Improving Bread Sales Predictions through Extreme Learning Machine (ELM)

Abstract

The bakery sector is experiencing growth, with enterprises like XY Bakery & Cake Shop providing their products directly to consumers. The company's bread sales have exhibited irregular pattern, rendering the task of establishing precise sales forecasts difficult. This inconsistency has resulted in financial losses, such as the spoilage of bread within three days, the squandering of raw materials, labor, and other forms of inefficiency. The objective of this study is to forecast bread sales and evaluate the accuracy of the predictions using the Extreme Learning Machine (ELM) approach. The accuracy will be assessed using the Mean Square Error (MSE) measure at AR Bakery. The ELM approach is utilized to predict bread sales, using normalized data divided into 80% for training and 20% for testing. The experimental tests conducted with 8 neurons produced mean squared error (MSE) value of 0.27402, whereas utilizing 5 neurons resulted in an MSE of 0.28761. The minimum error value was achieved using 5 neurons. 10 predictions were made using a dataset consisting of 101 data points for each type of bread. In December, the sales reached their peak, with a total of 198,850 units sold.