International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024
p-ISSN: 2395-0072
www.irjet.net
An Empirical Study on Identifying the Best Qualitative Methods for Sales Forecasting Models of the Top Five FMCG Companies in India Based on Market Capitalization as of March 19, 2024, Using JMP Software. Shreyas S Vanjare Student, pursuing MBA at PES University Bengaluru, Banashankari - 560085 ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract - This paper examines sales forecasting for the five largest FMCG companies as of March 19, 2024: Hindustan Unilever Limited (HUL), ITC Limited, Nestlé India Limited, Varun Beverages Limited, and Godrej Consumer Products Limited. We obtained 12 years of sales data for these companies from the Screener website to predict sales for the next three years, from March 2024 to March 2027. Various forecasting methods were employed, utilizing JMP software to identify the most effective techniques for each company. Our study revealed that each FMCG company has an optimal forecasting model tailored to its specific sales data. For ITC, Nestlé India Limited, and Godrej Consumer Products Limited, the ARIMA model proved most effective. In contrast, Varun Beverages Limited benefited more from the Seasonal Exponential Smoothing model. This highlights the importance of selecting the right forecasting method for accurate predictions and positive outcomes. Additionally, the paper provides an overview of the FMCG sector and its growth trends, driven by urbanization, rising incomes, and changing consumer preferences. Despite economic fluctuations, the FMCG sector remains resilient, offering lucrative investment opportunities. The findings are valuable for industry stakeholders, including companies, investors, and policymakers. Accurate sales predictions enable effective inventory management and strategic marketing. Overall, this study enhances our understanding of sales forecasting in the FMCG industry, underscoring the utility of diverse quantitative models in decision-making. Key Words: Business forecasting, sales forecasting, ARIMA model, Seasonal Exponential Smoothing, quantitative analysis, economic fluctuations, Screener website, growth potential.
1. INTRODUCTION The long-term success of any firm greatly depends on its management's ability to recognize trends and make sound decisions. Top company executives typically know just when to make changes to stay ahead of the competition. These companies rarely make mistakes by misjudging consumer demand, unlike many others. Being adept at forecasting can make all the difference. Due to intense competition and challenging times, determining how much product to sell is difficult. Various factors influence consumer demand, but if a company can accurately forecast sales, it can lead to increased customer satisfaction, higher revenue, and more efficient production planning. Accurate and timely forecasting is crucial for inventory management. If forecasting methods are flawed, a company may end up with too much or too little stock, which can negatively impact profitability and competitiveness. Forecasting helps predict and explain potential outcomes, and incorporating these predictions into planning can lead to better decision-making for the company (Makridakis and Wheelwright, 1989). For example, forecasting sales can enhance the effectiveness of the sales or marketing team. The same predictions can aid other departments in production planning and scheduling. There are various methods for forecasting in business, ranging from simple to sophisticated approaches. The most common method appears to be relying on intuition, as many decision-makers base their predictions on experience and past observations (Makridakis, 1989; DeLurgio, 1998; Wright & Goodwin, 1998). While most companies follow this approach, some combine it with data-driven techniques, and only a few rely solely on statistical methods (Makridakis, 1989). 1) Sales Forecasting: Sales forecasting involves analyzing past and current data to predict future sales. Every company that sells products must estimate consumer demand. Manufacturers need to know how much to produce, and retailers need to determine optimal stock levels. Failing to understand consumer demand can result in lost sales, dissatisfied customers, and a weakened market position.
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