Management and Production Engineering Review

Content

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

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Abstract

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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Authors and Affiliations

Mikael KUCEJKO
Marek BUGDOL
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Abstract

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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Authors and Affiliations

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

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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Authors and Affiliations

Fatima Ezzahra Sebtaoui
Anwar Meddaoui
Ahmed Ennhaili
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Abstract

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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Authors and Affiliations

Karol Chrzanowski
Dariusz Dobrowolski
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Abstract

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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Authors and Affiliations

Ivan BULEEV
Natalya BRYUKHOVETSKAYA
Tetyana KORYTKO
Iryna BRYL
Oleksandra PRYKHODKO
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Abstract

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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Authors and Affiliations

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

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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Authors and Affiliations

Krzysztof STALL
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Abstract

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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Authors and Affiliations

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

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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Authors and Affiliations

Sławomir Luściński
Mariusz Bednarek
Marek Jabłoński
ORCID: ORCID
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Abstract

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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Authors and Affiliations

Ireneusz ZAGÓRSKI
Jarosław KORPYSA
Monika KULISZ
Agnieszka SKOCZYLAS
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Abstract

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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Authors and Affiliations

Patricio SÁEZ
JAIME RIVERA
PATRICIO SALAS
VICTOR PARADA
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Abstract

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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Authors and Affiliations

Marcela MALINDZAKOVA
Lubomir AMBRISKO
Timea SIMONOVA
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Abstract

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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Authors and Affiliations

Stanisław LEGUTKO
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Abstract

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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Authors and Affiliations

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

Instructions for authors

REVIEW PROCESS

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 (https://www.editorialsystem.com/mper/). 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 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 system ( 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 material formatted in the MPER format must be unpublished and not under submission elsewhere.

REVIEWERS
Once a year a list of co-operating reviewers is publish in electronic version of MPER. All articles published in MPER are published in open access.


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Maximum length of the article is 18 pages (using MPER template).
There is no submission charge.

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Additional info

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/.

Publication Ethics Policy

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.
For Editor-in-Chief: The editor is responsible for decision which of the articles submitted to the journal should be published. The editor and editorial board and office must not disclose any information about a submitted manuscript to anyone other than the corresponding author, reviewers, potential reviewers, other editorial advisers, and the publisher, as appropriate. Unpublished materials disclosed in a submitted manuscript must not be used in an editor's own research without the express written consent of the author.
For Reviewers: Peer review helps the editor in making editorial decisions and also assist the author in improving the paper. Any selected referee who feels unqualified to review the research reported in a manuscript or knows that its prompt review will be impossible should notify the editor and excuse himself from the review process. Any manuscripts received for review must be treated as confidential documents. They must not be shown to or discussed with others except as authorized by the editor. Reviews should be conducted objectively. Personal criticism of the author is inappropriate. Reviewers should identify relevant published work that has not been cited by the authors. Any statement that an observation, derivation, or argument had been previously reported should be accompanied by the relevant citation. A reviewer should also call to the editor's attention any substantial similarity or overlap between the manuscript under consideration and any other published paper of which they have personal knowledge. Information obtained through peer review must be kept confidential and not used for personal advantage. Reviewers should not consider manuscripts in which they have conflicts of interest resulting from competitive, collaborative, or other relationships or connections with any of the authors, companies, or institutions connected to the papers. Other sources: http://apem-journal.org/


Peer-review Procedure

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

Reviewers

2024
No Name Surname Affiliation
1 Abd El-Rahman Abd El-Raouf Ahmed Agricultural Engineering, Agricultural Engineering Research Institute, Giza , Egypr
2 Wiktor Adamus Jagiellonian University, Poland
3 Shoaib Akhtar Fatima Jinnah Women University, Pakistan
4 Mohammad Al-Adaileh "COLLEGE OF ENGINEERING Engineering, Technology, and Management Assistant Professor of Instruction, United States"
5 Hind Ali University of Technology, Iraq
6 Katarzyna Antosz Rzeszow University of Technology, Poland
7 Muhammad Asrol Binus University, Indonesia
8 Lucia Bednarova Technical University of Kosice, Slovak Republic
9 Haniyah Bilal Haverford university, United States
10 Berihun Bizuneh "Bahir Dar University Bahir Dar Univ, Ethiopian Inst Text & Fash Technol, Bahir Dar, Ethiopia, Ethiopia"
11 Łukasz Brzeziński Katedra Organizacji i Zarządzania, Wyższa Szkoła Logistyki w Poznaniu, Poland
12 Waldemar Budner Katedra Logistyki, Uniwersytet Ekonomiczny w Poznaniu, Poland
13 Anna Burduk Wrocław University of Science and Technology, Poland
14 Vishnu C R Department of Humanities and Social Sciences, Indian Institute of Technology Tirupati, India
15 Fatih Çetin Başkent Üniversitesi, Turkey
16 Danylo Cherevatskyi Institute of Industrial Economics of NAS of Ukraine: Kiev, UA, Ukraine
17 Claudiu Cicea Bucharest University of Economic Studies Romania, Romania
18 Hasan Huseyin Coban Department of Electrical Engineering, Bartin University, Turkey
19 Juan Cogollo-Florez Universidad Nacional de Colombia, Colombia
20 David Coopler Universitat Politècnica de València, Romania
21 Ömer Cora Karadeniz Technical University, Turkey
22 Margareta Coteata Gheorghe Asachi Technical University of Iasi, Department of Manufacturing Engineering, Romania
23 Szymon Cyfert Poznań University of Economics and Business, Poland
24 Valentina Di Pasquale Department of Industrial Engineering, University of Salerno, Italy
25 Milan Edl University of West Bohemia, Czech Republic
26 Luis Edwards Cornell University, United States
27 Joanna Ejdys Bialystok University of Technology, Poland
28 Abdellah El barkany Sidi Mohamed Ben Abdellah University Faculty of Science and Technology of Fez, Morocco
29 Chiara Franciosi CRAN UMR 7039, Université de Lorraine, France
30 Mose Gallo Materials and Industrial Production Engineering, University of Napoli Federico, Italy
31 Tetiana Galushkina State Ecological Academy of Postgraduate Education and Management, Ukraine
32 Józef Gawlik Cracow University of Technology, Institut of Production Engineering, Poland
33 Rohollah Ghasemi, College of Management, University of Tehran, Iran
34 Arkadiusz Gola, Lublin University of Technology, Faculty of Mechanical Engineering, Poland
35 Alireza Goli Department of industrial engineering, Yazd university, Yazd, Iran
36 Magdalena Graczyk-Kucharska, Politechnika Poznańska, Poland
37 Adriana Grenčíková Industry 4.0, Human factor, Ergonomic, Slovak Republic
38 Patrik Grznár, Department of Industrial Engineering, University of Žilina Faculty of Mechanical Engineering, Slovak Republic
39 Anouar Hallioui INTI International University, Malaysia
40 Adam Hamrol Mechanical Engineering, Poznan University of Technology, Poland
41 ni luh putu hariastuti itats, Indonesia
42 Paula Heliodoro, Polytechnic Institute of Setubal, Portugal
43 Vitalii Ivanov Department of Manufacturing Engineering, Machines and Tools, Sumy State University, Ukraine
44 Ali Jaboob Dhofar University, Oman
45 Zamberi Jamaludin Universiti Teknikal Malaysia Melaka, Malaysia
46 Izabela Jonek-Kowalska, Wydział Organizacji i Zarządzania Politechnika Śląska, Poland
47 Satishbabu ACE India
48 Prasad Kanaka Institute of Industrial Relations and Human Resource Development, India
49 Anna Karwasz Poznan University of Technology, Poland
50 Waldemar Karwowski University of Central Florida, United States
51 Osmo Kauppila University of Oulu, Finland
52 Tauno Kekale Merinova Technology Centre, Finland
53 Mahmoud Khedr Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt, Egypt
54 Peter Kostal Department of Production Systems, Metrology and Asembly, Slovenská Technická Univerzita V Bratislave, Faculty of Material Science and Technology, Slovak Republic
55 Boris Kostow University of Angela Kyncheva in Ruse, Bulgaria
56 Martin Krajčovič, University of Žilina, Faculty of Mechanical Engineering, Slovak Republic
57 Caroline  Kristian Uppsala University, Sweden
58 Robert Kucęba Wydział Zarządzania, Politechnika Częstochowska, Poland
59 Agnieszka Kujawińska Poznan University of Technology
60 Edyta Kulej-Dudek Politechnika Częstochowska, Poland
61 Bhakaporn Kuljirundhorn Foxford University, Canada
62 Rajeev Kumar Doon University, India
63 Sławomir Kłos Institute of Mechanical Engineering, University of Zielona Góra, Poland
64 Yu Lee National Tsing Hua University, Taiwan
65 Anna Lewandowska-Ciszek Department of Logistics, Poznań University of Economics and Business, Poland
66 Wojciech Lewicki West Pomeranian University of Technology in Szczecin, Poland
67 Tetiana Likhouzova National Technical University of Ukraine, “Igor Sikorsky Kyiv Polytechnic Institute”, Ukraine
68 Damjan Maletič University of Maribor, Faculty of Organizational Sciences, Slovenia
69 Marcela Malindzakova Technical University, Slovak Republic
70 Ildiko Mankova Technical University of Košice, Slovakia
71 Arnaud  Marcelline University of Nantes, France
72 Józef Matuszek University of Bielsko-Biała, Poland
73 Marcin Matuszny Department of Production Engineering, Faculty of Mechanical Engineering and Computer Science, University of Bielsko-Biala, ul. Willowa 2, 43-300 Bielsko-Biała
74 Giovanni Mazzuto Università Politecnica Delle Marche, Italy
75 Tomasz Małkus Uniwersytet Ekonomiczny w Krakowie, Katedra Procesu Zarządzania, Poland, Poland
76 Rafał Michalski Katedra Systemów Zarządzania i Rozwoju Organizacji, Politechnika Wrocławska, Poland
77 Jerzy Mikulik AGH University of Krakow, Poland
78 Rami Mokao MIS - Management Information Systems, HIAST, Syria
79 Norsyahida Mokhtar International Islamic University Malaysia, Malaysia
80 Ig. Jaka Mulyana Industrial Engineering, Widya Mandala Surabaya Catholic University, Indonesia
81 Nor Hasrul Akhmal Ngadiman School of Mechanical Engineering, Universiti Teknologi Malaysia, Malaysia
82 Duc Duy Nguyen Department of Industrial Systems Engineering, Ho Chi Minh Technology University (HCMUT), Viet Nam
83 fernando Nino Polytechnic University of San Luis Potos, Mexico
84 Filscha Nurprihatin Sampoerna University, Indonesia
85 Rebecca Oliver Stockton University, United States
86 Anita Pavlenko Kryvyi Rih State University of Economics and Technology, Ukraine
87 Aleksandar Pesic, MB University, Faculty of Business and Law, Belgrade, Serbia, Serbia
88 Huy Phan Education Technology University, Vietnam, Viet Nam
89 Anna Piekarczyk Poznan School of Logistics (WSL), Poland
90 Alin Pop University of Oradea, Romania
91 Humiras Purba Industrial Engineering, Associate Professor, Universitas Mercu Buana, Jakarta, Indonesia, Indonesia
92 Tengku nur Azila Raja Mamat Universiti Tun Hussein Onn Malaysia (UTHM), Malaysia
93 Silvijo  Renato University of Rijeka, Croatia
94 Piotr Rogala Department of Quality and Environmental Management, Wroclaw University of Economics and Business, Poland
95 Michał Rogalewicz, Faculty of Mechanical Engineering, Poznan University of Technology, Poland
96 Izabela Rojek Institute of Computer Science, Kazimierz Wielki University, Poland
97 Adam Sadowski Katedra Strategii i Zarządzania Wartością Przedsiębiorstwa, Uniwersytet Łódzki, Poland
98 Mansia Sadyrova Al-Farabi Kazakh National University, Kazakhstan
99 Nadia Saeed University of the Punjab, Pakistan
100 Sebastian Saniuk Uniwersytet Zielonogórski, Poland
101 Krzysztof Santarek Faculty of Mechanical and Industrial Engineering, Warsaw University of Technology, Poland
102 shankar sehgal Panjab University Chandigarh, India
103 Piotr Senkus University of Warsaw, Poland
104 Jarosław Sęp Politechnika Rzeszowska, Wydział Budowy Maszyn i Lotnictwa, Poland
105 Robert Sika Faculty of Mechanical Engineering and Management, Institute of Materials Technology, Poland
106 Dariusz Sobotkiewicz Instytut Nauk o Zarządzaniu i Jakości, Uniwersytet Zielonogórski, Poland
107 Beata Starzyńska Poznan University of Technology
108 Klaudia Tomaszewska Faculty of Management Engineering, Bialystok University of Technology, Poland
109 Stefan Trzcielinski Poznan University of Technology, Poland
110 Cang Vo Binh Duong University, Viet Nam
111 Somporn Vongpeang Faculty of Technical Education, Rajamangala University of Technology Thanyaburi, Thailand
112 Jaroslav Vrchota University of South Bohemia České Budějovice, Faculty of Economics, Czech Republic
113 Gerhard-Wilhelm Weber Poznań University of Technology, Poland
114 Ewa Więcek-Janka Wydział Inżynierii Zarządzania, Politechnika Poznańska, Poland
115 Linda Winters Czech University of Life Sciences, Czech Republic
116 Zbigniew Wisniewski Lodz University of Technology, Poland
117 Piotr Wróblewski Faculty of Engineering, University of Technology and Economics H. Chodkowska in Warsaw, Poland
118 Iseul  Young Hanyang University, Korea (South)
119 Chong Zhan Hubei University, China
120 Sylwia Łęgowik-Świącik Czestochowa University of Technology Poland, Poland


2025
No. Name Surname Affiliation
1 akshat gaurav akshat Asia University, Taiwan
2 luma Al-kindi University of Technology, Iraq
3 Hind Ali University of Technology, Iraq
4 Katarzyna Antosz Rzeszow University of Technology, Poland
5 Gilmar Batalha Universidade de Sao PauloUniv Sao Paulo, Mech Engn Dept, Escola Politecn, Sao Paulo, SP, Brazil, Brazil
6 Lucia Bednarova Technical University of Kosice, Slovak Republic
7 Anna Burduk Wrocław University of Science and Technology, Poland
8 Danylo Cherevatskyi Institute of Industrial Economics of NAS of Ukraine: Kiev, UA, Ukraine
9 Dorota Czarnecka-Komorowska Faculty of Mechanical Engineering, Poznan University of Technology, Poland
10 SUGANYA Devi National Institute of Technology,Silchar, India
11 Jacek Diakun Poznan University of Technology, Poland
12 Milan Edl University of West Bohemia, Czech Republic
13 João Furtado Santa Cruz do Sul University, Brazil
14 Bożena Gajdzik "Politechnika Śląska Wydział Inżynierii Materiałowej Katedra Informatyki Przemysłowej, Poland"
15 Mose Gallo Materials and Industrial Production Engineering, University of Napoli Federico, Italy
16 Remigiusz Gawlik Department of Public Management, Krakow University of Economics (KUE), Poland
17 Raja Reddy GNV University of Saskatchewan, Canada
18 Arkadiusz Gola Department of Production Informatisation and Robotisation, Lublin University of Technology,Poland
19 Alireza Goli Department of industrial engineering, Yazd university, Yazd, Iran Iran, Iran
20 Cristian Gómez Universidad Nacional de Colombia, Colombia
21 José-Armando HIDALGO CRESPO ENSAM, Spain
22 Magdalena HRYB Faculty of Mechanical Engineering, Poznan University of Technology, Poland
23 Katarzyna Hys Opole University of Technology, Poland
24 Izabela Jonek-Kowalska "Wydział Organizacji i Zarządzania Politechnika Śląska, Poland"
25 Amirhossein Karamoozian, University of Chinese Academy of Sciences, China
26 Anna Karwasz Poznan University of Technology, Poland
27 khaoula khlie Liwa college, Morocco
28 Jerzy Kisilowski
29 Peter Kostal, Slovenská Technická Univerzita V Bratislave, Faculty of Material Science and Technology, Slovak Republic
30 Herbert Kotzab Institute for Logistics and Supply Chain Management, University of Bremen, Germany
31 Martin Krajčovič University of Žilina, Faculty of Mechanical Engineering, Slovak Republic
32 Krzysztof Krystosiak Toronto Metropolitan University, Graphic Communications Management, Canada
33 Wiesław Kuczko Poznan University of Technology, Poland
34 Agnieszka Kujawińska Poznan University of Technology, Poland
35 Edyta Kulej-Dudek Politechnika Częstochowska, Poland
36 Anup Kumar Inst Management Technol NagpurInst Management Technol Nagpur, Nagpur, Maharashtra, India, India
37 Sławomir Kłos Institute of Mechanical Engineering, University of Zielona Góra, Poland
38 Quynh Le Song Thanh Ho Chi Minh Technology University, Viet Nam
39 Yu Lee National Tsing Hua University, Taiwan
40 Stanisław Legutko Faculty of Mechanical Engineering, Poznan University of Technology, Poznan, Poland, Poland
41 Anna Lewandowska-Ciszek Department of Logistics, Poznań University of Economics and Business, Poland
42 José Machado University of Minho · School of Engineering, Portugal
43 Damjan Maletič University of Maribor, Faculty of Organizational Sciences, Slovenia
44 Marcela Malindzakova Technical University, Slovak Republic
45 Tomasz Malkus Department of Management Process, Cracow University of Economics, Poland
46 Mengistu Manaye, Kombolcha Institute of Technology, Wollo University, Ethiopia, Ethiopia
47 Marcin Matuszny, Faculty of Mechanical Engineering and Computer Science, University of Bielsko-Biala, Poland
48 Tomasz Małkus, Uniwersytet Ekonomiczny w Krakowie, Katedra Procesu Zarządzania, Poland, Poland
49 Rami Mokao MIS - Management Information Systems, HIAST, Syria
50 Beata Mrugalska Poznan University of Technology, Poland
51 Ig. Jaka Mulyana Industrial Engineering, Widya Mandala Surabaya Catholic University, Indonesia
52 fernando Nino Polytechnic University of San Luis Potos, Mexico
53 Shimon Nof Purdue University, United States
54 Hana Pacaiová KLI, Faculty of Mechanical Engineering, Faculty of Aeronautics, Technical University of Košice, Slovak Republic
55 Arun Kiran Pal Printing Engineering Department, Jadavpur University, India
56 Michal Patak University of Pardubice, Czech Republic
57 Ivan Pavlenko Department of General Mechanics and Machine Dynamics, Sumy State University, Ukraine
58 Miriam Pekarcikova Department of industrial and digital engineering, Technical University of Košice, Faculty of Mechanical Engineering, Slovak Republic
59 Alin Pop University of Oradea, Romania
60 Praveen Prabhu School of Engineering and Technology, Shivaji University, Kolhapur., India
61 Humiras Purba Industrial Engineering, Associate Professor, Universitas Mercu Buana, Jakarta, Indonesia, Indonesia
62 Paulina Rewers Faculty of Mechanical Engineering, Poznań University of Technology, Poland
63 Michał Rogalewicz Division of Production Engineering, Institute of Materials Technology, Faculty of Mechanical Engineering, Poznan University of Technology, Poland
64 Izabela Rojek Institute of Computer Science, Kazimierz Wielki University, Poland
65 David Romero Tecnológico de Monterrey, Mexico
66 Adam Sadowski Katedra Strategii i Zarządzania Wartością Przedsiębiorstwa, Uniwersytet Łódzki, Poland
67 Abdu Salam Abdul Wali Khan Univ MardanAbdul Wali Khan Univ Mardan, Dept Comp Sci, Mardan 23200, Pakistan, Pakistan
68 fernando sampaio KMITL, Brazil
69 Sebastian Saniuk Uniwersytet Zielonogórski, Poland
70 Iman Sharaf "Higher Technological Institute - Egypt Higher Technol Inst, Dept Basic Sci, Cairo, Egypt, Egypt"
71 Robert Sika Faculty of Mechanical Engineering and Management, Institute of Materials Technology, Poland
72 Beata Starzyńska Poznan University of Technology
73 Robert Ulewicz Politechnika Częstochowska, Poland
74 Wiesław Urban Politechnika Białostocka, Poland
75 Cang Vo Binh Duong University, Viet Nam
76 Jaroslav Vrchota University of South Bohemia České Budějovice, Czech Republic
77 Ewa Więcek-Janka Wydział Inżynierii Zarządzania, Politechnika Poznańska, Poland
78 Sylwia Łęgowik-Świącik Czestochowa University of Technology Poland, Poland

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