The past year has brought numerous challenges for logistics-based companies. Rapidly shifting economic conditions, unpredictable consumer behaviors, an ongoing labor shortage, and an increasingly competitive landscape have forced those in material handling to adapt quickly or fall behind. Supply chain managers are looking toward scalable, flexible solutions that will increase their operation’s preparedness for future disruptions. The first step in achieving this is by integrating robotic material handling.
Benefits of Automating Logistics Workflows
Logistics automation is crucial in meeting the challenges faced by the industry. It helps reduce costs, increase efficiency, and improve worker safety. By allowing robots to take over dangerous and repetitive tasks that workers don’t want to do, workers can be allocated to higher value tasks that humans are more suited for, increasing employee satisfaction and safety.
There are five signs that indicate when it’s time to automate logistics workflows, including:
- When a worker’s job is dangerous, repetitive, or hard to fill
- When labor scheduling for flexible demand is not working
- When order fulfillment or per square foot costs are high
- When you’re handling more SKUs or non-conveyable shipments
- When you want to stay ahead of the competition
Vecna Robotics’ Automated Mobile Robot (AMR) solutions are scalable, flexible, and can be adapted to any point in operations. The company’s workflow orchestration software, Pivotal, can double throughput with optimized movements and dynamic task assignments to both humans and robots.
Enhancing Safety with Autonomous Industrial Vehicles
The adoption rate of self-driving industrial vehicles such as autonomous forklifts, pallet trucks, and tuggers is on the rise. Warehouses are making the shift towards automation for many reasons, including a lack of available labor and the need to increase efficiency as demand fluctuates and e-commerce grows. Safety is a significant benefit of autonomous vehicles. In recognition of National Forklift Safety Day, it is essential to note the current incident rate of manually operated vehicles and consider ways in which automation can reduce it.
Forklifts are one of the greatest hazards in the warehousing industry. OSHA estimates that on a yearly basis, forklifts cause about 85 fatal accidents, 34,900 accidents resulting in serious injury, and 61,800 non-serious accidents. According to the Industrial Truck Association, there are about 855,900 forklifts in the U.S. Autonomous forklifts can significantly reduce these accidents while enhancing worker safety.
By making the switch to autonomous forklifts, warehouse managers can introduce a reliable and thoroughly tested navigation element. Autonomous forklifts use high-technology sensors and recognition to detect, report, and solve obstacles and workflow issues consistently. They can adapt to changing environmental factors and reassess routes as a human would, leading to a more efficient, reliable, and safe working environment.
Autonomous mobile robots such as Vecna Robotics’ AMRs offer a more flexible and easily integrated solution. They use natural feature localization, a technique that combines Lidar and camera-based sensors to make a virtual map of its environment in real-time. This enables the robot to locate static features such as walls, racking, and pillars and use them to orient themselves, rather than relying on infrastructural landmarks like AGVs do.
Smarter Navigation with Autonomous Mobile Robots
Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) are two types of self-driving vehicles used in warehouses and manufacturing plants. Although both perform similar tasks, there is a variance in their methods and capabilities.
Traditional AGVs navigate using infrastructural landmarks, such as wires, magnets, tape, reflectors, or QR codes placed throughout the facility. They move along predetermined paths like a train on a track. However, because these features are permanently and geographically set from the start, the robot is limited to operating on fixed routes. This means any changes would require further infrastructural updates, newly planned routes, and a loss of valuable operating time.
On the other hand, AMRs use a technique called natural, feature-based localization. They use a combination of Lidar and camera-based sensors to make a virtual map of their environment in real-time, locate static features such as walls, racking, and pillars, and use these features to orient themselves. Due to this, AMRs are not confined to any specific route and are able to navigate dynamic and ever-changing environments easily. Paths can be changed or interrupted with little consequence. This method forgoes the inconvenience and cost of infrastructure changes associated with traditional AGV installation and provides a more flexible form of navigation.
What distinguishes the best AMRs from the rest of the pack is their uniquely intelligent ability to engage in path-planning. This feature allows the robots to choose the most efficient route to accomplish their task, taking into account a multitude of actively changing environmental factors. When AMRs encounter obstacles, they can reroute efficiently and get back on task, saving workers from having to unblock a gridlock caused by a stuck AGV.
Vecna Robotics has employed a combination of path-planning and natural feature localization that mirrors how a human thinks. The AMR can move around effectively using real-time intelligence rather than initial inputs of precise measurements of the warehouse. An AMR that relies on a combination of obstacle avoidance, smart localization and path planning will not be thrown by an environment in flux. The AMR’s performance will never degrade as the facility changes – in fact, it has the potential to improve its performance over time.
The Future of Self-Driving Vehicles in Logistics
The potential of self-driving vehicles in revolutionizing in-house logistics for both smart warehouses and flexible manufacturing is immense. With the ability to handle an increasing number of SKUs or non-conveyable shipments, self-driving vehicles can provide a scalable, flexible solution for companies preparing for future disruption. The benefits of self-driving vehicles extend beyond increased efficiency and reduced labor costs. They also offer a safer work environment, as highlighted earlier, and the ability to allocate human workers to more high-value tasks.
One major barrier to the adoption of self-driving vehicles in logistics is their price. Traditional purchasing models require a significant upfront investment, which can be a challenge for many companies. However, the emergence of robot-as-a-service (RaaS) models has made self-driving vehicles more accessible. Under this model, companies can pay a monthly fee to lease the robots and take advantage of ongoing support and upgrades.
Despite the potential benefits and the availability of RaaS models, the transition to self-driving vehicles in logistics is not without its challenges. One of the major challenges is adapting the workforce to new technology. Workers must be trained to work alongside the new autonomous technology and understand how to interact with it. This requires significant investment in employee training, which can be a difficult sell for some companies.
However, the benefits of self-driving vehicles in logistics are clear. With the potential to reduce labor costs, increase efficiency, and create a safer work environment, self-driving vehicles are the future of logistics. The transition to automation is necessary for companies to stay ahead of the competition and prepare for future disruption.
Conclusion
In summary, the benefits of self-driving vehicles in logistics are clear. Automation can help companies reduce labor costs, increase efficiency, create a safer work environment, and stay ahead of the competition. The importance of scalable, flexible solutions for increased preparedness for future disruption cannot be overstated.
Adopting self-driving vehicles in logistics can be challenging, but the benefits are worth the investment. The emergence of RaaS models has made self-driving vehicles more accessible, and with the potential to improve performance over time, the investment is well worth it. Companies must be prepared to adapt their workforce to new technology, but with proper training and education, the transition can be a smooth one.
In conclusion, we encourage companies to consider self-driving vehicles as a solution to the challenges they face in logistics. With the potential to revolutionize the industry, the future is here, and the time to act is now.
