Predict Labor Needs With Labor Management Software
March 16, 2020
During the last decade, the focus of Labor Management Software (LMS) has gradually shifted from interpreting the past to predicting the future. About 10 years ago, the insights it generated were largely limited to the previous day’s performance, highlighting which areas met their goals or needed improvement.
More recently, details started coming faster as LMS evolved. It was possible to get quick feedback about the previous shift, or even to receive iterative updates once or twice during the current shift. This data had value — and it still does — but for the most part, it only enabled reactive responses to past results.
Fast-forward to today. With the advent of advanced machine-learning algorithms, LMS now can use this historical data to predict a DC’s future labor needs.
Today’s LMS lets you look into the future
Recent advances in data science, enabled by the development of cutting-edge artificial intelligence (AI) and machine learning, now can provide unprecedented levels of real-time data monitoring and predictive modeling. For example, your execution history can be used to predict your resource needs for the next day (or even the next week), calculating the precise number of workers you’ll need to run your operation at peak efficiency.
In a scenario like this, the LMS might notice that there are too many workers concentrated in one area while another will have a shortage soon. If the potential benefit of reassigning workers outweighs the opportunity cost of moving them, the system could automatically recommend an intra-day change for labor balancing.
Improve performance by reducing psychological obstacles
A similar strategy also can be applied to product rather than workers, particularly in locations like goods-to-operator (GTO) stations or tote picking areas. When too much product piles up at once in places like these, the sheer volume of work can create a psychological drain on workers that depletes their energy and reduces performance.
To prevent this effect, smart routing and release tools are being developed to optimize the flow of items throughout the entire material handling equipment infrastructure. This approach ensures that product is released into the system at the ideal times to keep the entire operation flowing efficiently, without creating physical or psychological congestion along the way.
Discover the full power of data science
The capabilities described in this blog are reality today and have established promising track records in real-world pilot tests. But they’re only a few of the emerging data science capabilities that are bringing game-changing insights to the supply chain. LMS now makes it possible to predict where each of your employees will work most effectively, which incentives will motivate them to get their jobs done, who’s thinking about quitting in the next few weeks, and more.
To learn more about how some of the world’s most successful DC operators are using these insights, download our free white paper, The Impact of Data Science Upon Labor in the Supply Chain. You’ll not only discover what today’s LMS technology can do, but learn how to apply it to your own operation in the most effective way to meet the growing demands of e-commerce, overcome the challenges of tight labor markets, and drive the development of autonomous systems.
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