Implementation of the SARIMA Statistical Algorithm for Sales Prediction in a Nigerian Medium Scale Enterprise

Abstract

A sales prediction is a projection of future sales figures for a business over a given period of time. Forecasts are predicated on conjectures drawn from examining the trends or behavior of historical sales data. Sales forecasting has an impact on most of the factors that lead to impact optimization, profit maximization, and cost minimization. An efficient forecasting system can help a business raise profits, increase equipment utilization, reduce inventory, and achieve more changeability. But the majority of medium-sized businesses, like the Nigerian fast-food sector, have not yet adopted this technology or business model, which results in poor planning and ultimately, early closure or bankruptcy. This led to the research's objective, which is to apply the statistical method to anticipate sales in a medium-sized fast-food company. Data collection, data exploration, and statistical model creation were used to accomplish this. The machine learning model was constructed using the statistical approach known as Seasonal Autoregressive Integrated Moving Average, or SARIMA. To use its features, the model was made available as a web application. The outcome demonstrates that, if implemented, it would allow Nigeria's fast-food industry, as well as any other medium-sized industry in the nation, to make well-informed projections, maximize profits and as a result guaranteeing their survival in the competitive business environment of the developing world.