How is AI impacting operations in the Distribution Warehouse

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Senior UX Designer for ADLINK Technology

Most distribution warehouses can be broken down into various tasks, all of which can benefit greatly from leveraging AI technologies. The tasks in order of product lifecycle include but are not limited to: 

  1. Receiving
  2. Storing
  3. Picking
  4. Packing
  5. Shipping

The more efficient a distribution warehouse can be in handling each of the tasks above, the more profitable it can be, allowing them to expand and offer additional higher-value services to their clients.

As we continue to embrace and invest in the technologies responsible for the fourth industrial revolution in manufacturing, we must consider how the same technologies can be leveraged in other areas of our business or industries, namely distribution. 

Arguably, manufacturing is responsible for Industry 4.0 technologies like robotics, automation, and AI. This is likely why the plant floor is years ahead of the distribution warehouse.

Companies like Amazon.com have had the luxury of reinventing the distribution warehouse and processes therein. They continue to innovate and experiment with Industry 4.0 technologies. In order to keep up with industry leaders like Amazon.com it’s not necessary to start from scratch, therefore the AI technologies that we will highlight in this article not only impact operational effectiveness but are also considered non-invasive.

Receiving and Storing – Machine vision with AI automates and improves the traceability of all assets throughout their lifecycle. Machine vision and beaconing equipped forklifts, for example, remove the human error during the receiving and storing process. Automating the scanning and data entry required for inventory management allows forklift drivers and receiving staff to make speed and safety their first priority.

Picking – Whether this is used for assembly or order fulfillment, robots, equipped with machine vision and AI accurately pick parts and place them in totes or boxes. AI allows users to take this a step further, by identifying the best “pick point” and placement orientation speeding up the packaging process and maximizing package volume.

Packing – Machine vision with AI audits each order for correctness. After the order is picked, these solutions automatically validate the order against the contents. This can support and/or replace a quality assurance technician allowing them to be reallocated to other tasks throughout the warehouse. Orders marked as incomplete/incorrect can be marked for re-packing prior to shipping.

Shipping – Machine vision and AI continue to audit each order for correctness during palletization. All packages placed on a pallet are marked based on unique identifiers, shape and size. Validating pallets for order correctness and fulfillment traceability can net massive gains in profitability. The industry is plagued with a human error during the palletization process, and high-mix orders can not currently benefit from the cost/capabilities of robotics.

Safety and Security – Machine vision and AI are used to determine if and when employees should be allowed entry, have the proper PPE, or in unauthorized areas. This same technology can be used to identify slick spots, trip spots, and/or pinch points.

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