Top 3 takeaways from 2021: Scope of vision software in the logistics industry

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The embarkment of 2021 meant a recovery from the repercussions of the pandemic and adapting to a new normal. The logistics industry was also recuperating from an unprecedented disruption from 2020, stepping up to advanced and automated processes. What was surprising was how the term ‘warehouse automation’ quickly became a buzzword among companies owning warehouses. Automating warehouse operations was always in discussion. However, it spurred only after the social distancing measures came into place and consumers were forced to do their shopping mostly, if not completely, online.

Parcel picking

Veloxity. (2015). [Warehouse with bulk parcels in peak hours]. https://veloxity.us/. https://veloxity.us/wp-content/uploads/2015/11/Cell-Phones-on-Singles%E2%80%99-Day-China-640×375.jpg

 

Nonetheless, the year 2021 turned out to be a game-changer. This was especially true for the companies that ultimately took a deliberate step of choosing sustainable options for warehouse operations. As this year comes to an end, here are the top three developments regarding vision software for logistics:

  1.  In an annual study conducted by Logistics management, “Warehouse Operations & Trends Survey,” about 66% of respondents were improving warehouse processes as a means to reduce costs. Out of them, 39% improved on the software front, which increased by 4% from the last year. Considering how software drives decisions relating to warehouse automation, the system integrators will see a CAGR of 16% in their sales of software solutions up to 2025.
  2. Earlier, machine-vision and gripper technology were not advanced enough to pick and place items in harsh environments of the logistics industry. For instance, identifying overlapping items in low lighting conditions, picking transparent objects, or even segmenting shiny items etc. All these instances highlight how manual labor was inherent to traditional or even semi-automated warehouse environments. Interact analysis reported that early adopters have already employed piece picking robots into their warehouses, especially within e-commerce facilities, where picking accounts for more than 50% of the labor spend.
  3. In 2020, e-commerce exploded by a 33% growth rate to $792 billion—some 14% of all retail sales, with the term ‘direct-to-customer’ becoming a common retail language. Many manufacturing plants shut down due to the crisis, disrupting the supply chain process. Amidst that, those who preferred to adopt dynamic measures found ways to increase revenue. This highlighted the extent to which the customer demand picked up a pace and has only accelerated since then. Moreover, the introduction of new products in the market leads to an increasing number of SKUs. This has led to supply chain executives rethinking ways to meet consumer expectations as quickly as possible.

What’s next?

Early this year, Amazon stated that the warehouse costs spike up to $90 bn per year, out of which about $20bn is estimated to be the cost of warehouse staff. Since then, they have taken measures to employ AI and automation to improve warehouse efficiency. One of their objectives is automating item picking with perfect object identification for optimizing the picking process. Furthermore, the company also plans to automate truck unloading and stacking higher racks.

Another instance redefining the future of the logistics industry is the implementation of robotics technology by Walmart for boosting warehouse capacity. Companies like Walmart and Amazon understood how managing the labor expenses, which account for up to 65% of the warehouse costs, was the need of the hour. In that case, the rise of industrial robots  implies that streamlining the warehouse processes can only benefit the industry.

However, warehouse staff still holds the upper hand in managing a warehouse optimally with 95% of the current warehouses still reliant on manual labor. The purpose of employing robots is to assist the warehouse staff by taking over repetitive dangerous activities. Thanks to deep-learning algorithms, the software-enabled robot is deployed with a trained neural network of millions of images.

Thinking of automation always puts a spotlight on the hardware, but what about the software and camera technology that defines the quality of the automation process. From object placement to segmenting shiny objects, computer-vision technology has evolved to take up tasks that were only possible through human vision. Having realized the importance of machine-vision technology, this market  is estimated to reach USD 15.5 billion by 2026, with warehouse automation itself expected to be worth USD 30 billion.