We live in an increasingly intelligent environment, where every decision has a greater impact on the world around us. It is therefore important that every company makes considered and informed decisions that lead not only to increased profit but also to the well-being and satisfaction of its customers.
One context in which it is crucial to make informed decisions is inventory management and optimisation. How many products should the company have in stock? How often should they be replenished? These are some of the extremely relevant questions in inventory management, and the answers can make all the difference to the company’s success.
What is demand forecasting all about?
This is where the concept of demand forecasting arises, which consists of using the historical sales records of a given product to estimate future demand. Having an estimate of how many products will be sold allows for better financial management, and also the calculation of profit margins, cash flows, the allocation of resources, and the optimisation of the production and storage of products.
Incorrect inventory management can lead to two types of problems:
👉 The customer wants to purchase a product, but there is no stock available. If this happens, not only have you lost a vital sales opportunity, leading to a decrease in profits, but also generated customer dissatisfaction.
👉 Too many products have been made and stay unsold. This problem is especially relevant when the product shelf life is short, such as in baked goods. In this case, production and storage costs will never be recouped, i.e. there is inevitably a loss.
How can we help?
Since demand forecasting takes advantage of historical data to make an estimate for the future, we are facing a Data Science problem. We can build mathematical models, using Machine Learning, that simulates market behaviour with the highest level of detail possible, reducing the difference between the estimated value of purchases and the real value of purchases made.
The biggest challenge for this kind of analysis is the high number of external factors directly affecting the number of purchases, which are not always easy to take into account. Seasonality, weather, events near the site, competitive analyses and promotions are just some of these factors, and it is essential to enrich historical sales data with this kind of information in order to find more significant sales patterns.
A major advantage of using Machine Learning models to forecast market demand is their explainability. From these models, it is possible to extract what factors are contributing positively or negatively to sales figures, and the decision-making process can take this into account in order to minimise negative factors in future wherever possible.
Azure Machine Learning
The Azure Machine Learning platform streamlines the entire Data Science process, from the feasibility analysis with interactive Notebooks to the creation and production of models, facilitating their registration and the continuous delivery of new models. The platform also makes it possible to trigger monitoring resources using Azure Application Insights, automatically recording information about the model in production; that is, what values were received and returned by the model (enabling the identification of data deviations or drifts that indicate the need to retrain the model), as well as response times, the number of requests and identification of any errors during the stock forecast.
Watch our video and discover how to predict sales!