Now It’s Humans Assisting Robots | Sheelah Kolhatkar | The New Yorker

Steelcase ology

“[…]As a zone leader, Stinson is responsible for about fifteen employees on a section of the production line that makes parts for Steelcase’s Ology series—height-adjustable tables built for the standing-desk craze. Until last year, the plant workers had to consult a long list of steps, taking pains to remove the correct parts out of a cart filled with variously sized bolts and screws and pins and to insert each one in the correct hole and in the correct order. Now computerized workstations, called ‘vision tables,’ dictate, step by step, how workers are to assemble a piece of furniture. The process is virtually mistake-proof: the system won’t let the workers proceed if a step isn’t completed correctly. We stood behind a young woman wearing a polo shirt and Lycra shorts, with a long blond ponytail. When a step was completed, a light turned on above the next required part, accompanied by a beep-beep-whoosh sound. A scanner overhead tracked everything as it was happening, beaming the data it collected to unseen engineers with iPads.[…] ”

Sourced through The New Yorker

Michel Baudin‘s comments: This is excerpted from a long article entitled Welcoming Our New Robot Overlords, from the 10/23/2017 issue of The New Yorker that caught my attention because it’s not about robots and it seems to be in the same spirit as Omron’s Digital Yatai back in 2002: using technology to eliminate hesitation and to mistake-proof operations that are too long or have too many variants to allow operators to go “on automatic” while performing them.

When repeating the same 60 seconds of work 400 times in a shift, operators quickly develop the ability to execute rapidly and accurately with their minds elsewhere. If on the other hand, the takt time is 20 minutes or the work is customized for every unit, the work requires the operators’ undivided, conscious attention and their productivity is increased by systems like the vision tables described in the article, that prompt them for every step and validate its completion.

The author contends that the evolution towards this kind of system reduces the skill levels of the operators, and I don’t think it has to be true. It can instead be a shift in focus from the execution of the process to the quality of its output, with a corresponding shift for operators from process skills to product knowledge.

This is what happens in chaku-chaku lines, where the operators work is to pick parts automatically ejected from one machine, apply go/no-go gauges, and load them onto the next machine. The operator does not need the ability to transform the parts — the machines take care of this — but needs to be able to detect when they don’t do it right.

Assuming that this kind of work requires a lower level of skills is like saying the same about digital photography. Just because a camera has automatically adjustments doesn’t mean you have to be less of a photographer to take good shots. Instead, it means that, instead of manually setting distance, aperture, and speed, you can focus on the composition of the picture.

Whether, as a manager, you adopt this perspective is a choice. You can employ people at lower wages with lower skills to follow the prompts of your system, but it has consequences on process troubleshooting and on product quality.

#robots, #automation, #autonomation, #jidoka, #digitalyatai

 

 

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