Time
9 hours 53 minutes
Difficulty
Intermediate
CEU/CPE
10

Video Transcription

00:00
Welcome back, guys. I'm Katherine MacGyver, and today we're going to go over designing data collection system.
00:05
So in our last couple of modules, we talked about our project objectives and benefits and how we calculate those in this particular module or that this one on the next one, we're gonna talk about that. A collection systems. This is going to be something that is applicable to your benefits, but not necessarily on direct 1 to 1 relationship.
00:24
If there's anything in these modules that seems unclear, we did talk about data collection systems in the measure phase of your yellow belt.
00:32
So go back and reference those lessons as well.
00:36
So when we talk about our data collection systems we're talking about is what is the information that we're going to capture to show that we did our project well, or that we met our objectives.
00:49
So the first thing that you're going to want is what are you trying to accomplish? What are you going to want from this project? What it
00:56
if where this project was wildly successful, what would that mean is that we're going to decrease our cycle times. So how fast it takes to get something done. Are we going to increase our throughput? So we're getting more products out in the same amount of time. Those tend to be similar measurements. Are we going to decrease or defects?
01:15
That question of what are we trying to accomplish
01:19
should show up in two places. First, the forefront of your mind as you're going through your projects, Remember, With my projects, I tend to like to pull up the charter ahead of time and remind everybody why we're here. Of course, that second place that this should show up is as an objective in your project charter a new objective or benefit. So remember,
01:38
capacity relates back to time.
01:41
So when we say that maybe our benefit is we're going to increase our employees capacity by 10%. What our objectives are going to be is then we're going to decrease, um cycle, time, non value. Add those sorts of things. So keep those in mind. They tend to pair together quick refresher.
02:00
We talked about this and benefits. We have talked about this throughout the course, um input, process, output, these air, the different areas that we're going to want to look for things to measure
02:09
inputs are supplier metrics, so either what comes in externally from the organization or what comes in in the previous step. If we're talking about a value stream and we stack their site box on top of each other,
02:22
our process this is really where the bulk of our work tends to be, or outputs, which is how happy our customers, and remember from yellow bow happy customers, equal happy. Cos so we want our customers to be really, really happy with us or have their needs and requirements met.
02:42
So when we start when we're talking about, what are we going to accomplish? We want to talk about Okay, we're going to accomplish X now. Let's develop some measurements around it. You know, of course, we can talk about smart goals, which are specific, measurable, actionable, reasonable on time bound.
02:59
But that's not what we're talking about here. We're going to talk about what good measurements are.
03:04
Good measurements are easy to understand straight up. If you have to explain it in more than three sentences like your business case, your measurements are too complicated, and you're not going to necessarily get buy in across your organization So you want to make it easy, easy to understand they're linked to the customer.
03:23
So remember the foundation for Lean and six Sigma is your customer input in your customer feedback.
03:30
The foundation for your culture of kaizen or ongoing improvement is your customer requirements. So you want these measurements toe actually mean something to your customers. So if it's a cycle time, it means that we're going to be able to get our pizza to our customer faster. If it's a, um,
03:45
throughput time and we're going, we're going to be able to get two pizzas to our customer in the same amount of time. If it's a quality thing, we're going to make sure that the toppings on our pizza are accurate 100% of the time. Because we've used mistake proofing. We want them. We want our measurements to provide quick feedback.
04:02
If we have to wait a long amount of time to get our measurements,
04:06
we're not going to be ableto act on them in a very effective manner. So if you had something done in January and we're talking about this now in June, what what's the likelihood that you're going to be able to do an intervention and remediated.
04:21
We also want them to be actionable. So I want to be able to do something with this information.
04:28
So we talked a little bit of remediation really quickly. If there is nothing that you can do about it,
04:33
then what you are talking about is a Vanity Metric and Vanity metrics or one of my big pet peeves. What they are is these are things that we measure to make ourselves look good or to feel pretty. Eso vanity metrics are things that are inevitable
04:47
that you don't necessarily take action on, so they're not something that you can change so like a. Really a great example of this is
04:56
vanity Metrics would be customer preference for, um,
05:03
customer preference for all wheel drive cars. So you make cars and, um, the customer. The industry landscapes of a little bit on the condo model tends to move towards all all wheel drive cars. So you're going to report on that. But there's nothing that you can do
05:23
that's going to change your customer's requirements for over the drive. I mean,
05:26
the marketing people in the audience were like, Whoa, no, we can we can do that with marketing, but for the large amount of things, these are things that are going to exist regardless. Um,
05:34
we let's see another way that we calculate vanity metrics would be something like We create x number of widgets. We're not going to change our widgets. For some reason, we're at capacity. We've done, you know, we're at our entitlement, But we're really proud of ourselves because we did X number of widgets.
05:50
Um, vanity metrics, Huge pet peeve of mine. If you ever want to ask me more, talk to me on Lincoln.
05:57
Also, the last thing about a good metric is it's not used as a form of punishment.
06:01
So we have to stop for a second and really listen to that. It is not used as a form of punishment is not punitive. It's We're not going to come in and say you only performed at 55% and we wanted you to perform it 60% because that it undermines the culture of employee collaboration
06:19
and it undermines ongoing improvement.
06:21
Met measurements are purely a measurement. It is a starting point. Remember, we talked about how we need to have a baseline we're not going to use. This is our wacky stick for those of you who played whack a mole and come in and hit you on the head because of your measurement, this is a little bit different from an organization. But trust me, it helps you with your adoption of your improvement.
06:43
So there are two types of metrics in your organization there are leading, which are things where we can see what's happening in the future. They're going to be predictive and were generally measured is indices. So a probability of change or what is the likelihood that this is going to happen?
07:01
Leading measures. You see this in staffing models, we tend to be more heavily. We tend to have more calls on Mondays,
07:10
so we're going to predict that we're going to have more calls on Mondays and staff accordingly. The other Petar, the other type of measurement is the lagging indicators thes air easy. We like these because these are things that have already happened. If you're going to tell me
07:25
how many phone calls we're going to get on Monday Ah, leading Inter Keter would say, Well, historically, we've gotten 100 so we can expect that we're going to get 100 plus or minus would have work on
07:33
confidence. Interval is ah, leading indicator says. Well, we can't tell until Tuesday and then we got 97. So when we talk about leading indicators, they're going to be measured as indices or probabilities. Lagging measures are at absolute values. We know what's happened because they have already happened.
07:51
They tend to be reported out for things like financials.
07:55
Where is Lee? Leading indicators tend to be things in projections. So when we're talking about data collection systems, you only need one process measure.
08:05
Whatever you decided is, pick it, stay with it. Your collection systems could be his complex or simple is the benefits or objectives need them to be?
08:15
My answer for that is going to be simple, is better. There is elegance and simplicity, but not to undercut yourself or misrepresent your metrics.
08:26
Um, and then the last one is this. Think about the people who are doing the data collection so I can have a very elaborate data collection system. But when I put it out on a very, very busy group of people who already have too much to dio, um, they may not necessarily give us the most accurate data back,
08:46
so try to be mindful of the people who will actually be doing the collection. If it's a database dump,
08:50
it's a little bit different than if we need to do tally sheets to keep track of stuff.
08:54
So your homework assignment for this module is to identify both a leading and a lagging a measure in your organization. I assure you, they're both there if you need. Ask some questions, but get a sense of how did your organisation predict versus what are they calculate, as or what are they report as absolute values?
09:13
Um, in today's module, we went over some considerations for our data collection system. And in our next module, we're going to be going over how to actually create a data collection system.

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