Management and Production Engineering Review

Zawartość

Management and Production Engineering Review | 2025 | Vol. 16 | No 3

Abstrakt

This paper focuses on the application of machine learning in the Failure Mode and Effects Analysis (FMEA) process for analyzing failure modes and effects using data modeling. FMEA is a recognized methodology used to detect and assess potential problems in products and processes before they occur. The main objective was to develop a neural network model that could predict potential failure modes and their effects, using a specially prepared anonymised table derived from industrial DFMEA records. Utilizing machine learning in the context of FMEA opens new perspectives in terms of accuracy, objectivity, and efficiency of analysis, while reducing subjectivity and the time required for the traditional FMEA analysis approach. The proposed neural network model performs calculations and analyses, enabling a deeper understanding of the patterns in the data and their potential applications in the industry.
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Autorzy i Afiliacje

Mikael KUCEJKO
Marek BUGDOL

Abstrakt

Lean manufacturing (LM) aims to improve production efficiency by systematically reducing waste and fostering continuous improvement. This research analyzes how LM strategies are carried out, what is important for them to succeed and the benefits they bring to the manufacturing industry in Morocco’s north. Using a comprehensive questionnaire survey, the author shows that Kaizen and similar incremental improvement techniques matter and detailed their effect on top performance metrics, like how much a company produces and its cost efficiency. Using advanced statistical methods, the study investigated whether employee engagement, managerial support, and process adaptability act as mediators between lean practices and performance outcomes. The results confirm that adopting LM significantly enhances operational performance, emphasizing its central role in achieving manufacturing excellence. This paper illustrates the importance of LM as well as the practical advice to industries that wish to pursue sustainable competitive advantage by adopting lean transformation.
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Autorzy i Afiliacje

Fouad MIRALI
Ikhlef JEBBOR
Youssef RAOUF
Zoubida BENMAMOUN
Jessnor Elmy Mat JIZAT
Hanaa HACHIMI

Abstrakt

This article’s primary objective is to demonstrate the influence of the human-centric technology adoption factor on JIT4.0 implementation by displaying the best practices used in Moroccan JIT4.0 organizations and the benefits obtained. By analyzing the critical success factors (CSFs) or activities that manufacturing organizations perform when implementing Just-In- Time (JIT) and Industry 4.0, three latent variables are identified: production strategy (PS), relation with suppliers (RS), and human-centric technology (HCT). Based on the benefits obtained from JIT4.0 implementation, three latent variables are identified and analyzed: the benefits of the production process (BPP), the benefits of inventory management (BIM), and economic benefits (EB). The study also proposes a structural equation model that considers the human-centric technology adoption factor as the leading factor in the implementation of JIT4.0. Additionally, it demonstrates that this factor interacts with other CSFs and benefits as the primary independent latent variable.
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Autorzy i Afiliacje

Fatima Ezzahra Sebtaoui
Anwar Meddaoui
Ahmed Ennhaili

Abstrakt

The rapid development of solutions based on modern information technologies (i.e., Semantic ML or ChatGPT) has emerged in the industry and its management methods. During the analysis of the possibilities of using these technologies in the processes of operation and maintenance of machines and devices, the options of using ontology for monitoring production processes based on measurement data obtained from vibration sensors located on the CP Factory production line in the Laboratory of Modeling of Intelligent Production Systems of the Kielce University of Technology were considered. The article aims to present the possibility of using measurement data to build an ontology of machine and equipment maintenance processes and to indicate the possibility of using it to create scenarios of events affecting the monitoring of appropriate operational parameters of the production process with the use of controlled natural language.
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Autorzy i Afiliacje

Karol Chrzanowski
Dariusz Dobrowolski

Abstrakt

Today, the development of a scientific and methodological approach to modelling the impact of digital transformation on enterprise management is highly relevant. This approach should be based on the rules of fuzzy logic and be adaptable to environmental changes. Consequently, the purpose of this study is to develop an optimal tool for modelling the decision-making process in enterprise management under the influence of digital transformation. The study’s outcome is a model for presenting fuzzy knowledge, demonstrated through examples of models designed to assess the impact of digital transformation on enterprise management, based on input from expert assessments. The developed model interprets the scored expert points for a loosely structured or unstructured task, thereby revealing the subjectivity of experts and providing a quantitative assessment for non-formalised tasks.
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Autorzy i Afiliacje

Ivan BULEEV
Natalya BRYUKHOVETSKAYA
Tetyana KORYTKO
Iryna BRYL
Oleksandra PRYKHODKO

Abstrakt

Excavated material transportation is crucial in mining operations, requiring optimal efficiency. Since the early 2000s, various aspects of transportation network optimization have been researched, often producing methods with overlapping objectives and outcomes. This work consolidates and analyzes existing methods and artifacts related to decision support for optimizing ore transportation networks. A systematic literature review, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, was conducted using sources such as Scopus, Web of Science, and Google Scholar. Out of 170 initial research papers, 46 were selected for detailed analysis. The review highlights the current state of decision support in ore transportation, focusing on supported decisions, optimized processes, and applied methods. It also identifies research gaps and future trends in this field.
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Autorzy i Afiliacje

Artur Skoczylas
Wiesława Gryncewicz
Paweł Stefaniak
ORCID: ORCID
Natalia Duda-Mróz
ORCID: ORCID

Abstrakt

This paper examines whether retrofitting a 1997 four-color offset press, Heidelberg Speedmaster SM74-4P, with Industrial Internet of Things (IIoT) solutions, preserves its performance compared to a 2021, digitally integrated Heidelberg Speedmaster CX75-4. For this purpose, observational studies with data collection were conducted. The study calculated descriptive statistics and Mann-Whitney U tests for print volume, job set-up time, production speed measures, waste, and overall equipment effectiveness (OEE). The results show that retrofitting an older press achieves comparable print volumes, and even the 1997M press had an average gross throughput that was 9.73% higher and a median that was 22.86% higher than the 2021 press. However, the new press achieves 63.14% higher average run length as well as significantly reduces make-ready time and waste. Despite the benefits that new machines offer due to technical advances, modernizing older machinery through IIoT solutions can therefore be a cost-effective strategy. In the discussed case, adaptation to the requirements of work in the modern IIoT environment by a relatively cheap modernization kit compared to a new machine, allowed for better operational efficiency, thus reducing costs and contributing to the sustainable development of the company without the need to invest in a new machine.
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Autorzy i Afiliacje

Krzysztof STALL

Abstrakt

This study examines the impact of makespan on production costs by comparing the Toyota Production System through simulations with three scheduling algorithms: the Initial Method, Simulated Annealing (SA), and Tabu Search (TS). This analysis explores the makespan, total production costs, and unit costs across three varying demand levels. Results show that SA and TS achieve a lower makespan than the Initial Method, although their total production costs are slightly higher. However, as demand increases, unit costs decrease in SA and TS, suggesting improved economies of scale with these methods. These findings highlight critical trade-offs between time and cost, emphasizing the importance of aligning scheduling choices with the company’s strategic goals. Additionally, the study addresses managerial aspects such as Break-even analysis, production constraints, and technology investments. Limitations include a restricted demand range and the exclusion of external factors, suggesting areas for further research on production quality in manufacturing.
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Autorzy i Afiliacje

Saiful MANGNGENRE
A. Besse Riyani INDAH
Diniary IKASARY
Ahmed HAMZI
Olyvia NOVAWANDA

Abstrakt

This article examines the advancement of predictive maintenance (PdM) for industrial assets through an innovative methodology that categorises diagnostic parameters into coherent groups. Predictive maintenance constitutes a vital component in mitigating unforeseen downtime and improving operational efficiency within manufacturing settings. The authors recommend a centralised framework for PdM, effectively addressing the complexities arising from data saturation by numerous sensor nodes. The proposed methodology refines the predictive maintenance process by systematically organising diagnostic parameters based on their significance and interconnections, thereby enhancing its effectiveness and efficiency. The study utilises the KNIME software platform for comprehensive data analysis and validation of the proposed approach, demonstrating its practicality with datasets obtained from SCADA/MES systems. The results confirm the robustness and accessibility of the methodology, highlighting its potential applicability across various industrial sectors. Future research directions include the integration of advanced machine learning techniques and the exploration of the methodology’s relevance in diverse industries.
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Autorzy i Afiliacje

Sławomir Luściński
Mariusz Bednarek
Marek Jabłoński
ORCID: ORCID

Abstrakt

From the point of view of production and manufacturing processes, issues related to surface quality and machining efficiency are very important. This paper presents the results of a study investigating selected problems of quality and efficiency in dry rough milling. Roughness parameters 2D and 3D were analysed. Additionally, 3D surface topography maps and Abbott– Firestone curves were generated. Carbide end mills with different helix angles were used in the study. Experiments were conducted on AZ91D magnesium alloy specimens. The machining process was conducted using high-speed machining parameters. The results showed that the surface roughness of the AZ91D alloy depended to a great extent on the tool geometry and applied machining parameters. Moreover, ANOVA statistical analysis and post-hoc tests (Tukey) were performed to assess the differences between individual groups of the specimens. Additionally, an artificial neural network (ANN) model was developed to predict the Ra parameter, and the results demonstrated its high predictive accuracy (R = 0.966).
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Autorzy i Afiliacje

Ireneusz ZAGÓRSKI
Jarosław KORPYSA
Monika KULISZ
Agnieszka SKOCZYLAS

Abstrakt

In modern manufacturing, addressing disruptions across multi-stage production requires adaptive and intelligent scheduling. This study evaluates two rescheduling strategies within a product-driven system for the Job Shop Scheduling Problem under disturbances: one based on the Shifting Bottleneck Heuristic (PDS-SBH), and another using a Monte Carlo Reinforcement Learning agent (PDS-RL). Products act as intelligent agents capable of autonomous decisions. A total of 151 simulations were conducted across 14 benchmark instances, with machine-level disruptions modeled as 100%, 200%, and 300% increases in processing times. PDS-SBH achieved average makespan reductions up to 5.2%, serving as a reactive and interpretable baseline. In contrast, PDS-RL consistently outperformed it, achieving reductions of 22.12%, 37.13%, and 53.87%, respectively. These results highlight the superior adaptability of reinforcement learning in uncertain production contexts. The study contributes to the understanding of how combining product-driven architectures with heuristic and learning-based strategies enables the development of intelligent, autonomous, and resilient scheduling systems.
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Autorzy i Afiliacje

Patricio SÁEZ
JAIME RIVERA
PATRICIO SALAS
VICTOR PARADA

Abstrakt

This study examines the use of the R&R (Repeatability and Reproducibility) method to improve measurement accuracy in quality control. By analysing single-operator (repeatability) and inter-operator (reproducibility) variability, the R&R method assesses overall system reliability. A case study on automotive part measurements shows acceptable variability levels but highlights repeatability as the main source of inconsistency. To enhance accuracy, the study recommends operator training, standardized procedures, regular calibration, and a stable measurement environment (consistent temperature, humidity, and low vibration). These improvements aim to reduce variability and increase system reliability, ensuring more precise quality control. The findings demonstrate the R&R method’s value in identifying variability sources and guiding measurement process enhancements.
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Autorzy i Afiliacje

Marcela MALINDZAKOVA
Lubomir AMBRISKO
Timea SIMONOVA

Abstrakt

The changing needs of modern manufacturing require a re-examination of the maintenance management role in achieving key cost and service benefits. The development of maintenance requirements is supported by the progress of information technology, which provides new opportunities for the implementation of maintenance processes. The aim of the article is to describe the latest trends in the field of maintenance management from the perspective of the challenges of the fourth and fifth industrial revolution as well as economic, environmental, and social challenges. The five stages of the machine maintenance approach, related to the five industrial revolutions, are characterized, along with the advantages and weaknesses of each machine maintenance approach. The operating data in different operating periods were characterized. Digitalization can empower machine maintenance services by using collected data and advanced technologies to monitor equipment health, diagnose faults, predict and prevent failures long before they occur and ensure performance optimization.
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Autorzy i Afiliacje

Stanisław LEGUTKO

Abstrakt

This study applied Lean tools without reducing staff, focusing instead on continuous improvement through enhanced machine efficiency, reduced waiting time, and optimized labor allocation. By using tools such as Value Stream Mapping, Balanced Transfer, Plant Simulation, and the E-Kanban system, the study reduced production time from 122.72 to 88.21 minutes and significantly improved overall productivity. Besides the workstation performance was improved, this study also addressed system-wide impacts, enabling effective reuse of labor when customer demand increases. The results show that a flexible application Lean tools is more effective than a rigid one and offers considerable potential for innovation in other manufacturers environments. However, the limitation of the study is that improvements have so far been implemented only within the production department, without involving other functional areas. In future research, we extend the scope of improvement to the entire enterprise and adapt this human-centered model to other manufacturing firms to promote sustainable growth without workforce reduction.
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Autorzy i Afiliacje

Thiet VAN DUONG
Linh THI DINH
Chinh NGOC NGUYEN
Anh Hai CONG NGUYEN
Tan MINH NGUYEN
Tuan DUC NGUYEN

Instrukcja dla autorów

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https://www.editorialsystem.com/mper/

They 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 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 at https://www.editorialsystem.com/mper/ 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.

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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

Dodatkowe informacje

The non-commercial use of the article will be governed by the Creative Commons Attribution license as currently displayed on https://creativecommons.org/licenses/by/4.0/.

Zasady etyki publikacyjnej

The ethics statements for the journal Management and Production Engineering Review are based on the guidelines of Committee on publication ethics (COPE) and the ELSEVIER publishing ethics resource kit.
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.
Authors are accountable for the originality, validity and integrity of the content of their submissions. In choosing to use AI tools, authors are expected to do so responsibly and in accordance with our editorial policies on authorship and principles of publishing ethics. Authorship requires taking accountability for content, consenting to publication via an author publishing agreement, giving contractual assurances about the integrity of the work, among other principles. These are uniquely human responsibilities that cannot be undertaken by AI tools. Therefore, AI tools must not be listed as an author. Authors must, however, acknowledge all sources and contributors included in their work. Where AI tools are used, such use must be acknowledged and documented appropriately.
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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; mper@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://www.editorialsystem.com/mper/) 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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