Suzuki Swift Marketing Data Comparative Study of Different Forecasting Methods

Authors

  • DR. PRIYANKA RAWAL

Keywords:

ARIMA, forecasting, moving average, Management, supply chain Management.

Abstract

 Sincere approaches to practical forecasts in organisations have been accomplished through operative research (OR) since its inception. Scientists have affected forecasts in other disciplines. Forecasting has an enormous social, economic and environmental impact and has a very important aspect of every business. Several prediction models have been developed to help people decide correctly against future uncertainties. However, there are distinct advantages and limitations for every prediction model. It is important to succeed in selecting correct forecasting methods from other alternatives. This paper aims to analyse predictive techniques to forecast car sales results, Ford Mustang. Companies depend on precise projected data to make the right decisions and to predict the business results over a long and short time. Predictions are usually based on historical results, industry comparisons and developments in the sector. Different model time series foresees were used in this phase, for example the moving average, exponential smoothing, the Holt double exponential smoothing, winter’s three times exponential smoothing and ARIMA. The predictions were made on the basis of the annual (non-seasonal) data and the cumulative annual data (seasonal) in the ARIMA model. For both   the   threefold exponential smoothing system of winter and the ARIMA model, Minitab was used to produce forecast. In addition, the best prediction method for this given set of data was found to be the double-exponential smoothing process used by Holt when calculating the mean absolute deviation.

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Published

2021-06-30

How to Cite

RAWAL, D. P. . (2021). Suzuki Swift Marketing Data Comparative Study of Different Forecasting Methods. The Journal of Contemporary Issues in Business and Government, 27(3), 1067–1073. Retrieved from https://cibgp.com/au/index.php/1323-6903/article/view/1701

Issue

Vol. 27 No. 3 (2021): The journal of contemporary issues in business and government

Section

Articles

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