Indian Journal of Marketing

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Forecasting Butyl Price:A Case of India's Tire Industry


Affiliations
1 Department of Management, Amrita University, Bangalore - 560 035., India
2 School of Management, Presidency University, Bangalore - 560 064., India
     

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In this work, a modest attempt was taken to forecast Butyl prices in setting market prices of tires more accurately because it is an important issue for managers of firms in the tire industry. In this context, two types of models were taken into consideration on the basis of sources of information (i.e. analogous time series or conventional time series). A linear regression model and a Box – Jenkins autoregressive integrated moving average (ARIMA) model were fitted to data on Exxon Butyl price (EBP), Russian Butyl price (RBP), and Crude oil price (RBRTE). Results showed that the ARIMA model was superior to the regression model in case of predicting Butyl prices. Marketing practitioners will benefit from the findings of this work in various aspects such as in setting tire prices more precisely. Moreover, the findings should assist managers in managing inventory costs more accurately. This study showed how a significant improvement can be achieved at a much lower cost and with a much lesser effort for forecasting Butyl prices in case of the tire industry.

Keywords

Butyl, Prices, ARIMA, Regression, Prediction.
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  • Albuquerque, P., & Bronnenberg, B. J. (2012). Measuring the impact of negative demand shocks on car dealer networks. Marketing Science, 31(1), 4 – 23. https://doi.org/10.1287/mksc.1110.0659

  • Aribarg, A., Arora, N., & Kang, M. Y. (2010). Predicting joint choice using individual data. Marketing Science, 29(1), 139 – 157. https://doi.org/10.1287/mksc.1090.0490

  • Armstrong, J. S. (2012). Illusions in regression analysis. International Journal of Forecasting, 28(3), 689 – 694.

  • Armstrong, J. S. (ed.). (2001). Principles of forecasting : A handbook for researchers and practitioners. New York : Springer Science & Business Media. DOI : 10.1007/978-0-306-47630-3

  • Banerjee, S., & Mandal, K. (2018). Advertisement vs. societal based marketing communication : An empirical analytic comparison vis-a-vis profit contribution. Indian Journal of Marketing, 48(6), 7 – 21. https://doi.org/10.17010/ijom/2018/v48/i6/127833

  • Beal, M., & Wilson, J. H. (2015). Forecasting in the marketing curriculum. International Journal of Business, Marketing, & Decision Science, 8(1), 109 – 118.

  • Brockwell, P.J., & Davis, R.A. (2016). Introduction to time series and forecasting. Berlin: Springer. DOI: 10.1007/978-3-319-29854-2

  • Canitz, H. (2016). Overcoming barriers to improving forecast capabilities. Foresight : The International Journal of Applied Forecasting, 41, 26 – 34.

  • Chase Jr., C. W. (2013). Demand-driven forecasting : A structured approach to forecasting. New Jersey : John Wiley & Sons.

  • Chintagunta, P., Hanssens, D. M., & Hauser, J. R. (2016). Editorial – Marketing science and big data. Marketing Science, 35(3), 341 – 342. https://doi.org/10.1287/mksc.2016.0996

  • Currie, C.S.M., & Rowley, I.T. (2010). Consumer behaviour and sales forecast accuracy: What's going on and how should revenue managers respond ? Journal of Revenue and Pricing Management, 9(4), 374 – 376.

  • Danese, P., & Kalchschmidt, M. (2011). The role of the forecasting process in improving forecast accuracy and operational performance. International Journal of Production Economics, 131(1), 204 – 214.

  • Draganska, M., & Klapper, D. (2011). Choice set heterogeneity and the role of advertising: An analysis with micro and macro data. Journal of Marketing Research, 48(4), 653–669. https://doi.org/10.1509%2Fjmkr.48.4.653

  • Fildes, R., Nikolopoulos, K., Crone, S. F., & Syntetos, A. A. (2008). Forecasting and operational research : A review. Journal of the Operational Research Society, 59(9), 1150 – 1172.

  • Gebre, T. W., & Singh, R. (2016). Comparative analysis and short term sales forecasting for an Ethiopian shoe manufacturing company. Indian Journal of Marketing, 46(10), 40 – 52. https://doi.org/10.17010/ijom/2016/v46/i10/102853

  • Hanssens, D.M., Parsons, L.T., & Schultz, R. L. (2009). Market response models econometric and time series analysis. Boston : Kluwer Academic Publishers.

  • Henderson, J. V., Storeygard, A., & Weil, D. N. (2012). Measuring economic growth from outer space. American Economic Review, 102(2), 994 – 1028. DOI : 10.1257/aer.102.2.994

  • Hoshmand, A. R. (2010). Business forecasting : A principal approach. New York: Routledge.

  • Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting : Principles and practice. OTexts.

  • Klindokmai, S., Neech, P., Wu, Y., Ojiako, U., Chipulu, M., & Marshall, A. (2014). Evaluation of forecasting models for air cargo. The International Journal of Logistics Management, 25(3), 635 – 655. https://doi.org/10.1108/IJLM-05-2013-0049

  • Luan, Y. J., & Sudhir, K. (2010). Forecasting marketing - mix responsiveness for new products. Journal of Marketing Research, 47(3), 444 – 457. DOI : 10.1509/jmkr.47.3.444

  • Mela, C. F. (2011). Structural workshop paper – Data selection and procurement. Marketing Science, 30(6), 965 – 976. DOI: 10.1287/mksc.1110.0650

  • Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting. New Jersey : John Wiley & Sons.

  • Narayanan, S., & Nair, H. S. (2013). Estimating causal installed-base effects : A bias-correction approach. Journal of Marketing Research, 50(1), 70 – 94. https://doi.org/10.1509%2Fjmr.11.0183

  • Ni, J., Neslin, S. A., & Sun, B. (2012). Database submission – The ISMS durable goods data sets. Marketing Science, 31(6), 1008 – 1013. https://doi.org/10.1287/mksc.1120.0726

  • Ramanathan, U. (2012). Supply chain collaboration for improved forecast accuracy of promotional sales. International Journal of Operations & Production Management, 32(6), 676 – 695. https://doi.org/10.1108/01443571211230925

  • Sarkar, A., Jha, A., & Mukherjee, D. (2018). Rise in sales of multi axle trucks in India: Governmental initiatives, industrial development, and operator preferences. Indian Journal of Marketing, 48(7), 7 – 22. https://doi.org/10.17010/ijom/2018/v48/i7/129717

  • Singh, S. (2016). Forecasting: Academia versus business. Foresight : The International Journal of Applied Forecasting, 41, 46 – 47.

  • Yang, S., Zhao, Y., Erdem, T., & Zhao, Y. (2010). Modeling the intra-household behavioral interaction. Journal of Marketing Research, 47(3), 470 – 484. https://doi.org/10.1509%2Fjmkr.47.3.470


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  • Forecasting Butyl Price:A Case of India's Tire Industry

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Authors

Mehir Kumar Baidya
Department of Management, Amrita University, Bangalore - 560 035., India
Bipasha Maity
School of Management, Presidency University, Bangalore - 560 064., India
P. Srinivasan
School of Management, Presidency University, Bangalore - 560 064., India

Abstract


In this work, a modest attempt was taken to forecast Butyl prices in setting market prices of tires more accurately because it is an important issue for managers of firms in the tire industry. In this context, two types of models were taken into consideration on the basis of sources of information (i.e. analogous time series or conventional time series). A linear regression model and a Box – Jenkins autoregressive integrated moving average (ARIMA) model were fitted to data on Exxon Butyl price (EBP), Russian Butyl price (RBP), and Crude oil price (RBRTE). Results showed that the ARIMA model was superior to the regression model in case of predicting Butyl prices. Marketing practitioners will benefit from the findings of this work in various aspects such as in setting tire prices more precisely. Moreover, the findings should assist managers in managing inventory costs more accurately. This study showed how a significant improvement can be achieved at a much lower cost and with a much lesser effort for forecasting Butyl prices in case of the tire industry.

Keywords


Butyl, Prices, ARIMA, Regression, Prediction.

References





DOI: https://doi.org/10.17010/ijom%2F2020%2Fv50%2Fi8-9%2F154688