Operators are striving to build dynamic and agile DCs, and data science can help by providing timely information, the orchestration of tasks, and employee engagement.

A study of warehouse workers showed they understood that a company’s investment in technology is an investment in them — 90% believed new technology helps attract and retain floor workers. Taking tasks away from human workers, such as poring over endless data streams or traveling from one end of the warehouse to the other, makes the jobs of human workers more efficient and rewarding.

The product slotting process is one example of how data science can help optimize the DC. Slotting has been traditionally performed with Excel spreadsheets, with planners trying to match fast-moving SKUs with advantageous warehouse locations. Slotting decisions based on product flow takes a great deal of data analysis. Using machine learning, it’s possible to tease out weekly and daily trends, build better forecasts and make better product slotting decisions. Optimizing product slotting reduces travel time and causes picking productivity to increase.

Managing the workforce in near real time also benefits from data science. Individual performance can be monitored so that operations managers can fix problems surgically. Deploying robots in the DC also reduces human travel time, yielding ahigher ratio between material handling time and travel time. Collaboration between human labor and robots requires software and AI with visibility into the location of both.

Data analysis can also be used to compare the performance of different facilities to understand where improvements need to be made. Companies considering technology investments to optimize DC operations should do so sooner rather than later.

Why?  Read on to learn more.

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