<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The purpose of the article is to present the method for forecasting one of the three categories of exploitation costs, i.e., operational costs. The article analyses the available subject literature discussing the methods of measuring operational costs used in the LCC analysis. The presented method for forecasting operational costs of technical objects applies econometric modelling, probability distributions and certain elements of descriptive and mathematical statistics. The statistical data analysis was performed using the functions and commands available in Microsoft Excel. Weibull++ application was also used for constructing probability distributions for random variables and verifying hypotheses. The method was tested on eight single-mode railbuses, operated by one of the regional railway companies providing passenger transport. An ex-post relative forecast error was used to measure the level of accuracy of the operational cost forecast. The analysis of the compliance between forecasted cost value and the actual costs showed extensive convergence as evidenced by the level of estimated relative errors. In forecasting the operational costs of railbuses, the average error was approx. 2.9%. The presented method can, therefore, constitute the basis for the estimation of both operational costs and exploitation costs, which represent an important cost component considered when assessing the profitability of purchasing one of the several competing technical objects offered by the industry.</p></abstract>

The purpose of the article is to present the method for forecasting one of the three categories of exploitation costs, i.e., operational costs. The article analyses the available subject literature discussing the methods of measuring operational costs used in the LCC analysis. The presented method for forecasting operational costs of technical objects applies econometric modelling, probability distributions and certain elements of descriptive and mathematical statistics. The statistical data analysis was performed using the functions and commands available in Microsoft Excel. Weibull++ application was also used for constructing probability distributions for random variables and verifying hypotheses. The method was tested on eight single-mode railbuses, operated by one of the regional railway companies providing passenger transport. An ex-post relative forecast error was used to measure the level of accuracy of the operational cost forecast. The analysis of the compliance between forecasted cost value and the actual costs showed extensive convergence as evidenced by the level of estimated relative errors. In forecasting the operational costs of railbuses, the average error was approx. 2.9%. The presented method can, therefore, constitute the basis for the estimation of both operational costs and exploitation costs, which represent an important cost component considered when assessing the profitability of purchasing one of the several competing technical objects offered by the industry.

Forecasting operational costs of technical objects based on the example of railbuses

Engineering Management in Production and Services

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The purpose of the article is to present the method for forecasting one of the three categories of exploitation costs, i.e., operational costs. The article...

Railbus number	Last month of the railbus exploitation t	$K U_{t}^{rz}$ {\bf{\it{KU}}}_{\bf{\it{t}}}^{{\bf{\it{rz}}}}	KU_t	γ_t	Mean relative error
1	50	3379357	3340998	−1.1%	2.9%
2	50	3156515	3340998	5.5%
3	50	3416362	3340998	−2.3%
4	19	1226472	1269579	3.4%
5	19	1276996	1269579	−0.58%
6	16	1140901	1069119	−6.7%
7	13	873708	868659	−0.58%
8	12	825694	801839	−3.0%

		Parameters of the operational cost components
		K_1/2	K₁₂	K₁₄	K₂₂	K₂₃
Stage 4	Correlation coefficient (r)	−0.22	−0.07	0.06	0.18	−0.25
	For ∝= 0,05	t = −1.59p = 0.12p > ∝	t = −0.51p = 0.61p > ∝	t = 0.45p = 0.65p > ∝	t =1.27p =0.21p > ∝	t = −1.78p = 0.08p > ∝
	Accepted hypothesis	H₀: ρ = 0	H₀: ρ = 0	H₀: ρ = 0	H₀: ρ = 0	H₀: ρ = 0
Stage 5	Type of probability distribution	Normal	Log-normal	Normal	Normal	Normal
	Distribution matching (φ)	0.97	0.99	0.99	0.99	0.88
	E(X)	367.85	1.23	2.07	4.75	0.58
	F(x_0,95)	480.95	1.46	2.41	5.60	0.69
	F(x_0,05)	254.76	0.02	1.72	3.91	0.47

Forecasting operational costs of technical objects based on the example of railbuses

Online veröffentlicht: 31. Dez. 2020

Seitenbereich: 52 - 63

Eingereicht: 05. Mai 2020

Akzeptiert: 15. Nov. 2020

DOI: https://doi.org/10.2478/emj-2020-0027

Schlüsselwörter
forecasting, operational costs, analysis of the life-cycle cost (LCC)

© 2020 Izabela Dziaduch, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Relative errors in measuring operational costs of railbuses

Parameters of the operational cost components for time intervals presented as the months of vehicle exploitation

Forecasting operational costs of technical objects based on the example of railbuses

Online veröffentlicht: 31. Dez. 2020

Seitenbereich: 52 - 63

Eingereicht: 05. Mai 2020

Akzeptiert: 15. Nov. 2020

DOI: https://doi.org/10.2478/emj-2020-0027

Schlüsselwörterforecasting, operational costs, analysis of the life-cycle cost (LCC)

© 2020 Izabela Dziaduch, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Relative errors in measuring operational costs of railbuses

Parameters of the operational cost components for time intervals presented as the months of vehicle exploitation

Schlüsselwörter
forecasting, operational costs, analysis of the life-cycle cost (LCC)