Hyperlocal forecasting is an artificial intelligence (AI) solution that considers the context of each demand driver within each individual department or location in order to generate the most precise demand forecasts possible. Consider a typical fast food chain. This approach generates a separate demand forecast for each individual data driver (e.g., transactions, total items sold, hamburgers, beverages, deliveries, and sales revenue) at each location. This means that the forecasts and resulting schedules take into account local events, seasonality, trends, and weather patterns, among other factors.
One of the benefits of hyperlocal forecasting is that it enables much more accurate forecasts. Rather than having a broad picture of your demand for a given month, hyperlocal forecasting algorithms can identify, for example, that a fast food restaurant is likely to sell more hamburgers during the first three days of November. Additionally, it will demonstrate a significant increase in foot traffic and a decrease in deliveries compared to other specific days. In this case, the fast food chain can schedule her employees accordingly, knowing that she will require fewer drivers and more cleaners on those days. Where do these sudden surges in demand originate? This is where forecast accuracy is enhanced by event management. If the fast food restaurant were to analyze those days, she would realize that a local event is taking place on that day, which explains the forecasted peaks. This may seem self-evident, especially if the event occurs annually. However, simplistic methods frequently ignore these types of events in favor of manager overrides. When compared to averaging sales over the last three months, using hyperlocal forecasting algorithms equipped with machine learning methods to crunch all available historical data results in significantly higher accuracy. Events are less likely to be missed, and their impact on each demand driver is quantifiable.
To generate accurate forecasts for each demand driver and location, a variety of forecasting techniques must be used. What may be the best method for one driver may not be the best method for another. This means that in order to determine which method works best for a particular demand driver, the results from all methods must be compared and the best one saved. That is why hyperlocal forecasting algorithms include customization features. This feature automatically trains, selects, and saves the best methods on a massive scale, which is critical for businesses that require accurate forecasts at the most granular level, every 15 minutes. Enterprise businesses benefit from customization because it enables them to scale easily, forecasting specifically and uniquely for each individual component of their organization, regardless of size.
Both historical and real-time data are required by the algorithm. The more data that is available, the better. If a business has a large amount of data, the algorithms can learn from it and produce extremely accurate hyperlocal demand forecasts that no other technique can match. Experience and gut feelings will be minimized, and new store managers will immediately have highly accurate forecasts thanks to hyperlocal forecasting.
Forecasting hyperlocally: generating the most accurate demand forecasts for your business. It's fast, accurate, and rapidly scales across departments and locations thanks to our OnShift API. It is the bedrock upon which cost-effective and efficient workforce management is built.
