The most common methodology that is used in building data analysis solutions is called CRISP-DM, that is, Cross-Industry Standard Process for Data Mining. To forecast demand using machine learning you need to perform next:
  • Understand the business
  • Understand the Data
  • Preparу the Data
  • Choose and train the models
  • Evaluate the models’ accuracy
  • Get the best model to work for you
  • Implement in a business process on the real data

In the context of the retail business, it looks like this:

- Firstly data is collected for a specific period, such as information about purchases.

- Then the data is analyzed, the system finds patterns, for example, the relation between the volume of sales and the season, the release of new collections, individual promotions, and other factors.

- Based on the data, the AI makes a prediction. A retailer, for example, can figure out that the next period it is necessary to purchase more of a certain product, based on the influence of specific factors.

Customizable algorithms can look too complex even at the first stage, and with a responsible approach from the automation team, they turn into multivolume instructions with many references to other algorithms that are indirectly involved in planning. Modelling and Evaluation of model accuracy should be dealt with by mathematicians - Data Scientists. Financial specialists can take an active part in the implementation of the first three items and the sixth one.

Most financial accounting systems have built-in planning and forecasting modules. At the beginning of work, these modules must be configured based on existing business processes, regulations, and procedures.

Prospects for forecasting with ML and how it relates to small retailers

The prospects for using neural networks in retail are promising, since any business needs forecasts, and technologies increase their accuracy. Major global retailers are already actively using neural networks, AI, and machine learning to increase sales. Over the past year, more than 35% of companies in the world's largest economies are already using artificial intelligence and machine learning, and this share will only grow further.

Large corporations most often use ML to cut costs, better understand customer behaviour, and improve customer service. They use technology to help customers select products online, streamline their sourcing process using predictive analytics.

With the help of neural networks, systems are being created already that can accumulate information of the same type, analyze it, and create algorithms for answers and decisions. Neural networks have a much greater potential in forecasting demand, because, unlike an analyst, they can use several orders of magnitude more factors in calculations.

The use of ML technologies in full will lead to the creation of fundamentally new formats of doing business. So far, only the largest market players can now fully unleash the potential of technologies. But the technology will become more accessible for use in medium and small retail after the technology has already been tested by large organizations that could afford to experiment.

As a result, solutions appeared that were easy to use and affordable even for small companies. Although it is more difficult to implement ML methods in offline retail, we can count on their spread here as well.