Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 46
items per page: 25 50 75
Sort by:
Download PDF Download RIS Download Bibtex

Abstract

Industry 4.0 (I4) as a concept offers powerful opportunities for many businesses. The set of Industry 4.0 technologies is still discussed, and boundaries are not perfectly clear. However, implementation of Industry 4.0 concept becomes strategic principle, and necessary condition for succeeding on turbulent markets. Radio Frequency Identification (RFID) was used before I4 emerged. However, it should be treated as its important part and even enabler. The question arises how adoption of RFID was impacted by I4 paradigm. Therefore, to answer this question a set of technology management tools was selected and applied to forecast RFID potential development in forthcoming years. Moreover, case studies were conducted for technology management tools and their applications for RFID for qualitative discussion of its relevance. It aimed to prove that existing toolset should be applied for modern technologies related to I4. Tools were proven to be necessary and successful. However, some specific challenges were observed and discussed.
Go to article

Authors and Affiliations

Bartlomiej Gladysz
1
Donatella Corti
2
Elias Montini
2

  1. Warsaw University of Technology, Institute of Production Systems Organization, Warsaw, Poland
  2. University of Applied Science and Arts of Southern Switzerland, Department of Innovative Technologies
Download PDF Download RIS Download Bibtex

Abstract

Industry 4.0 promises to make manufacturing processes more efficient using modern technologies like cyber-physical systems, internet of things, cloud computing and big data analytics. Lean Management (LM) is one of the most widely applied business strategies in recent decades. Thus, implementing Industry 4.0 mostly means integrating technologies in companies that already operate according to LM. However, due to the novelty of the topic, research on how LM and Industry 4.0 can be integrated is still under development. This paper explores the synergic relationship between these two domains by identifying six examples of real cases that address LM-Industry 4.0 integration in the extant literature. The goal is to make explicit the best practices that are being implemented by six distinct industrial sectors
Go to article

Authors and Affiliations

Beatrice Paiva Santos
1
Daisy Valle Enrique
1 2
Vinicius B.P. Maciel
1
Tânia Miranda Lima
1
Fernando Charrua-Santos
1
Renata Walczak
3

  1. Electromechanical Department, C-MAST, University of Beira Interior, Covilhã, Portugal
  2. Industrial Engineering Department, Federal University of Rio Grande do Sul, Brazil
  3. University of Technology, Warsaw, Poland
Download PDF Download RIS Download Bibtex

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.
Go to article

Authors and Affiliations

Tuğba Sari
1

  1. Konya Food and Agriculture University, Department of Management Information Systems, Turkey
Download PDF Download RIS Download Bibtex

Abstract

The profile of the Polish foundry engineer in the Industry 4.0 age is presented in the present paper. The presented results were obtained by means of three research methods consisting of: analyses of professional expertise documents, questionnaires filled-up by the executive staff of foundry enterprises and analyses of work offers for the foundry engineer position. The investigations indicated the key competences of the foundry engineer, demanded currently by employers and meeting the requirements of the Polish foundry sector. The obtained results were discussed in relation to the fourth industrial revolution and its requirements with regard to the engineering staff. This concept is based on information technology and robotizing, which means the total automation of industrial production processes as well as the widespread access to data and machines. Such an approach requires changes in applied machines, technologies and employees’ competencies. The competences of employees constitute the element deciding on the company success, aimed at obtaining a competitive advantage. Therefore adjusting the employees’ competencies to continuously changing reality is so essential.

Go to article

Authors and Affiliations

K. Liszka
K. Klimkiewicz
P. Malinowski
Download PDF Download RIS Download Bibtex

Abstract

The aim of our research is to gain understanding about material flow related information sharing in the circular economy value network in the form of industrial symbiosis. We need this understanding for facilitating new industrial symbiosis relationships and to support the optimization of operations. Circular economy has been promoted by politics and regulation by EU. In Finland, new circular economy strategy raises the facilitation of industrial symbiosis and data utilization as the key actions to improve sustainability and green growth. Companies stated that the practical problem is to get information on material availability. Digitalization is expected to boost material flows in circular economy by data, but what are the real challenges with circular material flows and what is the willingness of companies to develop co-operation? This paper seeks understanding on how Industry 4.0 is expected to improve the efficiency of waste or by-product flows and what are the expectations of companies. The research question is: How Industry 4.0 technologies and solutions can fix the gaps and discontinuities in the Industrial Symbiosis information flow? This research is conducted as a qualitative case study research with three cases, three types of material and eight companies. Interview data were collected in Finland between January and March 2021. Companies we interviewed mentioned use-cases for sensors and analytics to optimize the material flow but stated the investment cost compared to the value of information. To achieve sustainable circular material flows, the development needs to be done in the bigger picture, for the chain or network of actors, and the motivation and the added value must be found for each of them.
Go to article

Authors and Affiliations

Anne-Mari Järvenpää
Vesa Salminen
Jussi Kantola
Download PDF Download RIS Download Bibtex

Abstract

The article refers to the idea of using the software defined network (SDN) as an effective hardware and software platform enabling the creation and dynamic management of distributed ICT infrastructure supporting the rapid prototyping process. The authors proposed a new layered reference model remote distributed rapid prototyping that allows the development of heterogeneous, open systems of rapid prototyping in a distributed environment. Next, the implementation of this model was presented in which the functioning of the bottom layers of the model is based on the SDN architecture. Laboratory tests were carried out for this implementation which allowed to verify the proposed model in the real environment, as well as determine its potential and possibilities for further development. Thus, the approach described in the paper may contribute to the development and improvement of the efficiency of rapid prototyping processes which individual components are located in remote industrial, research and development units. Thanks to this, it will be possible to better integrate production processes as well as optimize the costs associated with prototyping. The proposed solution is also a response in this regard to the needs of industry 4.0 in the area of creating scalable, controllable and reliable platforms.

Go to article

Authors and Affiliations

D. Mazur
A. Paszkiewicz
M. Bolanowski
G. Budzik
M. Oleksy
Download PDF Download RIS Download Bibtex

Abstract

The spread of digital technologies dramatically changes production processes. The fourth

industrial revolution opens up new opportunities for the introduction of technologies, having

a significant impact on the production cycle, starting with highly automated production lines

and ending with the large-scale implementation of technological solutions designed to improve

productivity, optimize costs, quality and reliability. Defining digital transformations,

primarily in the manufacturing industry, as a strategic imperative for the entire economy

based on opinions and intentions of entrepreneurs (short and medium-term), key aspects of

the digitalization process in Russian medium, high-tech and low-tech manufacturing industries

are revealed. A set of tendencies in the development of digital technologies by their main

types is presented, the level of industry participation in digital transformation is shown, as

well as many other important digital transformation processes in enterprises that are not

measured by quantitative statistics.

Go to article

Authors and Affiliations

Inna S. Lola
Murat Bakeev
Download PDF Download RIS Download Bibtex

Abstract

Today, the changes in market requirements and the technological advancements are influencing

the product development process. Customers demand a product of high quality and fast

delivery at a low price, while simultaneously expecting that the product meet their individual

needs and requirements. For companies characterized by a highly customized production, it

is essential to reduce the trial-and-errors cycles to design new products and process. In such

situation most of the company’s knowledge relies on the lessons learnt by operators in years

of work experience, and their ability to reuse this knowledge to face new problems. In order

to develop unique product and complex processes in short time, it is mandatory to reuse

the acquired information in the most efficient way. Several commercial software applications

are already available for product lifecycle management (PLM) and manufacturing execution

system (MES). However, these two applications are scarcely integrated, thus preventing an

efficient and pervasive collection of data and the consequent creation of useful information.

The aim of this paper is to develop a framework able to structure and relate information

from design and execution of processes, especially the ones related to anomalies and critical

situations occurring at the shop floor, in order to reduce the time for finalizing a new product.

The framework has been developed by exploiting open source systems, such as ARAS

PLM and PostgreSQL. A case study has been developed for a car prototyping company to

illustrate the potentiality of the proposed solution.

Go to article

Authors and Affiliations

Giulia Bruno
Alberto Faveto
Emiliano Traini
Download PDF Download RIS Download Bibtex

Abstract

The industry transformation to the digital model 4.0 will be a significant change from

the perspective of the organisation and processes. In the context of the above, the research

was undertaken, the principal aim of which constituted the attempt to answer the question

concerning the technological advancement level of manufacturing companies operating in

the agricultural machinery sector. It is about identifying what adaptation projects in the

context of the fourth generation industry era should be undertaken by the Polish manufacturers operating in the agricultural machinery sector. The achievement of the main

objective required formulation and implementation of partial objectives, which, according

to the authors, include: C(1) – defining the Industry 4.0 axiom merit; C(2) – using the

subject literature reconstruction and interpretation methods – nomination of areas, on the

one hand essential from the perspective of the model 4.0, and on the other hand those that

may demonstrate the maturity in the domain of the adopted desiderata; C(3) – compilation

of the research model, in the form of an assessment sheet, being a resultant of literature

studies and research conducted among deliberately selected domain experts; C(4) – based

on the selected indicators, the technological advancement level recognition of the studied

companies; specification of a technological gap (questioning among experts).

Go to article

Authors and Affiliations

Bogdan Nogalski
Przemysław Niewiadomski
Download PDF Download RIS Download Bibtex

Abstract

The realization of digitalization in production companies – currently also referred to as Industry

4.0 – aims for reduction of internal value creation costs as well as costs for intercompany

collaboration and plays a key role in their current strategy development. However, related

strategy research still lacks to provide operationalized digitalization methods and tools to

practitioners with scientific rigor as well as real-world relevance. To challenge this status

quo, we present a scientifically grounded 14-step procedure model including 11 practically

tested tools, developed specifically for real-world application. The model leads practitioners

from their first contact with industrial digitalization, through the maturity assessment of

143 digitalization items, until the implementation of a KPI-monitoring system and a continuous

improvement process. We applied and re-worked the procedure model during three

years of application. Validation and Feedback from practitioners and scholars indicate, that

the model drives strategy development towards objective and data-based decision making

and increases stakeholder engagement in organizations considerably.

Go to article

Authors and Affiliations

Andreas Schumacher
Wilfried Sihn
Download PDF Download RIS Download Bibtex

Abstract

The main aim of the article is to develop a simulation model of flexible manufacturing

system with applying the ontology on flexibility. Designing manufacturing systems matching

both production and market requirements becomes more and more challenging due to the

variability of demand for a large number of products made in many variants and short

lead times. Manufacturing flexibility is widely recognised as a proven solution to achieve

and maintain both the strategical and operational goals of the companies exposed to global

competition. Generic simulation model of flexible manufacturing system was developed using

FlexSimr 3D software, then the example data were used to demonstrate the developed model

applicability. “The Ontology on Flexibility” was applied for evaluation of achieved flexibility

of manufacturing system.

Go to article

Authors and Affiliations

Sławomir Luscinski
Vitalii Ivanov
Download PDF Download RIS Download Bibtex

Abstract

Rescheduling is a frequently used reactive strategy in order to limit the effects of disruptions

on throughput times in multi-stage production processes. However, organizational deficits

often cause delays in the information on disruptions, so rescheduling cannot limit disruption

effects on throughput times optimally. Our approach strives for an investigation of

possible performance improvements in multi-stage production processes enabled by realtime

rescheduling in the event of disruptions. We developed a methodology whereby we

could measure these possible performance improvements. For this purpose, we created and

implemented a simulation model of a multi-stage production process. We defined system

parameters and varied factors according to our experiment design, such as information delay,

lot sizes and disruption durations. The simulation results were plotted and evaluated

using DoE methodology. Dependent on the factor settings, we were able to prove large improvements

by real-time rescheduling regarding the absorption of disruption effects in our

experiments.

Go to article

Authors and Affiliations

Peter Burggraf
Johannes Wagne
Oliver Bischoff
Download PDF Download RIS Download Bibtex

Abstract

The article presents tools, methods and systems used in mechanical engineering that in

combination with information technologies create the grounds of Industry 4.0. The authors

emphasize that mechanical engineering has always been the foundation of industrial activity,

while information technology, the essential part of Industry 4.0, is its main source of innovation.

The article discusses issues concerning product design, machining tools, machine tools

and measurement systems.

Go to article

Authors and Affiliations

Adam Hamrol
Józef Gawlik
Jerzy Sładek
Download PDF Download RIS Download Bibtex

Abstract

The article focuses on selected problems which have now appeared and fall under the ideas “industry 4.0” and “society 5.0”, namely on anthropological issues. Changes in the relationships between man and technology based on trust lead to an increase of the role of the technological factor in these relations. Other aspects of the analyzed changes concern the new requirements of the responsibility and changes of human subjectivity and rationality. The future of man appears to be an area of uncertainty related to inter alia the conditions of functioning and living in the order of the post-digital world.

Go to article

Authors and Affiliations

Andrzej Kiepas
Download PDF Download RIS Download Bibtex

Abstract

This paper points out assumptions and reasons for using digital technologies, the importance of using digital technologies in teaching and management. It also refers to the digital technologies and digital competences as an essential part of the competency model of a teaching staff in education. It also points out the fact that existing competency models need to be further explored, decomposed, and formulated as an illustration by the digital competences extensions.

Go to article

Authors and Affiliations

Petr Svoboda
Download PDF Download RIS Download Bibtex

Abstract

The evolution of the economy and the formation of Industry 4.0 lead to an increase in the importance of intangible assets and the digitization of all processes at energy enterprises. This involves the use of technologies such as the Internet of Things, Big Data, predictive analytics, cloud computing, machine learning, artificial intelligence, robotics, 3D printing, augmented reality etc. Of particular interest is the use of artificial intelligence in the energy sector, which opens up such prospects as increased safety in energy generation, increased energy efficiency, and balanced energy-generation processes. The peculiarity of this particular instrument of Industry 4.0 is that it combines the processes of digitalization and intellectualization in the enterprise and forms a new part of the intellectual capital of the enterprise. The implementation of artificial intelligence in the activities of energy companies requires consideration of the features and stages of implementation. For this purpose, a conceptual model of artificial intelligence implementation at energy enterprises has been formed, which contains: the formation of the implementation strategy; the design process; operation and assessment of artificial intelligence. The introduction of artificial intelligence is a large-scale and rather costly project; therefore, it is of interest to assess the effectiveness of using artificial intelligence in the activities of energy companies. Efficiency measurement is proposed in the following areas: assessment of economic, scientific and technical, social, marketing, resource, financial, environmental, regional, ethical and cultural effects as well as assessment of the types of risks associated with the introduction of artificial intelligence.
Go to article

Bibliography

Armenakis et al. 1993 – Armenakis, A.A., Harris, S.G. and Mossholder, K.W. 1993. Creating Readiness for Organizational Change. Human Relations 46, pp. 681–703.
Artificial intelligence the next digital frontier? McKinsey Global Institute. July 2017. 80 p. [Online] https://www.mckinsey.com/~/media/mckinsey/industries/advanced%20electronics/our%20insights/how%20 artificial%20intelligence%20can%20deliver%20real%20value%20to%20companies/mgi-artificial-intelligence- discussion-paper.ashx [Accessed: 2021-07-15].
Bakke, D. 2005. Joy a work: A Revolutionary Approach to Fun on the Job. Seattle: PVG, 314 pp.
Behrens, W. and Hawranek, P.M. 1978. Manual for the preparation of industrial feasibility studies. NY: Unated Nations, 404 pp.
Berger, R. 2013. How to Survive in the VUCA World. Hamburg: Roland Berger, 245 pp.
Blommaert, Т. and Broek, S. 2017. Management in Singularity: From linear to exponential management. Vakmedianet; 1 edition, 172 pp.
Borowski, P.F. 2016. Development strategies for electric utilities. Acta Energetica 4, pp. 16–21.
Borowski, P. 2021. Innovative Processes in Managing an Enterprise from the Energy and Food Sector in the Era of Industry 4.0. Processes 9(2), 381, DOI: 10.3390/pr9020381.
Bostrom, N. 2014. Superintelligence: Paths, Dangers, Strategies. Oxford University Press, 352 pp.
Cheatham et al. 2019 – Cheatham, B., Javanmardian, K. and Samandari, H. 2019. Confronting the risks of artificial intelligence. [Online] https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/confronting-the-risks-of-artificial-intelligence [Accessed: 2021-07-15].
Doroshuk, H. 2019. Organizational development: theory, methodology, practice (Організаційний розвиток: теорія, методологія, практика). Odesa: Osvita Ukrainy, 368 pp. (in Ukrainian).
Doroshuk, H. 2020. Reform of the electricity sector in Ukraine – liberalization of the market and corporatization of companies. Polityka Energetyczna – Energy Policy Journal 23(4), pр. 105–122. [Online] https://epj.min-pan.krakow.pl/Reform-of-the-electricity-sector-in-Ukraine-liberalization-of-the-market- and-corporatization,127664,0,2.html/ [Accessed: 2021-07-15].
Edvinsson, L. and Malone, M. 1997. Intellectual Capital: Realizing your Company’s True Value by Finding its Hidden Brainpower. New York, NY: Harper Collins.
Firer, S. and Williams, S.M. 2003. Intellectual capital and traditional measures of corporate performance. Journal of Intellectual Capital 4(3) , pp. 348–360, DOI: 10.1108/14691930310487806.
Hoe, S.L. 2019. The topicality of the learning organization: Is the concept still relevant today? [In:] The Oxford Handbook of the Learning Organization, Oxford University Press: Oxford, UK, pp. 18–32.
Jackson, P.C. Jr. 2019. Introduction to Artificial Intelligence. New York: Dover Publication Inc., 170 pp. Jensen, P.E. 2005. A contextual theory of learning and the learning organization. Knowledge Process Management 12, pp. 53–64, DOI: 10.1002/kpm.217.
Jones, M.T. 2017. A Beginner’s Guide to Artificial Intelligence, Machine Learning and Cognitive Computing. [Online] https://developer.ibm.com/articles/cc-beginner-guide-machine-learning-ai-cognitive/ [Accessed: 2021-07-15].
Kinelski, G. 2020. The main factors of successful project management in the aspect of energy enterprises’ efficiency in the digital economy environment. Polityka Energetyczna – Energy Policy Journal 23(3), pр. 5–20, DOI: 10.33223/epj/126435.
Koistinen, P. 2021. Toward learning organization – Practices in nuclear power plants. [In:] Human Factors in the Nuclear Industry, Elsevier BV: Amsterdam, The Netherlands, pp. 239–247.
Laloux, Fr. 2014. Reinventing Organizations: A Guide to Creating Organizations Inspired by the Next Stage of Human Consciousness. Brussels: Nelson&Parker, 379 pp.
Levy, F. 2009. A simulated approach to valuing knowledge capital. Washington: The George Washington University, 189 pp.
Nazari, J.A. and Herremans, I.M. 2007. Extending VAIC model: measuring intellectual capital components. Journal of Intellectual Capital 8(4), DOI: 10.1108/14691930710830774.
Oklander et al. 2018 – Oklander, M., Oklander, T., Yashkina, O., Pedko, I. and Chaikovska, M. 2018. Analysis of technological innovations in digital marketing. Eastern-European Journal of Enterprise Technologies 5/3 (95), pp. 80–91, DOI: 10.1088/1755-1315/440/2/022026.
Pan et al. 2020 – Pan, T., Hu, T. and Geng, J. 2020. View learning organization in a situational perspective. IOP Conference Series: Earth and Environmental Science 440 pp.
Piano, S.L. 2020. Ethical principles in machine learning and artificial intelligence: Cases from the field and possible ways forward. Humanities and Social Science Communication 7, DOI: 10.1057/s41599-020-0501-9.
Romer, P.M. 1994. The Origins of Endogenous Growth. The Journal of Economic Perspectives 8(1), pp. 3–22.
Sozontov et al. 2019 – Sozontov, A., Ivanova, M. and Gibadullin, A. 2019. Implementation of artificial intelligence in the electric power industry. [In:] E3S Web of Conferences 114, DOI: 10.1051/e3sconf/201911401009. EDP Sciences.
Toffler, A. 1984. The Third Wave. NY: Bantam, 560 pp.
Tortorella et al. 2020 – Tortorella, G.L., Vergara, A.M.C., Garza-Reyes, J.A. and Sawhney, R. 2020. Organizational learning paths based upon Industry 4.0 adoption: An empirical study with Brazilian manufacturers. International Journal of Production Economics 219, pp. 284–294, DOI: 10.1016/j.ijpe.2019.06.023.
Von Ketelhod, Wöcke, A. 2008. The impact of electricity crises on the consumption behaviour of small and medium enterprises. Journal of Energy in Southern Africa 19(1), pp. 4–12
Go to article

Authors and Affiliations

Hanna Doroshuk
1
ORCID: ORCID

  1. Department of Menegement, Odessa Polytechnic State University, Ukraine
Download PDF Download RIS Download Bibtex

Abstract

The study is devoted to the co-design concept which is not widely studied in the manufacturing industry area. The concept is just practiced but not theorized and not investigated enough, although it greatly deserves it because of its perspectives and advantages potential in the technology changes era. This study aims to present an investigation of literature views on co-design in manufacturing operations, with the comparison to service literature where it is widely discussed; the study also aims at in-depth investigations of co-design occurrences in two industrial cases of product development to understand their nature and circumstances. In addition, the influence of Industry 4.0 technologies and their coexistence with the concept of sustainability will also be strongly taken into consideration in the empirical part of this study. The process of the individualized production of the industrial line for animal food packing and cardboard packaging production has been studied according to case study methodology. The study demonstrates that co-design could contribute to bettering the process of new product development and achieving products more accurate for the final users’ requirements. It goes hand in hand with one of the core ideas of sustainability, which is to have long-lasting products, exploited by the customer with a high level of satisfaction for a longer time. The study implies that the technologies of Industry 4.0 could support wider and more effective co-design exploitation by manufacturing entities.
Go to article

Authors and Affiliations

Elżbieta Krawczyk-Dembicka
1
ORCID: ORCID
Wiesław Urban
1
ORCID: ORCID
Krzysztof Łukaszewicz
1
ORCID: ORCID

  1. Bialystok University of Technology, Wiejska 45A Street, 15-351 Białystok, Poland
Download PDF Download RIS Download Bibtex

Abstract

The publication reflects the current situation concerning the possibilities of using augmented reality (AR) technology in the field of production technologies with the main intention of creating a tool to increase production efficiency. It is a set of individual steps that respond in a targeted manner to the possible need for assisted service intervention on a specific device. The publication chronologically describes the procedure required for the preparation and processing of a CAD model. For this preparatory process, the PTC software package is used which meets the requirements for each of the individual operations. The first step is the routine preparation of CAD models and assemblies. These are prepared based on real models located on the device, and their shape and dimensions correlate with the dimensions of the model on the device. The second phase is the creation and timing of the disassembly sequence. This will provide the model with complete vector data, which is then paired with the CAD models in AR. This phase is one of the most important. It determines the location of the model concerning its relative position on the device, provides information on the relocation of parts of the model after the sequence is started, and essentially serves as a template for the interactive part of the sequence. The last two phases are used to connect CAD models with vector data, determine their position for the position mark, and prepare the user interface displayed on the output device. The result of this procedure is a functional disassembly sequence, used for assisted service intervention of a worker in the spindle drive of the Emco Mill 55 device.
Go to article

Authors and Affiliations

Justyna Trojanowska
1
Jakub Kašcak
2
ORCID: ORCID
Jozef Husár
2
ORCID: ORCID
Lucia Knapcíková
3
ORCID: ORCID

  1. Poznan University of Technology, Faculty of Mechanical Engineering, Department of Production Engineering, Piotrowo Street 3, 61-138 Poznan, Poland
  2. Technical University of Košice, Faculty of Manufacturing Technologies with a seat in Prešov, Department of Computer Aided Manufacturing Technology, Šturova 31, 080 01 Prešov, Slovak Republic
  3. Technical University of Košice, Faculty of Manufacturing Technologies with a seat in Prešov, Department of Industrial Engineering and Informatics, Bayerova 1, 080 01 Prešov, Slovak Republic
Download PDF Download RIS Download Bibtex

Abstract

Production is becoming more customer-focused as it departs from delivering standardized mass products to market segments, and the emerging Industry 4.0 technologies render this much easier than before. These technologies enable two-way information exchange with customers throughout all the steps of product development, particularly in terms of tailor-made products. This study aims at presenting proposals of implementing Industry 4.0 technologies into the process of tailored products, where the product is customized for the customer from the start and where adjustments are also made at the manufacturing stage. The study also aims to build a concept of intensification of customer contact and to improve the process flow by applying Industry 4.0 technologies. The study’s subject is tailor-made furniture production, with individually designed products that are manufactured and installed at a customer’s facilities. The company in the study operates on a small scale. The study employs a case study methodology that shows how the process can be improved in terms of real-time effective customer contact and process flow. The huge potential of 3D visualization as well as augmented and virtual reality technologies are also demonstrated. The study concludes with several directions for further development of existing technology solutions.
Go to article

Authors and Affiliations

Krzysztof Łukaszewicz
1
ORCID: ORCID
Wiesław Urban
1
ORCID: ORCID
Elżbieta Krawczyk-Dembicka
1
ORCID: ORCID

  1. Faculty of Engineering Management, Department of Production Management, Bialystok University of Technology, Wiejska 45A Street, 15-351 Białystok, Poland
Download PDF Download RIS Download Bibtex

Abstract

Artificial intelligence (AI) is changing many areas of technology in the public and private spheres, including the economy. This report reviews issues related to machine modelling and simulations concerning further development of mechanical devices and their control systems as part of novel projects under the Industry 4.0 paradigm. The challenges faced by the industry have generated novel technologies used in the construction of dynamic, intelligent, flexible and open applications, capable of working in real time environments. Thus, in an Industry 4.0 environment, the data generated by sensor networks requires AI/CI to apply close-to-real-time data analysis techniques. In this way industry can face both fresh opportunities and challenges, including predictive analysis using computer tools capable of detecting patterns in the data based on the same rules that can be used to formulate the prediction.
Go to article

Bibliography

  1.  Artificial intelligence quotient –2nd edition, Digital Poland Microsoft Foundation, 2019.
  2.  IoT in the Polish economy, Report of the working group for the Internet of Things at the Ministry of Digital Affairs, Ministry of Digital Affairs, 2019.
  3.  Towards the Industry 4.0, HBR Polska, ICAN, How to do IT, ASTOR, 2019.
  4.  Industry 4.0, The new industrial revolution, How Europe will succeed, Company Report Roland Berger, 2019.
  5.  S. Tay, L. Te Chuan, A. Aziati, and A. Nur Aizat “An Overview of Industry 4.0: Definition, Components, and Government Initiatives”, J. Adv. Res. Dyn. Control Syst. 10(14), 1379‒1387 (2018).
  6.  H. Kagermann, W. Wahlster, and H. Johannes, “Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0”, Forschungsunion, 2013.
  7.  J. Qin, Y. Liu, and R. Grosvenor, “Categorical Framework of Manufacturing for Industry 4.0 and Beyond”, Procedia CIRP 52, 173–178 (2016).
  8.  D. Gorecky, M. Schmitt, M. Loskyll, and D. Zühlke “Human-Machine-Interaction in the Industry 4.0 Era”, in Proc. 12th IEEE International Conference on Industrial Informatics, 2014, pp. 289–294.
  9.  Y. Liao, F. Deschamps, E.D. Freitas, and R. Loures, “Past, present and future of Industry 4.0 – a systematic literature review and research agenda proposal”, Int. J. Prod. Res. 55(12), 3609–3629 (2017)).
  10.  D. Mourtzis, “Simulation in the design and operation of manufacturing systems: state of the art and new trends”, Int. J. Prod. Res. 58(7), 1927‒1949 (2020), doi: 10.1080/00207543.2019.1636321.
  11.  E. Chlebus, CAx computer techniques in production engineering, WNT, Warsaw, 2000, [in Polish].
  12.  W. Paprocki, “The concept of Industry 4.0 and its application in the conditions of a market economy”, in Digitization of the economy and society. Opportunities and challenges for infrastructure sectors, pp. 39‒58, eds. J. Gajewski, W. Paprocki and J. Pieriegud, Research Institute for Market Economics – Gdansk Academy of Banking, Gdansk, 2016 [in Polish].
  13.  B. Denkena, J. Schmidt, and M. Krüger, “Data mining approach for knowledge-based process planning”, Procedia Technol. 15, 406‒415 (2014).
  14.  D. Trentesaux and A. Thomas, “Product-Driven Control: Concept, Literature Review and Future Trends”, in Orientation in Holonic and Multi Agent Manufacturing and Robotics, Studies in Computational Intelligence, vol. 472, pp. 135‒150, eds., T. Borangiu, A. Thomas, and D. Trentesaux, Springer, Heidelberg, 2013.
  15.  E. Uhlmann, E. Hohwieler, and M. Kraft, “Self-organizing production with distributed intelligence. Intelligent workpieces control their way through production”, Werkstattstechnik 103(2), 114‒117 (2013) [in German].
  16.  X. Xu, L. Wang, and S.T. Newman, “Computer-aided process planning –A critical review of recent developments and future trends”, Int. J. Comput. Integr. Manuf. 24(1), 1‒31 (2011).
  17.  U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From Data Mining to Knowledge Discovery in Databases”, AI Magazine 17(3), 37‒54 (1996).
  18.  G. Köksal, I. Batmaz, and M.C. Testik, “A review of data mining applications for quality improvement in manufacturing industry”, Expert Syst. Appl. 38(10), 13448‒13467 (2011).
  19.  T. Sánchez López, D.C. Ranasinghe, B. Patkai, and D. McFarlane, “Taxonomy, technology and applications of smart objects”, Inf. Syst. Front. 13(2), 281‒300 (2009).
  20.  L. Wang, “Machine availability monitoring and machining process planning”, CIRP J. Manuf. Sci. Technol. 6(4), 263‒273 (2013).
  21.  S. Wang, X. Lu, X.X. Li, and W.D. Li, “A systematic approach of process planning and scheduling”, J. Clean Prod. 87(1), 914‒929 (2015).
  22.  V. Roblek, M. Meško, and A. Krapež, “A complex view of industry 4.0”, SAGE Open 6(2), 1–11 (2016).
  23.  X.G. Ming, J.Q. Yan, X.H. Wang, S.N. Li, W.F. Lu, Q.J. Peng, and Y.S. Ma, “Collaborative Process Planning and Manufacturing in Product Lifecycle Management”, Comput. Ind. 59(2‒3), 154‒166 (2008).
  24.  C.M. Gonzalez, “The future of artificial Intelligence domination”, Mechanical Engineering, The Magazine of ASME 8, 2020.
  25.  O. de Weck, D. Reed, S. Sarma, and M. Schmidt, Trends in Advanced Manufacturing Technology Research, MIT, 2013.
  26.  J. Nukeaw and W. Pecharpa, Advances in Material Science and Technology, vol. 802, TTP, 2013.
  27.  J. Zhou, “Digitalization and intelligentization of manufacturing industry”, Adv. Manuf. l(1), 1–7 (2013).
  28.  G. Lanza, B. Haefner, and A. Kraemer, “Optimization of selective assembly and adaptive manufacturing by means of cyber-physical system based matching”, CIRP Ann-Manuf. Technol.64(1), 399‒402 (2015).
  29.  L. Monostori, “Cyber-physical Production Systems: Roots, Expectations and R&D Challenges”, Procedia CIRP 17, 9‒13 (2014).
  30.  J. Becker, R. Knackstedt, and J. Pöppelbuß, “Developing Maturity Models for IT Management: A Procedure Model and its Application”, Bus. Inf. Syst. Eng. 1(3), 213‒222 (2009).
  31.  R. Gao, L. Wang, R. Teti, D. Dornfeld, S. Kumara, M. Mori, and M. Helu, “Cloud-enabled prognosis for manufacturing” , CIRP Ann- Manuf. Technol.64(2), 749‒772 (2015).
  32.  R. Berger, “INDUSTRY 4.0 – The new industrial revolution”, [Online]. Available: www.rolandberger.com [Accessed: 21. Dec. 2015]
  33.  G. Schuh, T. Potente, R. Varandani, and T. Schmitz, “Global Footprint Design based on genetic algorithms – An ‘Industry 4.0’ perspective”, CIRP Ann-Manuf. Technol.63(1), 433‒436 (2014).
  34.  M. Kohlegger, R. Maier, and S. Thalmann, “Understanding Maturity Models Results of a structured Content Analysis”, presented at The 9th International Conference on Knowledge Management and Knowledge Technologies (IKNOW ‘09), Graz, Austria, 2009.
  35.  K. Lichtblau, V. Stich, R. Bertenrath, M. Blum, M. Bleider, A. Millack, K. Schmitt, E. Schmitz, and M. Schröter, IMPULS – Industrie 4.0- Readiness, Impuls-Stiftung des VDMA, Aachen-Köln, 2015.
  36.  E.W. Ngai, D.C.K. Chau, J.K.L. Poon, and C.K.M. To, “Energy and utility management maturity model for sustainable manufacturing process”, Int. J. Prod. Econ. 146(2), 453–464 (2013).
  37.  D.C.A. Pigosso, H. Rozenfeld, and T.C. McAloone, “Ecodesign maturi-ty model: a management framework to support ecodesign implementation into manufacturing companies”, J. Clean Prod.59, 160‒173 (2013).
  38.  M.A. Maasouman and K. Demirli, “Assessment of Lean Maturity Level in Manufacturing Cells”, IFAC-PapersOnLine 48(3), 1876‒1881 (2015).
  39.  M. Pérez-Lara et al., “Vertical and horizontal integration systems in Industry 4.0”, Wireless Netw. 26, 4767–4775 (2020).
  40.  A.C. Pereira, and F. Romero, “A review of the meanings and the implications of the Industry 4.0 concept”, Procedia Manuf.13, 1206‒1214 (2017).
  41.  A. Schumacher, S. Erolb, and W. Sihna, “A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises”, Procedia CIRP 52,161‒166 (2016).
  42.  D.M. Gligor and M.C. Holcomb, “Understanding the role of logistics capabilities in achieving supply chain agility: a systematic literature review”, Supp. Chain Manag. 17(4), 438‒453 (2012).
  43.  J. Schlechtendahl, M. Keinert, F. Kretschmer, A. Lechler, and A. Verl, “Making existing production systems Industry 4.0-ready: Holistic approach to the integration of existing production systems in Industry 4.0 environments”, Prod. Eng 91(1), 143 (2014).
  44.  U. Dombrowski and T. Wagner, “Mental strain as field of action in the 4th industrial revolution”, Procedia CIRP 17, 100–105 (2014).
  45.  E. Fielt, “Conceptualising business models: Definitions, frameworks and classifications”, J. Bus. Mod. 1(1), 85 (2013).
  46.  B. Schumacher, S. Erol, and W. Sihn, “A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises”, Procedia CIRP 52, 161 (2016).
  47.  J.A. Saucedo-Martinez, M. Perez-Lara, J.A. Marmolejo-Saucedo, T.E. Salais-Fierro, and P. Vasant, “Industry 4.0 framework for management and operations: A review”, J. Ambient Intell. Humaniz. Comput. 9, 789–801 (2018).
  48.  B. Ślusarczyk, “Industry 4.0: are we ready?”, Pol. J. Manag. Stud. 17(1), 232‒248 (2018).
  49.  S.A. Abbas, “Entrepreneurship and information technology businesses in economic crisis”, Entrep. Sustain. Issues 5(3), 682‒692 (2018).
  50.  F. Baena, A. Guarin, J. Mora, J. Sauza, and S. Retat, “Learning Factory: The Path to Industry 4.0”, Procedia Manuf. 9, 73‒80 (2017).
  51.  J.K. Allen, S. Commuri, R. Jiao, J. Milisavljevic-Syed, F. Mistree, J. Panchal, D. Schaefer, and W. Chen, “Design Engineering in the Age of Industry 4.0”, J. Mech. Des. 142(8) 088001 (2020).
  52.  T.D. Oesterreich and F. Teuteberg, “Understanding the implications of digitization and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry”, Comput. Ind. 83, 121‒139 (2016).
  53.  J. Oláh, G. Karmazin, K. Pető, and J. Popp, “Information technology developments of logistics service providers in Hungary”, Int. J. Logist. Res. Appl. 21(3), 1‒13 (2017).
  54.  R. Krishnamurthi, A. Kumar, “Modeling and Simulation for Industry 4.0”, in: A Roadmap to Industry 4.0: Smart Production, Sharp Business and Sustainable Development, eds., A. Nayyar, A. Kumar, Springer, 2019.
  55.  B. Rodič, “Industry 4.0 and the New Simulation Modelling Paradigm”, Organizacija 50(3), 193–207 (2017).
  56.  I. Sittón Candanedo, E. Hernández Nieves, S. Rodríguez González, M.T. Santos Martín, and A. González Briones, “Machine learning predictive model for Industry 4.0”, in Knowledge Management in Organizations Project: IOTEC – Development of Technological Capabilities in the industrial Application of the Internet of Things, 13th International Conference KMO, Žilina, Slovakia, pp 501‒510, 2018, doi: 10.1007/978-3-319-95204-8.
  57.  I. Rojek and E. Dostatni, “Machine learning methods for optimal compatibility of materials in ecodesign”, Bull. Pol. Acad. Sci. Tech. Sci. 68(2), 199‒206 (2020).
  58.  I. Rojek, “Classifier Models in Intelligent CAPP Systems”, in Man-Machine Interactions. Advances in Intelligent and Soft Computing, vol. 59, pp. 311–319, Eds., K.A. Cyran, S. Kozielski, J.F. Peters, U. Stańczyk, and A. Wakulicz-Deja, Springer, Berlin, Heidelberg, 2009.
  59.  I. Rojek and J. Studzinski, “Detection and Localization of Water Leaks in Water Nets Supported by an ICT System with Artificial Intelligence Methods as a Way Forward for Smart Cities”, Sustainability 11(2), 518 (2019).
  60.  J. Kopowski, I. Rojek, D. Mikolajewski, and M. Macko, “3D Printed Hand Exoskeleton – Own Concept”, in Advances in Manufacturing II. MANUFACTURING 2019. Lecture Notes in Mechanical Engineering, pp. 298‒306, Eds., J. Trojanowska, O. Ciszak, J. Machado, and I. Pavlenko, Springer, Cham, 2019, https://doi.org/10.1007/978-3-030-18715-6_25.
  61.  W. Żagan, S. Zalewski, S. Słomiński, and K. Kubiak, “Methods for designing and simulating optical systems for luminaires”, Bull. Pol. Acad. Sci. Tech. Sci. 68(4), 739‒750 (2020).
  62.  M. Gad-El-Hak, “Coherent structures and flow control: genesis and prospect”, Bull. Pol. Acad. Sci. Tech. Sci. 67(3), 411‒444 (2019).
  63.  Ch. Cimini, G. Pezzotta, R. Pinto, and S. Cavalieri, “Industry 4.0 technologies impacts in the manufacturing and supply chain landscape: an overview”, in Service Orientation in Holonic and Multi-Agent Manufacturing. SOHOMA 2018. Studies in Computational Intelligence, vol. 803, pp. 109‒120, Eds., T. Borangiu, D. Trentesaux, A. Thomas, and S. Cavalieri, Springer, Cham, 2019 doi: 10.1007/978-3-030- 03003-2_8.
  64.  J. Flizikowski and M. Macko, “Competitive design of shredder for plastic in recycling”, in Proc. 5th International Symposium on Tools and Methods of Competitive Engineering, Lausanne, Switzerland, 2004, vol. 1‒2, pp. 1147‒1148.
  65.  J. Flizikowski and M. Macko, J. Czerniak, A. Mroziński “Implementation of genetic algorithms into development of mechatronic multi- edge’s grinder design”, in Proc. of ASME International Mechanical Engineering Congress and Exposition (IMECE), Denver, 2012, vol. 7, pp. 1227‒1235.
  66. M. Macko, A. Mroziński, and A. Prentki “Simulations CAE of wood pellet machine”, in MATEC Web of Conferences, 2019, vol. 254, art. no. 02028.
  67.  A. Napoleone, M. Macchi, and A. Pozzetti, “A review on the characteristics of cyber-physical systems for the future smart factories”, J. Manuf. Syst. 54, 305‒335 (2020).
  68.  L. Monostori, “Cyber-physical production systems: roots, expectations and R&D challenges”, Procedia CIRP 17, 9‒13 (2014).
  69.  K.D. Thoben, S. Wiesner, and T. Wuest, “Industrie 4.0 and smart manufacturing – a review of research issues and application examples”. Int. J. Autom. Technol. 11, 4‒16 (2017).
  70.  M. Garetti, L. Fumagalli, and E. Negri, “Role of ontologies for CPS implementation in manufacturing”, Manag Prod Eng Rev 6, 26‒32 (2015).
  71.  L. Shi, G. Guo, X. Song, “Multi-agent based dynamic scheduling optimisation of the sustainable hybrid flow shop in a ubiquitous environment”, Int. J. Prod. Res. 59(2), 576‒597 (2021).
  72.  M. Thürer, H. Zhang, M. Stevenson, F. Costa, and L. Ma, “Worker assignment in dual resource constrained assembly job shops with worker heterogeneity: an assessment by simulation”, Int. J. Prod. Res. 58, 6336‒6349 (2020).
  73.  C. Wang, G. Zhou, and Z. Zhu, “Service perspective based production control system for smart job shop under industry 4.0”, Robot. Comput.-Integr. Manuf. 65, 101954 (2020).
  74.  W. Polini and A. Corrado, “Digital twin of composite assembly manufacturing process”, Int. J. Prod. Res. 58, 5238‒5252 (2020).
  75.  D. Mourtzis, J. Angelopoulos, and G. Dimitrakopoulos, “Design and development of a flexible manufacturing cell in the concept of learning factory paradigm for the education of generation 4.0 engineers”, Procedia Manuf. 45, 361‒366 (2020).
  76.  Q. Demlehner and S. Laumer, “Why Context Matters: Explaining the Digital Transformation of the Manufacturing Industry and the Role of the Industry’s Characteristics in It”, Pac. Asia J. Assoc. Inf. Syst. 12(3), 3 (2020).
  77.  G. Genta, M. Galetto, and F. Franceschini, “Inspection procedures in manufacturing processes: recent studies and research perspectives”, Int. J. Prod. Res. 58, 4767‒4788 (2020).
  78.  D. Mourtzis, V. Siatras, and J. Angelopoulos, “Real-Time Remote Maintenance Support Based on Augmented Reality (AR)”, Appl. Sci. 10(5), 1855 (2020).
  79.  R. Davidson, “Cyber-Physical Production Networks, Artificial Intelligence-based Decision-Making Algorithms, and Big Data-driven Innovation in Industry 4.0-based Manufacturing Systems”, Econ. Manag. Financ. Mark. 15(3), 16‒22 (2020).
  80.  D. Mourtzis, G. Synodinos, J. Angelopoulos, and N. Panopoulos, “An augmented reality application for robotic cell customization”, Procedia CIRP 90, 654‒659 (2020).
  81.  T. White, I. Grecu, and G. Grecu, “Digitized Mass Production, Real-Time Process Monitoring, and Big Data Analytics Systems in Sustainable Smart Manufacturing”, J. Self-Gov. Manag. Econ.8(3), 37‒43 (2020).
  82.  D. Mourtzis, “Adaptive Scheduling in the Era of Cloud Manufacturing”, in Scheduling in Industry 4.0 and Cloud Manufacturing, International Series in Operations Research & Management Science, vol. 289, pp. 61‒85, Eds., B. Sokolov, D. Ivanov, and A. Dolgui, Springer, Cham, 2020 https://doi.org/10.1007/978-3-030-43177-8_4.
  83.  N. Hangst, S. Junk, and T. Wendt, “Design of an Additively Manufactured Customized Gripper System for Human Robot Collaboration”, in Industrializing Additive Manufacturing. AMPA, pp. 415‒425, Eds., M. Meboldt and C. Klahn, Springer, Cham, 2021, http://doi-org-443. webvpn.fjmu.edu.cn/10.1007/978-3-030-54334-1_29.
  84.  Y. Tirupachuri, “Enabling Human-Robot Collaboration via Holistic Human Perception and Partner-Aware Control”, arXiv-CS-Robotics, 2020, doi: arxiv.org/abs/2004.10847v1
  85.  W. de Paula Ferreira, F. Armellini, and L.A. de Santa-Eulalia, “Simulation in industry 4.0: A state-of-the-art review”, Comput. Ind. Eng. 149, 106868 (2020).
  86.  R. Cioffi, M. Travaglioni, G. Piscitelli, A. Petrillo, and A. Parmentola, “Smart Manufacturing Systems and Applied Industrial Technologies for a Sustainable Industry: A Systematic Literature Review”, Appl. Sci. 10(8), 2897 (2020).
  87.  G. Li, S. Yang, Z. Xu, J. Wang, Z. Ren, and G. Li, “Resource allocation methodology based on object-oriented discrete event simulation: A production logistics system case study”, CIRP J. Manuf. Sci. Technol. 31, 394‒405 (2020).
  88.  R. Davis, M. Vochozka, J. Vrbka, and O. Neguriţă, “Industrial Artificial Intelligence, Smart Connected Sensors, and Big Data-driven Decision-Making Processes in Internet of Things-based Real-Time Production Logistics”, Econ. Manag. Financ. Mark.15(3), 9‒15 (2020).
  89.  C.H.D. Santos, J.A. de Queiroz, F. Leal, and J.A.B. Montevechi, “Use of simulation in the industry 4.0 context: Creation of a Digital Twin to optimise decision making on non-automated process”, J. Simul.(2020) doi: 10.1080/17477778.2020.1811172.
  90.  H. Sun, G. Pedrielli, G. Zhao, C. Zhou, W. Xu, and R. Pan, “Cyber coordinated simulation for distributed multi-stage additive manufacturing systems”, J. Manuf. Syst. 57, 61‒71 (2020).
  91.  G. Chryssolouris, N. Papakostas, and D. Mavrikios, “A Perspective on Manufacturing Strategy: Produce More with Less”, CIRP J. Manuf. Sci. Technol. 1(1), 45–52 (2008), doi: 10.1016/j.cirpj.2008.06.008.
  92.  D. Mourtzis, M. Doukas, and D. Bernidaki, “Simulation in Manufacturing: Review and Challenges”, Procedia CIRP 25, 213–229 (2014), doi: 10.1016/j.procir.2014.10.032.
  93.  T. Burczyński, W. Kuś, W. Beluch, A. Długosz, A. Poteralski, and M. Szczepanik, Intelligent computing in optimal design, Springer, 2020.
Go to article

Authors and Affiliations

Izabela Rojek
1
ORCID: ORCID
Marek Macko
2
ORCID: ORCID
Dariusz Mikołajewski
1
ORCID: ORCID
Milan Sága
3
ORCID: ORCID
Tadeusz Burczyński
4
ORCID: ORCID

  1. Institute of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland
  2. Faculty of Mechatronics, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland
  3. Department of Applied Mechanics, Faculty of Mechanical Engineering, University of Zilina, 010 26 Zilina, Slovakia
  4. Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B; 02-106 Warsaw, Poland
Download PDF Download RIS Download Bibtex

Abstract

Since the beginning of the Fourth Industrial Revolution, enterprises have been promising the main advantages and benefits of implementing the Industry 4.0 technologies. However, the perception of new Industry 4.0 technologies may vary between different types of enterprises. The paper focuses on the main advantages of Industry 4.0 technologies for manufacturing enterprises. We analyze the difference of enterprise size and technological intensity in enterprise managers’ perception. The research was conducted based on a questionnaire survey that participated 217 enterprises from the Czech Republic. Statistical analysis showed that higher productivity and production volume are the main advantages of Industry 4.0. The present results show differences between enterprises according to their size. However, differences related to the technological complexity of enterprises have not been confirmed as an essential factor.
Go to article

Authors and Affiliations

Martin Pech
1
ORCID: ORCID
Drahoš Vaněček
ORCID: ORCID

  1. University of South Bohemia in Ceske Budejovice, Czech Republic
Download PDF Download RIS Download Bibtex

Abstract

The purpose of servitization is to provide new business opportunities mainly to manufacturing companies. Companies strive to develop new services through utilizing servitization models, which are required to be applicable in several servitization scenarios. The main objective of this study is to propose a servitization model, known as “end-to-end servitization model” suitable for servitization purposes in companies. The model was developed based on several validated and commonly utilized service design models. Moreover, testing the validity of the model was implemented with the usability survey (usefulness, ease to use, easy of learning and satisfaction) with the Master’s level students, while they were developing new services by utilizing the proposed model. The results of this study indicate that the proposed servitization model can be utilized in different organizations to provide new services. Furthermore, the model can be concluded as useful, easy to use, easy to learn and it is at a satisfactory level based on the empirical evidence.
Go to article

Authors and Affiliations

Ari Sivula
1 2
Ahm Shamsuzzoha
2
Emmanuel Ndzibah
2
Binod Timilsina
2

  1. Seinäjoki University of Applied Sciences, Finland
  2. University of Vaasa, School of Technology and Innovations, Finland
Download PDF Download RIS Download Bibtex

Abstract

Leadership research is an essential part of all areas of organisational science worldwide, and there is still a lack of studies in this research area. The paper aims to determine leadership competency perceptions and their sub-competencies characteristics and determinants in the fourth industrial revolution era. The research survey, conducted in 2018-2021, covered a sample of 100 respondents from organisations from the Czech Republic. The most important competencies for leadership are effective communication, innovation, cooperation, creativity, solving problems, lifelong learning, Information and Communication Technology (ICT) and motivation and support of others. We selected statistical methods ANOVA and linear regression for the characteristics of the respondents and the cluster analysis for the leaders’ 4.0 types determination. The linear regression results showed that age, the field of education, position in the organisation and tenure in the organisation of the respondents affect their assessment of the level of leadership competency. We identified three management types that are currently facing the challenges of Industry 4.0: ICT-oriented Junior Managers, Top 4.0 Prepared Leaders, and Non-Creative Unmotivated Senior Directors. The contribution of this paper is the in-depth study in the area of perceived levels of partial competencies for leadership for different criteria of respondents.
Go to article

Authors and Affiliations

Julie Čermáková
1
Michal Houda
2
Ladislav Rolínek
1
Martin Pech
1
ORCID: ORCID

  1. University of South Bohemia: Jihoceska Univerzita v Ceskych Budejovicich, Department of Management, Faculty of Economics, Czech Republic
  2. University of South Bohemia: Jihoceska Univerzita v Ceskych Budejovicich, Department of Applied Mathematicsand Informatics, Faculty of Economics, Czech Republic
Download PDF Download RIS Download Bibtex

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.
Go to article

Authors and Affiliations

Leszek Pacholski
1
ORCID: ORCID

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

This page uses 'cookies'. Learn more