Even before the logistical challenges that the eCommerce explosion of 2020 brought, businesses large and small have long struggled to adequately address packing inefficiency in the warehouse. It’s not for a lack of trying—investments are made in box on demand machines, carrier rates get negotiated, and consultants get consulted. Despite these efforts, cost and waste control are only marginally improved.
Warehouse personnel, under pressure to keep cartons flying out the door, simply don’t have the time to consider all the variables that may affect the optimal box configuration for any given shipment. Determining the most efficient, sustainable and transportation cost-effective way to pack items of various sizes and weights into different-sized cartons, going from and to various locations, is complex and beyond the ability of real-time human judgment. Material waste, labor costs, and the intricate pattern of packing incentives woven throughout negotiated rate tables are just some of the converging factors that have to be considered on a per-order basis.
In this white paper we’ll explore how these issues affect optimal box selection, and walk through some examples of how the right, cost-efficient packing configuration for a shipment is sometimes surprising and counterintuitive. We’ll also make the case that while these concerns are impractical for human consideration in real-time, they’re ideal candidates for specialized artificial intelligence. Until recently, only the largest retailers in the US had the resources to develop internal technology that was up to the challenge, but modern cartonization AI democratizes cost-efficient packing for businesses of all sizes.
Please CLICK HERE to download the white paper.
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