The weakest points in warehouse management are often the human ones. Even smart, safe, and focused people are limited by their physical and cognitive capabilities. That’s why AI-based “predictive picking” plays such an important role in warehouse management.
What Is Predictive Picking?
Predictive picking uses AI to analyze historical demand data, as well as unstructured information on factors such as the current popularity and seasonal demand of products, to anticipate which products customers will order next. This analysis helps warehouse managers pick and pack the most commonly ordered products first, rather than guesstimate what may be needed in the future.
Switching to predictive picking helps reduce the possibility of stockouts and overstocking, and it optimizes the fulfillment process. In turn, it helps cut unnecessary costs, streamlines operations, and minimize errors.
Predictive Picking Explained
Data-driven decision-making has revolutionized warehouse management. Such analyses can help dictate which products are moved to shelves nearest to the packing zone. Workers scan RFID codes when compiling orders to ensure the inventory is updated in real time.
Predictive picking helps warehouse managers take this efficiency to the next level. By analyzing data on historical demand and predicting future demand AI algorithms can help managers decide which items to order and when, and even the most efficient sequence to pick and pack stock on hand.
Consider this basic example. Weather forecast data indicates a heat wave ahead. That information could encourage an electronics store to move its fans and AC units to a more prominent part of the warehouse for speedier delivery, or a supermarket to stock up on ice cream, beer, and picnic foods. Both applications help the stores maximize sales, reduce waste, and increase accuracy.
Predictive picking is just one cog in the machine of supply chain optimization. Spatial analysis can help companies identify the ideal number, size, and spacing of shelves in a warehouse. Robotic pickers can increase speed and accuracy. Analyzing traffic data can help companies plot the best routes for product deliveries. Scheduling systems can help ensure the right people are working at the right time. Data-based decision-making can support businesses with all this and much more.
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