Factory Robots Need a Workforce That Can Keep Them Running
Factory automation projects can reduce dependence on repetitive manual work while creating a different staffing requirement: people who can operate, diagnose and restore the equipment. Ross Barnard, Chief Engineer at Chang Robotics, argues that manufacturers need to design those capabilities into their automation plans. His analysis places workforce development alongside equipment selection, rather than treating training as a final implementation step. Earlier comments from robotic-hand manufacturer Tesollo identify the corresponding equipment requirement: sustained performance and a practical way to recover when something fails.
Training Shapes Usable Capacity
In an explainer submitted to The Supply Chainer, Barnard described manufacturers struggling to recruit and retain workers while also facing gaps in the skills needed to support advanced equipment. His argument challenges the assumption that buying automation independently resolves a labor constraint. The equipment still needs an operating model suited to the people available to run it. Otherwise, a project can exchange difficulty staffing a manual task for difficulty maintaining an automated one.
“The answer isn't simply to deploy more robots or recruit more workers. It's to redesign manufacturing around a workforce whose capabilities are expanded by automation, AI and accessible technical training,” Barnard wrote. He described intelligent diagnostic tools and structured upskilling as ways to broaden what factory operators can do. Those proposals concern how work could be redesigned; the submission did not provide a measured training program or a quantified productivity result. Their practical test would be whether operators can resolve more problems reliably within the limits of their training.
A Successful Pick Does Not Qualify a Shift
Youngjin Kim, President and Chief Executive Officer of Tesollo, previously told The Supply Chainer that qualification should begin with the customer’s actual products and tasks. Tesollo develops multi-finger grippers and robotic hands, which Kim described as particularly relevant to varied objects and changing handling requirements. After establishing technical feasibility, he said buyers should measure cycle time and throughput to assess economic viability. Extended operation testing should then examine grasping success, repeatability and reliability across a full production shift. This sequence prevents an impressive demonstration from standing in for evidence of sustained operation.
Kim also identified recovery as a separate requirement from inherent reliability. “However, even reliable products can experience unexpected collisions or operational failures. For this reason, the ability to repair and restore the system quickly is equally important.” He said Tesollo uses a modular joint architecture that allows customers to keep replacement modules and change individual joints. That describes a repair approach, but the response did not provide a guaranteed restoration time or evidence that every customer could perform the work without additional support.
Scale the Support Model with the Equipment
The two perspectives meet when manufacturers move beyond a pilot cell. Additional installations increase the amount of productive equipment that depends on maintenance coverage, available parts and clear escalation arrangements. Kim recommended establishing spare-parts inventory and maintenance procedures before scaling across multiple cells. Barnard’s workforce argument adds another condition: the relevant knowledge must be accessible to the people expected to act. A spare component has limited operational value if nobody on the available team can diagnose the fault or complete the approved replacement.
These responses suggest that automation acceptance should assess the operating team alongside the machine. Full-shift testing can show whether the equipment performs consistently, while recovery exercises can establish how faults are identified, escalated and resolved. The two exercises answer different questions about usable production capacity. Neither source presented evidence that automation inevitably reduces staffing needs or improves retention. The stronger investment case is one that demonstrates reliable output, supportable maintenance and work that the available workforce can actually perform.
