Ever since numbers were invented, the value of forecasting has been apparent. Making sure that inventory and other factors that determine capacity are adequate but not excessive requires an accurate idea of what demand will be over future periods. Guessing too low leads to out of stock situations and lost sales, while guessing too high creates a situation where waste and obsolescence of inventory is more likely. Staffing levels are also affected by proper forecasting.
Bigger Data Sets Allow for Improved Forecasting
In the past, forecasts were sometimes wrong because of the limited data that was available to analyze in a reasonable time. But with today’s computing power, you can use machine learning tools to forecast accurate results quickly, even when the data sets involved are fairly massive. The greater capacity of modern data systems also allows you to store more data, even data that might not seem critical, and store data for longer historical periods.
Better Tools Create Pinpoint Results
Having a bunch of data isn’t all that it takes. Analytics tools help structure queries so that they produce the results you’re looking for. Improved dashboards and assistance from the software in creating these queries allows you to delegate ad-hoc requests to less technical personnel. For example, restaurant sales numbers are often compared to the same period from a year ago to account for seasonality. But a sales period from a year ago might be quite different if a holiday or a period of extreme weather had affected it. An advanced analytic tool, therefore, will warn you about major variances in the data set.
Better forecasting can help manage resources. It can also guide strategy. What stores should be closed, and where should new stores be opened? Is staffing adequate across the board in your company? These are the kinds of questions that forecasting can answer.