Labour demand forecasting is the first step in the workforce optimisation process before anonymous shift creation and shift filling where accurate demand is predicted based on different demand signals for any time interval. It correspondingly translates that demand into the appropriate labour headcount based on advanced labour standard models.
Scheduling the right people at the right place at the right time should be simple, right? Unfortunately, that is not often the case. With unpredictable demand, seasonality, local events, holidays, etc., there are just too many factors that have to be taken in account, especially when considering that these factors and their effect can differ per location. That’s why more and more companies use AI to optimise their schedules and scheduling process, often times starting with labour demand forecasting as their base.
Labour forecasting allows workforce planners to get accurate projections of their demand signals such as sales, transactions, items sold, and foot traffic. Accurate demand forecasts give the insight needed to build accurate schedules (and minimise over or understaffing). Staffing on its own is a major cost driver: losing sales due to understaffing or incurring additional labor costs due to overstaffing has a significant impact on the overall business, especially when staffing impacts not just customer satisfaction, but also employee satisfaction.
As established before, there are many factors that can influence demand: foot traffic, sales, volume trends, seasonality, events, weather, etc. On top of that, each store or restaurant most likely has their own demand patterns, unique seasonal impacts, and local events that impact sales. Leaving us with another question: How does one make the most accurate forecasts given so many different factors?
More traditional forecasting methods capture some but not all of this complexity, resulting in forecasts that are not accurate enough to be useful in creating labour projections. However, with the introduction of new methods like AI and machine learning, labour demand forecasts are more accurate than ever. These intelligent algorithms learn from past data to continuously improve the quality of the results and they open up more possibilities to scale and complete automation. However, there are a few things that should be considered when implementing labour demand forecasting.
Labour demand forecasting algorithm should for instance be equipped with automatic quality checks and corresponding retraining to ensure that forecasts are not only accurate on the data set it is trained on now, but also produce high-quality results with future data sets that may be different because of changing conditions and another forecasting algorithm may produce better results. That’s important because you could spend time more wisely on other tasks than on manually checking and retraining each individual forecasting algorithm you have for each location. The latter is also called hyperlocal forecasting and that is of significant value because each location needs to have a different forecasting method to make accurate forecasts for that location, simply because each location has different demand signals and different environments.
Labour demand forecasting is a proven solution for those who are looking to automate their scheduling process, maximise sales and minimise labour costs. With hyperlocal and accurate labour demand forecasting, under and overstaffing are officially things of the past.
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.
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