That said, it all depends on what you're trying to accomplish. No matter
what, if you're trying to model collection efficiencies, you need something
to pin your hopes on, don't you? If you can't measure it, how can you manage
it?
I would submit then that for planning purposes the best thing to do is to hop
aboard a recycling truck, carrying your trusty stop watch and clipboard, and
do your own study. You need a lot of data points (at least a week's worth) to
create a "statistically significant" sample...meaning you need to be focussed
and willing to commit an ample amount of time. Ideally, you should do a week
or two during each of the four seasons and grow your database over time.
What you'll have in the end is a fair though very generalized and approximate
idea of what's going on. A number of studies have been done along these
lines. In extrapolating the data, most do not take into account season (lots
of paper during winter holidays...and glass; oodles of beverage containers
during summer); geography (is there a deposit law, is it always hot?) etc.,
etc.
Most importantly, people rarely consider the issue of actual collection time
vs. travel and "down" time. For instance, to collect materials on dead end
streets, trucks may have to stop traffic at the head of the street in order
to back down the street, meaning that they take longer to get on to the
street than to actually do the collection (and if only three houses out of,
say, 20 have put out their recycling, 30 seconds per stop doesn't tell you a
thing). Or, another example, in a "get it and go" scenario crews have been
known to cover their territory lickety split and then make a beeline to the
nearest cafe (or bar) or to literally disappear for a time (personal errands
and issues). And in situations where OT is available, it is not unheard of
for crews to lollygag...for obvious reasons (though in our world of
increasing accounatability this kind of thing is becoming harder to get away
with). And in bad weather, collections often happen faster...again, for
obvious reasons.
One way to overcome these details is to simply count the number of collection
points (stops) and divide that number into the hours for the shift. This
gives you a more realistic understanding of time and resources. We've used
this method in efficiency tests for commercial collection programs in
comparing one person crews to two person crews. But no matter what, you still
need to ride that truck (or follow it). Don't ask the driver to do the
counting for you. You can't trust that data. He/she has to navigate and worry
about safety. There's no way they need to think about productivity data.
So, you have to be careful with your information and, hopefully, willing to
collect your own data. I know this doesn't give you a quick answer, but
hopefully it will lead to something that actually gives you something
meaningful.
David Biddle
Center for Solid Waste Research
Philadelphia, PA
215-247-2974
In a message dated 9/3/99 1:07:21 PM Eastern Daylight Time,
RESRECYCLE@aol.com writes:
<< Paul: There is no useful "average" time per stop in curbside recycling
collection because there are too many variables, including:
-- number of separations
-- types of household containers used and their size(s)
-- height of truck bins
-- use of mechanical dumping and compacting mechanisms
-- use of low-entry, right-side cabs
-- age of worker
-- amount of material at each stop (many programs are now promoting that
only
full bins should be set out. This slows down the collection time per stop
but greatly reduces the number of stops and thus improves collection
efficiency.)
So watch out of someone offers you an "average." >>