approach to predicting the completion time of production operations using supervised machine learning techniques. The analysis was conducted on a database extracted from ERP and MES systems, comprising over 150,000 records containing technological and production completion times, operation numbers, and textual descriptions of operations. The dataset preprocessing involved cleaning, feature encoding, and text vectorisation using the TF-IDF method to represent semantic patterns within operation descriptions. Regression models, including Linear Regression, Random Forest, and XGBoost, were trained and evaluated using Google Colab. Model performance was assessed using standard evaluation metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R2). Experimental results showed that ensemble-based methods achieved the highest predictive accuracy, outperforming the baseline model based solely on technological completion time. In addition, the study examines the sensitivity of selected models to hyperparameter settings and analyses the impact of alternative categorical feature encoding methods on prediction accuracy. The proposed approach enables more accurate estimation of production durations and supports data-driven decision-making in manufacturing environments.
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Autorzy i Afiliacje

Jadwiga Krupnik-Worek
Sebastian Skoczypiec

Abstrakt

The rapid development of Industry 4.0 has introduced advanced technologies such as IoT, cyber-physical systems (CPS), and industrial IoT into manufacturing environments. However, traditional production management systems remain largely reactive, operating in discrete modes with fragmented interfaces. This paper presents a concept for a production management support system that integrates large language models (LLMs) that enable natural language interaction. This solution concept addresses the key challenge of data fragmentation by creating an intelligent digital twin that acts as a production expert capable of contextual reasoning, information synthesis from multiple sources, and real-time decision support. This concept demonstrates the potential to transform production management from a reactive to a proactive operating model by leveraging LLM’s capabilities in pattern recognition, predictive analysis, and automated recommendation generation. Future development directions focus on optimizing business intelligence integration, improving automated recommendation mechanisms, and standardizing natural language user interfaces for industrial applications.
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Autorzy i Afiliacje

Jakub PIZOŃ
Łukasz WÓJCIK
Arkadiusz GOLA

Abstrakt

The design of machine control systems requires the correct selection of safety logic devices to ensure functional safety and compliance with international standards such as EN ISO 13849-1 and IEC 62061. This process is typically based on expert knowledge and manual evaluation of design parameters, which can be time-consuming and error-prone. In this study, machine learning techniques are applied to automate and improve the selection of safety logic devices using real industrial data originating from various types of machinery and automated manufacturing real-world projects. This work introduces a significantly extended industrial dataset comprising 670 labelled machine configurations derived from real anonymized engineering projects and performs a comprehensive comparison of ten representative ML algorithms implemented in WEKA. The main novelty of the study is a unified large-scale comparative evaluation of heterogeneous machine learning classifiers on real industrial decision data, enabling joint assessment of scalability, generalization, interpretability, and computational efficiency under identical experimental conditions. The results demonstrate that increasing dataset size considerably enhances model stability and generalization. The Averaged 2- Dependence Estimator (A2DE) achieved the highest performance with an accuracy of 86% and Kappa = 0.81, followed by REPTree and Random Forest classifiers. Rule-based methods such as PART and NNge maintained strong interpretability with competitive predictive power. The findings confirm that probabilistic and ensemble algorithms provide reliable and practically applicable solutions for data-driven decision support in industrial safety engineering, paving the way for deployable, explainable, and adaptive decision-support tools in smart manufacturing environments.
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Autorzy i Afiliacje

Karel STIBOR
Sona SEDIVA
Radek ŠTOHL
Lenka ŠTOHLOVÁ PUTNOVÁ

Abstrakt

The aim of this study was to analyse the impact of technological modifications in the production cycle of flat components on reducing production costs. The research involved a detailed analysis of production processes, identification of key areas requiring improvement, and the development of new technological solutions. As part of the study, changes were implemented in the production process, and their effect on production costs was thoroughly evaluated. The technological modification in the manufacturing of the “metal support bracket” was designed to enhance efficiency by reducing unit production time and assessing the effect of this change on overall production performance. The main innovations included the introduction of a machining centre that integrated drilling, chamfering, and threading operations into a single process. This significantly reduced both unit production time and cost while eliminating machine downtime. Additionally, powder coating was replaced with electroplating, which resolved issues related to hole narrowing and ensured the maintenance of precise technical dimensions. The implemented changes resulted in a shorter production cycle, improved product accuracy and quality, and reduced machine downtime. The analysis demonstrated that these modifications positively influenced the enterprise’s competitiveness, generating substantial cost savings. This work provides a practical example of the application of industrial innovation, contributing to cost reduction, shorter production cycle times, and enhanced precision and quality of products.
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Autorzy i Afiliacje

Mariusz NIEKURZAK

Abstrakt

Three-dimensional (3D) printing is an attractive method for creating gel geometries for specific applications. In this research, the 3D printing parameters for direct and indirect extrusion of alginate-based hydrogels are optimized using the Taguchi and Analysis of Variance methods for output responses, including printed shape retention and the height of the 3-printed layers. The results show that for an indirect extrusion, the optimal values of extrusion speed, printing speed, and nozzle distance for the shape retention ability are 13 steps/mm, 50 mm/s, and 0.5 mm, respectively, while those values for the printed height are 13 steps/mm, 60 mm/s, and 0.5 mm, respectively. For direct extrusion, the optimal parameter set for former response is 35 steps/mm, 60 mm/s, and 0.6 mm, respectively, while the set for the latter is 35 steps/mm, 40 mm/s, and 0.6 mm, respectively. The findings in this report can be used as data for related research.
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Autorzy i Afiliacje

Thanh Tan NGUYEN
Ngoc Hieu PHAM
Hoai Nam NGUYEN
Quoc Bao PHAN
Van Tron TRAN

Abstrakt

The article addresses the challenges associated with the Product-as-a-Service (PaaS) concept in the context of the circular economy. The use of simulation technologies within this concept can accelerate the adoption of circular economy principles in companies. It enables faster modelling and optimisation of value-retention processes such as remanufacturing and refurbishment. The aim of the paper is to propose a simulation approach that allows engineers to develop simulation models (digital twins) in the same way they design and redesign closed-loop manufacturing systems. A further objective is to highlight the specific requirements for testing such simulation models and for sizing planned digital-twin projects. The article presents the development of a hybrid model that combines discrete piece-flow for remanufacturing with mass flow (in kilograms) for recycling, using household appliances as an example and identifying sources of uncertainty that affect the organisation of PaaS processes, particularly remanufacturing and recycling. The main contribution is the development of a hybrid simulation approach based on operations in the PaaS domain, i.e. closed-loop manufacturing with the 6R framework, and the application of the COSMIC methodology to size simulation projects. The article also discusses methods for simulation modelling of individual sources of uncertainty and uses an operation-based simulation method instead of conventional discrete-event simulation.
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Autorzy i Afiliacje

Paweł PAWLEWSKI
Dominik kAŹMIERCZAK

Abstrakt

This study investigates the impact of Industry 4.0 on sustainable supply chain practices in Ghana’s beverage industry, emphasizing the role of managerial support. Data were collected from 200 beverage firms in Greater Kumasi using a structured questionnaire and analyzed with SPSS 23 and SmartPLS SEM. Results show that Industry 4.0 positively influences both sustainable supply chain practices and managerial support. Managerial support also enhances sustainability and partially mediates the relationship between Industry 4.0 and supply chain sustainability. The findings suggest that the adoption of Industry 4.0 technologies, coupled with active managerial involvement, strengthens sustainable practices in the beverage sector. The study contributes to understanding how digital transformation and leadership jointly promote sustainability in developing economies. Future research should examine other manufacturing sectors and the role of Industry 4.0 in driving creativity and innovation.
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Autorzy i Afiliacje

Elizabeth KOOMSON
Piotr PIETRZAK
Joanna PALISZKIEWICZ
Prince Junior AMOH
Harrietta AKROFI-ANSAH

Abstrakt

The paper compares the results of protective measures used to maintain business continuity of Polish production enterprises during a crisis situation. It examines how these results differ depending on the type and form of production organization. A mix-model CATI/CAWI survey was performed on a representative group of 600 enterprises. Over 40 individual protection measures were identified and assigned to one of five predefined groups. The research specified three production types (unit/job, batch, mass) and two production forms (line production, job-shop production). A chi-square test was used for the statistical analysis. The results indicate that production volume, defined as the number of manufactured, repetitive products, affects the selection of protection measures. The production form, reflecting the way a product flows through the production process, is also significant. The findings provide a basis for future preparations aimed at ensuring business continuity in the event of similar disruptions to production processes.
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Autorzy i Afiliacje

Anna KOSIERADZKA
Justyna SMAGOWICZ
Cezary SZWED

Abstrakt

Conveyor equipment is commonly used in packaging systems, and operators must stand for long periods and perform a similar, monotonous task. This might increase the operator’s physical and mental fatigue. A hypothesis is, that the speed, noise, and direction of material flow influence the operator’s workload. A work system optimization could be proposed using response surface methodology. The Central Composite Design was proposed as an experimental design. Both physical and mental workload are assessed as the response. The results showed that the optimal combination of physical workload response parameters was a speed of 25.8579 cm/s, a noise of 89.3569 dB, and a flow direction from right to left. Meanwhile, the optimal parameter combination for mental workload response was a speed of 25.8579 cm/s and a noise level of 77.9289 dB, with the most optimal flow direction from right to left.
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Autorzy i Afiliacje

Nachnul ANSORI
Ernaning WIDIASWANTI
David KRESTIYANTO

Abstrakt

The aim of this publication is to synthesize dynamic models for selected innovation diffusion models. For this purpose, modeling in the system dynamics (SD) convention was applied to represent the flow between the stock of potential and current users of innovations in three selected diffusion models: the Bass model, the source model, and the contact model. The AnyLogic software was used as the simulation environment. As a result of the study, simulation models were developed that enable forecasting the behavior of participants in a given population depending on predefined coefficients. This solution is particularly useful for the cost optimization of promotional activities in enterprise departments responsible for marketing innovative products, as well as for diffusion understood as the dissemination of modern organizational and process methods among employees of an organization.
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Autorzy i Afiliacje

Kamil WYSOCKI
Zbigniew WIŚNIEWSKI
Aleksandra LOTA

Abstrakt

In the automotive industry, sustainable manufacturing involves integrating the triple bottom line of economic, environmental, and social aspects into manufacturing operations. However, the automotive industry faces challenges in prioritizing sustainability due to its interdependence and complexity, where effective decision-making requires identifying influential factors and understanding their relationship. To address the challenge, a hybrid method combining Interpretive Structural Modeling (ISM) and MICMAC analysis is utilized. ISM establishes connections between specific criteria, enabling a comprehensive understanding of their interdependencies. MICMAC analysis then helps the prioritization process by classifying factors according to their driving and dependency power. This approach helps stakeholders identify the most crucial factors and develop action plans to reduce or eliminate obstacles hindering the adoption of sustainable manufacturing practices. This study addresses the sustainability issues in the automotive sector in Kerala, India. Furthermore, the study suggests the potential expansion by conducting a large-scale survey to include additional criteria, thereby enhancing the understanding of sustainable practices in the automotive sector. The results indicate the proposed ISM-MICMAC model outperforms existing methods in several areas, including accuracy of prioritization (92.5% vs. 70% for AHP), resource efficiency (85% vs. 60% for Carbon Footprint Analysis), emission reduction (30% vs. 20% for LCA), and stakeholder engagement (85% vs. 80% for LCA).
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Autorzy i Afiliacje

S.S. SAJI
N. RAMASAMY
M. Dev. ANAND
N. SANTHI

Instrukcja dla autorów

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The average time during which the preliminary assessment of manuscripts is conducted - 14 days

The average time during which the reviews of manuscripts are conducted - 6 months

The average time in which the article is published - 12 months

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For Authors: All articles, published in the journal Management and Production Engineering Review have to comprise a list of references which correspond with the journal’s Instructions to authors for paper preparation. The authors should ensure that they have written entirely original works, and if the authors have used the work and/or words of others that this has been appropriately cited or quoted. All articles are tested using antyplagiarism programme. An author should not in general publish manuscripts describing essentially the same research in more than one journal or primary publication. Submitting the same manuscript to more than one journal concurrently constitutes unethical publishing behaviour and is unacceptable. Authorship should be limited to those who have made a significant contribution to the conception, design, execution, or interpretation of the reported study. The corresponding author should ensure that all co-authors have seen and approved the final version of the paper and have agreed to its submission for publication. All authors should disclose in their manuscript any financial or other substantive conflict of interest that might be construed to influence the results or interpretation of their manuscript. All sources of financial support for the project should be disclosed.
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Procedura recenzowania

Received manuscripts are first examined by the Management and Production Engineering Review Editors. Manuscripts clearly not suitable for publication, incomplete or not prepared in the required style will be sent back to the authors without scientific review, but may be resubmitted as soon as they have been corrected. The corresponding author will be notified by e-mail when the manuscript is registered at the Editorial Office (marta.grabowska@put.poznan.pl). The ultimate decision to accept, accept subject to correction, or reject a manuscript lies within the prerogative of the Editor-in-Chief and is not subject to appeal. The editors are not obligated to justify their decision. All manuscripts submitted to MPER editorial office (https://wydawnictwo.pan.pl/index.php/mper/about/submissions) will be sent to at least two and in some cases three reviewers for passing the double-blind review process. The responsible editor will make the decision either to send the manuscript to another reviewer to resolve the difference of opinion or return it to the authors for revision.

The average time during which the preliminary assessment of manuscripts is conducted - 14 days
The average time during which the reviews of manuscripts are conducted - 6 months
The average time in which the article is published - 8.4 months

Recenzenci

Name Surname Affiliation Hind Ali University of Technology, Iraq Katarzyna Antosz Rzeszow University of Technology, Poland Bagus Arthaya Mechatronics Engineering Universitas Parahyangan, Indonesia Sarini Azizan Australian National University, Australia Zbigniew Banaszak Management and Computer Science, Koszalin University of Technology, Poland Lucia Bednarova Technical University of Kosice, Slovak Republic Kamila Borsekova UNIVERZITA MATEJA BELA V BANSKEJ BYSTRICI, Slovak Republic RACHID Boutarfa Hassan First University, Morocco Anna Burduk Wrocław University of Science and Technology, Poland Virginia Casey Universidad Nacional de Rosario, Argentina Claudiu Cicea Bucharest University of Economic Studies Romania, Romania Ömer Cora Karadeniz Technical University, Turkey Wiesław Danielak Uniwersytet Zielonogórski, Poland" Jacek Diakun Poznan University of Technology, Poland Ewa Dostatni Poznan University of Technology, Poland Marek Dźwiarek Milan Edl University of West Bohemia, Czech Republic Joanna Ejdys Bialystok University of Technology, Poland Abdellah El barkany Sidi Mohamed Ben Abdellah University Faculty of Science and Technology of Fez, Morocco Francesco Facchini Università degli Studi di Bari, Italy Mária Magdolna Farkasné Fekete Szent István University, Hungary Çetin Fatih Başkent Üniversitesi, Turkey Mose Gallo Materials and Industrial Production Engineering, University of Napoli Federico, Italy Mit Gandhi Gujarat Gas Limited, India Józef Gawlik Cracow University of Technology, Institut of Production Engineering, Poland Andrzej Gessner Politechnika Poznańska, Poland Pedro Glass Universitatea Valahia din Targoviste, Romania Arkadiusz Gola Lublin University of Technology, Faculty of Mechanical Engineering, Poland Alireza Goli Department of industrial engineering, Yazd university, Yazd, Iran Iran, Iran Magdalena Graczyk-Kucharska Politechnika Poznańska, Poland Damian Grajewski Poznan University of Technology, Poland Łukasz Grudzień Production Engineering Department, Poznan University of Technology, Poland Patrik Grznár University of Žilina, Slovak Republic" Anouar Hallioui INTI International University, Malaysia Ali HAMIDOGLU Adam Hamrol Mechanical Engineering, Poznan University of Technology, Poland ni luh putu hariastuti itats, Indonesia Christian Harito Bina Nusantara University, Indonesia Muatazz Hazza "Mechanical and Industrial Engineering Department; School of Engineering. American University of Ras Al Khaimah. United Arab Emirates, United Arab Emirates" Ali Jaboob Dhofar University, Oman Małgorzata Jasiulewicz-Kaczmarek Poznan University of Technology, Poland Oláh Judit University of Debrecen, Hungary Jan Klimek Szkoła Główna Handlowa, Poland Nataliia Klymenko National University of Life and Environmental Sciences of Ukraine, Ukraine Peter Kostal Slovenská Technická Univerzita V Bratislave, Slovak Republic Martin Krajčovič University of Žilina, Slovak Republic Robert Kucęba Wydział Zarządzania, Politechnika Częstochowska, Poland Agnieszka Kujawińska Poznan University of Technology Edyta Kulej-Dudek Politechnika Częstochowska, Poland Sławomir Kłos Institute of Mechanical Engineering, University of Zielona Góra, Poland Christian Landschützer Graz University of Technology, Austria Anna Lewandowska-Ciszek Department of Logistics, Poznań University of Economics and Business, Poland Damjan Maletič University of Maribor, Faculty of Organizational Sciences, Slovenia Marcela Malindzakova Technical University, Slovak Republic Józef Matuszek Janusz MLECZKO Rami Mokao MIS - Management Information Systems, HIAST, Syria Maria Elena Nenni University of Naples, Italy Nor Hasrul Akhmal Ngadiman School of Mechanical Engineering, Universiti Teknologi Malaysia, Malaysia Dinh Son Nguyen The University of Danang, University of Science and Technology, Viet Nam Duc Duy Nguyen Department of Industrial Systems Engineering,
Ho Chi Minh Technology University (HCMUT), Viet Nam Filscha Nurprihatin Sampoerna University, Indonesia Filip Osiński Poznan University of Technology Ivan Pavlenko Department of General Mechanics and Machine Dynamics, Sumy State University, Ukraine Robert Perkin BorgWarner, United States Alin Pop University of Oradea, Romania Ravipudi Venkata Rao "Department of Mechanical Engineering S. V. National Institute of Technology, Surat, India" Marta Rinaldi University of Campania, Italy Michał Rogalewicz Division of Production Engineering, Institute of Materials Technology, Faculty of Mechanical Engineering, Poznan University of Technology, Poland David Romero Tecnológico de Monterrey, Mexico ELMADANI SAAD Hassan First university of Settat, Morocco Krzysztof Santarek Faculty of Mechanical and Industrial Engineering, Warsaw University of Technology, Poland shankar sehgal Panjab University Chandigarh, India Robert Sika Faculty of Mechanical Engineering and Management, Institute of Materials Technology, Poland Chansiri Singhtaun Department of Industrial Engineering, Faculty of Engineering, Kasetsart University, Thailand Bożena Skołud Silesian University of Technology, Poland Lucjan Sobiesław Jagiellonian University, Poland Fabiana TORNESE University of Salento, Italy Stefan Trzcielinski Poznan University of Technology, Poland Amit Kumar Tyagi Centre for Advanced Data Science, India Cang Vo Binh Duong University, Viet Nam Jaroslav Vrchota University of South Bohemia České Budějovice, Faculty of Economics, Department of Management, Studentská, 370 05 České Budějovice, Czech Republic Radosław Wichniarek Poznan University of Technology, Poland Ewa Więcek-Janka Wydział Inżynierii Zarządzania, Politechnika Poznańska, Poland Josef Zajac Uniwersytet Techniczny w Koszycach, Slovak Republic Aurora Zen Universidade Federal do Rio Grande do Sul, Brazil

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