It’s important to find the right balance between demand and the number of employees you need to satisfy that demand. Having too many employees present for a certain level of demand is demotivating for the employees and expensive for you. Having too few employees present puts unwanted pressure on those employees and might have you lose out on sales.
One way to find this balance is by starting off with accurately forecasting labour demand. Knowing what demand and the required headcount will be, leaves the user to decide whether they want to perfectly cover or over/undercover that demand. While labour demand forecasting is a critical component of workforce optimisation, doing it right is easier said than done. After all, demand fluctuates on the basis of many variables: seasonality, trends, local weather, events, and more. Forecasting demand and the number of employees you’ll need on the basis of that demand is currently an exercise that often times involves experience and some guess work. This ‘gut feeling’ based method could understandably lead to overlooking important elements that influence demand and therefore having less accurate demand forecasts as a results. Luckily, that’s where algorithms can help.
For a number of reasons, AI-driven algorithms can significantly improve the speed and accuracy of labour demand forecasting. Firstly, algorithms can crunch much more data than people. Whereas people and even legacy systems and other types of software like Excel can get overwhelmed when they’re given too much data, algorithms thrive on it. It allows them to forecast more accurately by noticing patterns in historical data that would otherwise have gone missed.
Secondly, algorithms can make forecasts at hyperlocal level. This doesn’t pose anything new: planners and store manager already do it on a timely basis. Labour demand forecasting algorithms can however help improve forecasts by taking different demand signals and forecast the demand for every signal per hour and location. This type of forecasting is also called hyperlocal labour demand forecasting. It allows, for example, restaurants to not only know exactly how many hamburgers and ice creams will be sold at four in the afternoon, but also how many visitors they can expect during that hour. This demand will correspondingly be translated into required headcount and indicate how many cooks, cashiers and cleaners are needed at that particular restaurant at that moment in time. In short, these algorithms bring more accuracy and granularity to current forecasting expertise.
Finally, algorithms increase in value the more they’re used throughout the organisation. While an isolated algorithm has value on its own, algorithms that help plan the workforce optimisation process from A to Z are significantly more valuable. For example, a labour demand forecasting algorithm that translates the predicted demand into how many employees will be required, an algorithm that creates shifts to cover that demand and another one that determines the right employees to fill those shifts. Having a complete end-to-end solution which planners can use to fill schedules, is more useful and valuable than an algorithm that just predicts demand.
If you’re looking to streamline your workforce, labour demand forecasting is usually the first step you’ll look at. Algorithms can turn this step from a complex and time-consuming problem into an efficient and accurate solution.
Looking to reduce costs and improve sales with hyperlocal and accurate demand forecasts? Widget Brain’s labour demand forecasting algorithms are the answer. Our algorithms are designed, scaled, and monitored all in one easy-to-use management application. This ensures we’re using and updating the best forecasting algorithms that work for your business. Go to www.widgetbrain.com/labour-demand-forecasting for more information or schedule a Widget Brain demo today.
Learn more about how algorithms contribute to a level of employee happiness at CitizenM that drives their guest satisfaction and commercial success.
“We wanted to provide a better solution and now we capture all that expertise and knowledge into a system that will be widely available and doesn’t require as much human interaction.” - Jan Gielen (Manager Climate & Energy at Delphy)