Improving sales forecast accuracy for restaurants

Abstract: Data mining and machine learning techniques are becoming more popular in helping companies with decision-making, due to these processes’ ability to automatically search through very large amounts of data and discover patterns that can be hard to see with human eyes. Onslip is one of the companies looking to achieve more value from its data. They provide a cloud-based cash register to small businesses, with a primary focus on restaurants. Restaurants are heavily affected by variations in sales. They sell products with short expiration dates, low profit margins and much of their expenses are tied to personnel. By predicting future demand, it is possible to plan inventory levels and make more effective employee schedules, thus reducing food waste and putting less stress on workers. The project described in this report, examines how sales forecasts can be improved by incorporating factors known to affect sales in the training of machine learning models. Several different models are trained to predict the future sales of 130 different restaurants, using varying amounts of additional information. The accuracy of the predictions are then compared against each other. Factors known to impact sales have been chosen and categorized into restaurant information, sales history, calendar data and weather information. The results show that, by providing additional information, the vast majority of forecasts could be improved significantly. In 7 of 8 examined cases, the addition of more sales factors had an average positive effect on the predictions. The average improvement was 6.88% for product sales predictions, and 26.62% for total sales. The sales history information was most important to the models’ decisions, followed by the calendar category. It also became evident that not every factor that impacts sales had been captured, and further improvement is possible by examining each company individually.