There’s no denying the data: consumer demand for e-commerce shopping is rising. According to Statista, by 2026, the volume of small parcels processed each year is expected to reach 266 billion – more than double the volume of 2020. To meet this demand, the package, fulfillment, logistics, and delivery (PFLD) industry will invest in new technologies in the next three to five years that deliver demonstrable business outcomes.
An MHI 2022 Industry Report predicts a near 55% increase in the adoption of Artificial Intelligence (AI) over the next five years. Yet, supply chain leaders identified the lack of a clear business case as the most significant barrier to adopting all new technologies. The action now is to prepare the business case for these technologies, identify the financial model and pave the way to adoption and installation.
The case has already been made by leading retailers using AI-enabled robotics systems at other points of the supply chain and seeing rapid returns. Emerging business models like Robots-as-a-Service (RaaS) are making testing AI easier, as well as justifying the business case.
CEOs and organizations are open and indeed eager to explore technological innovations to address their challenges and increase their future resiliency. According to a Supply Chain AI survey from Gartner, supply chain leaders predict that AI will have the greatest impact on their industry during the next three years, with 64% of respondents citing AI in customer fulfillment as the most important emerging technology area.
The good news is AI is entirely achievable. For decades, warehouses, distribution, and fulfillment centers have employed robots to automate functions and support workers. Much newer to the scene is the integration of Artificial Intelligence capabilities. AI-enabled robots represent the next generation in automation and augmentation in PFLD.
AI Advantages in Picking, Sorting, and Induction
In general terms, AI refers to a computer’s ability to carry out functions usually performed by human associates. On the warehouse and distribution floor, those functions often include picking, sorting, and induction. With human-like intelligence, AI-powered robots employ vision, grasping, and manipulation algorithms to read barcodes and see, maneuver, and sort products. With each repeated task, AI-powered robots learn — becoming smarter, faster, more accurate, and more efficient. As they improve, so do the operations that employ them.
McKinsey & Co. reports that, over time, retailers may see as much as 87% incremental value from using AI in their warehouses. Over the past four years, leading retailers, including Gap Inc., J. Crew Group, and Under Armour, have proven that AI-powered robots are effective for pick-and-place applications. In total, these e-tailers, and others, have used AI-powered robotic systems to pick hundreds of millions of items.
With advancements in AI-enabled induction robots, operations can see the same improvements in speed, throughput, and productivity applied to their parcel induction processes. Highly intelligent robotic systems can also address the challenges of parcel induction, such as variations in size, weight, fragility, and materials. This increased efficiency also comes with enhanced speed. AI-enabled induction robots can process up to 2,000 units per hour (UPH) while maintaining a 99.5% uptime with the support of remote robot pilots. In addition to the speedy throughput, operators can use multiple robotic systems along the line so distribution centers, e-commerce fulfillment operations, and shippers can keep up with the breakneck pace of customer demands.
Smarter, Faster Data-Driven Decisions
The need for speed is critical in e-commerce. So is the need for data. AI-powered induction robots also deliver on that front, serving up robust business analytics that operators can leverage to make faster, smarter business decisions. As robots go about their tasks, they capture and evaluate millions of data points across a range of metrics, including:
· Throughput: How the robots are processing units and orders.
· Associate Impact: Where associates’ performance is affected.
· Utilization: How much time a robot is being fully used.
· Speed: How fast the robot processes items.
· Blocktime: The measure of the time the robot is unable to work.
· Accuracy: The error rate when sorting items and orders.
· Order Attributes: Real time order information, including order size, item composition, item velocity info, etc.
The supply chain team can leverage this information to drive continuous improvements throughout operations. AI-enabled robots allow operators to access analytics in real time, without having to pull information manually, for instant insight and outcomes.
From Business Case to Business Reality
Increasingly aware of the advantages of AI, businesses around the world are integrating AI-enabled technologies into their logistics operations. Research and Markets’ 2022 Logistics Robots Market Report estimates that the global logistics robots market will surpass $5.4 billion in 2020, with an expected 15% compound annual growth rate over the next six years. New business models like RaaS make the benefits of AI even more accessible.
Marin Tchakarov is the CEO of Kindred. A technology executive for over 28 years, Marin Tchakarov is passionate about connecting supply chain and fulfillment organizations to the advantages of artificial intelligence, machine learning, and next-gen robotics. Under his leadership as CEO, Kindred launched the INDUCT robotic system built on the proprietary CORE/AutoGrasp AI platform, and developed Kindred SORT to pick half a billion pieces of merchandise in real-world production environments. Learn more at www.Kindred.ai.
This article originally appeared in the September/October, 2022 issue of PARCEL.
