Management and Production Engineering Review

Content

Management and Production Engineering Review | 2022 | vol. 13 | No 3

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Abstract

The main purpose of the paper is to identify and analyse a state of exploratory motivating factors in terms of lean management as the instrument of a policy of human resource management in the face of COVID-19 pandemic implemented in service companies. The main question is: if the motivation system used in the companies works out up against the unpredictable situation such as COVID-19 pandemic? The secondary purpose of the paper is to recognise relations and dependencies between these factors, and the question is: what factors have the strongest or the weakest relations with Lean Staff Management (LSM) tools? This research designed based on interview was conducted due to the lack of existing studies on the current status of motivating factors in terms of lean management tools in two service companies (case studies) in the light of COVID-19. The results show that factors influencing work efficiency in a dominating manner were, primarily, financial incentives (almost 21%), communications (around 21%), and workplace atmosphere (almost 18%). The paper investigates also the benefits and concerns of implementing LSM in service companies during the pandemic. This research might help the service organization’s management to identify the employees‘ problems to implement more effective lean services.
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Authors and Affiliations

Patrycja Żegleń
1
Aldona Kluczek
2
Daniela Matusikova
3

  1. University of Rzeszów, Poland
  2. Warsaw University of Technology, Poland
  3. University of Presov, Slovakia
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Abstract

The automotive industry is characterized by a high degree of uncertainty. Companies are facing the challenge of producing different systems simultaneously. Additionally, the global quantity of electric vehicles is also expected to increase significantly. This results in the following capability to remain competitive: Effective and efficient adaptions of production systems to model variations and volume increases. While flexible production is identified as the most promising concept, defining the actual flexibility level of included production resources is essential for its proper realization. A literature review on existing flexibility assessment approaches revealed their emphasis on high-level enablers and limited practical applicability in the automotive industry. In contrast, focusing the assessment on single workstations supports the selection of appropriate production resources. Therefore, a simple and structured standard procedure for a production resource flexibility assessment was developed. This theoretical construct was subsequently complemented with practical insights through its application on two real-life case studies within one automotive engineering company. Summarizing and discussing the findings in combination with a conclusion completed this paper.
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Authors and Affiliations

Oliver Moerth-Teo
1
Gernot Schlögl
2
Muaaz Abdul-Hadi
3
Markus Brillinger
3
Martin Weinzerl
4
Christian Ramsauer
1

  1. Institue of Innovation and Industrial Management, Graz University of Technology, Austria
  2. Institue of Production Engineering, Graz University of Technology, Austria
  3. Pro2Future GmbH, Austria
  4. AVL List GmbH, Austria
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Abstract

The paper addresses a managerial problem related to ensuring cybersecurity of information and knowledge resources in production enterprises interested in the implementation of INDUSTRY 4.0 technologies. The material presented shows the results of experimental research of a qualitative nature, using two expert inventive methods: brain-netting and a fuzzy formula of inference. The experts' competences included the following three variants of the industrial application of the INDUSTRY 4.0 concept: (1) high production volumes achieved using a dedicated and fully robotic production line (2) the manufacture of short, personalized series of products through universal production cells, and (3) the manufacture of specialized unit products for individual customers. The Google Forms software was used to collect these expert opinions. The conclusions of the research carried out using the brain-netting method point to nine variants of the cybersecurity strategy of IT networks and knowledge base resources in manufacturing enterprises represented by the experts. The results of the research using the fuzzy formula of inference are numerically and situationally defined relations linking the above-mentioned nine strategies with five types of cyber-attacks. The summary record of these relations as the basis for managerial cybersecurity recommendations has a matrix form.
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Authors and Affiliations

Leszek Pacholski
1
ORCID: ORCID

  1. Poznan University of Technology, Faculty of Engineering Management, Poland
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Abstract

In many companies, along with the economic development, the use of integrated management systems is becoming more and more common, which are subject to evolution in terms of, inter alia, offered functions and new user requirements. The main purpose of this paper is to compare selected ERP (Enterprise Resource Planning) systems in the field of production planning and control on the example of the automotive industry. The paper presents the contemporary functioning of the automotive industry against the background of issues related to the integrated management systems used in them. The research part presents the proprietary methodology for the assessment of IT systems used in the automotive industry, which included a user survey. The obtained score allowed to indicate the optimal ERP class system supporting production planning and control.
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Authors and Affiliations

Robert Sika
1
ORCID: ORCID
Oliwia Wojtala
2
Jakub Hajkowski
1
ORCID: ORCID

  1. Poznan University of Technology, Faculty of Mechanical Engineering, Poland
  2. Poznan, Poland
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Abstract

Industry 4.0 is expected to provide high quality and customized products at lower costs by increasing efficiency, and hence create a competitive advantage in the manufacturing industry. As the emergence of Industry 4.0 is deeply rooted in the past industrial revolutions, Advanced Manufacturing Technologies of Industry 3.0 are the precursors of the latest Industry 4.0 technologies. This study aims to contribute to the understanding of technological evolution of manufacturing industry based on the relationship between the usage levels of Advanced Manufacturing Technologies and Industry 4.0 technologies. To this end, a survey was conducted with Turkish manufacturers to assess and compare their manufacturing technology usage levels. The survey data collected from 424 companies was analyzed by machine learning approach. The results of the study reveal that the implementation level of each Industry 4.0 technology is positively associated with the implementation levels of a set of Advanced Manufacturing Technologies.
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Authors and Affiliations

Tuğba Sari
1

  1. Konya Food and Agriculture University, Department of Management Information Systems, Turkey
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Abstract

This paper proposes the application of the digital numerical control (DNC) technique to connect the smart meter to the inspection system and evaluate the total harmonic distortion (THD) value of power supply voltage in IEEE 519 standard by measuring the system. Experimental design by the Taguchi method is proposed to evaluate the compatibility factors to choose Urethane material as an alternative to SS400 material for roller fabrication at the machining center. Computer vision uses artificial intelligence (AI) technique to identify object iron color in distinguishing black for urethane material and white for SS400 material, color recognition results are evaluated by measuring system, system measurement is locked when the object of identification is white material SS400. Computer vision using AI technology is also used to recognize facial objects and control the layout of machining staff positions according to their respective skills. The results obtained after the study are that the surface scratches in the machining center are reduced from 100% to zero defects and the surface polishing process is eliminated, shortening production lead time, and reducing 2 employees. The total operating cost of the processing line decreased by 5785 USD per year. Minitab 18.0 software uses statistical model analysis, experimental design, and Taguchi technical analysis to evaluate the process and experimentally convert materials for roller production. MATLAB 2022a runs a computer vision model using artificial intelligence (AI) to recognize color objects to classify Urethane and SS400 materials and recognize the faces of people who control employee layout positions according to their respective skills.
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Authors and Affiliations

Minh Ly Duc
1 2
Petr Bilik
2

  1. Faculty of Commerce, Van Lang University, 700000, Vietnam
  2. VSB–Technical University of Ostrava, Faculty of Electrical Engineering and Computer Science, Department ofCybernetics, and Biomedical Engineering, 708 00, Ostrava, Czech Republic
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Abstract

Value Stream Mapping has been a key Lean tool since its publication in 1988, offering a strategic view on the reconfiguration of an organization’s processes to reduce overall lead time. It has since been used in many different domains beyond (car) manufacturing. However, the potential offered by its concise representation of both material flow and its controlling information flow seems to have been largely underused. Most literature reports on VSM in the context of waste detection and local improvements. VSM also supports redesigning the material flow (even on a supply chain level) towards (pure) pull systems. However, it fails to adequately give guidance on how to gradually evolve towards this ultimate ideal state. This paper wants to offer a significant contribution to practitioners on how to use VSM to bridge this gap. Another key challenge that remains largely unpublished is how to adapt the planning systems accordingly at each reconfiguration of the material flow. This paper presents extensions to the basic VSM tool to meet these challenges. It includes a more comprehensive 5-level hierarchy that allows to position most lean flow-related techniques. It also extends the basic “door-to-door” VSM with new symbols to accommodate these techniques into the map. Finally, it introduces a new set of 13 questions to support redesigning not only the material flow, but also the information flow. The resulting richer future state maps better support the gradual evolution towards a leaner future shop floor, as illustrated with an example.
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Authors and Affiliations

Hendrik Van Landeghem
1 2
ORCID: ORCID
Johannes Cottyn
1 2

  1. Department of Industrial Systems Engineering and Product Design, Ghent University, Gent-Zwijnaarde, Belgium
  2. Industrial Systems Engineering (ISyE), Flanders Make vzw, Kortrijk, Belgium
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Abstract

Lean thinking and Industry 4.0 have been broadly investigated in recent years in intelligent manufacturing. Lean Production is still one of the most efficient industrial solutions in business and research, despite being implemented for a long time. On the other hand, Industry 4.0 has been introduced referring to the fourth industrial revolution. This study aims to analyze the combination of both Industry 4.0 and Lean production practices through a systematic literature review from a Lean Automation perspective. In this field, 189 articles are examined using VOSviewer for cluster analysis. Then, a more detailed analysis is provided to explore how Industry 4.0 and Lean techniques are integrated from a practical perspective. Results highlighted Big Data Analysis and Value Stream Mapping as the most common techniques, also emphasizing a growing trend toward new publications. Nevertheless, few practical applications are identified in the literature highlighting six gaps in the correlation of LA practices.
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Authors and Affiliations

Laura Lucantoni
1
Sara Antomarioni
1
Filippo Emanuele Ciarapica
1
Maurizio Bevilacqua
1

  1. Dipartimento di Ingegneria Industriale e Scienze Matematiche, Università Politecnica Delle Marche, Italy
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Abstract

Abstract Meeting quality characteristics of products and processes is an important issue for customer satisfaction and business competitiveness. It is necessary to integrate new techniques and tools that improve and complement traditional process variables analysis. This paper proposes a new methodological approach to analyze process quality control variables using Fuzzy Cognitive Maps. Application of the methodology in the production process of carbonated beverages allowed identifying process variables with the greatest influence on finished product quality. The process variables with the greatest impact on carbon dioxide content in the beverage were the beverage temperature in the filler, the carbo-cooler pressure, and the filler pressure.
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Authors and Affiliations

Juan Cogollo-Florez
1
ORCID: ORCID
Orfani Valencia-Mena
1
ORCID: ORCID

  1. Department of Quality and Production, Instituto Tecnológico Metropolitano – ITM, Colombia
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Abstract

The current market situation shows that enterprises are still struggling to digitize their business through the integration of the Internet of Things (IoT), artificial intelligence (AI), cloud technologies and other more advanced technologies, but the fifth industrial revolution is knocking on the door. This article deals with the analysis and evaluation of the impact of Industry 5.0 on entrepreneurs. Industry 4.0 analysis provides results based on interviews with practitioners as well as sales representatives. The main part of the article focuses on the business situation, where the goal was to identify existing gaps along with opportunities and threats. This analysis also describes the best way how to transform in times of the next industrial revolution. Study addresses the approach of integrating human workers in the supply chain in cooperation with automated processes. The purpose of this study is to confirm or refute whether companies are ready for another industrial revolution.
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Authors and Affiliations

Laura Lachvajderová
1
ORCID: ORCID
Jaroslava Kádárová
1
ORCID: ORCID

  1. Technical University of Košice, Department of Industrial and Digital Engineering, Slovakia
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Abstract

The optimal decision regarding the place of production is an essential, sometimes determining factor of its effectiveness. The main drawback in substantiating the optimal location of production is the lack of a system approach to accounting in the analysis of potential sales markets. Orientation, when justifying the optimal location of production, only to some particular sales market (and orientation to specific sales markets is necessary both in terms of taking into account the costs of moving the benefit from the place of production to the places of consumption, and in terms of production capacity, since it depends unit cost of production) is erroneous because it does not take into account many other competitive options. The article develops a system approach to rationale optimal locations and production capacity, based on a comparison of combinations of locally optimal places, the total production capacity of which is equal to the total (system) demand. The variant of combinations of locally optimal places with minimal total costs is systemically optimal. The result of solving the problem will be information about 4 parameters of the production of benefit: “where?” (in what places), “how much?” (in each of these places), “how?” (with what technology in each of these places), “for whom?” (sales markets for each of these places). The system approach proposed in the article to rationale the optimal location of the production of a single benefit can be adapted to a more complex situation, when the optimal location of the production of several benefits is justified at the same time. Further research is promising in the direction of a clearer determination of the boundaries of the space of possible location of production, as well as in the direction of studying the possibility of aggregating potential sales markets.
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Authors and Affiliations

Jerzy Stadnicki
1
Andrii Terebukh
2

  1. Faculty of Management and Computer Modelling, Kielce University of Technology, Poland
  2. Department of Tourism, Lviv Polytechnic National University, Ukraine
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Abstract

Sustainability manufacturing is crucial in many aspects in terms of environmental impact. It concerns the consumption of energy, raw materials and materials, as well as the emission of harmful substances and waste. The implementation of sustainability manufacturing requires many actions at various levels, including strategic, tactical and operational ones. In order to implement measures aimed at minimizing the negative impact of the company on the environment, employees’ competencies are needed. The article presents preliminary research on key green competencies for sustainability companies. The research was carried out in the form of individual interviews with medium and large production companies. The result of the research is the division of competencies (knowledge, skills and attitudes) into three stages of the organization’s development, indicating the key competencies for each stage of the development of sustainability management.
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Authors and Affiliations

Magdalena Graczyk-Kucharska
1

  1. Institute of Safety and Quality Engineering, Department of Marketing and Organization Development, PoznanUniversity of Technology, Poland

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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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.
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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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