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Demand Forecasting to Predict Future Demand of Sales Data: A Case Study from the Courier and Logistics Industry of Saudi Arabia
Banaja, Jana
Banaja, Jana
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Abstract
This research explores demand forecasting to predict future sales in the courier and logistics industry of Saudi Arabia, using Aramex as a case study. As e-commerce continues to expand rapidly across the Kingdom, accurate demand forecasting has become essential for maintaining efficiency, minimizing costs, and improving customer satisfaction. This quantitative research employs statistical forecasting models—specifically linear regression and moving averages—applied to real sales data provided by Aramex. Through Microsoft Excel, sales trends over a multi-year period were analyzed to identify seasonal fluctuations and forecast future demand patterns.
The findings revealed a clear upward trend in sales, with noticeable seasonality during specific months, notably in May and December. Among the techniques tested, the three-month moving average model yielded the most reliable short-term forecasts, whereas linear regression proved useful for identifying long-term growth patterns.
The study concludes that data-driven forecasting not only enhances strategic decision-making but also provides logistics firms with the agility to better align their operations with fluctuating market demands. This research contributes to the growing body of logistics literature in the Gulf region and offers actionable insights for companies aiming to implement or refine their forecasting strategies.