- Your team is out on the floor. Their hands have grease on them. Using tablets sounds great until you're trying to use it with a glove on it, or your hands are dirty, and it's hard to get grease off tablets. But they need the info out on the floor. Also, it can be noisy on the floor.
- The team tends to be very visual. They don't like tapping on computers a lot. Literacy ranges from pretty good to kinda OK. Sometimes they refuse to get (or wear) reading glasses for whatever personal reason.
- They're working on proprietary hardware, but technicians with the right knowledge are not nearby to come in and look at it. You really need to be able to see the issues visually. Sometimes even hear them. AR might be interesting here. (I spend $10k to fly a tech out for a few days to look at a machine. The bigger issue is that I lose $10k a day from one machine being down, and a tech might not be available to fly out for a week.)
- Predictive maintenance. The fancy sensors and whatnot mostly don't work. Tech people try it in a clean, quiet office and it works, and they can raise money on it from clueless VCs, so money keeps getting set on fire with smart AI machine learning magic motor sensor companies.
- Preventative maintenance. How to schedule, how to verify it was done, how to check whether it revealed an issue that needs a follow-up. Getting people to do it, and verify it was done, can be a challenge, but there are huge returns to preventative maintenance (for example: checking gearbox oil levels, verifying lubrication line function, checking valve temperatures.)
- Diagnosing machine problems. Using prior problem documentation helps team members see most likely issues. But many of these people don't really want to sort through a database of prior similar issues because they "know" what the problem is. How do you provide this information to them in a way that feels more approachable to them?
I could go on forever. Manufacturing is an interesting environment because downtime is usually hundreds to tens of thousands of dollars of hard cost per hour, depending on the operation, and they will spend quite a bit of money to stop it from going down, but culturally there's a vast gulf between the white collar SF tech bros and what actually happens in manufacturing plants, so innovation tends to be more limited.
HOAs, which we serve, are run by busy volunteers, yet expected to perform almost insane financial gymnastics, planning 30 years of major component replacement, e.g. common area roofs, piping, asphalt resurfacing. This involves (a) estimating each component's lifetime (total and remaining), (b) getting a cost estimate, and (c) coming up with a plan to spread paying for it out over however many years before it's needed, breaking that up between the units in the HOA, and collecting the funds, month after month.
People blame cultural issues ("people won't pay for maintenance") or "laziness" but the truth is, it's just too damn hard to do predictive/preventative without a very accurate inventory of what you have. You need to get all of this into a cloud environment, and then somehow expose it so that either internal staff or external vendors (more common) can see exactly what you have, bid on fixing it, and track status and work in a fine-grained way.
Our ultimate goal is doing the entire inventory automatically using computer vision (partner and I used to work in self-driving) and having enough data around that we can price and estimate everything accurately.
Nobody wants to pay for this as a standalone product so we just decided to build a payment collection product (for monthly dues), start with that, and build it up. It's going pretty well and we'd love to get more people on it. Email's in my profile in case you want to chat
How will you protect from incorrect estimates, insurance?
After typing up all that, I realise that most of this is applicable to every industry.
I think better training and investigation into best practices of organizing knowledge would benefit all industries.
Then, the tech bros arrived and after a few abortive attempts to catalog things for themselves (webrings, portals, yahoo) the industry collectively shrugged and decided to ignore the problem, assuming that a search engine would always be able to pluck your favorite needle out of the haystack.
Except that, today, it cant. Intranets are essentially corporate graveyards of content. The public web is a webring of 7 or 8 megasites that vacuum up all searches and make it all but impossible to break out of their domain. That article you read in 2005 about XYZ? Forget it, you're never finding that with Google.
Search isn't enough because search doesn't help you if the philosophy behind how the information is organized doesn't make sense -- if what you need is spread around between 100 slack messages, emails, and unconnected unmaintained wiki pages. You need someone (or ideally a team) whose job it is to one one hand organize that information themselves, and on the other hand create a framework so it's easy for the non-librarians to put things in the right place.
But right now the majority of tech companies are like libraries without librarians, the patrons are just wandering around sticking the books on random shelves and wondering why nobody can ever find anything.
But I think we need more structure to how we represent knowledge get to something like a semantic web.
If the knowledge was easier to parse, it would be easier to break out of the megasites.
The academic literature itself (even review papers) was way too terse and (I believe, semi-intentionally) obfuscatory.
This is a really interesting application I hadn't considered before. Having lots of blue-collar family, helping new members of the trades upskill fast would take a considerable load off that workforce.
Why is that important? Must every job be automated?
If it's something "artisanal", that not necessarily true, but even then, intentional "mistakes" can be added [1]. Having humans for the sake of having humans isn't a charity that non-luxury businesses can support (à la Snow Crash). It'll have to be something that governments subsidize or enforce tyrannically.
1. https://www.forbes.com/sites/nadiaarumugam/2012/04/24/new-yo...
If we need excavation, our worst-case scenario is that that we need excavation by someone who also knows
I think the long term best thing we can be doing is documenting the how and why of building, designing, fixing, working with etc... everything
Better and more granular documentation in a form that can be interpreted by humans and also machine readable would be a desirable outcome for any system. Especially true for systems in which the builders and operators are being replaced or EOLed
Better?
There's multiple parts to this field of work: networking to find leads, doing discovery to understand a potential client's problem, formulating a technical solution, creating/negotiating contracts, and implementing the solution. My experience in this area was at a company that was large enough so that these pieces were split into different roles within our organization, and I was mostly on the tech/solution implementation side.
- Language models / NLP applications for processing large amount of technical text data (SOP, documentation, technical data, machine text logs, voice to text, video data processing for speeding up corrective action, training, onboarding and highlighting areas of improvement / bottlenecks), digitising documents and extracting failure reasons / equipment names / spare parts / processes involved and making associations between them for pareto analysis, better search or process improvement recommendations
- Recommending the next steps to fix something / remote intervention / do something etc. Lowering the expertise threshold required for technicians, electricians, mechanics or reliability engineers to be effective.
- Enabling operators to become data scientists by enabling to train AI models via their day to day activities / analysis. Building better UX in general and providing simple tools that even a toddler could use.
- Autonomous factory use-cases / supply chain automation.
Would love to discuss with people who find these things exciting