Sales Forecasting: A Time Series Approach

- Punit Sharma, Student, Faculty of Management, GLS University, Ahmedabad

- Dr. Tanvi Pathak, Assistant Professor, Faculty of Management, GLS University, Ahmedabad

Abstract:

Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions ineffective operations of retail businesses. For profitable retail businesses, accurate demand forecasting is crucial in organizing and planning production, purchasing, transportation and labour force. Retail sales series belong to a special type of time series that typically contain trend and seasonal patterns, presenting challenges in developing effective forecasting models. Forecasting helps the investors to get a vague idea about the future scope of the company. The purpose of this paper is to validate the sales of top five FMCG companies of India using time series forecasting method. Classical decomposition and Autoregressive Integrated Moving Average model has been used in this paper to validate the sales revenue of the company by using the expenses as the dependent variable. Classical decomposition method is used to predict the future sales values and ARIMA was used to validate the past data points of the successive data series. Data was used from 2015-2019 on quarterly basis for both models. In this examination we are concentrating on Hindustan Unilever Ltd., ITC (Indian Tobacco Company), Marico, Dabur India and Colgate-Palmolive (India).

Keywords:

FMCG, Sales Forecasting, Time Series

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