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Pick & Pack Optimisation: How AI-powered algorithms streamline the process

Pick & pack is a central aspect of supply chain operations. When optimised, it can be the source of competitive differentiation through higher customer satisfaction and loyalty, lower fulfillment costs, and transparency that allows for smooth logistics inside warehouses and stores. If not proactively managed, however, pick & pack can be the cause of numerous inefficiencies, and the process may quickly turn into an operational nightmare.


Even minor picking errors can result in significant reworking, delays, and missed deliveries. Similarly, failure to package goods efficiently leads to unused capacity in boxes, crates, and trucks — a compounded waste of space — as well as higher transportation and handling costs that must be absorbed one way or another by shippers and recipients.


So pick & pack optimisation sounds like a priority, right? The short answer is yes. But in light of the expanding size of warehouses and stores, thousands of confusingly similar SKUs, and rising customer expectations for timeliness and accuracy, the task is more challenging than ever.  


Standard logistics systems may fall short of intelligence when it comes to carrying out complex calculations, enabling decision-making in real time, and recommending targeted actions. With that in mind, how can AI-powered algorithms help optimise pick & pack even though the process is naturally prone to chaos?



Incorrect inventory levels create a situation of imbalance. If the actual stock is lower than recorded, retailers and distributors may overconfidently sell products and later have to cancel or reschedule customer orders. In contrast, underestimating inventory generates higher warehousing costs in addition to missed sales opportunities, stock obsolescence, and lack of available storage space for new SKUs.


Fine-tuned algorithms provide visibility to retailers and manufacturers, which can connect physical touchpoints and IT systems to ensure accuracy and flag errors and discrepancies as they occur. Inventory planning then becomes more effective with fewer days in inventory, less or no excess stock, and better demand forecasting taking in consideration weather conditions, past purchases, and other factors.



What is the best picking strategy for a specific set of pieces with different dimensions and weights? Is it possible to reduce movement and effort by reorganising zones and reallocating workforce accordingly? What are the potential productivity gains and chances of errors corresponding to diverging scenarios?


AI-powered algorithms can provide answers to these questions and facilitate pick optimisation overall, notably by using the batching method:


  • Identifying ways to minimise the number of trips and travel distance
  • Re-prioritising orders and replenishment based on current traffic and out-of-stocks
  • Alerting workers of frequently confused SKUs, so they pay closer attention to these
  • Recommending more suitable storage strategy by volume, class, or else



Pack optimisation is equally important such that products are packed and transported most efficiently and arrive in good condition. Algorithms support 3D cubing, a method used to anticipate the volumetric size of an order and its SKUs to select the right packaging material and size.


Boxes that are too small or fragile, for instance, may require unpacking and, therefore, increase the number of touches and reworking. On the contrary, oversized or wrong crates result in mismanaged space, superfluous shipping material, and higher transportation costs.


Using algorithms enable retailers to streamline pick & pack in a variety of ways, supporting leaner operations alongside improving order-to-delivery lead times and creating a better customer experience. 


Would you like to learn more about how ALFA can help you optimise your retail operations? Contact us today to schedule a demo.