Effective data usage for the proper and beneficial automotive production cost improvement

• Autorzy: Krzesniak G.

Identyfikatory

DOI

10.5604/01.3001.0053.8696

• Języki publikacji: EN

Abstrakty

EN

Purpose The article aims to present a proposal and discuss the investment cost calculation procedures based on data collected during the manufacturing process, according to standard SPC control chart evaluation and standard PDCA. It is applied as a tool to support the process of continuous improvement of the manufacturing process and improve profitability by proper allocation the cost of investment and resources. Design/methodology/approach The study uses the results of a literature review on the issue of cost analysis and their modelling. Key elements are the main cost components, but also those that are considered less important and maybe overall decisive. Application cost to benefit relations – as a method of data evaluation for cost modelling to improve overall cost structure is proposed. Findings The relationship between return on investment and amortisation time allows to easily visualise which of the proposed changes are the most cost-effective over time. Based on the analysis conducted the results, the change is proposed below, in order from the most cost-effective. Research limitations/implications Further research should focus on the impact if a decision were based on the findings and proposals defined. Practical implications Each production process is based on the use of resources. This applies to both production plants and other activities. A resource can be anything that will be used in the manufacturing process. Of key importance for the success of the project is their proper use and not only effective but most of all efficient. Originality/value The considerations presented in the study may be the basis for determining the key factors of the cost of production and investment. The proposed simulation model allows for determining the efficient direction for investment. This, in turn, should enable us to define the main directions of searching for the optimisation of the product cost to achieve the expected cost and quality level.

Słowa kluczowe

EN

cost calculation; cost modelling; manufacturing costs; control chart; quality costs; Statistical Process Control; SPC

PL

kalkulacja kosztów; modelowanie kosztów; koszty produkcji; wykres kontrolny; koszty jakości; statystyczna kontrola procesu; SPC

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2023
• Tom: Vol. 119, nr 1
• Strony: 27--34
• Opis fizyczny: Bibliogr. 15 poz., rys., tab.

Twórcy

autor

Krzesniak G.

• ZF Automotive Systems, Poland

• https://orcid.org/0009-0003-2809-7131

Bibliografia

• [1] R. Wolniak, Quality Management Systems According to ISO 9001:2015 Requirements and its Improvements, The Publishing House of the Silesian University of Technology, Gliwice, 2018.
• [2] R. Wolniak, Basic Concepts of Operation Management and Its Control, The Publishing House of the Silesian University of Technology, Gliwice, 2018.
• [3] T. Sałaciński, SPC Statistical control of production processes, The Publishing House of the Warsaw University of Technology, Warszawa, 2009 (in Polish).
• [4] M. Aslam, A. Saghir, L. Ahmad, Introduction to Statistical Process Control, John Wiley & Sons, Hoboken, 2021.
• [5] R. Godina, J.C.O. Matias, S. Azevedo, Quality improvement with statistical process control in the automotive industry, International Journal of Industrial Engineering Management 7/1 (2016) 1-8.
• [6] D. Dobija, M. Kucharczyk, Management accounting, Wolters Kluwer, Warszawa, 2014 (in Polish).
• [7] S. Cavalieri, P. Maccarrone, R. Pinto, Parametric vs. neural network models for the estimation of production costs: A case study in the automotive industry, International Journal of Production Economics 91/2 (2004) 165-177. DOI: https://doi.org/10.1016/j.ijpe.2003.08.005
• [8] L. del Re, F. Allgöwer, L. Glielmo, C. Guardiola, I. Kolmanovsky (eds), Automotive Model Predictive Control: Models, Methods and Applications, Springer, London, 2010. DOI: https://doi.org/10.1007/978-1-84996-071-7
• [9] J.J. Dahlaard, K. Kristensen, G.K. Kanaj, Fundamentals of quality management, PWN, Warszawa, 2004 (in Polish).
• [10] W.J. Latzko, D.M. Saunders, Four days with Dr. Deming: A strategy for modern methods of management, Addison-Wesley Publishing, Boston, 1996.
• [11] D. Olsen, The Lean product playbook: how to innovate with minimum viable products and rapid customer feedback, John Wiley & Sons, Hoboken, 2015.
• [12] S. Matope, G.P. Chirinda, B. Sarema, Continuous improvement for cost savings in the automotive industry, Sustainability 14/22 (2022) 15319. DOI: https://doi.org/10.3390/su142215319
• [13] S. Jauhar, M. Asthankar, A.M. Kuthe, Cost benefit analysis of rapid manufacturing in automotive industries, Advances in Mechanical Engineering and its Applications 2/3 (2012) 181-188.
• [14] G. Krzesniak, Production Cost Calculation Approach Based on Data Collected During Manufacturing Process, System Safety: Human – Technical Facility – Environment 4/1 (2022) 19-27. DOI: https://doi.org/10.2478/czoto-2022-0003
• [15] W.M. Smith, Surface Materials Processing. Second Edition, Backmann Verlag, Berlin – London – Paris – Warsaw, 2006.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).