SALES FORECASTING EFFECT ON PHARMACIES
DOI:
https://doi.org/10.70454/IJMRE.2025.50202Keywords:
ARIMA model, Linear regression, Naive Forecasting, Predictions, Sales ForecastingAbstract
Nowadays, as technology is advancing to previously unheard-of levels, every company and organization is finding it difficult to balance inventory and customer expectations. Every organization relies heavily on sales, and being able to predict the future helps in making strategic and intelligent sales decisions. The majority of businesses still struggle with revenue forecasting because it is usually the first step in developing the company's annual budget. Over time, a company's estimation could suffer if its sales projections are consistently inaccurate. Sales forecasting therefore affects the entire company to improve their overall growth strategy. An essential part of any business's sales operations is sale forecasting.
For a business to supply the necessary quantity at the appropriate time, an accurate sales forecast is essential. Executives use the predictions to assess future performance and plan for organizational expansion. In this study, we use the machine learning techniques of naive forecasting and linear regression to try and predict a retail company's sales. The difference between the linear regression and naïve forecasting approaches is demonstrated using a computational example, and we have found that the linear regression yields better results than the naïve forecasting approaches. Additionally, we used the ARIMA model for the linear regression approach to forecast the sales for the upcoming five days.
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