Forecasting is the process of estimating future sales in an organization. For a particular field and future time frame, the sales forecast shadows the information about the future product’s sales with a specified price. From the organization’s perspective, sale forecasting is of utmost importance with the best accuracy for calculated production at the desired time, because production targets are based on sales forecasting. The forecasting of the raw materials makes the production houses operate easily with fewer issues, the arrangements of the labor and tools are arranged orderly by forecasting the sales.

In big organizations, the sales teams are distributed, everyone has their target and vision. To boost up their entire development strategy, these organizations are using the approach of sales forecasting more than ever. With the forecast results, the data-driven decision could be taken for the company’s growth and the expansion of their businesses. 

Impacts assessed when not forecasting your sales.

A single good big-decision would provide revenue for the whole organization, but missing the forecast could be a bad idea from the organization's point of view. Every company in the world are in strict competition applying every means of technology to win the race, the entire sales and revenue management should know the ‘Sales forecasting’ a powerful technology based on Artificial Intelligence. Whenever Artificial Intelligence is used, the automated approach is imbibed to get a better result.

Over-forecasting: Companies are at risk when failed to fulfill the expectations and targets of the businesses. These results in damaging long-term impacts on the company. Ideally, even over-forecasting can bring in the risk for the company

Under-forecasting: When the company forecasting over-delivers their expectations, the profits generation is good but the poor strategy made on R&D, marketing doesn’t provide exponential net growth to the company and the competitors' advances. Thus in all the scenarios, under-forecasting is almost avoided and is seen to be detrimental.

How is sales forecasting being done using artificial intelligence?

These are some famous and powerful AI-based models for time series forecasting:

●        AutoRegressive Integrated Moving Average (ARIMA)

●        Seasonal AutoRegressive Integrated Moving Average (SARIMA)

●        Vector AutoRegression (VAR)

●        Deep Learning using Long Short-Term Memory (LSTM)

Here we have forecasted sales for 6 months of a fashion retail company and the results predicted above is an Artificial Intelligence-based LSTM model. Historic data of the company is been used, analyzed, pre-processed, and forecasted using powerful algorithms. From the above figure, we can see that the predicted sales have been executed with the least error overlapping the actual sales. These types of time series forecasting enable the machine to understand the seasonal changes, holidays, and festivals of every month because of which the machine knows on which festival what type of products would give the company a better sale. When getting the forecasted results 6 months prior, the company’s management planning, changes in policies as well as targets been executed earlier and the decisions made by the executive won’t be a deadline panic decision as they would have a longer duration to think about the problems if any.

 The Sales Forecasting using artificial intelligence (AI) has gained popularity and organizations have started predicting  their sales  using AI and data analytics. The impact which has been discussed above has helped to finalize the aspects of why sales forecasting is important to organisations using artificial intelligence.

 Why do we need it?