Who we serve
Predion Labor improves the profitability of labor decisions for:
Why it matters
There has always been a drive for operational improvement in the labor domain, as it typically represents the single largest controllable expense at 15-30% of revenue. This is especially challenging in a tight labor market, where employees prefer to know their timetables in advance, scheduling rules are more restrictive and hiring costs are high.
It has become increasingly difficult to predict demand and staff accordingly. Altered shopping habits, new business models and competitor types, and different influence drivers have made many legacy approaches to predicting customer behavior (and therefore labor needs) less effective. The typical approach of ‘take last year and adjust for the past few weeks’ leads to both over- and under-staffing
Much of the technology in this area has focused on addressing the tactical question of how to assign individuals to specific shifts and manage shift swaps with little managerial overhead. This is indeed useful, but the highest point of profit leverage is determining how many people (and of what position) to schedule where and when. This is true because labor is not just a cost. There is almost always a direct relationship between staffing levels and realized sales, although identifying the size and shape of that relationship, and how it varies by location and time, is challenging.
What is it?
Modern AI techniques give the potential to improve forecasting. This is particularly relevant for businesses with hundreds or thousands of sites, where new techniques allow us to create forecasts that learn from other locations or skus. This can boost performance by identifying patterns that are shared — for example — by all suburban locations or all locations with a particular competitor present. These types of refinements improve performance, as does the incorporation of novel data sources such as local events.
How it works
Predion Labor is a cloud-based software that integrates data from both corporate and external sources, models it into a unified format, and then pushes scheduling recommendations into existing workforce systems. We have a single-minded focus on improving the profitability of the key decision step using data and modern AI techniques.