Vekia is the leading economic player offering Machine Learning technology in the retail industry.
The Mathematical Engineering is at the core of Vekia’s Expertise. Our technical team is led by experienced researcher in Machine Learning from high-level academic research (CNRS, – INRIA The University of Cambridge). They are surrounded by developers expert in scientific program.
Machine Learning brings robustness and a very fine precision to your forecasts. The fundamental principal of Machine Learning involves seeking the best match between the complexity of a mathematical model and those from data to be processed.
It allows a systematic and efficient research of the best model of forecast. This ensures that our methods are both simple to use and with a high robustness. Our sales forecasts are based on an optimum modelling of the past behaviours, crossed with all points and channels of sales, including specific events (commercial operations, seasonal sales, etc.). This forecasts can be used for calculating stores or warehouse purchase order proposals including packaging, purchase and selling prices, real delivery dates, transport, merchandising constraints, etc.
This is the first mission of our engines business software!
A standard algorithm, no matter how powerful or precise it may be, execute what it was programmed for and is limited to instructions that we gave. In a computer programme, the execution of tasks is automated. However, under any circumstances, it has the possibility to improve by itself the data modelling or understand new pieces of information it processed. It has even lower capacity to innovate in its way of functioning…unless to be modified by humans (updates and new versions).
This is one of the most fundamental differences between standard programming and programming based on Machine Learning.
This notion of automatic learning, specific to Machine Learning is essential. It is important to recall the definition: it is all processes implemented to develop or modify behavioural models influenced by activity and experience.
The automatic learning, and more broadly artificial intelligence are not new concepts. Certain factors although allow to apply them in a much more effective way nowadays, revolutionizing its use.
Machine Learning applied to retail chain
The current context of innovation, to speed up fast-to-market production and products customization forces retailers to rethink their logistics chain and stocks management in order not to hinder their growth.
With Machine Learning, data are fundamental and contribute to the definition of the statistical model to be applied, unlike standard algorithms, where the model is preliminary defined, before to be applied to data.
This difference is major, since it is the transition from an empirical scheme to an advanced system that adapts with absolute autonomy the solution and model that must be applied. The first objective is to constantly seek for the best economic performance in the short and long term.
It is a revolution for calculation and forecast models.