Chapter 1 Literature Review Order Forecasting Methods

Chapter 1 Literature Review

1. 
   Order Forecasting Methods
   

Order forecasting is the process of estimating the future demand or sales of products or services to determine the quantity and timing of orders that need to be placed with suppliers or manufacturers. Accurate order forecasting is crucial for effective supply chain management, inventory control, production planning, and meeting customer demand. It helps organizations optimize their operations, minimize stockouts or excess inventory, improve customer service levels, and reduce costs.
Order forecasting can be performed using various methods and techniques, depending on the available data, and the complexity of the demand patterns. The characteristics and features of moving average, exponential smoothing, and seasonal variations methods will be discussed as follows.

1. 
   The moving average method
   

Demand forecasting plays a vital role in the effective management of supply chains, production planning, and inventory control. Accurate predictions of future demand facilitate optimal decision-making, reduce operational costs, and ensure customer satisfaction. The moving average method, a classic time-series forecasting technique, has garnered significant attention due to its simplicity and ease of implementation.
The moving average method calculates the average demand over a specified period, providing a smooth representation of the demand trend. Historical demand data is used to calculate the moving average, which is subsequently employed as a forecasting tool for future demand estimation. The choice of the moving average window length depends on the specific demand planning requirements and the characteristics of the demand pattern under consideration. The window size of the moving average determines the smoothness of the curve. This technique is most commonly used for estimating the trend cycle from seasonal data.

2.