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

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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://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.
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

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

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