I honestly do not know much about the recycling numbers on the bottom or side of plastic items. I’m learning more and more, since I started working on a landfill reduction project and helping out at a full-service recycling center.
If you’re really interested, check out this article >>
All you need to know for this discussion is that the lower the number, the higher quality plastic material it is, and therefore, the higher the value it is for a recycling center (they get paid more for it).
The goal for this recycling center is to maximize profits (of course), so they have two objectives:
1) Separate the plastics into different bins, so they can get a higher price for the items from their customer, rather than leaving them all in one bin (current state).
2) Don’t contaminate the bins beyond the 5% contamination level, otherwise the value in the bin is lost.
Let’s focus this article on #1, sorting the plastics to increase profits. The goal was to get the plastics into 3 major bins: #1 plastics, #2 plastics and #3-7 plastics. Anything left over would go into a trash bin.
Just like every process, there are two ways in which you can make a mistake: Give the customer something wrong that they didn’t ask for, or disregard giving the customer something that they would have wanted. In other words, you sent them the wrong thing, or your process didn’t send them something they wanted.
Let’s review the traditional good/bad process decisions, to make sure we’re on the same page. Usually at the end of a process step, we label the quality of the item as either “good” or “bad.” There are four different scenarios that can occur:
#1: The item is acceptable for use (“good”), and the process determines it to be acceptable (“good”). This is a correct decision.
#2: The item is acceptable for use (“good”), but the process determines it to be unacceptable (“bad”), which is an incorrect decision (Type I).
#3: The item is unacceptable for use (“bad”), but the process determines it to be acceptable (“good”), which is an incorrect decision (Type II).
#4: The item is unacceptable for use (“bad”), and the process determines it to be unacceptable (“bad”), which is the correct decision.
So in our simple good/bad scenario, we can end up with two correct decisions, and two incorrect decisions. These incorrect decisions have a name, Type I and Type II error.
Type I error is calling a good item “bad” and reworking or scraping or fixing the item for no reason, which increases costs to the producer. For that reason, we call it producer risk. This is the same as result #2 above.
Type II error is calling a bad item “good” and sending to the customer, which causes problems for the consumer/customer, thus consumer risk. This is the same as result #3 above.
In a recycling facility, there are actually different levels of “goodness,” in addition to a good vs bad determination. This situation took me a little longer to figure out. For this process, we’ll consider each bin (or plastic #) as a good/bad decision on whether the right items got into the bin.
As the equipment processes the different plastic types, it attempts to sort the plastics into the three bins, and anything leftover will go into the trash/other bin. In order to assess producer and consumer risk, we will evaluate each bin as either good or bad. So which poor decision goes with which type of risk?
It’s easier to figure out if we go back to the idea of producer and consumer risk. If we have a #2 plastic PETE go through the process, there are two ways it can fail to get in the correct bin. The plastic could end up in the #1 bin (contamination), or fall into the #3-7 bin or trash/other bin (loss of money).
Contamination implies consumer risk, since the customer will get #2 contamination in their #1 bin. This is type II error.
Loss of money implies producer risk, since the recycling center will not get paid as much for the #2 plastic when it goes into the lower quality bins. This is type I error.
Your priority should always be to reduce Type II errors first (to make your customers happy), then focus on Type I errors. In a future article, I will discuss how we reduced the producer and consumer risk in this process.