Kheperer’s Deep Learning solutions enable your company to leverage all your complex and heavy data to automatically capture the relationships between multiples demand drivers for highly accurate demand forecasting, thus making possible to improve planning processes spanning from supply chain and operations management.
The strategic importance of demand forecasting
Even in environments with high demand uncertainty, being able to predict as accurately as possible future events, like market dynamics, makes possible to have an efficient management that can operate within a winning strategic vision.
Unfortunatelly, traditional statistical demand forecasting systems are not able to capture non-linear patterns present in historical data. Instead, our advanced solution leverages a full AI Toolbox to correctly handle the complexity of demand-influencing factors using all available internal and external data sources.
We are able to develop a tailored solution of forecasting methods based on Deep Learning and Machine Learning methods to automate forecasting predictions and enable you to optimize your processes.
This solution allows you to manage in advance all the variables that drive the variation in demand of your customers, to allow you to define an effective business strategy, based on your specific needs.
Thanks to Kheperer’s Demand Forecasting solution you can:
Better understand the influencing factors of the market
Profile customers and ad-hoc target your products
Anticipate and optimize procurement processes
Optimize inventory and supply chain management
Reduce the risk of ineffective market response
Reduce the risk of understock and overstock
How it works
The model is able to use heterogeneous sales data by analyzing historical time series of customer orders. However, the model is also able to make forecasts of new products, i.e. in the absence of historical data. The algorithm, in fact, based on learned metrics of similarity between products, is able to exploit historical data to produce forecasts even for products not yet sold. The system succeeds moreover to correctly exploit the multitude of categorical variable that normally describe the products, succeeding therefore to avoid to waste information. The model is capable of giving specific meaning to every variable, in function of the analyzed context.
From the data available, inner and external to the system, the models compose of the temporal series on the variable representative of the phenomenon.
This data base is used to train the Machine Learning and Deep Learning models, which learn to recognize the fundamental components of the phenomenon and predict the evolution of the process.