Model of human resource management in manufacturing companies using key performance indicators (KPIs)

• Autorzy: Czerwińska Karolina , Pacana Andrzej

Identyfikatory

DOI

10.2478/czoto-2024-0009

• Języki publikacji: EN

Abstrakty

EN

Measuring the performance of manufacturing enterprises is now a key component of developmental organizational management. Key performance indicators make it possible to determine the effectiveness of implemented changes, set new goals and confirm the reasonableness of incurred expenditures. However, a key difficulty is the selection of adequate metrics, which should correspond to strategic objectives and be tailored to the specifics of the enterprise. Therefore, the purpose of the study was to present a model for effective human resource management in manufacturing enterprises based on key performance indicators. The developed model makes it possible to justify the necessity and examine the relevance of decisions made and investments incurred. The originality of the study is due to the methodological nature and concerns the model used, which is the author's tool for supervising the development strategy of the organization taking into account the well-being of employees. The presented model can provide important support for managers shaping the personnel policy of the enterprise and carrying out tasks in the field of employer branding.

Słowa kluczowe

EN

key performance indicators; key performance measures; management and quality; strategic management; human capital

PL

kluczowe wskaźniki efektywności; kluczowe środki efektywności; zarządzanie i jakość; zarządzanie strategiczne; kapitał ludzki

• Wydawca: De Gruyter
• Czasopismo: System Safety : Human - Technical Facility - Environment
• Rocznik: 2024
• Tom: Vol. 6, iss. 1
• Strony: 69--78
• Opis fizyczny: Bibliogr. 41 poz., rys., tab.

Twórcy

autor

Czerwińska Karolina

• Rzeszow University of Technology; Faculty of Mechanical Engineering and Aeronautics, Rzeszow, Poland

• https://orcid.org/000-0003-2150-0963

autor

Pacana Andrzej

• Rzeszow University of Technology; Faculty of Mechanical Engineering and Aeronautics, Rzeszow, Poland

• https://orcid.org/0000-0003-1121-6352

Bibliografia

• 1.Bauer, K., 2004. KPIs-The metrics that drive performance management, Information Management, 14, 9, 63.
• 2.Bauer, K., 2004a. KPIs: Not all metrics are created equal, Information Management, 14, 12, 42.
• 3.Bean, C., Geraghty, K., 2003. Navigating the road to KPI success. Focus, 5(6), 37-41.
• 4.Blaskova, E., Blasko, R., Rostak - Szyrocka, E., Ulewicz, R., 2017. Flexibility and variability of motivating employees and managers in Slovakia and Poland, Polish Journal of Management Studies, 15, 1, 26-36.
• 5.Cervi, M., de Almedia Pires, F., Chang, LH., Novier, L., Wang, RY., 2017. Towards a Methodology of Data Management Metrics. MIT International Conference on Information Quality, UA Little Rock, October 6-7, 1-6.
• 6.Cronin, G., 2007. Measuring strategic progress. Choosing and using KPIs. Accountancy Ireland, 39 (4), 30-31.
• 7.Czerwińska, K., Pacana, A., 2020. Analysis of the exterior door production process using key performance indicators (KPI), Zarządzanie Przedsiębiorstwem, 23, 1.
• 8.Czerwińska, K., Pacana, A., 2022. Analysis of the maturity of process monitoring in manufacturing companies, Production Engineering Archives, 28, 3, 246-251.
• 9.Czerwińska, K., Pacana, A., Bednarova, A., 2023. Analysis of the Level of Sustainability with the Application of KPIS, Scientific Papers of Silesian University of Technology, Organization and Management Series, 178, 191-204.
• 10.Czerwińska, K., Pacana, A. Dwornicka R., 2020. Improvement of the production process with the use of selected KPIs, System Safety: Human-Technical Facility-Environment, 2, 1.
• 11.Deja, A., Ulewicz, R., Kyrychenko, Y., 2021. Analysis and assessment of environmental threats in maritime transport. Transportation Research Procedia, 55, 1073-1080. DOI: 10.1016/j.trpro.2021.07.078
• 12.Dwornicka, R., Pietraszek, J., 2018. The outline of the expert system for the design of experiment. Production Engineering Archives, 20(20), 43-48. DOI: 10.30657/pea.2018.20.09
• 13.Eckerson W. W., 2006. Creating Effective KPIs, Information Management, 16, 6, 15.
• 14.Eckerson, W.W., 2009. Performance Management Strategies. Business Intelligence Journal, 14(1), 24-27.
• 15.Gajdzik, B., Wolniak, R., 2022. Smart production workers in terms of creativity and innovation: The implication for open innovation, Journal of Open Innovation: Technology, Market, and Complexity, 8, 2, 68.
• 16.Gajdzik, B. Zarządzanie różnorodnością zasobów ludzkich w przedsiębiorstwie hutniczym, HUMANITAS Zarządzanie, 16, 4, 91-106.
• 17.Hursman, A., 2010. Measure what matters - Seven strategies for selecting relevant key performance indicators, Information Management, 20, 4, 24.
• 18.Ingaldi, M., Dziuba, Sz. T., 2020. Wykorzystanie wskaźników satysfakcji z pracy na przykładzie przedsiębiorstwa metalurgicznego, Hutnik, Wiadomości Hutnicze, 87, 1-2.
• 19.Jasiewicz, B., Pietraszek, J., Duda, S., Pietrzak, S., Pruszczyński, B., Parol, T., Potaczek, T., Gądek-Moszczak, A., 2021. Inter-observer and intra-observer reliability in the radiographic measurements of paediatric forefoot alignment. Foot and Ankle Surgery, 27(4), 371-376. DOI: 10.1016/j.fas.2020.04.015
• 20.Jemala, M., 2024. Recognizing Key Macro-factors of Technological Innovation Based on Leading Technology Companies’ Research. Production Engineering Archives, 30(4), 413-430. DOI: 10.30657/pea.2024.30.40
• 21.Kalak, T., Cierpiszewski, R., Ulewicz, M., 2021. High efficiency of the removal process of Pb(Ii) and Cu(ii) ions with the use of fly ash from incineration of sunflower and wood waste using the CFBC technology. Energies, 14(6), art. 1771. DOI: 10.3390/en14061771
• 22.Klimecka-Tatar, D., Ingaldi, M., Ulewicz, R., Dwornicka, D., 2021. Preparation for implementation of Industry 4.0 in small and medium-sized enterprises of metal industry, METAL 2021 : 30th Anniversary International Conference on Metallurgy and Materials, Brno, Czech Republic, EU, May 26 - 28, 2021: conference proceedings, 2021, Ostrawa, TANGER Ltd., ISBN 978-80-87294-99-4.
• 23.Knop, K., Olejarz, E., Ulewicz, R., 2019. Evaluating and Improving the Effectiveness of Visual Inspection of Products from the Automotive Industry. Lecture Notes in Mechanical Engineering, 231-243. DOI: 10.1007/978-3-030-17269-5_17
• 24.Kocyłowska, E., Pietraszek, J., 2010. Wspomaganie procesu dydaktycznego interaktywnymi narzedziami wskaznikowymi, Edukacja. Studia-Badania-Innowacje, 110, 2, 105-108.
• 25.Marković, S., Arsić, D., Nikolić, R.R., Lazic, V., Hadzima, B., Milovanovic, V.P., Dwornicka, R., Ulewicz, R., 2021. Exploitation characteristics of teeth flanks of gears regenerated by three hard-facing procedures. Materials, 14(15), art. 4203. DOI: 10.3390/ma14154203
• 26.Ogunlana S. O., 2010. Beyond the ‘Iron Triangle’: Stakeholder Perception of Key Performance Indicators (KPIs) for Large-Scale Public Sector Development Projects, International Journal of Project Management, 28.
• 27.Pacana, A., Czerwińska, K. Bednárová, L., 2018. Discrepancies analysis of casts of diesel engine piston, Metalurgija, 57(4), pp.324-326, 201754. https://hrcak.srce.hr/201754.
• 28.Pacana, A., Pasternak-Malicka, M., Zawada, M., Radoń-Cholewa, A., 2016. Decision support in the production of packaging films by cost-quality analysis.. Przemysł Chemiczny, 95(5), pp. 1042-1044.
• 29.Pacana, A., Czerwińska, K., 2020. Improving the quality level in the automotive industry, Production Engineering Archives, 26, 4, 162-166.
• 30.Pietraszek, J., 2013. The modified sequential-binary approach for fuzzy operations on correlated assessments. Lecture Notes in Computer Science, 7894, 353-364. DOI: 10.1007/978-3-642-38658-9_32
• 31.Popa B. M., 2015. Challenges When Developing Performance Indicators, Journal of Defense Resources Management, 6, 1 (10).
• 32.Radek, N., Pietraszek, J., Szczotok, A., Fabian, P., Kalinowski, A., 2020. Microstructure and tribological properties of DLC coatings. Materials Research Proceedings, 17, 171- 176. DOI: 10.21741/9781644901038-26
• 33.Radzymińska-Lenarcik, E., Ulewicz, M., 2015. The use of the steric effect of the carrier molecule in the polymer inclusion membranes for the separation of cobalt(II), nickel(II), copper(II), and zinc(II) ions. Polish Journal of Chemical Technology, 17(2), 51-56. DOI: 10.1515/pjct-2015-0029
• 34.Ramirez-Zuñiga, E.J., Castro-Silva, H.F., Velásquez-Pérez, T. & Garcia-Cruz, E.K., 2024. Unveiling Critical Innovation Factors in Sustainable Coffee Production: A Colombian Perspective. Production Engineering Archives, 30(4), 431-441. DOI: 10.30657/pea.2024.30.41
• 35.Siwiec, D., Pacana, A., 2022. A New Model Supporting Stability Quality of Materials and Industrial Products. Materials 15(13), 4440. DOI: 10.3390/ma15134440
• 36.Stacho, Z., Stachová, K., Barok, A. & Olexová, C., 2024. Trends and Perspectives in Enhancing the Competitiveness of Slovak Businesses Through Predictive HR Analytics. Production Engineering Archives, 30(3), 333-343. DOI: 10.30657/pea.2024.30.33
• 37.Tootell, B., Blackler, M., Toulson, P., Dewe, P., 2009. Metrics: HRM´s Holy Grail? A New Zealand case study. Human Resources Management Journal, 19(4), 375-392.
• 38.http://dx.doi. org/10.1111/j.1748-8583.2009.00108.x Toulson, P., Dewe,P. 2004. HR accounting as a measurement tool. Human Resource Management, 14 (2), 75-90. http://dx.doi.org/10.1111/j.1748-8583.2004.tb00120.x.
• 39.Ulewicz, R., Czerwińska, K., Pacana, A., 2023. A rank model of casting non-conformity detection methods in the context of industry 4.0, Materials, 16, 2, 723.
• 40.Ulewicz, R., Nový, F., Selejdak, J. 2014. Fatigue strength of ductile iron in ultra-high cycle regime. Advanced Materials Research, 874, 43-48. DOI: 10.4028/www.scientific.net/AMR.874.43
• 41.Wolniak, R., Dolata, M., Hadryjańska, B., Wysokińska-Senkus, A. 2024. Employing Business Analytics in Industry 4.0 Settings for Human Resource Analytics, Scientific Papers of Silesian University of Technology. Organization & Management, 197, 629- 640.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).