Smolyak, Sergey (2023): Оценка подержанных машин на основе новой модели их деградации. Published in: Applied Mathematics and Control Sciences , Vol. 2023, No. 1 (15 June 2023): pp. 116-132.
Preview |
PDF
MPRA_paper_119423.pdf Download (400kB) | Preview |
Abstract
We propose a new model of equipment degradation. In it, the machine is subjected to random latent failures, the danger of which depends on the equipment condition and after each failure the intensity of the equipment's benefits decreases by a random amount. Equipment that brings negative benefits is subject to decommissioning. The model parameters are found based on known information about the average value and the coefficient of variation of the equipment lifetime. Market value of equipment is determined by discounting the flow of benefits from its future use. This allows us to find the dependence of the equipment’s market value on the benefits it brings. Assessing the market value of new equipment is usually not difficult, but it is much more difficult to do this for used equipment items. Appraisers are usually unable to estimate the value of the work performed by equipment, and when valuing a used equipment, they have to rely on its age. To do this, the market value of a similar new equipment is usually reduced by a depreciation factor or multiplied by Percent Good Factor (PGF, relative value), depending on the age of equipment being valued. However, equipment of the same age may be in different conditions and, therefore, have a different market value. Therefore, such PGFs, in fact, relate to the average equipment that has survived to the appropriate age. Appraisers determine them by formulas or tables that are usually not supported by proper justifications. The proposed model makes it possible to build the dependence of the average PGF on age and calculate the market value of the work performed by machines, even if such works are not traded on the market. It turns out that it is possible to take into account the influence of the utilization cost of the machine and inflation in the model. The results of experimental calculations performed using the model (with calibration parameters selected appropriately) are in good agreement with the market prices of some types of construction equipment.
Item Type: | MPRA Paper |
---|---|
Original Title: | Оценка подержанных машин на основе новой модели их деградации |
English Title: | Valuation of used machinery based on the new model of its degradation |
Language: | Russian |
Keywords: | machinery, equipment, benefits, valuation, market value, degradation, failures, age, Percent Good Factors, mean useful life |
Subjects: | C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis D - Microeconomics > D6 - Welfare Economics > D61 - Allocative Efficiency ; Cost-Benefit Analysis |
Item ID: | 119423 |
Depositing User: | Mr Sergey Bushansky |
Date Deposited: | 11 Dec 2023 15:55 |
Last Modified: | 11 Dec 2023 15:55 |
References: | 1. Gorjian N., Ma L., Mittinty M., Yarlagadda P., Sun Y. (2009) A review on degradation models in reliability analysis. In: Proceedings of the 4th World Congress on Engineering Asset Management, 28-30 September 2009, Marriott Athens Ledra Hotel, Athens. 2. Li W., Pham H. (2005). Reliability modeling of multi-state degraded systems with multi-competing failures and random shocks. IEEE Transactions on Reliability. Vol. 54(2), pp. 297-303. 3. Lin Y.H., Li Y.F., Zio E. (2014). Integrating Random Shocks Into Multi-State Physics Models of Degradation Processes for Component Reliability Assessment. IEEE Transactions on Reliability. Vol. 64(1), pp. 154-166. 4. Nakagawa T. (2007). Shock and damage models in reliability theory: Springer. 5. Wang Z., Huang H.-Z., Li Y., Xiao N.-C. (2011). An approach to reliability assessment under degradation and shock process. IEEE Transactions on Reliability. Vol. 60(4), pp. 852-863. 6. System of National Accounts 2008. (2009). European Commission, International Monetary Fund, Organization for Economic Co-operation and Development, United Nations, World Bank. New York. 7. Smolyak S.A. (2022). Theory and methods of machinery and equipment valuation: Tutorial. Moscow, INFRA-M. 390 p. DOI: 10.12737/1031121 (in Russian) 8. Smolyak S.A. (2021). Valuation of machinery and equipment undergoing the Wiener degradation process. Economics and mathematical methods. No 3 (58), pp.97-109. DOI: 10.31857/S042473880016422-3 (in Russian) 9. Smolyak S.A. (2020). Poisson’s process of machinery degradation: Application to valuation. Journal of the New Economic Association, No 4 (48), 63–84. DOI: 10.31737/2221-2264-2020-48-4-3 (in Russian) 10. Smolyak S.A. (2022). Depreciation of machinery and equipment in the generalized Poisson model of degradation. Proceedings of the Institute of Systems Analysis of the Russian Academy of Sciences. 1(72), pp. 48-60, DOI: 10.14357/20790279220105 (in Russian) 11. International Valuation Standards. Effective 31 January 2020. International Valuation Standards Council. 12. Smolyak S.A. (2022). Valuation of machinery and equipment with randomly degraded operating characteristics. Applied Mathematics and Control Sciences, no 1, pp. 153-175. DOI: 10.15593/2499-9873/2022.1.08 (in Russian). 13. Alico J. (Ed.). (1989). Appraising machinery and equipment. McGraw-Hill. 14. Fedotova M. (Ed). (2018). Machinery and Equipment Valuation: textbook. 2nd ed. Moscow, INFRA-M Publ. (in Russian) 15. Leifer L.A. (Ed). (2019). Handbook of appraiser of machinery and equipment. / Correction factors and characteristics of the market of machinery and equipment. 2nd ed. Nizhny Novgorod: Volga Center for Methodological and Informational Evaluation. (in Russian) 16. Roslov V.Yu., Myshanov A.I. (2007). Modified lifetime method for calculating equipment depreciation. Voprosy otsenki. No 2. (in Russian) 17. 2022 Cost Index and Depreciation Schedules. Raleigh: North Carolina Department of Revenue. 18. 2020 Personal Property Manual. Arizona Department of Revenue. https://azdor.gov/sites/default/files/media/PROPERTY_pp-manual.pdf 19. Assessor’s Handbook, Section 582. The Explanation of the Derivation of Equipment Percent Good Factors. California State Board of Equalization. February 1981. Reprinted August 1997. 20. Barth N/, Cappelen Å., Skjerpen T, Todsen S., Åbyholm T. (2016). Expected service lives and depreciation profiles for capital assets: Evidence based on a survey of Norwegian firms. Discussion Papers No. 840. Statistics Norway, Research Department. 21. Mikhailitchenko S. (2016). Estimates of Net Capital Stock and Consumption of Fixed Capital for Australian States and Territories, 1990–2013. Regional Statistics, vol. 6, no 2, pp. 114–128; DOI: 10.15196/RS06206 22. Nomura K. (2005). Duration of Assets: Examination of Directly Observed Discard Data in Japan. KEO Discussion Paper No 99. 23. Grinchar N.G., Chalova M.Yu., Fomin V.I. (2014). Fundamentals of machine reliability. Part 1: Tutorial. Moscow: MGUPS. 98 p . (in Russian) 24. Ostreykovsky V.A. (2003). Reliability Theory: A Textbook for High Schools. Moscow, Higher School Publ. (in Russian) 25. Pitukhin A.V., Shilovsky V.N., Kostyukevich V.M. (2010). Reliability of forestry machines and equipment: Tutorial. St. Petersburg: Lan’ Publ.. 288 p. (in Russian). 26. Guidance document 26-01-143-83. Reliability of chemical engineering products. Assessment of reliability and efficiency in the design. (in Russian). 27. Livchits V.N. (1971). Selection of optimal solutions in technical and economic calculations. Moscow: Economics Publ. (in Russian) 28. Leifer L.A., Kashnikova P.M. (2008). Determination of the residual service life of machines and equipment based on probabilistic models. Property relations in the Russian Federation. No 1(76), pp. 66-79. (in Russian) |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/119423 |