Machine Learning and sales forecasts.

Today, intelligent systems that offer artificial intelligence capabilities are often based on Machine Learning (ML). Machine learning describes the ability of systems to learn from problem-specific training data to automate the process of creating analytical models and solving associated tasks. Deep learning is a machine learning concept based on artificial neural networks (Janiesch et al., 2021).

Interpretability in ML is crucial for making high-risk decisions and solving problems.

ML can be useful in the management of a bar or restaurant, with a focus on stock management and sales forecasting according to the day of the week.

By analysing historical sales data, seasonality, artistic events in the area, as well as the weather conditions to predict the flow of customers in the establishment, you can avoid wastage. Although it's not easy.

By predicting an event, you can adapt the selling price to demand, thus increasing revenue. In the same way, you can analyse which item on your list of delicacies is most in demand and increase the RRP.

ML can also help by monitoring feedback on social media or on platforms such as Google Reviews or TripAdvisor.

ML can also help with monitoring feedback on social networks or platforms such as Google Reviews or TripAdvisor.

Sales forecasting is key to staying competitive, but inaccurate product sourcing can lead to stock-outs or overstocking.

Author Pavlyshenko (2019) states that sales data can have many outliers and missing data, making it necessary to clean out the outliers and interpolate the data before using a time series approach.

This means that ML is a very important tool, but you need to know how to interpret the data, otherwise it becomes a paperweight.

References:

Janiesch, C., Zschech, P., & Heinrich, K. (2021). Electronic Markets. Machine learning and Deep Learning. https://doi.org/https://doi.org/10.1007/s12525-021-00475-2

Pavlyshenko, B. (2019). MDPI. Machine-Learning models for sles time series forecasting. https://doi.org/10.3390/data4010015