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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 dig deeper into predictive AI 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. This significantly decreases the satisfaction rate of our customers and causes losses as high as 40% in sales.

On the other hand, an excess of inventories can cause liquidity problems dueto an increase in the working capital necessary to have this estimated inventory. To avoid these issues, we can take advantage of PredictiveArtificial Intelligence in Retail.

The challenges which retailers are facing include:
  • Demand volatility is a natural process in the market, and organizations do not have enough flexibility to adapt their production processes to this volatility.
  • Organizations are facing constant changes to delivery dates.
  • Excess inventory is not a focus on the production process.
  • High costs associated with merchandise not sold are due to errors in the demand forecast.
  • High costs are caused by unsatisfied demand.
  • A lot of time is focused on the development of calculations, and not enough time is invested in the in-depth analysis of information.
  • Too much dependence on qualitative demand planning methodologies.

Given this scenario, organizations must have accurate sales forecasts fort heir products at each point of sales so that they can align inventory with demand peaks in all their locations, thereby avoiding unsold merchandise, unnecessary investments, or unsatisfied demands.

Usually, organizations use moving averages, aggregate information, and employee experience to make their estimates. But in most cases, these methods are not enough to solve this problem.

How can we reach a balance between demand and appropriate stock ofproducts?

How can we reach a balance between demand and appropriate stock ofproducts?

With an adequate strategy for estimating and planning demand, we can positively impact cost reduction, customer satisfaction and increased production.

Making accurate planning on demand allows an improvement of 15% to 30%in satisfactory order fulfillment

Proper detection and reaction to eventualities and exceptions in the organization’s performance help enormously in facing unexpected demand.

Having a solution that incorporates the trends, market conditions and seasonality of the products and intelligently assesses all these factors will determine how many products to distribute at each sales point without adding a significant operational burden to 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 HAS DEVELOPED A SOLUTION BASED ON PREDICTIVE ARTIFICIAL INTELLIGENCE

Axity has developed a solution based on Predictive Artificial Intelligence. PAIincorporates different techniques such as time series, neural networks,support vector machines, and hybrid models that compete to generate thebest estimate of future demand and can suggest orders for each product ateach point of sales, considering trends, segments and seasonality, whichdelivers to our clients multiple benefits:

Discover products or points of sales with 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.