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Predictive Artificial Intelligence in Retail

Predictive Artificial
Intelligence in Retail

Let's go into detail:

Every day we see how potential consumers leave a store without buying anything because there is no stock of the item they were looking for, significantly decreasing the satisfaction rate of our customers and causing losses that can reach 40% of sales. On the other hand, an excess of inventories can cause liquidity problems, due to the increase in working capital needed to dispose of this overestimated inventory.

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The challenges retailers typically face are:

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  • The volatility of demand is a process intrinsic to the market, and organizations do not have the flexibility to adapt their production processes to this volatility.
  • Organizations face constant changes to delivery dates.
  • Excess inventory is not a focus in the production process.
  • Predictive efforts are focused on establishing production requirements (rather than focusing on sales estimates).
  • High costs associated with unsold merchandise due to errors in forecasting demand.
  • High costs caused by unsatisfied demand.
  • A lot of time focused on the development of calculations and little on the deep analysis of information.
  • Dependence on qualitative demand planning methodologies.

Given this scenario, it is very important that organizations can count on accurate forecasts of sales of their products at each point of sale, in order to align inventory with the peaks of demand for all their branches, avoiding unsold merchandise, unnecessary investments or unsatisfied demands.

Usualmente las organizaciones, utilizan promedios móviles, información agregada y la experiencia de los empleados para realizar sus estimaciones, pero que en la mayoría de los casos no son suficientes para resolver esta problemática.

¿How then can we get a good balance
between demand and stock of products?

With an adequate strategy of estimation and planning of the demand that impacts in the reduction of costs, the satisfaction of clients and in greater production.

Making an accurate demand planning, which will allow an improvement between 15-30% improvement in the satisfactory fulfillment of orders.

Detect and react in a timely manner to eventualities and exceptions in the performance of the organization in relation to the expected demand.

Have a solution that incorporates trends, market conditions, and product seasonality and intelligently evaluates all these factors to determine how many products to distribute at each point of sale without placing a significant operational burden on the organization.

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Integrate this solution with the back office, in such a way that the recommendations based on this analysis are executed by distributors and production and purchasing areas.

Axity Chile, has developed a solution based on Predictive Artificial Intelligence, which incorporates different techniques such as time series, neural networks, vector support machines and hybrid models competing to generate the best estimate of future demand and suggested order of each product at each point of sale, considering trends, segments and seasons, which gives our customers multiple benefits:

  • Have greater certainty about the demand for products
  • Strengthen the supply chain planning and inventory management process.
  • A more agile adaptation to market changes.
  • Increase in sales and decrease in returns

Know products or points of sale of low performance.

¿Do you have an artificial intelligence project in Retail?

At Axity, we have developed solutions that incorporate different techniques, in order to improve the estimation of future demand for each point of sale.