4.1 Basing Decisions on Information

دوره: Effective Problem-Solving and Decision-Making / فصل: Implementing Decisions / درس 1

4.1 Basing Decisions on Information

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And gosh, we did some data gathering there we didn't really need to do but we thought we did, and I guess it's nice to have that but we really need to move forward with some other things. We keep going back and forth between our expert knowledge and the data that we have to finally give us good information that will help us move forward in the project. This third down here now, now this is what happens as they put the, the the total amount of the transaction, how much time were people spending at the window.

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Welcome back one more time. We’re discussing problem solving and decision making. This is brought to you by the University of California Irvine and I’m Rob Stone. This is module four, lecture four. We’re going to be talking about using data to make decisions. Data fits in also with expert knowledge. Sometimes we need data to make good decisions. And we need our expert knowledge to point us in the right direction to even figure out where to start gathering the data. In a group, when we’re making decisions, there are a lot of people sitting there that have some really good experience, a lot of expertise, and we need to draw on that. We also need to use data to support what our thinking is about things. Between the two things expert knowledge and data, going back and forth. The expert knowledge can point us in the right way, to find some data, find the data that we need, point us in the right directions. Where do we go gather data? What kind of data do we need? We bring that back to the group and the group says, that’s pretty good, we found some good things, but, you know, we sorta missed this little piece. And gosh, we did some data gathering there we didn’t really need to do but we thought we did, and I guess it’s nice to have that but we really need to move forward with some other things. Let’s go get some other information. We go gather some more data, bring it back to the group. Group analyzes that, points us to, in a direction to gather some more data. Group analyzes that. We keep going back and forth between our expert knowledge and the data that we have to finally give us good information that will help us move forward in the project. Move forward in our, with our team. Move forward in our processes we’re working with. Move forward in the efforts that we’re trying to undertake with our group of people that we have. Using data. We’re going to look at two tools in this module. One will be a run chart, and one will be a control chart. Run charts. Run charts and control charts both are ways to show us how things are working in our, in our group setting. It’s in our organization. If it’s in our volunteer group. If it’s at home in the family. No matter what our group setting is, this is going to show us how things actually operate over time. So, there’s something we’re doing and it works a certain way every day. This way, this way, this way. So this shows us data actually over time. And this is a data, these are both data tools where we gather actual hard data and analyze it using these tools and it’s time, it’s data over a time period. A lot of the other tools we’ve been looking at is data in static views. What’s a run chart? Well, it’s a quality control chart. It’s used to determine whether the long-run average of something is working out. If it’s a process, is working the way it’s supposed to, or if it’s starting to change a little bit. Let’s take a look at an example here. So we have one old guy who goes out and does some running. So he runs along and some days he runs pretty well and some days doesn’t run quite so well and this certainly is not a run chart that you would see with someone younger or more experienced or in better shape. But for this individual this is how they run. And some days they run a little slower than other days, other days they run a little faster. So we take a look at the first data point plot that on here, the second data point, and then we draw a line between them. So now we have all the different data points along the way here that say, okay, on this day this person ran this fast. They ran at this pace. The next day they ran at this pace. So we get all of the different dates along the way here and this is the pace the person ran. So this shows us, yeah, they, they had a pretty good day there. One of those, day 5, looks pretty darn good. That’s a lot faster than the next day, day 6. Somehow that was a lot slower. Probably sore, legs tired, went out the next day right away, took off and said, oh my, not gonna be able to run quite so fast today. Got through that, ran faster the next couple of days, and then, you know, kind of a regular sort of speed along there for a little while. You want to look at how does this person actually run? What’s kinda their average sort of running time? Remember that mean thing we were gonna look at? Let’s do the average here. Let’s do the mean. So this person has a mean pace of 10 and a half minutes. So that’s pretty slow. I know for a lot of you you’d think is that person even running or are they walking? Well, this is an old guy running best he can and, you know, so that this is what they do, 10 and a half minute pace. But some days they run faster, some days they run slower. So now we look at the range. We have a range all the way from 10 and three-quarter minutes down to a little less than 9 and a half minute pace. So 9 and a half minute pace, that’s considerably more than the average pace that this person has. Well, I wonder what that’s all about. That’s a good day. Somehow they did the right thing, slept right, ate right. Did all the right things along the way. Let’s look at how this kind of a tool can help us in a real process in a real organization. This is an example from a real organization. It’s a credit union that had a problem. People would come into the credit union and they would wait in line too long. And there were a lot of complaints about this. People didn’t like waiting in line this long. So the credit union started to look at this and say, well, you know, we really need to take good care of our customers. So, let’s figure out what’s going on. So they looked at the waiting time in the teller line. And then they also looked at the total time. So now we have the bottom line is the time waiting in the teller line. The top line is the total time from the time from they came in the front door of the credit union, until the person actually left the credit union. So that includes their time at the window as well. So this group gathered a couple kinds of data. First, how long are people spending in line before they get to the teller window and what does it look like from the time they walk in the door until the time they leave? And they also gathered some data along the way. Once they get to the teller line, and actually get to the teller window, how long do they spend interacting with the teller? And if you look at these two lines, you know it’s about the same amount of time, it looks like, at the teller window itself, most of the time. The big problem is waiting in line. So these are all the different individuals. What they did was they tracked individuals from the time they came in the door. Took a little stopwatch and said boom. This person’s coming in the door. Ding, ding, ding, ding, ding. This is how long they’ve been in line here. Boom. Now they’ve gone to the teller window. Boom. They’re done at the teller window. Now they go out. Boom. Now they’re out the door. So that’s the total amount of time we have in here and all these individual people. So, we have a, this is a sample, they did a lot more people than this, but it’s just a little sample of what they looked at over one little period of time. They put some times on here and said, you know, it’s okay to look at, gee, what’s going on with the people. But you know, I wonder what times of day this is happening, because we’re actually looking at with the run chart, is data over time. So they put a time scale across the bottom here. Credit union opened up at 7:30, and then it closes late in the afternoon. So they look at this and say, gosh what’s going on here? So they start to examine this data and say, I wonder if this tells us anything at all. What they realized was, we are a credit union for a very specific group of people. These people all are right across the street from us. These people come in when they’re not at work. So they come in in the morning early, they come in at lunch time, and they come in at the end of the day. So they took a look at this and they realized, you know, most of our problem is between 11:30 in the morning and 1:00 in the afternoon. That’s when we have people in here standing in line the longest amount of time. The longest, total amount of time here in the credit union. What’s going on during that time? You know what? Well those people that we are serving here with our credit union, when they have a lunch break, and they’re coming over here, guess what? That’s also when our tellers take their lunch break. So they realized, we need to fix something here. And it was a simple matter of looking at this chart, and as soon as they put times on there, they looked at it and said, oh, that’s during lunch hour. That’s when we don’t have very many tellers at the windows. That’s when most of our customers come in here. Oh my, we have to do something different about that. So one simple chart helped them solve a huge problem. So what happened? This third down here now, now this is what happens as they put the, the the total amount of the transaction, how much time were people spending at the window. They’re finding out it’s not time at the window that’s the problem. It’s standing in line that’s the problem. When they fixed things they had the old time to wait in line which has that big spike around noon. And then after they fixed things all of a sudden, look at this, we don’t have much of a problem at noon at all. And how did they fix that? Very simple. All the tellers had to either take their lunch break and be back by 11:30, and no tellers could take a lunch break there until one o’clock. All the tellers were at the window between 11:30 and 1:00. The tellers work their lunch hours around the people that come in. So now the people came in, short wait time, everybody was happy. Very simple solution. And it all came from just looking at a simple chart that says, what’s goin’ on here with our waiting time in the teller line? It’s not the time at the window. Our tellers are well trained, they’re doing a great job. Everything’s fine. And once people get to the window, the transactions go really well. Well, what’s goin’ on with waiting in line, here? Oh, we do have a problem there. All shows up right away on the run chart. These are the new times. Solution in place, everybody’s happy. So now with a run chart, one simple tool that just shows us what going on before and after we put our solutions in place. This one chart pointed them in the right direction immediately to solving this problem.

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