Below is a 4 minute overview on how SensrTrx helps manufacturing companies reduce downtime.

Cut Costs, Increase Performance, & Decrease Downtime With SensrTrx​

Want more detail? See Bryan Sapot, CEO of SensrTrx, deliver a comprehensive overview of how manufacturing analytics can impact the bottom line of manufacturers. For more 4-minute demos on how to reduce downtime, increase throughput, improve OEE:

Increase Capacity & Throughput
Replace Excel
Get Control of Your Schedule
Understand and Reduce Scrap


Want to see it in action? Get a personalized demonstration of SensrTrx tailored for your business.

Full Transcript

Hi my name is Bryan Sapot and i’m the
founder and CEO of SensrTrx.

Today we’re gonna talk about how
you can use SensrTrx to diagnose and fix

downtime issues so create more
availability for your machines increase

your capacity and throughput because
your machines are going to run more

often most companies talk about this (and) we see it

in marketing all the time but very few
people actually show how they do it so…

This is an example a very simple example
of a sandwich line. The sandwich line has

multiple steps to the process where
breads being fed in, we’re putting

various meats and cheeses on the breads,
and then maybe then maybe it’s frozen

towards the end and then it’s packaged
and we can see here we’re looking over a

24-hour period on this sandwich line
where we have quite a bit of red on this

timeline so each one of these red bars
represents some downtime sort of the

blue bars the difference between the
blue and the red is planned versus

unplanned maintenance so the blue is
planned downtime so for 28 minutes this

morning it was not scheduled then we
started up we ran a little bit we ran

two hundred sixty two sandwiches through
the line and then we had some more

downtime we had to have maintenance
come over and do some production support so

you can see every hour we have some
green and some red it kind of looks like

we have more red than more green here so let’s look at the details associated

with that so we can scroll down a little
bit and notice here we’ve had a total of

two hundred and fifty eight minutes of
downtime on this particular shift and we

can see it by hour as well so this is
showing us down time as the red line in

minutes by hour so you can see here we
had 42 minutes at the seven o’clock hour

and we were able to make 2440 sandwiches
so you can see that this increased

downtime is having some pretty big
impacts on our production so what is

this downtime actually that’s showing up
in this graph and this time line up

above well we can break it down by our
in terms of is it maintenance or is it

production support so meaning is
maintenance coming over and having to do

something or is it production related
downtime such as change over something

getting stuck a problem that
production team can fix so we can see that

we don’t have a ton of maintenance
support here most of it is coming

through the production team and then we
can dive down into and look at a Pareto

chart of where our biggest problems are
and we can see that the butter

applicator is causing almost all of our
downtime 58 minutes of that and then we

move to production support and then a
tunnel issue so now if we’re trying to

get to our Pareto number which is what’s
representing 80 percent of our downtime

we can take a look at that and see that
it is the first five so it’s also a

tunnel issue we also have some not
scheduled time that’s pretty big and a

cutting head adjustment so I can take a
look at another view of those downtime

details in the table so maybe I want to
look at total downtime in terms of

minutes so here we see the same thing
the Pareto is showing us that butter

applicator is our biggest problem next
production support and then a tunnel

issue but we can also see how what’s the
average duration of this downtime so the

butter applicators down for 15 almost 15
minutes at a time 14 and a half minutes

and that’s happened four times
production support doesn’t last as long

that’s happened four times as well same
with the tunnel issue we also noticed

that not scheduled from an average
perspective is one of the highest down

times that we have so we can make a
choice about what we attack first but it

seems like we need to deal with this
butter applicator and then we better see

what’s going on with production support
and then if you ever have to if you ever

have to moderate a dispute between the
maintenance team and the production team

as to who’s causing more downtime we can
categorize it by by category showing you

that 71 percent of the downtime is
associated with production and then 28

percent is associated with maintenance
activities so it gives you a high-level

view of how you can quickly diagnose
downtime issues on a more complex

production line but this works the same
whether you have multiple machines in a

line or you’re dealing with just a
single machine such as a machine tool

but you can find out more at our website
so take a look there