High forecast accuracy
Whether you manage hundreds or tens of thousands of SKUs, our scalable platform based on Microsoft Azure Cloud allows you to quickly generate and display forecast results, which saves you time and allows you to make better decisions. Our solution uses Artificial Intelligence (AI) and Machine Learning (ML) to simulate many forecasting models and choose the best one at any given time for each of your SKUs, ensuring more accurate demand forecasting.
Hundreds of hours saved
SMARTSTOCK not only increases the quality of forecasts, but also reduces tedious manual work.
Even the best forecasting model can generate forecasts with a high error if the input data contains noise in the form of promotions, individual large customer orders or the lack of sales resulting from an out-of-stock situation. The use of machine learning mechanisms for data imputation reduces the impact of noise on the quality of forecasts and saves time for performing these operations manually. When high quality goes hand in hand with automation, better decisions can be made in less time. You can invest the time saved in other operational activities.
None sales opportunity missed
The accuracy of forecasts is related to the quality of model fitting to the demand pattern. If your products behave differently throughout the year, regardless of the typical seasonality, then you are certainly dealing with the influence of so-called calendar days impact on your sales. Including the impact of calendar days in demand patterns allows you to reduce out-of-stock phenomena and maintain your customers’ loyalty. Using machine learning to deal with moving calendar days, e.g. Easter, saves time analyzing data and reduces the error of overlooking demand increases that are important to your business.
Better marketing decisions
The growing product portfolio affects not only the number of assortment groups, but also the number of products available in each group. It certainly takes a long time to analyze whether they behave similarly. Another important issue is their mutual influence on each other. Visualization of historical sales and forecasts data of individual groups and products relative to each other, taking into account the impact of calendar days and promotions, allows you to easily assess the relationships among products and make better decisions about future marketing and sales activities.