Demand forecasting in supply chain management and its application in a local textile business

In today's market conditions, forecasting future customer demand within supply chain systems has always been a challenging process. Accurate demand forecasts enable more realistic decision-making in supply chain management and enhance efficiency in operational processes. In an increasingly competitive environment, the importance of supply chain management has grown steadily and has become an indispensable aspect for businesses.The primary objective of supply chain management is to deliver products or services to the end customer at the right time, in the right quantity, and at the lowest possible cost. Achieving this goal depends on the effective management of the interdependent processes within the supply chain. However, forecasting errors at any stage of the supply chain can negatively impact overall performance. Therefore, demand forecasting plays a critical role in supply chain management. In this study, the conceptual structures, objectives, and importance of supply chain management are initially addressed, and the relationship between supply chain management and demand forecasting is examined. The research provides general information on demand forecasting and forecasting methods. Within the scope of the study, historical sales data from the past ten years of a local clothing supplier specializing in wholesale men's shirts were used. Forecasting methods such as the Naive Method (NM), Moving Average (MA), Exponential Smoothing Method (ESM), and Holt & Winters Method (HWM) were applied. The performance of these methods was then compared. To implement improvements in a system, a thorough examination of the existing system is required. In this context, the company's past sales data were analyzed, and the deviations between expected sales volumes and actual demand were evaluated. The main objective of this research is to improve the existing forecasting system and add value to the business by identifying the most accurate forecasting method through the application of Time Series Demand Forecasting approaches, in contrast to the previously used non-forecasting-based methods.