Sales Forecasting with No-Code, Explainable Machine Learning for Supply Chain Optimization

Large and mid-size retail organizations must stay on top of millions of product inventories every day to maintain high levels of margins and efficiency of their operations. Sales forecasting is the key to operational success in such a demanding environment where the flow of information is constantly changing.

Companies can use conventional forecasting methods to find patterns from previous sales information. However, these methods cannot extract information from complex data patterns. Therefore, they are not sufficient to make predictions in a dynamically changing world. Batch machine learning models can also be used for sales forecasting although these models rarely succeed in deployment as they are hard to retrain and environments like customer segments can change daily. These models also lack explanations and the ability to be understood by business experts. Sales representatives and inventory managers need a real-time prediction system to accurately satisfy the demand on time.

This paper will discuss how TAZI’s Continuously Learning ML platform enables AI-Assisted Sales Forecasting for supply chain optimization. TAZI’s patented continuous learning technology will help business experts analyze the number of products sold and the demand for the future.