![]() Complex relationships such as the one between customer demand, inventory list, and the automated logistics system can also be integrated into a digital twin. With the data collected from these automated processes, the logistics chain within a facility can be modeled into a digital twin and analyzed in real-time. The data automated assets and processes provide can be analyzed to enhance shop floor performance and the accuracy of logistics systems. The use of AGVs and robotic systems are examples of such subsystems and the digital twin can be used to extract important industrial insights from these systems, as well as, their relationship with an entire facility. It provides a comprehensive digital environment where the varying relationships among subsystems within a facility can be discovered, monitored, and enhanced. This is because of the multiple subsystems that communicate in complex patterns when achieving a common goal. Receiving Business Insight from Automated Guided Vehicles and Robotic SystemsĪutomating every process in large industrial facilities is a complex task. Thus, making robots and AGVs integral components in industrial automation and Industry 4.0. These systems also produce their fair share of data that define the manufacturing or industrial process occurring on the shop floor. Other robotic systems that deliver precision guidance or track production processes also rely on shop floor data such as inventory lists and workstation availability. The data produced by AGVs also provide more insight into the material handling and logistics activities that occur on the shop floor. Also, these automated shop floor assets produce their own data which could be the distance traveled during deliveries, the relationship between load weight and transportation safety, delivery timelines, and transportation speed. The increasing reliance on AGVs and robots are driven by the need to increase efficiency levels, reduce waste, and eliminate accidents from occurring within a facility’s logistics systems.ĭuring their operations, AGVs and robots rely on data such as mapping data, shop floor topology, and obstacle dimensions which may affect the accuracy of their performance. Industrial and manufacturing markets currently account for approximately 45% of the autonomous vehicles (AGVs) and robots currently deployed across the world. Simio software provides enterprises with the opportunity to digitize logistic systems and complex autonomous processes to receive applicable insights from them. ![]() Integrating automated guided vehicles and robotic systems into digital twin models increase accuracy and enhance simulation results. ![]()
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