Forecasting Demand and Optimizing Product Ordering in the Supply Chain Using Artificial Intelligence

The research aims to predict demand and optimize product ordering within the supply chain using artificial intelligence. Employing a purposeful sampling method, 12 managers from Calais were selected as the sample group. This study utilized two Delphi techniques and a neural network. Through semi-structured interviews conducted in a fuzzy Delphi panel, relevant components were identified. Predictions were made using the Multi-Layer Perceptron Neural Network toolbox in MATLAB software. The results from the fuzzy Delphi analysis indicated that the primary factors influencing the forecast included warehouse inventory, sales from the previous week, sales from the previous month, cargo in transit, fluctuations in customer numbers compared to the past, competitors' market status, government regulations, and the company's development plans. After finalizing the Delphi process, the key factors identified were warehouse inventory, sales from the previous week, sales from the previous month, changes in customer numbers compared to the past, competitors' market status, and government regulations. The neural network predictions demonstrated that, due to the fluctuating demand trends, the predicted values closely aligned with the actual values. According to findings, the neural network's predictions were deemed acceptable, even with the rapid fluctuations in actual demand. In the case of Kale Dairy Company, utilizing artificial intelligence can yield significant benefits. Firstly, more accurate demand forecasting through artificial intelligence algorithms can lead to a reduction in waste and excess inventory within the supply chain. This enables the company to better identify and meet customer needs, ultimately resulting in increased customer satisfaction.