Simulation: Catapult Exercise - Round 1
So our exercise now is going to be I'll walk it through it and then we're going to organize a little team up here and like I said everybody's going to get three shots so our catapult here is ah already preset for you the pin heights anything like so we don't have to worry about and the pen sites pen heights up their set but we do have to pull this down so that it's tight put on this little knob all right, so we've got some tension here all right and it says pull the arm backto one seventy seven now you'll see there's a little protractor here on the catapult one seventy seven there's a little pointer one seventy seven is almost all the way back but but not quite so it's almost all the way back so we're gonna pull it back to one seventy seven and shoot the ball not make sure you keep safety in mind with this because sometimes you know the catapults positioned up there and people just get a little too close no don't shoot the ball on your own here all right? Some people might laugh but ye...
ah, it can hurt so we're going to have somebody measured the distance of each shot so the catapults gonna be on this little stand we're all gonna take a shot. We're gonna have the, uh a measuring device take measuring device and we're just gonna put this right out on the floor I'll shoot the ball over this way. It should. It should go to about here, so we're just going to set this down now. The other thing is. Try not to step on the tape measure or move it, because that's kind of obviously introduced variation into our process so well. One shooters shooting. What I'd like to have his two team members standing, you know, roughly right up here and identifying where the ball lands on the first bounce. So it's, where the ball lands on the 00:01:59.203 --> 00:02:01. first bounce, that's, the that's, the data we want. 00:02:01.8 --> 00:02:04. So we're going to capture that, and I'll write it 00:02:04.44 --> 00:02:07. down. So I'll capture the data up here 00:02:08.28 --> 00:02:09. for the distance. 00:02:13.02 --> 00:02:16. Okay, and each person's going to shoot the ball three 00:02:16.08 --> 00:02:16. times 00:02:18.32 --> 00:02:20. and between shots, 00:02:21.12 --> 00:02:25. we've got this policy and this procedure, so between 00:02:25.96 --> 00:02:27. shots, if I shoot the ball 00:02:28.66 --> 00:02:32. all right between shots I loaded, I shoot it, I have 00:02:32.53 --> 00:02:36. to remove the rubber band and re tighten it 00:02:37.52 --> 00:02:39. while I'm doing that somebody's retrieving the ball 00:02:39.77 --> 00:02:43. for me. Two people are identifying where landed one 00:02:43.84 --> 00:02:47. person may is right now recording it, so we're all 00:02:47.23 --> 00:02:49. going to do this and then when I'm done shooting all 00:02:49.81 --> 00:02:54. right, my three shots I will change with somebody 00:02:54.52 --> 00:02:57. and they will come over and shoot three shots so that 00:02:57.65 --> 00:03:00. everybody gets to shoot three shots were going to 00:03:00.14 --> 00:03:03. collect the data and see what the variation looks 00:03:03.14 --> 00:03:04. like. All right, 00:03:05.17 --> 00:03:08. now we have attack time here too, and our tak time 00:03:08.99 --> 00:03:11. is fifteen seconds. What does that mean? 00:03:13.05 --> 00:03:15. That means we need to be getting a shot off every 00:03:15.27 --> 00:03:18. fifteen seconds, just like in the lean sigmund game, 00:03:18.11 --> 00:03:21. we were getting a a sheet off every twenty seconds, 00:03:21.32 --> 00:03:24. so fifteen second tak time that's our drum beat, we'll 00:03:24.63 --> 00:03:26. record the distance is and then we're going to calculate 00:03:26.7 --> 00:03:29. the range range is the distance between the longest 00:03:30.17 --> 00:03:33. and the shortest. Now what happens 00:03:35.27 --> 00:03:36. if somebody gets up here 00:03:38.28 --> 00:03:39. and they load the ball 00:03:40.59 --> 00:03:42. and then they pull it back and pull ups 00:03:45.22 --> 00:03:46. that's a zero. 00:03:47.62 --> 00:03:50. No mulligans. Do you know what that does to our range? 00:03:52.72 --> 00:03:56. That would make our range enormous because now we 00:03:56.61 --> 00:03:59. have a zero to whatever the longest shot this so we 00:03:59.85 --> 00:04:04. have to be very careful not to drop the ball so it's 00:04:04.52 --> 00:04:07. like that line and uh the movie stripes with bill 00:04:07.66 --> 00:04:10. murray's asking where's the sarge's he's blown up 00:04:10.41 --> 00:04:14. sir don't blow yourself up with the with the ball 00:04:14.29 --> 00:04:16. here by the way we run this exhibit we've run this 00:04:16.69 --> 00:04:19. exercise in the military for number of years in the 00:04:19.59 --> 00:04:21. defense industry and whatnot they love it because 00:04:21.6 --> 00:04:25. it represents you know shooting stuff but where I 00:04:25.54 --> 00:04:27. want to blow ourselves up with the ball all right 00:04:27.89 --> 00:04:30. so that's going to be that's basically the exercise 00:04:31.29 --> 00:04:35. and then like I said I will I'll capture the data 00:04:35.49 --> 00:04:38. well we do that so I just need everybody to think 00:04:38.38 --> 00:04:41. about now ok let's go back over the procedure shoot 00:04:41.74 --> 00:04:44. three shots pull it backto one seventy seven let it 00:04:44.79 --> 00:04:47. go don't drop the ball remove the rubber band put 00:04:47.79 --> 00:04:51. it back on and we get situated so we're just going 00:04:51.49 --> 00:04:54. to play with play with this exercise to capture current 00:04:54.32 --> 00:04:57. state data any questions on that 00:04:58.49 --> 00:05:01. we all good okay so 00:05:02.89 --> 00:05:07. why don't we uh curt you want to shoot first and while 00:05:07.1 --> 00:05:11. you're shooting why don't we have kate you and jane 00:05:11.99 --> 00:05:15. be ah measuring where's the ball gonna land I'll give 00:05:15.64 --> 00:05:16. the ball to kurt 00:05:17.74 --> 00:05:22. all right? And, uh I will record susan how about if 00:05:22.25 --> 00:05:25. you retrieve the ball and round, while kurt shooting, 00:05:26.14 --> 00:05:27. incidentally, when you're done 00:05:29.29 --> 00:05:33. let's, have, ah, let's, have cat follow kurt, all 00:05:33.39 --> 00:05:36. right, and kimberly follow cat in the shooting. 00:05:37.29 --> 00:05:40. Kurt. When you're done, you go retrieve the ball, 00:05:41.07 --> 00:05:44. susan, you, khun, you can get in line, all right? 00:05:44.32 --> 00:05:46. And we'll just kind of we'll rotate this way, and 00:05:46.39 --> 00:05:48. then we'll we'll get a couple more people measuring 00:05:49.79 --> 00:05:53. so that kate so that you and jane khun get get shots. 00:05:53.29 --> 00:05:55. So we're going to get, uh, get six people, three to 00:05:55.75 --> 00:05:57. three shots each. We're going to get eighteen data 00:05:57.77 --> 00:06:02. points, all right, and, uh, and have a little hopefully 00:06:02.02 --> 00:06:04. have a little fun here. This fund fun to play with. All right, kurt, you could start shoot whenever you want. Remember, we're on a fifteen second tak time, so we wantto keep it going backto one seventy seven. Alright, did you guys see where that landed? They won fifteen, one fifteen, one, seventeen. I tell you what, I'll give you one sixteen. Here we go again. No, you know it. Four. That one was one eighteen anything. I want you guys to move around, honey. Ugo, okay, I don't get to reconnect. The rubber band took it off, reconnected. Left handed. One thirteen. Dwight, take the rubber banana within. It is not between you. In between shots. There you go. Ninety nine. Yeah. I want to know how you transferred all of these with southwest airlines. Were you carrying them with you on the plane? 00:08:06.32 --> 00:08:08. Different distance every time. 00:08:09.4 --> 00:08:10. Ninety three, ninety eight. Which one? 00:08:14.59 --> 00:08:17. So has anybody hit the same? No it's being different 00:08:17.39 --> 00:08:19. every time no one said the same one yet. Yeah, so 00:08:19.99 --> 00:08:23. far, we've got a range from ninety five to a one twenty. 00:08:28.64 --> 00:08:32. Who? And I, like everyone, was technique. I'm gonna 00:08:32.8 --> 00:08:36. call this the one knee method here. Kimberly. Crowd. 00:08:36.78 --> 00:08:37. Yes. 00:08:49.88 --> 00:08:50. Oh! 00:08:54.94 --> 00:08:55. Wilson. 00:09:01.51 --> 00:09:04. In management system seems to have a little variability 00:09:04.53 --> 00:09:04. in it. 00:09:07.43 --> 00:09:12. Oh, that best one yet. Well, kimberly, sir. Kicker, 00:09:13.05 --> 00:09:13. which is 00:09:16.97 --> 00:09:18. take that off. 00:09:20.96 --> 00:09:23. Variation has a funny way of creeping in the most 00:09:23.77 --> 00:09:26. interesting places, but 00:09:29.24 --> 00:09:32. I think you inspired susan here. Really see some similarities. 00:09:35.1 --> 00:09:36. Oh, that was good. 00:09:37.84 --> 00:09:38. Six 00:09:40.24 --> 00:09:41. no landed on the wood. 00:09:43.04 --> 00:09:45. You know, it's pissed someone on one o nine. 00:09:48.04 --> 00:09:48. What? 00:09:49.93 --> 00:09:49. Yeah. 00:09:52.44 --> 00:09:54. I just I want a. Was it right there? 00:09:57.24 --> 00:09:57. Yeah. 00:09:59.98 --> 00:10:01. Let's. Just be fun if I got 00:10:03.07 --> 00:10:04. this to limbo. I think that was the winner, one e one twenty to wear green, one twenty four. Okay, next stuck. Oh, what did you do differently that time? All right, so, yeah, who's, who hasn't shot you, have kate, your skates shooting without measure that I did, because you got, like, straight. You have tio around your house growing out, really, actually, I hit him, probably. Dad's gonna have to work with these things. Really? What does he do? Yeah. What for? Can I do the rubber band before the balls there? Me. One of six. Thank you. You want to? One twenty two. Wow, did you do anything differently? That on that lost? I didn't think so didn't seem to be eyes. Everybody shot. She did. James gotta shoot yet chain. All right, haven't I? Okay, you want to help? Just, uh, help kurt measure. We'll get this, too. A couple of dewpoints here, look thirteen, one. Forty. Wait, one of them, it land on wood. To make it. Yeah, right. One night. All right, good. So we've collected our data. I think everybody has shot three shots, correct, okay, thank you very much. So what we just did is we uh we've armed ourselves with some facts we've gone out we've collected some data we said all right let's listen to the voice of our process and let's observe what it's telling us so we might say oh yeah yeah we can shoot we can meet that deadline we could hit that price we can deliver on that date whatever the case might be but do we know with some degree of certainty and confidence or are we guessing so this is about knowing not guessing this is about shifting from I think two I know by the way I had a ceo of a hospital tell me years ago john we're just trying to get from I hope too I think I can't make this stuff up I was like really all right then we better think prevention when it comes to health right because uh hopes not a good strategy all right so we've collected our data now and well let me let me just capture the range real quick so one of the most common measures in statistics his range range is a measure of variations simple difference between longest and shorts nobody dropped the ball so that was a good thing we were very careful about that looks like our shortest we've got a ninety five years at the shortest I think looks like it and our longest so one twenty four so we've got a twenty nine inch range, right. Twenty. So we have a twenty nine and strange. We could calculate other statistics from this is well we could say well, what's our average sometimes we've heard of that is the x bar the y bar what's the average you know, I had up all these numbers and divide by the this case we had eighteen shots to get an average shot so on average you know we shoot the ball you know, whatever that might turn out to be a hundred ten inches but think about statistics his average is never happened because there'd be decimal points to probably so we're always landed on one side of the average of the other so one of the lessons we learned with six segments that customers feel variation they don't feel averages I always said this is like the pilot coming on after you've mr connecting flight and you're three hours late and they go on average we're only five minutes late you don't care you just missed your flight so averages you know we use them a lot to measure performance in business but we also have to measure range or variation now another measure variation would be things like standard deviation all right, so you're measuring range in different ways measuring variation in different ways but range is pretty simple I think it's probably about fifth grade so anyone you know with a little mathematical sense can do it. So now we go you know what I don't john is that good is that bad how do we, how do we know it is a twenty nine inch range? Is it good way, pretty good at our business. So, without context, without comparison, without benchmark data, without a clear understanding of voice of customer truth is, we don't know. What if the customer says, you know, I need you. I need the balto land within, uh, with within a forty inch range. We're not. We're not bad. What if the customer says I need the ball to land within a six inch range? Oh it was like well I got most of the car in the garage no that doesn't always work so the idea here is to compare voice of process that would be the car with voice of customer which would be the garage one we take in the sides off the mirrors off we're getting it in there without any risk all right that's what we're going to play with here so we have to check out I do have to define our process could we mapped this process sure we could step one what load the ball step to pull it back step three make sure you're that one seventy seven step for let it go you know we can we can map it out we'll do that we can calculate that x bar that average on the range what could calculate our standard deviation we can calculate our voice of process can calculate our voice of customer we could talk to the customers say what do you need all right I need you to hit this target I need you to hit this date I needed you to meet these specifications okay we've got the voice of customer that we calculate some states six sigma measures like cp and cpk which is essentially process capability we can we can actually calculated a couple different ways and populate them populate with that data are measures and measure our performance and things like that and then take that into our analysis to our cause and effect and this is essentially where we're going to be going um with with this exercise alright to learn more about variation so mapping the current state so this would be like no well let's uh let's pull out our define tool from domestic and map the current state and a simple illustration of that we'll just do right now let's uh incidentally, a lot of organizations khun spend weeks and weeks and weeks and perhaps even months mapping things my experience is that it really doesn't take much time at all teo to map a lot of processes we get the right people together we use post its and wallpaper to do it we don't sit and spend lots of time on video initially later on if we want to fancy it up and make it look good to distribute and publish that's that's fine, but for the purposes of mapping get people on their feet I know what you're doing know how to use the tool but say all right, well let's uh let's walk through this process together this is part of that alignment process right up front that so important. So all right um first thing we want to do is, uh, identify what happens right up front and, uh, I'm just going to call the first step here because we sort of did it for you set up so we, we set up the catapult, and we set up the tape measure way, set up the operation, okay, so set up and then set up, goes to now it's, time to shoot the ball. All right, so let's, uh, let's, shoot the ball or start would start preparing to shoot the ball. So what do we do? We're all set up, kurt, you were first. What what what was next? No already set for me, so I didn't need to do that. So is basically placed the ball in the cup, okay, so I just got load the ball. All right, so there's a step. Um, ok, good, then what do we do, then I pulled the lever back, okay, so now we, we pull back. Okay, then what check for a hundred and seventy seven degrees, all right? Yeah. Now, a lot of times on a on a process map, we get to one of these decision points. You can see it represented here by a little diamond, and so we'll throw a decision point on here. She was a different color for it, too, and we so we pull it backto a decision point. That decision point happens to it's. A question mark, usually, in this case, is at one hundred seventy seven degrees. All right, and if the answer is yes out, what do we do that a girl? Okay, let's, go. All right, so we release it. We let it go. And if it's no. We got to go back and adjust our pull back, right until we get it. Till we get this to be yes, and then we let it go. Sooner or later, we'll let it go. All right, what do we do after we let it go? You just come back this way. We could map it out any way we want to. Measure. All right, now, we measure it, okay? What else do we do? Retweeted. Okay, incidentally, while we were measuring, I was also documenting. So I'll just capture that. And then we had somebody retrieving the ball. Teo after that for a reset. So I'm gonna go. We retrieved the ball all right and return it. Okay and meanwhile while that's happening is part of the setup the operator in this case is taking the rubber band off and putting it back on again there's an assumption there that maybe that has something to do with the elasticity of the rubber band so we release it and we put it back it's going to give us a more consistent distance now that's an assumption okay that's so it's been designed into the process but we don't know whether or not that's a fact or not but it's something took something to consider that's where that take it off and put it back on because it seems like that's a crazy activity it's redundant thing does that matter? Well, the truth is it didn't seem to help much but that doesn't mean in isolation there are other things you know when we look at it outside of the isolated incidents there's a whole lot of other stuff going on so one of the reasons we process map things and incident, how long did it take us to do that like five minutes at the most the process mapping again? This is a simple little process, but we can process map just about anything in a relatively short period of time if we have the experts helping us walk me through this process and it's a brilliant way to get people across functionally to look at things from a more cross functional systems perspective because a lot of us just don't know what's going on outside our own little world, so process mapping allows us to see something beyond our own world and the interaction of it. So process, map, whatever business you're in, all right, how do you go about selling whatever it is you sell and then delivering it? Take it right, take it right on through a value stream, all right, and, uh, color coded if you want, but keep it relatively simple. Now, the idea here is that there's an opportunity for variation to creep into this system. Where everywhere, everywhere. Could we have variation creeping into our set up. Hint. What's this all about. All right, um, we have we have opportunity for variation right here. What about loading the ball? Is it possible we have a variation, you know, creeping into the way we load the ball, and I'll give you a hint? Maybe, maybe that has something to do it. I want to hurt myself, uh, that could have something to do with it. So the way we load the ball, while we certainly don't want to drop it, okay, so if we put that ball in there, good and tight, you know, that's going to affect the distance. We have more variation creeping in. What about our pull back, this whole equation going on over there? Any variation going on there? These things are very shady things going on the iie miles. Some of you. I heard a couple of times from that what? The seventeen wasn't being followed. And I heard a couple times from our commentary. Hey, good shot. Well, what's, a good shot in this game. You hear that from me? This is your leader. Wait. Everyone. Pizza party. Okay, but it's funny because what happens is we get somebody that's, some thinking distance, maybe no, maybe like the longest drive in golf. If I can really get it out there. Boy, I'm gonna I'm going to hit a home run. So you get people that will do something like this will get it back to that one seventy seven and then it's, you know, there goes a nice one. Good shot. So that's that, huh? I think I saw susan do that. One is just you're knocking it out of the park. It was funny. It was there's. Actually, when you go back the data, susan, you're like what may be the fourth shooter. Yeah, you've got the long drive but you were real consistent here that I was just I'm gonna let this one go so we think you know what we've got variation going on here we might have variation going on with our release method you got the thumb you got the two finger you got the one finger they've got the different release methods if you noticed really gamble and you go see where the work is done and you watch we got variation creeping in here now was there any variation in our measurement system you know let's just go in because I was standing here watching too and I was listening and documenting this we had all kinds of variations creeping in our measurement system now I think I I documented what you told me but there could have been variation in uh in the recording and reporting for the interpretation of the reporting you know, not everybody interprets a report the same way so there's variation that khun creep in there all right, we could have variation in terms of ah take it the time it takes to retrieve the ball, get it back it could have affected our attack time. I wasn't capturing the the tack time in detail, but we could add variation around that as well. So they put the point of of the mapping from this particular perspectives to say, well, we've got, uh we got a lot of opportunity for error here right, we've got a lot of opportunity for we're poor quality, all right, so way. Gather all of that from our process mapping activity, and then, you know, from the data we can start to also define and measure things like our range, like our average. You know, this is again basics statistic, so our longest from our shortest. This illustration. This has to 00:28:07.946 --> 00:28:10. do with the time it takes to do something from seven 00:28:10.84 --> 00:28:13. to three hours. Incidentally, if our ranges, it takes 00:28:13.44 --> 00:28:15. from seven to three hours, and somebody says, how 00:28:15.41 --> 00:28:17. long is it going to take? What do we tell him? 00:28:19.47 --> 00:28:21. This is like calling up a company to come in service 00:28:21.95 --> 00:28:24. something at your house and they go well okay you 00:28:24.59 --> 00:28:25. need to be home 00:28:26.6 --> 00:28:29. and we'll be there somewhere between eight a m and 00:28:29.01 --> 00:28:29. eight pm 00:28:31.2 --> 00:28:33. you know on a tuesday when you're supposed to be at 00:28:33.27 --> 00:28:35. work how do you know anybody to do with that right 00:28:36.2 --> 00:28:39. now try that with lexus they uh they'll ask you what 00:28:39.77 --> 00:28:42. time and I've got friends that have done this uh I 00:28:42.39 --> 00:28:45. haven't tried it on lexis yet put good friends that 00:28:45.18 --> 00:28:48. say I want you to be there at seven fifty nine a m 00:28:48.7 --> 00:28:52. just to play with him seven fifty nine or eight twelve 00:28:53.17 --> 00:28:55. some random time you know we're used to the paradigm 00:28:55.91 --> 00:28:57. of eight o'clock or eight thirty years 00:28:58.7 --> 00:29:00. they'll knock at the door eight seven fifty nine or 00:29:00.95 --> 00:29:01. twelve 00:29:02.9 --> 00:29:05. it's hilarious yeah they're they're really that precise 00:29:05.78 --> 00:29:09. about it okay so we could calculate our statistics 00:29:09.14 --> 00:29:12. here we can calculate our standard deviation incidentally 00:29:13.2 --> 00:29:17. the days of manually hand hand calculating this stuff 00:29:17.25 --> 00:29:20. which which was quite scary toe a lot of people now 00:29:20.15 --> 00:29:23. it's a simple is loaded in a spreadsheet and hit a 00:29:23.69 --> 00:29:26. few commands and you get your you get your data pretty 00:29:26.18 --> 00:29:30. quick all right so we can calculate our standard deviation 00:29:30.87 --> 00:29:33. which is essentially the square root of something 00:29:33.83 --> 00:29:37. we call variants all right, incidentally, it's also 00:29:37.53 --> 00:29:40. uh on a graph on a visual aid if this is a district 00:29:40.86 --> 00:29:43. normal distribution curve right here. One standard 00:29:43.9 --> 00:29:47. deviation visually represents the distance from the 00:29:47.49 --> 00:29:50. mean toe where the curve changes directions where 00:29:50.67 --> 00:29:54. it goes from this direction to this direction that's 00:29:54.98 --> 00:29:57. called point of inflection and that's. Where the that's 00:29:57.77 --> 00:30:00. what? That the measure of standard deviations from 00:30:00.1 --> 00:30:03. the mean to that point. So that'll come in handy in 00:30:03.37 --> 00:30:04. a few minutes. 00:30:05.7 --> 00:30:08. All right. And this is process variance again. Not 00:30:08.13 --> 00:30:11. to get too scary. We don't have to manually calculate these things anymore. We just loaded into a software. It punches it right out force. But it tells us variants standard deviation and range are all measures of variability. So what we really need to so whatever measure we use, we need to know now, how much do we vary? All right, now, this is an interesting little chart because what this tells us is that our true voice of process all right is going tio fall within this distribution. All right, so we have ah, when we we lay this out, we have our average plus or minus one standard deviation plus or minus two standard deviations plus or minus three standard deviations. And, statistically, we can predict with confidence that sixty eight percent are going to fall within the average plus or minus one standard deviation it's just that's gonna happen ninety. Five's gonna fall within plus or minus two standard deviations, and over ninety nine percent will fall within plus or minus three standard deviations. We know that. So we essentially no, with with a great deal of confidence that, in our process, all right. Ninety nine percent of our shots are going to fall within this within this range, roughly good as long as we have enough data points. Soto. Actually, we have eighteen data points here. We should have a least twenty, preferably thirty two have a really good, reliable prediction, so we have quite that many. But the idea is, if we have enough data and it's it's just it is what it is we can project takes reliably, that we're going to fall within this range now, that's, his voice of process. So, john, we're pretty confident we're willing to bet good money. We can fall within this twenty nine inch range, okay, but I need it in six that's, a whole different ball game. This is voice of process. This has nothing to do yet with voice of customers. All right, now we start talking about voice a customer. This is the garage we got to fit the car in. This is the goal post these air the goalposts. So are slower spec limit and are up respect limit let's. Just call that a twelve inch range. So we got a twelve inch wide garage and a twenty nine inch wide car you're comfortable with that process. Probably not right. Just leave the car in the driveway. So centering is not even an issue in this case, are our process is just way outside our our boundaries. We're just not going to compete, so we've got to figure out a way to get that variation out. So what we're now doing with process capability, things like cp and cpk is we're comparing our voice of process with voice of of customers. So if this is our voice of process and these are our goal posts, okay, so we've got our speck with divided by our process with well if our suspect with is twelve inches and our process with his twenty nine inches we have a problem right? I have a big problem all right and c p doesn't really consider centering it doesn't it's just it's looking at potential all right so the potential is we've got a twelve inch range if we were at six signal by the way we'd have a ratio of two to one we'd have ah a process of a customer with of twelve inches and a process with of six think of a basketball in a basketball rim it's a two to one ratio that's actually six sigma potential ratio so we've got a a rim and we've got a basketball doesn't mean we're going to make every shot because of variability but we certainly can it's going to go through now if we go to the fairgrounds and they put that little tempting room right up in front of your face and give you a basketball in go you can win this giant teddy bear by by shooting the ball in that room you think it's easy pay attention to the signal level because it's not a two to one ratio right it's a smaller room and perhaps he perhaps even a bigger ball but they're stacking the odds against you so that's essentially what's going on with six sigma so I'll go back to the lean sigma game that we played earlier and round three when we went from that square diamond to the round diamond what did we do in terms of sigma put six signal potential we made the goalpost bigger right so we actually made it easier to get the red circle in there so we designed in six signal in fact it's very difficult to get to six sigma it's almost impossible really to get to six signal level of performance without designing it and you can't make your way there you've got you've got to think about it from an engineering perspective and design it in so either you design it in through clever engineering and design question just related teo you know uh diane longer and is back in here saying she has a start up photography business is a solo preneurs can you just make it a little bit more of an example or or give us some concrete ways to actually take this sort of big concept of measuring and repeatability sure and I don't mean it's not just uh it photography and I am it'd anybody who's listening online or anybody anybody here whatever business you're and step number one to start to brainstorm where do we see there's variation in our process to our customers experience variation in any way to a variation in our vendors variation in our family different films films do we use how many different photography instruments materials machines to be his cameras all right where a czar variation in the business and if the answer is we don't have any you're not you're not getting it so you gotta pay attention to where you have variation in your business in terms of product material performs go back to the issue cow a diagram that fish bone that we talked about were goingto look at that in a few minutes and think about ok from a machine from the material some of methods from a measurement systems where is the variation so just like we process map before there's variation in the process and think about it from a process standpoint so in my business what is the process that I used to get value to my customers map that process out and then search for the variation it's there it's there in a certain degree of variation is a good thing you certainly want just a little bit of variation going on with your heart is that true otherwise your flat line so I was kind of a joke but the idea is you know a little bit of variation is helpful okay but we want to be careful of where it really hurts so that's just it and when we come back later to some some q and a and that I'd like to hear from people specific examples of we've got variation here we've got variation here we've got variation here and then we can start to root cause it and get after it so this this this leads us to how predictable and reliable are we as a vendor, as a supplier, as a as a business owner in our market. Okay, another measure is six. Cpk. Cpk is a capability index just again, another measure, and you can read more about this stuff you don't have to get to, too into the weeds on it. These air simply measures to say, how do we know what our signal level is? And you can find out by calculating some of these things or simply 00:38:10.055 --> 00:38:12. entering the data into a software, like I said before 00:38:12.34 --> 00:38:15. and letting it do it for you that's the simplest way 00:38:15.23 --> 00:38:18. there's, some so great software's available to do 00:38:18.69 --> 00:38:18. that. 00:38:20.79 --> 00:38:22. The voice of customer, voice of process what are we 00:38:22.82 --> 00:38:24. doing? We're comparing the two. 00:38:25.11 --> 00:38:25. All right, 00:38:26.65 --> 00:38:29. this is the car in the garage metaphor. What what's 00:38:29.45 --> 00:38:32. the relationship. All right. And what we know so far 00:38:32.66 --> 00:38:34. from our exercise in round one is that we're not going 00:38:34.93 --> 00:38:38. to get twenty nine inch car into a twelve inch garage 00:38:38.85 --> 00:38:42. just that's not gonna happen. All right, so 00:38:44.49 --> 00:38:47. this would be a three signal process where essentially 00:38:47.42 --> 00:38:51. is a one to one ratio so where did we have three sigmund 00:38:51.52 --> 00:38:54. the in the in the lane sigma game 00:38:56.61 --> 00:39:00. think yellow circle department that yellow circle 00:39:00.5 --> 00:39:04. was almost the same with if you will or diameter as 00:39:04.71 --> 00:39:07. the ring wasn't it jane you were putting them in there 00:39:07.99 --> 00:39:10. it's a it was a tight fit it's about a three sigma 00:39:10.79 --> 00:39:15. ratio you did remarkably well at a three sigma design 00:39:16.29 --> 00:39:19. okay to get those in there without any errors because 00:39:19.53 --> 00:39:22. we didn't see any years there but if we did it a million 00:39:22.77 --> 00:39:26. times we'd see close to sixty seven thousand years 00:39:27.55 --> 00:39:30. okay in that kind of a design the three sigma design 00:39:30.75 --> 00:39:32. so by figuring out a way and we didn't do this in 00:39:32.63 --> 00:39:34. that round we could have gone out and try to get you 00:39:34.99 --> 00:39:38. know get smaller yellow dots or get the ring's bigger 00:39:38.46 --> 00:39:40. and we weren't we weren't allowed to do that in that 00:39:40.76 --> 00:39:43. round but we did come up with a creative alternative 00:39:43.44 --> 00:39:46. in the diamond department to essentially do a design 00:39:46.08 --> 00:39:51. for six sigma bye bye thinking creative it creatively 00:39:51.22 --> 00:39:53. about that diamond shape all right 00:39:55.09 --> 00:39:56. that's what six sigma would look more like 00:39:58.05 --> 00:40:01. it's all we've essentially done is going back here 00:40:01.65 --> 00:40:04. is we've grabbed this distribution and we've just 00:40:04.95 --> 00:40:05. pulled it up 00:40:06.65 --> 00:40:07. so that now we have a much 00:40:09.25 --> 00:40:12. a much better chance of scoring that's essentially six sigma you know in a nutshell is we've we've we've gone after the the variation and we've taken it out now that's what we're going to do with kaizen in the next segment is we're going to say all right let's let's take that catapult let's go backto our process map and let's start the brainstorm where the variation is and then where it's coming from using cause and effect analysis and using to make and so I challenge the folks at home and online take your take your business take your process take take whatever it is you're doing and start looking for that variation or dr variation and billing and how long it takes to answer the phone and right first time quality in responding to customer enquiries it's it's it's it's amazing it's all over they're surrounded by you know I do a lot of times when I call it an eight hundred numbers customer service line and I need help on something and they can't help me I hang up and I dialed in same number again you know I do that because I'll get a different answer that's variation sometimes it works for you sometimes it works against you but so often you hang up you pick up the phone you dial it back you get a different person see here's my problem this is what I need and they'll help you where is the one the one before won't that's variation so sometimes we can actually use it to our advantage if we know those little tricks of the trade because not every customer service rep is following the same script or answering the questions the same way. Okay, it's ah, it's all over the place, so just some measures and again, if if you're into this particular ah, you know, module or segment, you want to learn more about six sigma, these air, just classic measures and, you know, you can google this stuff, you can being it, you can go after it and, uh, and learn a lot more about it. But the way I use this isn't a very user friendly way. We don't need to get too into the weeds. Let's, just let's understand what it is and let's focus more on the inputs that lead to these to these outputs. All right, so cps to a cpk, a one and a half and a defects per million what that stands for sometimes you'll see dpm oh defect per million opportunities hey, d, p amor de pmo of three point for that street point, four defects per million opportunities would be sixty six sigma level of performance. So we go from, define out and measure. All right. So we've defined our current state. You know, we've gone ahead and mapped it out, appear and and, uh that's not what I'm after the shoot curtis all right we've mapped it out we've done the process flow we could pull out the aipo diagram we could start looking at other tools like the history graham the run chart the control chart to get a good idea just what is our process currently capable of someone walking through a few of those now here's an I p o diagram it's an illustration of how an I p o diagram can be blown out of a lack of manufacturing example all right so if in this case we've got raw materials we've got manufacturing packaging distribution customer and we say well let's we want to zero in on manufacturing and run on I p o on that what would be some and it happens to be tablet manufacturing what would be some of the inputs that are critical here in the outputs that are critical to measure here so starting with the outputs we want to measure the tablet wait could we have variation with the weight most certainly thickness most certainly hardness potency dissolution rate these air critical measures and this is a real example so all right what what the critical inputs over here while the amount of ingredient a the amount of the ingredient b the pressure that we compress it at things like that time temperature humidity those air all this is this is where we have an opportunity for variation to enter the equation and this is where we're going to experience it so variations always propagating from this side to this side and so the secret being we've got to come back here and we got to control it so if you ever see a really good recipe it's very precise you know this amount of this and this amount of this you put it in the oven for this amount of time okay if you follow this recipe very specifically you will get predictable outputs variations that are being shared ready flame ire a different name but the other day um they're variations are client outcomes and client expectations result of lead generation and other types of marketing so would that be on spot? Sure so the result is we've got that variation in the with the client the customer and it sounds like the second half of that was we've already gone upstream and figured out at least one of the reasons why on terms a lead generations there's lead generations perhaps another marketing okay so what we're doing now is we're capturing the variation in our process and were captured was showing it over here so this could be the distance the ball goes it could be the amount of time it takes to generate a lead it could make it be in the amount of time it takes to fill up on order. Okay it's it's everywhere we captured over here and then we use a tool like the aipo to gain knowledge about it's variation over here and if it's undesirable then let's take it over here and figure out what's causing it may be the oven's turned up too high maybe we've left it in the oven too long maybe we didn't put enough sugar in the in the recipe so speak metaphorically but there's going to be something going on over here that's causing that variation and lee generations and inclined experience this is so this is this is how we'll use it I'll come back and try to continue to build and build on that with the examples that we have variations that are outside of your control oh yeah yeah I'm going to cover that we call those noise variables so there's controllable variables and there's noise we're abel's so like in the airline industry look we can we can control a lot of things but we can't control the weather so if we're going to fly and hot and cold and rain and snow we've got to make a process robust is the typical term to that we've got to make it insensitive to those noise variables or we're at risk and that actually can relate back even to photography in baking in like a weather delay if you're doing a commercial shoot like that's right this is where failure mon effect analysis comes in where we say what could go wrong look good rain so what's our countermeasure what's there what's our pre programmed response we're not gonna wait until it's raining to figure out what to do we're gonna have a pre programmed response a back up plan all right that's what smart companies do they have backup plans not like they're caught by surprise when it rains because it's sooner or later it's going it's going to rain you know what I'm saying okay so now we could actually dig down even deeper let's just pretend in this example again this is just a illustration that we've got problems going on with our dissolution rate the tablets aren't dissolving the way they should and customers are complaining again you can use any example you want here so we could say well let's drill down deeper into dissolution rate this might be the not just the distance that's what we're really focusing on here what's going on with that well dissolution is really relies on these x variables you know that you want to die the amount tell him ana lak toasty amount of wax amount of magnesium and potassium this is a real example by the way so we need to again investigate more to figure out just exactly what that cause and effect relationship is if we're going to fix our dissolution right this is again it's a knowledge gaining tool so we start with the variation wherever it is that's on an output and then we go from there everybody should not be thinking about where do I see the variation, and then we can display the variation, slay the data in different ways. So one way would be a history. Graham. Okay, so we've got no. We've got a number of shots landing between two and four, a number of shots between four and six number shots between six and eight, so to speak, things like that, and you can actually see now, if we threw a blanket over this, we'd have that normal distribution that curve that we want to, then, huh, used to statistically, learn more about our process and there's information. Now, in your material is well on how to actually create a history. Graham. The simplest way to do it today is put the data into a spreadsheet and click on history. Yeah I have to walk you through all the mathematics behind it but it's here for your leisure all right we run charts I love run charts because you could just look at it go really that's what our process looks like you know you could just see the variation in a different kind of way so if this is the amount of time it takes to fill in order so we put time up here and it takes us on some days it takes us four hours to fill an order and other days we can fill it instantaneously and on other days we take eight hours or something like that to fill an order okay we can actually if you just think about it you could throw a great big distribution curve over this because we could calculate an average we could calculate a standard deviation you turn that ninety degrees you've got your your normal distribution this just visually shows us and then every time this is like isaac example of golf you know you finished the golf hole what do you do it right down your score so we finished our changeover let's write down how long it took we finished doing in a processing our film we finished. How many? How many deals did we sell this week or this month or today? You can use this on anything. Write down your score and then you could start to really learn about your performance from your data. A lot of times I go into companies. They say, well, show me the day that we don't have it. We don't keep sort. We don't keep that score. We don't have that day 00:50:16.28 --> 00:50:18. case well, if it's important, that we should probably 00:50:18.04 --> 00:50:21. start. So it starts, starts measuring and keeping 00:50:21.75 --> 00:50:23. track of what's important. Let's, get that done, 00:50:24.78 --> 00:50:27. and then we can, how we supposed to gain information 00:50:27.98 --> 00:50:29. and intelligence and knowledge without data? 00:50:32.58 --> 00:50:34. So data's data collection is a very important step 00:50:34.91 --> 00:50:35. in all of this 00:50:36.58 --> 00:50:38. from the run charts we can build control charts and 00:50:38.55 --> 00:50:40. again the easiest way to do this is load software 00:50:40.67 --> 00:50:45. up with the uh the data points and so I want a control 00:50:45.58 --> 00:50:47. chart is going to ask you what kind of control charges 00:50:47.73 --> 00:50:51. different types of control charts and expert on our 00:50:51.27 --> 00:50:54. charts and yu charts and p charts and things like 00:50:54.07 --> 00:50:56. that you got a research a little bit into just what 00:50:56.88 --> 00:50:59. kind of chart you want to use it if you will but this 00:50:59.4 --> 00:51:02. is going to do just what I just showed you from that 00:51:02.12 --> 00:51:04. data we get enough data points 00:51:06.38 --> 00:51:10. we could calculate the average the mean we can calculate 00:51:10.09 --> 00:51:12. the standard deviation we can populate the upper and 00:51:12.46 --> 00:51:13. lower control limits 00:51:14.68 --> 00:51:18. okay and we've got nowt we've now got a model to use 00:51:19.71 --> 00:51:25. to monitor to measure and to use to improve our process 00:51:25.85 --> 00:51:27. whatever that happens to be 00:51:28.61 --> 00:51:32. and it's we're talking about minutes a day not hours 00:51:32.83 --> 00:51:34. and hours and hours it doesn't take you long to write 00:51:34.81 --> 00:51:38. down your golf score bless you play golf like I do 00:51:38.63 --> 00:51:39. and you can't find the ball 00:51:41.48 --> 00:51:45. all right so again here's ah a little information 00:51:45.56 --> 00:51:48. in your booklets on control charts and how to do those 00:51:48.68 --> 00:51:51. again the simplest thing to do is let the software 00:51:51.92 --> 00:51:54. help you a little bit. The bottom line here though 00:51:54.23 --> 00:51:56. is we want to make sure that we can determine the 00:51:56.44 --> 00:51:59. stability of our process, how stable 00:52:01.18 --> 00:52:04. I'ii, how predictable is our process. 00:52:05.48 --> 00:52:08. You look at that catapult that we shot earlier, how 00:52:08.88 --> 00:52:12. predictable and stable is our process. You'd say it's, 00:52:12.29 --> 00:52:12. not 00:52:13.68 --> 00:52:16. wei, don't know where. We don't know where the ball on the land next truly, with any degree of certainty other than it's gonna land, probably somewhere within that twenty nine inch range. But if I said, I want you to shoot it into a cup, where do I put the cup? You want me shoot the ball onto a cup like a little coffee cup? Yeah, that's, exactly what I want, that's, our six sigma test for the next session, where I put the cup. I don't know. All right, so in the next round, we should know we're gonna put the cup right here, and we're going to see if we could shoot the ball in the cup. Come on. Then statistical process control it's just a way of listening to our process so now we can listen to it we can monitor and from there we can begin to interrogate it a little bit and we could start to read for trends. So what happens if for example if we've got a whole lot of data points on one side of the center line what's that all about that's like flipping in fact the magic numbers statistically is seven if we get seven data points in a row on one side of the means or the other that tells us their process is out of control the reason for that is puts the odds of flipping a heads or a tail seven times in a row with a coin because to do it once it's fifty fifty right to do two heads in a row is point five times is point five point two five about three heads in row foreheads five heads in rome when you get to seven it's less than one percent point oh seven so now all of a sudden we just flip seven heads in a row or seven tails in a row that tells us something fishy is going on with our process we need to investigate it so we use simple statistical measures to monitor a process okay to find out just how stable it isn't how in control it is and if if the data tells us there's something fishy going on it tells us to investigate and by the way let's investigate before it goes hey, wire goes out of out of control we have a lot of cost of poor quality but we have two different types of things going on here one is that it's called special cause and if it's a special cause situation um our strategy is number one get timely data gets something immediate we've got to take special immediate action on it some something something crazy went on okay, we're out of bounds okay? The space shuttle blew up something serious happened that's uh that's not supposed to happen that's not normal let's uh let's check it out and and learn from it retained the lesson so that's what we classify his special cause we'll spend more time on this and the next segment as well common causes normal variation this is its normal supposed to happen so we've got some normal variation going on all right but uh let's not try to explain away every little data point on a run chart every little one it shouldn't be flatlined flatline would being dead potentially so we're just trying to treat a common cause like a special cause we're actually going to make the process work let's just let's understand the difference alright or otherwise we could actually make things make things a whole lot worse. So we come back to this food for thought our systems and structure perfectly designed for the results we're currently getting so right now. Our system from round one is perfectly designed to get us a twenty nine inch range, okay, so if we're before good with that, we could go on and kaizen something else, because we got data to prove that we're good at getting a twenty nine inch range. On the other hand, if we learn from the customer where that's just not acceptable, I got to see a six inch range, okay, not a or twelve and strange, but certainly not a twenty nine inch range. We got work to do. So that's, what we're going to do in our next, our next segment is we're gonna because we're going to get into how do we drive out that variation?