Per the Association for Advancing Automation (A3), North American robot sales declined 30% from record 2022 sales levels. Companies bought 31,200 robots in 2023 versus 44,200 in 2022 and 39,700 in 2021. Sales in 2023 were almost equally divided between automotive (15,700) and non-automotive (15,400) companies. Sales to automotive-related companies dropped 34% from 2022 while sales to non-automotive companies fell 25%.
A3 attributed the decline to a few factors: (1) slow U.S. economy (2) higher interest rates (3) over-purchasing of robots in 2022 due to supply chain concerns (4) a slowdown in the manufacturing of electric vehicles and (5) fewer new distribution centers.
In 2023, the strongest demand from non-automotive companies was in the metals industry, followed by semiconductor/electronics/photonics, food and consumer goods, life sciences, pharmaceutical & biomedical, and plastics & rubber. While each of these industries declined in 2023 versus 2022, the fourth quarter of 2023 had 20% higher sales than the third quarter, including growth in multiple industries – automotive (OEM and components), metals, semiconductor/electronics/photonics, plastics & rubbers, and the “all other industries” category (includes areas newer to robotics such as construction, hospitality and agriculture).
MOSIMTEC has significant experience with robots. For a robotic technology division within a multinational retail corporation that supports warehouse logistics, they wanted a simulation model to test a concept for warehouse logistics utilizing a transportation robot that collects and distributes product totes to designated locations. The main goal was to understand if the conceptual design would adequately meet throughput goals. For a robotics manufacturer in North America serving the optics industry, MOSIMTEC custom-developed a detailed simulation model to represent a new standalone contact lens manufacturing device to test throughput and refine control variables.
MOSIMTEC is often asked to evaluate automation initiatives and develop control algorithms, test sequencing rules, and stress-test the ‘whole system’ under various demand profiles to predict performance and identify risks/bottlenecks. Using simulation modeling to compare an autonomous mobile robot system versus adding labor while factoring in capacity, productivity, downtime, charging time, labor cost, etc. is critical to know if robots are the right fit. MOSIMTEC data scientists and industrial engineers can help you future-proof your business.
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