Sales and Demand Forecasting using Machine Learning

In today’s world of extreme competition, cost reduction is of utmost importance for organizations, primarily in the retail and consumer product goods (CPG) industries. All the major players in these industries try to focus on cost-cutting and maintaining optimum inventory levels to gain a competitive edge. In addition to cost optimization, having just the right amount of inventory is also becoming important for consumer satisfaction especially in the perishable retail goods market.

This is where demand forecasting helps these companies. Efficient and accurate demand forecasts enables organizations to anticipate demand and consequently allocate the optimal amount of resources to minimize stagnant inventory.

Sales and demand are typically forecasted by methods of linear regression, gradient boosting with decision trees or recurrent neural networks based on historical data and some additional data like:

  • weather conditions​
  • market situation​
  • currency exchange rates​