Ai-based demand forecast in supply chain management: İmplementation in a textile company

In today's globalizing world, businesses have to produce effective solutions and decide on various problems they encounter in order to survive against the changing competitive conditions as a result of increasing costs and digitalization. In this context, it is very important to manage the Supply Chain correctly. It is possible to avoid surprises, to predict and predict costs, and to make these predictions consistent with forecasting. Obtaining demand forecasts with high accuracy is a key factor for the success of all supply chain management processes. Restrictions on international travel and commercial activities due to the global pandemic have caused the biggest changes in the supply chain and adversely affected the supply chain management. These effects continued to affect many sectors for a long time, from the automotive spare parts sector to the food sector, from textiles to tourism. For this reason, accurate and fast estimation of the changing demand in the textile sector, which is one of the most affected sectors, is important in terms of effective workforce and resource planning. The purpose of forecasting is to predict the situations that businesses may encounter in the future and to take precautions in advance by using various data and techniques. For this reason, in this study, a forecasting model was proposed to the enterprise by using artificial neural networks, one of the statistical demand forecasting methods. In the study, a business operating in the textile sector is discussed. The sales data of the company operating in the textile sector between the years 2018-2021 were taken and included in the research. A forecasting model was created using the sales data with the MATLAB program. Training data and output data were compared. Error test results after the application showed that the predictions made by the model were reliable and consistent.