Optimizing Order Picking to Increase Omnichannel Profitability with Databricks
The core challenge most retailers are facing today is not how to deliver goods to customers in a timely manner, but how to do so while retaining profitability. It is estimated that margins are reduced 3 to 8 percentage-points on each order placed online for rapid fulfillment. The cost of sending a worker to store shelves to pick the items for each order is the primary culprit, and with the cost of labor only rising (and customers expressing little interest in paying a premium for what are increasingly seen as baseline services), retailers are feeling squeezed.
But by parallelizing the work, the days or even weeks often spent evaluating an approach can be reduced to hours or even minutes. The key is to identify discrete, independent units of work within the larger evaluation set and then to leverage technology to distribute these across a large, computational infrastructure. In the picking optimization explored above, each order represents such a unit of work as the sequencing of the items in one order has no impact on the sequencing of any others. At the extreme end of things, we might execute optimizations on all 3.3-millions simultaneously to perform our work incredibly quickly.
Walmart Is Pulling Plug on More Robots
The retailer is phasing out the hulking automated pickup towers that were erected in more than 1,500 stores to dispense online orders. The decision reflects a growing focus on curbside pickup services that have become more popular during the Covid-19 pandemic and continues a broader retreat from some initiatives to use highly visible automation in stores.