There's a lot of fear with automation going around. It'll be interesting to see how the next couple of decades to with it but the fear is over hyped, the looms didn't ruin textile industry neither did sheet music ruin live music.
Every time we've seen a major change in industry there's been massive leaps in safety and productivity. As we see regularly, artisan crafts remain popular and in demand. Home automation with IoT is a emergent field providing a market for many a start-up. There is still employment available, with any of these leaps in industry jobs have evolved out of what was new, there will obviously be a field for automation repair, work flow development and a perfect opening for kaizen process improvement.
Some benefactors of automation.
Agriculture:
High definition cameras in sheds save a farmer a lot of time in checking, similarly the user of drones checking livestock and searching for lost livestock, combined with RFID or similar the tracking could be made a lot quicker and easier.
Stock delivery:
Most large shops already use this, entering incoming stock in to there point of sale systems, tracking sold goods and triggering alerts when goods need to be replaced, but it could be expanding to when and which shelves need to be restocked. This can be extended to vending machines, using something small like the raspberry pi, stock can be monitored, fed back to a central server, where the person handling stock can download there orders and load their van with exactly what they need. This cuts back on excess fuel consumption and journeys to machines that don't need it.
Retail:
Very few 'bricks and mortar' retailers take advantage of reservations like Argos or Schuh. Whilst it's a natural extension of Argos' retail model, Schuh have blended convenience and consumers want to view, handle and try a product. This is a very simple integration into a shops POS and massively improves the consumers perception of a company whilst helping solidify there place competing in an internet environment.
The automation resistance seems like a reason to bring in universal basic income, UBI a rant for another post. I worry people will cheat themselves out of opportunity being co-opted in to opposing what should make there lives better.
Those who fight change will die out.
Showing posts with label Nerd Stuff. Show all posts
Showing posts with label Nerd Stuff. Show all posts
20170414
Rant on analysis 1 of many
There's nothing I enjoyed more in my old job than a fresh streaming pile of raw data, give me a months data and a quarter to compare it to, I'm like a pig in shit. Nothing teaches you more about statistics than wading into it up to the knees. From fucking around with SQL to force a database into giving you what you need, to performing black magic with vlookups holding a concatenate referencing a pivot table makes my dick tingle. It's not just the joy of a good script coming together but the overview showing you in clear numbers or charts for the discalcic.
Then you must do the hard work RCA (Root Cause Analysis), comparing the results to the reality, then the horror kicks in. When looking into the causes of contact, often by listening in to calls you slowly realise that data gets skewed by laziness, things like 'billing' isn't the top call driver it's the top of the list. Then you need to start a new table, list the user, the reason given and the actual cause. From that you get what I call "frequent fuckers",
essentially an exclude list of users that misuse logging and cause anomalous data. Go back to the original piece of data, create a new tab with a table of the "frequent fuckers" to to your raw data putting in a vlookup against the new tab, if they're on the table get it to give a mark, throw in a ifna to keep it clean, rebuild your pivot table but this time add a filter to exclude the "frequent fuckers" and then this look different.
The worst thing someone can do is over use averages, averages are often the bit in the middle that no one uses, providing solutions on averages gives these best solution for no one.
Try it yourself, http://www.pewresearch.org/data/ has lots of data sets, see can you get to the same results they do, more interesting is if you don't, then you get to figure out why not. Any questions leave a comment, who knows I might even get back to you.
Then you must do the hard work RCA (Root Cause Analysis), comparing the results to the reality, then the horror kicks in. When looking into the causes of contact, often by listening in to calls you slowly realise that data gets skewed by laziness, things like 'billing' isn't the top call driver it's the top of the list. Then you need to start a new table, list the user, the reason given and the actual cause. From that you get what I call "frequent fuckers",
essentially an exclude list of users that misuse logging and cause anomalous data. Go back to the original piece of data, create a new tab with a table of the "frequent fuckers" to to your raw data putting in a vlookup against the new tab, if they're on the table get it to give a mark, throw in a ifna to keep it clean, rebuild your pivot table but this time add a filter to exclude the "frequent fuckers" and then this look different.
The worst thing someone can do is over use averages, averages are often the bit in the middle that no one uses, providing solutions on averages gives these best solution for no one.
Try it yourself, http://www.pewresearch.org/data/ has lots of data sets, see can you get to the same results they do, more interesting is if you don't, then you get to figure out why not. Any questions leave a comment, who knows I might even get back to you.
Subscribe to:
Posts (Atom)