GetScale’s (YC S15) QC System Protects Factory Workers and Hardware Companies
techcrunch.com
techcrunch.com
> All information is permanently archived on GetScale’s servers, which saves workers from being held responsible for mix-ups that aren’t their fault.
This is presumably meant as a way to try and fix the issue referenced in the article's title and hook:
> He realized that many line workers have very few legal protections, and if something goes wrong while an order is being produced, they often end up absorbing their employer’s losses by taking wage cuts or deductions even if they aren’t at fault.
Is there any actual evidence that in legal regimes with inadequate worker protections, increasing transparency of the workers' labor efforts improves fairness or pay for the workers?
Instead we can provide insight to the OEM - verified information on the status of what they're paying for. Avoiding miscommunications that can push losses onto the factory (and subsequently negatively impacting the worker).
You're right in that if the factory wants to misbehave and the owner/GM is a tyrant, you aren't likely to directly impact worker treatment (we have in-house auditors in China that can check on the factory for you if you have that specific concern). Fortunately, the overwhelming number of factory owners/GM's are fundamentally decent people caught up in difficult circumstances.
Typical contracts are structured 30% deposit and 70% when it ships. Margins to the factory aren't 70% so they wind up quite leveraged. Most of the scenarios I saw came out of the factory not understanding or executing on the customer requirements correctly. If they build units and the customer rejects them, they have to cover those material costs so they can repair or otherwise try again. This tremendous financial pressure causes otherwise good GM's to cut corners every possible way they can to try to keep the lights on.
GetScale impacts worker treatment in several ways:
(1) it eliminates waste and errors that result from misunderstandings in customer acceptance procedures. After deploying us in a factory you just don't encounter thousands of bad units because the factory is confirming against the exact acceptance procedure as you go (instead of build a lot, ship, customer receives and rejects).
(2) it is possible to audit your suppliers and if you do that, GetScale provides the ground truth of which workers were working at which stations when and for how long (down to the millisecond!). Since you have to login-logout to do any work our records are very accurate.
(3) it enables you to vote with your dollars. Without invasive monitoring (like GetScale) it is very difficult to determine which factories are "good" because reputation is easy to coopt and places like Alibaba can be bought. GetScale data collection is automatic and the data is held off-shore (in the US). We already see factories in China using GetScale as a selling point to foreign clients since it provides quantitative proof of their compliance and performance. A factory that is willing to be open about its performance and let you document workers on the line is the factory proud of its worker treatment, not the one trying to hide it.
As to studies, I have seen some in both the psychology of management literature and the manufacturing management literature, but as to specific citations I will need some time to dig them out of my research notes.
In any case, I hope I've answered your concerns. If I missed anything, please follow-up!
Of the three ways that GetScale "impacts worker treatment", the first doesn't mention workers at all, the second only improves record quality (not worker treatment), and the third depends on foreign buyers to demand good treatment of workers (which there is no evidence they'll do) and does nothing to address the single abuse mentioned in the article, higher water bills for "unproductive" workers.
This story seems set on the idea that increasing productivity (benefiting the owners directly) will increase conditions for the workers. I think it's great to increase productivity, but the link to worker conditions still seems like magical thinking, and as a result I find the linked article deceptive.
- a set of communications tools for enabling the assembly workers to have good assembly instructions. This is an iterative process as the design engineers watch the assembly.
- video is now cheap enough that you can video all the assembly and trace QA failures to a particular step.
There's a generally humane tone to the writeup, one of giving people the right tools to do a good job. If it can improve DfM that's great. The number of devices I've had to take apart with ridiculous numbers of screws or blind clips is too high. On the other hand, who among us would accept continuous surveillance every second of being clocked on?
My thought is improving yields by reducing defect rates caused by those hopelessly imperfect humans (like me) might be a great selling point for some types of manufacturing.
The practical application is this is sort of an 80/20 proportion though. Currently we see fairly significant improvements in some situations just by the system being there. Factories will pre-test units before passing them over to our system in order to demonstrate better quality, when prior to installation they weren't testing a majority of units at all.
It'd still be really cool to try, haha.
The problem with pre-test is it can both decrease defect rate _and_ decrease throughput rate. Depending on how much each decrease is, you can actually decrease yield/time rather than increase it. This exact problem exists on PCB manufacturing. It's certainly possible to pre-test every component as it comes off the reel and before it's used on the PCB. The component pre-test does decrease defect rates of the finished boards, but it also decreases throughput rate, so it can negatively impact yield/time. The decision then becomes an investment/accounting problem. Often, post-mfg circuit/component test automation and reworking failed boards makes more sense financially.
As far as I know, no one does hard or soft real-time verification of the human side of manufacturing and lab experimentation, but _my_ knowledge of the current state of the art is admittedly outdated. Using CV and ML to spot human manufacturing errors (or inefficiencies) seems feasible if you throw enough compute power at it, but whether or not it's practical and financially viable is another (more important) question altogether.
Though CV+ML may not be reasonable in the real world, I agree with you; it sure does sound like fun to try.
You are describing a type of machine called an AOI (automated optical inspection). It is a common piece of equipment in electronics assembly lines. But AOI equipment is very expensive (~$250k+) and only works on a specific set of products. For example, it doesn't work well when the product contains soft or flexible surfaces, or has a highly non-planar shape.
GetScale terminals can be interfaced with AOI machines in the factory to collect and store the findings along with our human-led inspections and catalog everything by serial number and we do have ambitions to add AOI-like capabilities to our hardware down the line.
Sounds like you have a project in mind. What are you working on? ;-)
I'm not very familiar with the more advance microscopy based AOI systems used on silicon and chip level manufacturing (e.g. wire bonding checks). I'm also not familiar with advanced AOI used in robotic assembly. On the bright side, I am familiar with the comparatively "simple" form of AOI used on PCB assembly lines.
With PCB's most of the AOI identification and spatial orientation issues are solved through Fiducial Marks [1] and pattern recognition. Some PCB AOI systems even read/record chip/board lettering with OCR, as well as handling Bar and QR codes. Most of the rules and routines controlling the AOI are consistently derived directly from the PCB design files, so there is little need for custom setup programming on each new PCB run.
That last point is critical; The rules and routines are automatically and consistently _derived_ from the authoritative data sources. Manually writing every tiny step in the AOI automation routines for each PCB design would never be time/cost effective, so it has never been done.
Given sufficient example 2D images, 3D scan models, or CAD files, doing the object recognition and spatial orientation with OpenCV is reasonably straight forward, even without fiducials. The extremely tricky part will be automatically deriving rules and routines for the GetScale AOI-like capabilities from authoritative data sources. If the human assembly task is to attach the top cover and secure it with four screws from the other side, you should _never_ need to manually write the AOI routine for checking the existence of a screw in each of the four corners when viewing the bottom of the product.
I'm sure you're really busy, so I think I'll stop here. ;)
If you want to know about the projects I'm working on, email would be a lot better for me. My address is on my HN profile page. Also, I spotted a few places where your website could use a little TLC, and it's better to point them out through email rather than publicly.
We think GetScale works great for anything that:
(1) Is valuable enough to warrant a serial number (unique ID)
(2) Has unit margin of at least a few dollars
(3) Is complicated to build or inspect, or has to be held to an extremely high aesthetic standard (e.g. luxury goods)
That covers almost all of your examples. One counter-example might be T-shirt manufacturing.