Forecasting monthly farm tractor demand for India using MSARIMA and ARMAX models

DOI: 10.18805/IJARe.A-5185    | Article Id: A-5185 | Page : 315-320
Citation :- Forecasting monthly farm tractor demand for India using MSARIMA and ARMAX models.Indian Journal Of Agricultural Research.2019.(53):315-320
Alok Yadav and Sajal Ghosh alok.yadav@escorts.co.in
Address : Management Development Institute, Gurgaon-122 001, Haryana, India.
Submitted Date : 1-12-2018
Accepted Date : 28-03-2019

Abstract

Because of long product development cycles, effective production planning of automobiles requires accurate demand forecasting in order to effectively managing resources and maximizing revenue. Errors in demand forecasts have often led to enormous costs and loss of revenue due to suboptimal utilization of resources. Since early 2000 India has been the largest manufacturer and consumer of farm tractors in the world. This paper develops multiplicative seasonal autoregressive integrated moving average (MSARIMA) and autoregressive moving average model with exogenous variable (ARMAX) to forecast monthly demand for farm tractor. The result indicates that ARMAX with real agriculture credit has found to be outperformed MSARIMA model in forecasting demand of farm tractors in the horizon of six months. The accurate monthly forecasting of farm tractor would help the manufacturers for better raw material, inventory and supply chain management.

Keywords

ARMAX Demand forecasting Farm tractor demand MSARIMA.

References

  1. Bai J., Perron, P., (1998). Estimating and testing linear models with multiple structural changes. Econometrica 66, 47–78.
  2. Biondi, P., Monarca, D., Panaro, A. (1998). Simple forecasting models for Farm Tractor Demand in Italy, France and the United States. Journal of Agricultural Engineering Research, 71: 25–35.
  3. Bottinger, S. (2013). Agricultural Development and Mechanization in 2013 A Comparative Survey at a Global Level. Working Paper United Nations Industrial Development Organization (UNIDO).
  4. Das A., Manjusha S., and Joice J. (2009). Impact of Agricultural Credit on Agriculture Production: An Empirical Analysis in India. Reserve Bank of India Occasional Papers, 30, No.2, Monsoon.
  5. Evcim, H. U., Sindir, K. O., (1993). Tractor Sales Projection (Demand Projection of Tractor). E.U Z.F. Yayýn No. 30
  6. Gautam, A. K., Shrivastava, A., Samariya, R. K., Jha, A. (2018). Design and development of tractor drawn seed cum pressurized aqueous fertilizer drill. Indian Journal of Agricultural Research, 52 (3): 257-263
  7. Ghosh S. (2009). Univariate forecasting of day-ahead hourly electricity demand in the northern grid of India. Int. J. Indian Culture and Business Management, 2, No. 6.
  8. Kim B. Shin, S-Y ; Kim, Yu Y ; Yum, S ; Kim, J (2013). Forecasting demand of agriculture tractor, riding type rice transplanter and combine harvester by using an ARIMA model. Journal of Biosystem Engineering, 38(1):9-17.
  9. Mandal, S.K. and Maity, A. 2013. Current Trends of Indian Tractor Industry: A Critical review. Applied Science Report, 3(2): 132-139.
  10. Mui H. W. et. al., (1986). Modelling the demand for durable inputs: Distributed lags and causality. Southern Journal of Agriculture Economics, 273-279.
  11. Natarajan, A., Chander. M. and Bharathy, N. (2016). Relevance of draught cattle power and its future prospects in India: A review, Agricultural Reviews, 37 (1): 2016: 49-54
  12. Pawlak, J. (1999). Impact of some selected factors on the sale of agriculture tractors. Problemy Inzynierii Rolniczej, 7(4).
  13. Sharan, G. (1995). Demand for Farm Tractors: Two Models. IIM Ahmedabad Working Paper No. 1995/1278.
  14. Singh, G. (2005). Estimation of a mechanisation index and its impact on production and economic factors – a case study in India. Biosystem Engineering, 93(1): 99-106.
  15. Sriram M. S. (2007). Productivity of Rural Credit: A Review of Issues and Some Recent Literature. Indian Institute of Management Ahmedabad, Working Paper No.2007-06-01.
  16. Uankitan G. (2007). Tractor demand projection in Turkey. Biosystem Engineering 97: 19-25.
  17. Zivot, E. and D. Andrews, (1992), Further evidence of great crash, the oil price shock and unit root hypothesis, Journal of Business and Economic Statistics, 10: 251-270. 

Global Footprints