# Prediction of Repair and Maintenance Costs of Massey Ferguson 285 Tractors

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1 Agricultural Engineering Research Journal 1 (3): 63-67, 011 ISSN IDOSI Publications, 011 Prediction of Repair and Maintenance Costs of Massey Ferguson 85 Tractors Fereydoun Keshavarzpour Department of Agriculture, Shahr-e-Rey Branch, Islamic Azad University, Tehran, Iran Abstract: Prediction of repair and maintenance (R&M) costs of tractors and agricultural machinery in any mechanized farm is essential for owners and managers to achieve information on overall costs and to control financial balance and production economy. As Massey Ferguson 85 (MF-650) tractors are widely used by Iranian farmers and agro-industry companies, a study was conducted to model accumulated R&M costs of MF-85 tractors as percentage of initial purchase price (Y) based on accumulated usage hours (X). Recorded data of an Agribusiness Company in Iran were used to determine regression model (s). The statistical results of the study indicated that in order to predict accumulated R&M costs of MF-85 tractors with service life of 75 h or less the power regression model Y = (X/100) with R = and to predict accumulated R&M costs of MF-85 tractors with service life of 75 h or more the polynomial regression model Y = (X/100) 0.8 (X/100) with R = can be properly suggested. Key words: R&M costs Tractor MF-85 Modeling Prediction Iran INTRODUCTION models for predicting R&M costs. Fairbanks et al. [7] working in Kansas collected R&M costs data through Machinery ownership (fixed) and operating (variable) investigation from 114 farm managers. At the end, costs represent substantial portion of total production accumulated R&M costs were predicted using a power expenses. Machinery ownership costs usually include regression model based on cumulative usage hours of charges for depreciation, interest of investment tractors. Ward et al. [8] obtained a power regression (opportunity cost), taxes, insurance and housing facilities. model for predicting accumulated R&M costs based on Operating costs include repair and maintenance, i.e. accumulated usage hours for 63 forestry tractors in spare-parts, wages and lubricants [1, ]. Repair and Ireland which gave very high cost estimates compared to maintenance (R&M) costs of farm machinery are those other references. They concluded that the observed R&M expenditures necessary to restore or maintain technical costs variation on tractors was so high as to preclude the soundness and reliability of the machine [3]. Accurate use of an obtained model for predicting R&M costs for a prediction of R&M costs trends is critical to determine single tractor. They suggested this variation was most optimum economical life of machine and to make likely attributable to differences in tractor operation, appropriate decisions for machinery replacements and maintenance services, operating practices and inherent also for general farm management purposes [4]. tractor qualities, but they were not in a position to Since variation in R&M costs depends on site and time substantiate this claim. Morris [9] collected R&M costs specifications, a general relationship can not be data of 50 tractors in Weasenham Farm Company in suggested. But prediction of these costs at an acceptable Norfolk and used them to obtain R&M costs prediction level can be made by fitting a regression model based on model. His study showed that hours of use he could the previous data [5]. account for, shared no more than 16% of the observed Bower and Hunt [6] surveyed around 1800 farmers in variations in R&M costs. Skill of operator, working Illinois and Indiana and used R&M costs data to develop conditions and maintenance standards were reported as Corresponding Author: Fereydoun Keshavarzpour, Department of Agriculture, Shahr-e-Rey Branch, Islamic Azad University, Tehran, Iran. 63

3 Agric. Engineering Res. J., 1(3): 63-67, 011 Table 1: Mean annual values and mean annual percent of R&M costs fractions (spare-parts, wages and lubricants), average of whole annual R&M costs, average of annual usage hours and average of R&M costs per hour per unit of MF-85 tractors for different ages of them Spare-parts Wages Lubricants Average of whole annual Average of annual Average of R&M costs Age (years) Value (Rials) %* Value (Rials) % Value (Rials) % R&M costs (Rials) usage hours (h) per hour (Rials) Average * As percentage of average of whole annual R&M costs Table : Mean accumulated usage hours and mean accumulated R&M costs as percentage of initial purchase price per unit of MF-85 tractors for different ages of them Mean accumulated Mean accumulated R&M costs as Age (years) usage hours(h) percentage of initial purchase price(%) Table 3: Description, coefficients and coefficient of determination (R ) of the four regression models obtained for MF-85 tractors under study Model Description a b c R Linear Y = a (X/100) + b Exponential b(x/100) Y = a e Power b Y = a (X/100) Polynomial Y = a (X/100) + b (X/100) + c Fig. 1: Mean R&M costs fractions, i.e. spare-parts, wages and lubricants for MF-85 tractors under study Table provides information on mean accumulated usage hours and mean accumulated R&M costs as percentage of initial purchase price per unit of all tractors for different ages of them which were used as base data for regression analysis. In this study, tractors' initial purchase prices declared by the Agribusiness Company were adjusted for mean annual inflation rate for a period of 15 years. Table 3 shows linear, exponential, power and polynomial models. Considering R values, there is a significant correlation between X and Y variables in all 65

4 Agric. Engineering Res. J., 1(3): 63-67, Y = (X/100) (X < 75 h) (1) On the other hand, as the polynomial model conforms well to actual data trend particularly at later life time of tractors, the polynomial model is preferred to the power one for the remaining service life of tractor, i.e. accumulated usage hours above 75 h as equation : Y = (X/100) 0.8 (X/100) (X > 75 h) () Fig. : Curves of predicted accumulated R&M costs as percentage of initial purchase price based on accumulated usage hours using the power and polynomial regression models for MF-85 tractors under study Fig. 3: Actual data curve and modeling curve of accumulated R&M costs based on accumulated usage hours for MF-85 tractors under study four models. However, R values indicate that the power and polynomial models have higher conformity with actual data trend in comparison with the linear and exponential models. For prediction of accumulated R&M costs, the power model can be applied because of its simple structure and easiness of calculating procedure, but this model has lower R value than the polynomial model. Moreover, as the polynomial model shows accumulated R&M costs to be lower than the actual data for the first period of machine life and also predicts some fixed amount of costs before binging service life of tractor, the power model can be suitably applied for the first period of machine life, i.e. accumulated usage hours up to 75 h as equation 1: Figure indicates the curves of predicted accumulated R&M costs based on accumulated usage hours using the power and polynomial models together with the actual data and the line of X = 75 h. Figure 3 shows the curve of predicted accumulated R&M costs based on accumulated usage hours using the power model for the first period of machine life and the polynomial model for the remaining service life of tractors (modeling curve) along with actual data curve. From comparison of two curves, it can be concluded that modeling curve and actual data curve give almost the same trend. It can also be observed that the rate of accumulated R&M costs at earlier life time of tractors was fairly low. However, trend of R&M costs was increasing thereafter and the rate of increase was moderately high. This increasing rate of R&M costs may be attributed to the facts like quality in design and manufacturing, scarcity and higher cost of some spare-parts and also much frequent need for repair in MF-85 tractors. This can also be related to more frequent break-downs, inferior production technology, inherent deficiencies and also incompatible field operations to their power and efficiencies. CONCLUSION Results of this study indicated that average R&M costs per hour increased with tractor age. These results also indicated that in order to predict accumulated R&M costs of MF-85 tractors with service life of 75 h or less the power regression model Y = (X/100) with R = and to predict accumulated R&M costs of MF-85 tractors with service life of 75 h or more the polynomial regression model Y = (X/100) 0.8 (X/100) with R = can be properly suggested. 66

5 Agric. Engineering Res. J., 1(3): 63-67, 011 REFERENCES 9. Morris, J., Estimation of tractor repair and maintenance costs. J. Agric. Eng. Res., 41: Bowers, W., Fundamentals of Machine 10. Rotz, C.A. and W. Bowers, Repair and Operation (FMO): Machinery Management. Second maintenance cost data for agricultural equipment. Edition. Deere & Company. Moline. IL. USA. In: Proc. of International Winter Meeting Sponsored. Morris, J., Tractor Depreciation, Repair and by the American Society of Agricultural Engineers, Holding Cost: A Case Study. Silso College. Silso. Paper Bedford. UK. 11. Lazarus, W.F., 008. Estimating farm machinery 3. Srivastava, A.K., C.E. Georing, R.P. Rohrbach and repair costs, faculty/ D.R. Buckmaster, 006. Engineering Principles of wlazarus/documents/mf008.pdf 8/10/008. Agricultural Machines. Second Edition. American 1. Almassi, M. and H.R. Yeganeh, 00. Determination Society of Agricultural and Biological Engineers, a suitable mathematical model to predict the repair Niles Road, St. Joseph, MI, USA. and maintenance costs of farm tractors in Karoon 4. Hunt, D.R., 001. Farm Power Machinery Agro-industry Company. Iranian J. Agric. Sci., Management. Tenth Edition. Iowa State University 33: Press. Ames. Iowa. USA. 13. Ashtiani-Eraghi, A.R., I. Ranjbar and M. Toorchi, 5. Rotz, C.A., A standard model for repair costs of 006. Optimum mathematical model for predicting agricultural machinery. Applied Eng. Agric., 3: 3-9. R&M costs of operation tractors in Mazandaran 6. Bowers, W. and D.R. Hunt, Application of Dasht-e-Naz Farm Company. J. Agri. Sci., 15: mathematical formulas to repair cost data. 14. Ajabshirchi, Y., I. Ranjbar, M.H. Abbaspour, Transactions of ASAE, 13: M. Valizadeh and A. Rohani, 006. Determination of 7. Fairbanks, G.E., G.H. Larson and D.S. Chung, a mathematical model for estimating tractor repair The cost of using farm machinery. Transactions of and maintenance costs. J. Agric. Sci., 16: ASAE, 14: Ward, S.M., P.B. McNulty and M.B. Cunny, Repair costs of WD and 4WD tractors. Transactions of ASAE, 8:

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