The shortage of workforce due to coronavirus has disrupted many supply chain industries, which has affected the global market. Autonomous robotic systems are changing the automation landscape from logistics, manufacturing to retail, these robots are the perfect tool for dynamic environments with on-demand automation playing a significant role in increasing productivity. With the latest advancements in AI, it has been possible to make many robots smart enough to adapt them in agile and tricky environments.
Digital Supply Chain Market with IoT & AI
To establish a demand-driven supply chain network, organizations are leveraging IoT in supply chain management with sensors and communication devices to improve inventory management, achieve accurate asset tracking and predictive maintenance. Also, the use of smart sensors to increase automated data collection, processing and GPS are improving the visibility of shipments and fill rate. In a world of self-driving cars, smart algorithms, big data and Siri, we know that artificial intelligence (AI) is getting smarter every day. AI focussed machine learning and vision systems are used to address key challenges in supply chain management with planning and optimization, warehouse and inventory management, fleet management and demand forecasting.
The supply chain market is projected to grow $75B by 2030.
Key areas enabling these technology advances and market maturity are:
- A wide range of low-cost, small and power-efficient sensors allow remote devices to capture and transmit enormous amounts of data about the robot’s immediate, anticipated and extended environment.
- AI-focused processor architectures are available from major Semicon players in the field, including Microsoft and Google. Cost-effective and low-power AI processors can be incorporated into remote devices for onsite fast computation and efficient decisions.
- Advanced software algorithms analyze and process data in the robot, cloud, or even in remote, extended sensors that provide intelligence data for the robot to anticipate needs and proactively adapt its behavior.
- Wireless communication and Cloud computing allow the data to be stored, accessed and processed instantly.
Key Market Trends and Their Use Cases
Autonomous robotic solutions rely on picking optimization and fleet management solutions. Pick optimization robots integrates the movement of machines and helps improve throughput and optimize existing picking methods and strategies. Fleet management solutions (FMS) typically manage and control multiple robots operating within the units with bigger payloads and route the robots from an origin to a destination enabling centralized management.
Last-mile Delivery (LMD) - From renowned retailers to local businesses, Last-mile service with delivery robots and drones have been identified as a key differentiator. Last-mile delivery workflows include unmanned ground vehicles (UGVs) & drones. Amazon is testing its Scout delivery robots to deliver the package. FedEx collaborated with Pizza Hut to test the “SameDay Bot” for pizza delivery.
Famous Dog Robot – It has great agility to go in all the places to perform tasks in terrains inaccessible to humans and unstructured environments, allowing you to automate routine inspection process and data capture accurately, safely and regularly.
Healthcare Assistant - Robots are helping hospitals run 24/7 while assisting the healthcare staff. It helps gather deliver lab samples, patient supplies, distribute PPEs, fetch items and deliver medication. It has gained great significance post-pandemic as it can reduce human intervention. With human-like designed features, it is supposed to be socially intelligent, that can make eye contact and provide a supporting arm.
Recycling Infrastructure Robots - It’s been difficult for recyclers to accurately separate different materials. Robot’s arms glide over a conveyor belt and are trained to recognize objects, guided with cameras and applied computer vision technology to process millions of images to classify complex material for waste segregation. Deep learning algorithms are applied to improve its classification and identification of materials like metal, plastic, paper and other factors.
Humanoid Robots – These AI-powered humanoid robots have an upper torso and four arms that can overcome the limitations of traditional robots. They have human-like capabilities that make it possible to work alongside us in factories, offices, or homes while navigating stairs and obstacles. Their growing popularity is resulting in increasing demand across various industrial and commercial sectors.
Forklifts – Driverless forklifts are highly in demand to provide both vertical and horizontal movement of the loads with increasingly intelligent features like front and rear scanners, navigation laser, 3D camera and visual/acoustic warning indicators that enable it to move safely around a warehouse in the vicinity of human workers. Also, for cross-docking where they transport pallets directly from inbound and outbound shipment areas.
Inventory Robots - Autonomous robots offer new opportunities for inventory monitoring. Combined with RFID-tagged products and equipment, these machines can now conduct their shelf insights autonomously at schedules determined by the warehouse.
With RFID interrogators mounted for optimal coverage, they can reliably and consistently detect tagged products. It reduces the need for manual inventory counts and offers real-time mapping to managers to visualize product storage easily. For instance, they can identify storage and placement that is leading to inefficient movements of machinery or people. Also, identify goods that are nearing expiration dates.
The Bottom Line
With deep learning algorithms heavily being integrated into Industrial IoT (IIoT), it has exceeded expectations tremendously ensuring the entire manufacturing operations are working at their full potential with negligible inaccuracies and inefficiencies. AI processing and wireless technologies are intrinsically tied together for complex industrial automation applications, be it warehouse automation, inventory control/logistics, tracking of shipment and goods.
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