Integrating System Dynamics and Random Forest Algorithm for Sales Forecasting

This study proposes an approach to improve sales forecasting accuracy by integrating System Dynamics (SD) modeling with the Random Forest (RF) algorithm. The combination of these two methods offers a promising way to make sales predictions more reliable, especially in uncertain market conditions. Using SD principles within the RF algorithm helps us simulate expected sales growth, gain insights into market dynamics, and make our forecasting model more understandable to stakeholders. This study using synthetic sales data shows that the integrated approach is offered as an open idea and customizable, particularly in scenarios with dynamic growth patterns and feedback effects.

I. Introduction

The connection between sales and forecasting holds significant weight in the world of business management. Forecasting sales aids in business planning by providing estimates of future demand, enabling companies to create effective marketing, production, and distribution strategies [1]. Accurate sales forecasting can streamline inventory management, reducing the risk of understocking or excess inventory that can negatively impact profits and operating costs [2]. Sales forecasting also plays a crucial role in financial planning and budgeting, allowing companies to set revenue goals, predict cash flow, and make informed investment decisions [3]. A comparison between actual sales and forecasts can provide valuable insights into business performance. If sales exceed forecasts, it could indicate higher demand than anticipated, while sales below forecasts may require companies to evaluate their marketing or operational strategies [4].