Analytics for Demand and Inventory Optimisation
Analytics use different types of data to predict future demand and adapt inventories accordingly.
Analytics play a key role for Demand Prediction and Inventory Optimization in both B2C and B2B businesses. Algorithmic models use different types of data that may influence customer demand in order to predict future demand and adapt material purchases and inventories accordingly. This avoids unnecessary inventory levels as well as sales losses due to lack of material.
These Models use historical consumption data that characterizes the seasonality of demand as well as other complementary data such as changes in the cost of raw materials, regional and local consumption trends, etc.
How to start? Building a robust Analytical Model requires different algorithms (including linear and non linear regressions) to be developed, analyzed and tested. Even when the Model works, it usually needs some time for continuous learning and improvement before it is adopted as a regular tool for employees. Expertise, Talent and Method are key for designing a Model that can improve by understanding and learning from new data.
Demand and Inventory Analytics usually unlock exceptional results in Working Capital and available Cash with low investment, as well as facilitates the introduction of Data Science in day to day business operations.