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

Izabela Dziaduch

Izabela Dziaduch

Wrocław University of EconomicsPoland
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Dziaduch, Izabela
  
Dec 31, 2020
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Published Online: Dec 31, 2020
Page range: 52 - 63
Received: May 05, 2020
Accepted: Nov 15, 2020
DOI: https://doi.org/10.2478/emj-2020-0027
Keywords
© 2020 Izabela Dziaduch, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1

Algorithm followed in the method for forecasting the costs of a technical object operation
Algorithm followed in the method for forecasting the costs of a technical object operation

Fig. 2

Schedule of the performed research analysis
Schedule of the performed research analysis

Fig. 3

Structure of costs distribution of a railbus operation adopted for calculations
Structure of costs distribution of a railbus operation adopted for calculations

Fig. 4

Data for the time series analysis covering a given operational cost parameter of railbuses: a) daily mileage of railbuses, b) salaries of conductors per kilometre of the route, c) salaries of train drivers per kilometre of the route, d) diesel consumption per kilometre of the route, and e) purchase price of a litre of diesel
Data for the time series analysis covering a given operational cost parameter of railbuses: a) daily mileage of railbuses, b) salaries of conductors per kilometre of the route, c) salaries of train drivers per kilometre of the route, d) diesel consumption per kilometre of the route, and e) purchase price of a litre of diesel

Fig. 5

Distribution of operational cost parameters for test objects: a) daily mileage of railbuses, b) salaries of conductors per kilometre of the route, c) salaries of train drivers per kilometre of the route, d) diesel consumption per kilometre of the route, and e) purchase price of a litre of diesel
Distribution of operational cost parameters for test objects: a) daily mileage of railbuses, b) salaries of conductors per kilometre of the route, c) salaries of train drivers per kilometre of the route, d) diesel consumption per kilometre of the route, and e) purchase price of a litre of diesel

Fig. 6

Cumulative operational costs of railbuses in the analysed period presented as the months of exploitation
Cumulative operational costs of railbuses in the analysed period presented as the months of exploitation

Relative errors in measuring operational costs of railbuses

Railbus numberLast month of the railbus exploitation tKUtrz{\bf{\it{KU}}}_{\bf{\it{t}}}^{{\bf{\it{rz}}}}KUtγtMean relative error
15033793573340998−1.1%2.9%
250315651533409985.5%
35034163623340998−2.3%
419122647212695793.4%
51912769961269579−0.58%
61611409011069119−6.7%
713873708868659−0.58%
812825694801839−3.0%

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

Parameters of the operational cost components
K1/2K12K14K22K23
Stage 4Correlation coefficient (r)−0.22−0.070.060.18−0.25
For ∝= 0,05t = −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 hypothesisH0: ρ = 0H0: ρ = 0H0: ρ = 0H0: ρ = 0H0: ρ = 0
Stage 5Type of probability distributionNormalLog-normalNormalNormalNormal
Distribution matching (φ)0.970.990.990.990.88
E(X)367.851.232.074.750.58
F(x0,95)480.951.462.415.600.69
F(x0,05)254.760.021.723.910.47
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Business and Economics, Business Management, Industries, Transportation, Logistics, Air Traffic, Shipping, Engineering, Mechanical Engineering, Production Technology, Process Engineering and Industrial Engineering
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