Comment by mcorrand
9 years ago
Possibly not, right? If you don't sort but instead use the machine to do the inventory of your bins, then you could have your software tell you which bins you are going to need to fulfill your orders for the day. The big "if" in that scenario is that your machine is not categorizing anymore but identifying. I don't have the experience to assess how much harder it is.
Then you'd go and pick the bins, run them through, and the machine assembles the sets from those bins. That's similar to the way Amazon does it. Now they have the shelves on robotic trolleys that bring them directly to the packers, but that's just a required efficiency at their scale.
I guess the problem with this scheme is that you move the problem from classifying to identifying... twice. So the precision requirement goes up. I don't know how big your dataset would need to be to require minimum human intervention.
That, and you'd waste a ton of time because in such a re-run you'd be looking for a very limited number of parts from a much larger bin. So in the end you'd run around with bins rather than picking the parts from pre-sorted and binned by color and type parts. Either way it is a boatload of work.
The closest I've come to this is where you identify which sets are present in a batch by doing a trial on a sample (that's a pretty easy statistical job), and then sort directly into sets starting from the largest sets down. That way you reduce the parts count very rapidly. So, I did this for a bit and now have 18 60 liter crates of almost complete sets which all need to be manually completed and checked. Again, not profitable.
If you just want to do this to keep busy it is easy, if you want to make more than what you could make by flipping burgers it is surprisingly hard.