Demand Forecasting

Machine-learning engines use diverse incoming data about the environment to discover dependencies, trends, and patterns in customer behavior and as result – predict future demand.

For years, companies have been trying to forecast different aspects of customer behavior. The forecasting accuracy is constantly being improved with development of data science methods. Our experience in solving different data analysis problems helps us choose different machine-learning techniques for a particular demand-predicting solution. The method depends mainly on the dataset’s volume as well as diversity of the data the customer has collected over time. The more data that’s available, the more complex the solution we offer, the better the training available for our learning model, and the more accurate the results the company receives.

The dataset can include details of sales history for a period of several years. Our machine-learning model can incorporate different kinds of data; therefore, it is preferable to use additional environmental data such as:

  • Product availability
  • Promotion campaigns
  • Competitors’ prices
  • Weather
  • Social media feeds
  • Trends from search engines

Apart from demand prediction itself, our solution discovers dependencies and correlations between sales of different products, and calculates the efficiency of advertisements.

We believe that not much time will pass before most supply chains will rely on machine-learning solutions to perform forecasting. The first step toward accumulating environmental information into big datasets has already been taken on this roadway. The next step is to start refining data into insights. This task is our company’s mission. BitRefine group offers invaluable tools for companies whose business demands solving complex forecasting problems.

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