Forecast: demand for picking robots to jump by 2030

Market demand for inventory picking robots in warehouses is poised to leap from less than 2,000 annual shipments in 2022 to just above 50,000 per year by the end of the decade, according to a report from Interact Analysis.

That trend will be driven by rising labor costs and a dropping cost of robots, motivating companies to have installed just over 150,000 picking robots by 2030, the UK-based firm forecasted. More specifically, the uptake will be driven by rising wage costs in warehouses, labor shortages, and the rapid development of artificial intelligence (AI) and machine vision technology.

According to Interact Analysis, picking tasks in the warehouses are extremely repetitive, and companies across the world have struggled to recruit and retain warehouse operators for several years, contributing to a higher cost of labor. At the same time, the firm expects the price of robotic picking to come down significantly over the coming years, largely driven by pricing pressures for robotic arms and machine vision software. “By 2030, we expect the average price of picking robots will drop by 40%, while the cost of warehouse labor will increase by approximately 30% over the same period,” Irene Zhang, senior analyst with the firm, wrote in the report.

Those pressures will also force the types of roles carried out within a warehouse to change over time. Historically, most employees have been very mobile due to the nature of person-to-goods picking, but the rise of automation is leading to the growth of “static manipulation roles” within warehouses. The firm forecasts that there will be around 7.5 million global full-time equivalent (FTE) employees performing static manipulation tasks by 2030, which is more than double that of 2022.

The employees assigned to those jobs will also see their work assignments change, aided by improvements to AI machine vision technology that increase the range of items that can be manipulated by robots and boost the success rate of each pick. “We currently assume that for every 3-5 robots, there will be a full-time equivalent (FTE) employee supervising them. However, we expect this will increase to one FTE per 7-10 robots by 2030, improving the unit-economics,” Zhang wrote.