Everyone who has had the responsibility to order stock for a retail store or to order materials for a manufacturer or production facility has longed to have a crystal ball to be able to see what consumers will be in the market to buy in the upcoming months.
In the past, there was limited science behind how to complete these orders. Often companies went by last year’s sales as a guideline to expect what will happen this year over the same period. The problem with this is the historical nature of the data and the rapid change in consumer demand for production in virtually all sectors of sales.
Today, retailers and manufacturers can use a more refined approach to demand forecasting. While there is still somewhat of a historical aspect to the process, modern software systems and highly advanced data collection on consumer behavior provides more precise information than ever before.
Big data is just what it sounds like. It is massive databases that use hundreds of thousands to millions of data points to look for trends, changes or shifts in how people are buying and what they are looking for.
One of the important differences between big data use in demand forecasting, and historical sales data is the timeliness of the collection of big data. This can be easily captured and analyzed by powerful software systems to provide immediate information on consumer trends.
Models and Methods
Aside from the use of big data, demand forecasting also uses a variety of different models that are highly effective in predicting consumer behavior. By inputting current data into the model, a forecast will be provided that will show a range of possible outcomes and trends.
This is provided as a range rather than an absolute. However, given the accuracy of the specific models in different industries, it is an effective modeling and forecasting tool to set production, purchases, and sales.
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