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Predictive Artificial Intelligence in Retail

Predictive Artificial Intelligence in retail

Predictive Artificial
Intelligence in retail

ia - retail ia - retail

Let's go into detail about Predictive Artificial Intelligence in Retail:

Every day we see how potential consumers leave a store without buying anything because the item they were looking for is not in stock, significantly reducing 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 the working capital necessary to have this estimated inventory, for that we need Predictive Artificial Intelligence in Retail.

The challenges that retailers usually face are:
  • Demand volatility is an intrinsic process 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 (instead of focusing on sales estimates).
  • High costs associated with merchandise not sold due to errors in the demand forecast.
  • 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 have accurate forecasts of sales of their products at each point of sale, in order to align inventory with demand peaks for all their branches, avoiding unsold merchandise, unnecessary investments or unsatisfied demands. .

Usually organizations use mobile averages, aggregate information and the experience of employees to make their estimates, but in most cases they are not enough to solve this problem.

How then can we reach a good balance between demand and the stock of products?

How then can we reach a good balance between demand and the stock of products?

With an adequate strategy for estimating and planning demand that impacts on cost reduction, customer satisfaction and increased production.

Making a precise planning of the demand, 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 the trends, market conditions and seasonality of the products and intelligently assess all these factors to determine how many products to distribute at each point of sale without implying a significant operational burden for the organization.

Integrate this solution with the backoffice, so 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

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 sales, considering trends, segments and seasonality, which gives our customers multiple benefits:

Know products or points of sale of low performance.

Do you have a Predictive Artificial Intelligence project in Retail? Let's talk

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

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