Management and Production Engineering Review

Content

Management and Production Engineering Review | 2026 | No 1

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Abstract

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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Authors and Affiliations

Ajay Kumar BOYAT
Vinit GUPTA
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Abstract

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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Authors and Affiliations

Jadwiga Krupnik-Worek
Sebastian Skoczypiec
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Abstract

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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Authors and Affiliations

Jakub PIZOŃ
Łukasz WÓJCIK
Arkadiusz GOLA
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Abstract

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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Authors and Affiliations

Karel STIBOR
Sona SEDIVA
Radek ŠTOHL
Lenka ŠTOHLOVÁ PUTNOVÁ
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Abstract

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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Authors and Affiliations

Mariusz NIEKURZAK
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Abstract

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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Authors and Affiliations

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

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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Authors and Affiliations

Paweł PAWLEWSKI
Dominik kAŹMIERCZAK
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Abstract

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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Authors and Affiliations

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

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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Authors and Affiliations

Anna KOSIERADZKA
Justyna SMAGOWICZ
Cezary SZWED
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Abstract

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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Authors and Affiliations

Nachnul ANSORI
Ernaning WIDIASWANTI
David KRESTIYANTO
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Abstract

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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Authors and Affiliations

Kamil WYSOCKI
Zbigniew WIŚNIEWSKI
Aleksandra LOTA
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Abstract

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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Authors and Affiliations

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

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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://wydawnictwo.pan.pl/index.php/mper/about/submissions). 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://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 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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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/.

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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). 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

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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