Transactions of FAMENA, Vol. 44 No. 4, 2020.
Izvorni znanstveni članak
https://doi.org/10.21278/TOF.444004719
The Model Prediction of Life Cycle Ownership Costs of Special Motor Vehicles
Jan Furch
orcid.org/0000-0002-7332-1580
; Department of Combat and Special Vehicles, University of Defence, Brno, Czech Republic
Sažetak
The paper deals with the prediction of life cycle costs related to special motor vehicles. In the first part, there is an analysis of the applied commercial software programs used for calculating and predicting the life cycle cost of vehicles. Next, there is a description of risks which might occur when calculating the life cycle cost and of the possible risk management. In the second part of the paper it is suggested that the motor vehicle life cycle cost can be predicted based on accurate data which are generally difficult to obtain, e.g. failure intensity z(t) or mean time between failures (MTBFs) used for calculating the cost after maintenance. The final part includes a proposal for the prediction of the ownership life cycle cost which consists of the operating and maintenance costs of special motor vehicles. This proposal is based on the company logistic information system, which at regular intervals assesses special vehicle life cycle cost during operation and maintenance. Under special motor vehicles here we understand the vehicles which are equipped with a chassis and a special vehicle superstructure which consumes operation units and on which maintenance is performed. Such vehicles are used in the construction or agricultural industry as well as in the military environment. The paper focuses on the design of a prediction model of the ownership life cycle cost of the military environment, where a relevant military logistic information system is used.
Ključne riječi
motor vehicle life cycle cost; prediction model; life cycle cost prediction; ownership cost; operating cost; preventive maintenance cost; corrective maintenance cost
Hrčak ID:
247506
URI
Datum izdavanja:
22.1.2021.
Posjeta: 1.431 *