Figure 1: Autonomous Systems in Distribution Centers
Per a recent study by BCG, approximately 70% of survey participants expect advanced robotics to become a very important productivity driver in production and logistics by 2025, apart from benefits reaped in quality and maintenance. The same survey also estimated that Autonomous Mobile Robots and Drones for the movement of goods would be more important than conventional AGVs by 2025.
Pillars of Autonomous Operations
Contactless operations and advanced automation in supply chain are derived from synergies among the following core autonomous technologies communicating in accordance with Industry 4.0.
1. Automated Guided Vehicles (AGVs)
The earliest instances of automation in warehouses involve the use of AGVs, as early as the 1950s. These driverless machines are usually pre-programmed to follow fixed routes to aid in material movement, bringing in unique advantages such as increased efficiency, productivity, and safety while reducing costs manifold.
Flexibility of operations becomes a limiting factor though, since any expansions in the warehouse or changes in the layout call for a complete halt of operations to reprogram the AGVs.
2. Robots for Autonomous Mobility
Autonomous Mobile Robots (AMRs), in contrast to AGVs, use on-board sensors and processors to autonomously move materials without the need for physical guides or markers. AMRs employ learning algorithms to perceive their environment, remember their location, and dynamically plan their own paths while avoiding obstacles.
Much like Self Driving cars, AMRs use HD Maps to enable these complex mobility functions. The minimal impact AMRs have on infrastructure make them highly cost-effective, especially with increase in warehouse size.
Drones find application in inventory counting, health check applications, and airborne transport of goods. According to supply chain specialist, Argon Consulting, two drones working in a pair in certain use-cases can do the work of 100 humans over the same period, while drastically reducing errors associated with the traditionally manual process.
4. Industrial Robotics
Advanced robotics including articulated robotic arms are an important element of contactless operations that bring in the capability to pick and place, inspect, sort, package, and palletize items. They can be exceptionally versatile since they can move, turn, lift, and manoeuvre items, making them a robust technology that adapts easily to multiple applications.
5. Computer Vision
Deloitte Consulting recently stated, “Computer vision together with machine learning is going to add the missing sense in our IoT endeavors and we are already seeing this at our industrial IoT clients today.”
Increasing labor costs and decreasing costs of automation have contributed to companies turning to CV to automate a range of labor-intensive tasks, such as quality inspection of supplier parts, stock counting and locating, in-line quality checks, inventory counting, and inspection among others. With improvements in computational capacity, CV solutions are becoming especially effective in catching errors as they happen, thus reducing bottlenecks.
Autonomous Trucks and the Relevance of Platooning
Autonomous Trucks (ATs) have seen heavy investment in the past, driven primarily by driver shortage, among other factors. Although the primary focus to date has been on passenger automobiles, buses, yard, and shuttle trucks are slowly carving a niche for themselves in the autonomous realm – primarily because they operate in more controlled environments such as ports, manufacturing plants, and distribution centre yards. In fact, companies have already made fully autonomous beer deliveries and actively struck alliances to operate ATs jointly.
Full autonomy, however, is still a long way off. McKinsey Center for Future Mobility, estimates that the deployment of ATs will happen in 4 stages, the cornerstone in the short to medium term being “platooning” - a technique to connect wirelessly a convoy of trucks to a lead truck, allowing them to operate safely much closer together and realize fuel efficiencies. The report estimates that each wave will lower the operators’ total cost of ownership (TCO) in stages—moderately at first with the bulk of savings expected with implementation of higher autonomy levels.