Predicting Labor Attrition With Labor Management Software

Predicting Labor Attrition With Labor Management Software

The evolution of data science and sophisticated machine learning is opening new frontiers for data science systems, from lower costs to higher throughput rates to more engaged workers. One of the emerging benefits of this technology is the ability to predict labor attrition. Modern Labor Management Software (LMS) can forecast which individuals are most likely to quit next week — and get it correct 19 out of 20 times.

By drawing on years of historical data from multiple facilities worldwide, machine-learning algorithms can track employees from their start dates as they transition to new roles, quit, get fired or laid off, etc. Once the model gains enough local data from a given site, it’s capable of identifying risk factors that signal when a worker is at risk for quitting. 

The next best thing to a crystal ball

While predicting attrition isn’t exactly a “crystal ball,” early tests suggest the turnover of any given individual can be determined in advance with accuracy rates around 95 percent after the model has been programmed. Local data is key to this process, because what workers do differently before they leave can vary by organization and even by location within the same organization. 

The LMS only needs about six months to learn the patterns of an individual site. Once programmed, it can be leveraged to develop engagement models and incentives to retain and develop your best workers, creating measurable improvements for both employees and the warehouse.

The same machine-learning model also can be used to help pinpoint factors that are encouraging or discouraging employee retention, such as which supervisors are managing the largest number of satisfied employees, and what their peers could be doing to improve their own rates.

Great power creates great opportunities

Early adopters are using this powerful information with varying degrees of sophistication. Some companies simply use this kind of data to automatically fire workers with the highest risk scores in a kind of “preemptive strike.” This strategy may eliminate some problems before they escalate, but it also overlooks the greater levels of value the data science can provide. 

Considering the lack of skilled workers available in today’s tight labor environment — which shows every sign of getting worse long before it improves — this is also a short-sighted approach that can create unnecessary costs and inefficiency.

A more enlightened and profitable approach is detailed in our new white paper, The Impact of Data Science Upon Labor in the Supply Chain. In this informative and quick-reading briefing, you’ll learn how successful DCs are using attrition risk data to inspire, engage and retain their best workers by utilizing the powerful outputs of LMS data science to drive resource-focused engagement. By minimizing the costs of rehiring and retraining, early tests suggest these strategies easily could create six-figure savings each year for medium-sized DCs. 

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