Rises in e-commerce and consumer expectations have created complex productivity issues for distribution and fulfillment (D&F) centers. The popularity of e-commerce has created service level agreements (SLAs) that shrink delivery windows after purchases are made. For distributions centers (DCs), this means an increase in productivity and a decrease in the amount of time allocated to execute orders. Thus, DCs and fulfillment centers of today are no longer able to rely on old strategies to meet the new demands of consumers. They must find ways to adjust to the fluctuating environment. 

There are constant changes in product demand and resources; machine learning must be able to accommodate those needs without affecting output. Operators now can rely on machine learning and sophisticated techniques that couple data analysis with shifts in resources in order to reach optimal productivity in ever-changing order fulfillment environments. By using historical data, DC operators can make better, more informed decisions on how to run their warehouse and determine the best ways to meet productivity goals.

An increase in the amount of warehouse automation integration techniques for operators means a proportional increase in the amount of information and data coming in to them. Knowing how to properly manage machine learning and data science will ensure you are using your workforce and automation investments to their highest potential. 

This white paper explores how to best leverage data science techniques in your distribution center.