Prediction of Repair and Maintenance Costs of Two-wheel Drive Tractors in Iran

Predicting of repair and maintenance （R＆M） costs of tractors in any mechanized farm is necessary for owners and managers to obtain information on overall costs and to control financial balance and production economy. In this context a study was conducted to predict accumulated R＆M costs （Y） of two-wheel drive （2WD） tractors based on accumulated usage hours （X） in Misagh-e-Sabz Agribusiness Company in Iran. Recorded data of the company were used to determine regression models for predicting accumulated R＆M costs （as percentage of initial purchase price） based on accumulated usage hours. The statistical results of the study indicated that in order to predict accumulated R＆M costs of 2WD tractors with accumulated usage hours of 2260 h or less the power regression model Y=0.013 （X/100）1.677 with R2=0.976, and to predict accumulated R＆M costs of 2WD tractors with accumulated usage hours of 2260 h or more the polynomial regression model Y=0.004 （X/100）2-0.181 （X/1 00）＋4.373 with R2=0.998 can be strongly suggested.

Artificial neural network-based repair and maintenance cost estimation model for rice combine harvesters

This research proposes an artificial neural network(ANN)-based repair and maintenance(R&M)cost estimation model for agricultural machinery.The proposed ANN model can achieve high estimation accuracy with small data requirement.In the study,the proposed ANN model is implemented to estimate the R&M costs using a sample of locally-made rice combine harvesters.The model inputs are geographical regions,harvest area,and curve fitting coefficients related to historical cost data;and the ANN output is the estimated R&M cost.Multilayer feed-forward is adopted as the processing algorithm and Levenberg-Marquardt backpropagation learning as the training algorithm.The R&M costs are estimated using the ANN-based model,and results are compared with those of conventional mathematical estimation model.The results reveal that the percentage error between the conventional and ANN-based estimation models is below 1%,indicating the proposed ANN model’s high predictive accuracy.The proposed ANN-based model is useful for setting the service rates of agricultural machinery,given the significance of R&M cost in profitability.The novelty of this research lies in the use of curve-fitting coefficients in the ANN-based estimation model to improve estimation accuracy.Besides,the proposed ANN model could be further developed into web-based applications using a programming language to enable ease of use and greater user accessibility.Moreover,with minor modifications,the ANN estimation model is also applicable to other geographical areas and tractors or combine harvesters of different countries of origin.