0:02 hello there now that we know that data
0:04 is important in Lean Six Sigma not only
0:07 in measure face but across all the faces
0:09 of the might methodology it is also
0:12 important for us to determine how can we
0:13 collect the data that we will process
0:16 moving forward presented here is an
0:19 example of a simplified general steps on
0:22 how to collect and stablish the baseline
0:24 measurement first select the measure
0:27 measure refers to the metric or the data
0:29 that we need to capture or collect in
0:32 order for us to have a good baseline of
0:34 the existing performance of the process
0:37 second is to develop a data collection
0:39 plan in data collection plan will serve
0:42 as a guide in order for us to know what
0:43 are the data that are need to be
0:45 collected and who will collect it in
0:48 ving forward and third is the actual
0:51 execution of the data collection plan or
0:53 the data gathering procedure when we
0:56 talk about data and data collection it's
0:58 very important for us to know what is a
1:01 primary data and what is irrelevant data
1:04 primary data is the main metric or main
1:06 KPI it will be the basis of
1:10 understanding the problem however having
1:13 a primary data is not enough that is why
1:15 we need to collect what we call relevant
1:18 data let's take the two examples given below
1:18 below
1:21 first is yield just to put context yield
1:24 is the ratio of good parts well over
1:27 total parts produce perish of the good
1:29 service all over the total number of
1:31 service rendered let's say we're talking
1:33 about yield in manufacturing setup not
1:36 only taking you will give the benefit of
1:39 what we want to know it's also important
1:41 to get relevant data about yield for
1:44 example the model of the product that we
1:46 are taking yield considerations the line
1:49 where it has been produced the day time
1:52 per shift of production or manufacturing
1:55 who are the operators involved during
1:58 its production the age of the products
2:01 and the tenure of the operators or
2:03 technicians could also help us in
2:06 understanding the root causes of the
2:09 problem in yield this is how we want to
2:10 understand the relationship between
2:13 primary and relevant data
2:16 let's take another example let's say
2:18 we're having a problem regarding a
2:20 tendency it's important for us to
2:24 determine relevant data such as gender
2:27 of employees their marital status their
2:30 tenure in our organization the process
2:32 of assignment the shift that they are
2:35 working for and the group that we belong
2:38 to all of this data relevant data as we
2:40 may see will be helpful in understanding
2:42 our problem with attendance now
2:45 understanding primary data and relevant
2:48 data presented in this slide is a data
2:50 collection plan this is a simplified
2:52 version of a full-blown data collection
2:56 plan first column talks about data data
2:59 means the basis of the problem for
3:01 example we have a problem with the
3:04 orderly time in a restaurant so let's
3:07 use an example of order lead time and
3:10 how do we measure order lead time unit
3:12 of measurement tells us that it is in
3:15 minutes it's important for us to know
3:18 and establish what called an operational
3:21 definition this will give a picture to
3:23 all the team members of how we
3:26 understood the definition of the data
3:29 for the material for this case order
3:32 lead time as defined order lead time is
3:34 the amount of time it takes from the
3:37 moment the customer started to order to
3:39 the moment the order has been served to
3:41 the customer this will be the basis of
3:44 our measurement for the order the time
3:47 sampling plan talks about the frequency
3:50 of getting the data for this case we
3:53 plan to get the data for orderly time
3:56 every order collection method will give
3:58 us an idea of how can we collect the
4:01 data for this case we plan to have a
4:05 time study for other cases it could be a
4:08 little review it could be cooling out of
4:11 data from the system and last column
4:13 talks about who will collect the data in
4:15 this example the restaurant systems
4:18 officer will be the one in charge in
4:20 collecting the data so this is how you
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