Lessons

Lesson Info

Error Free Performance

You know do it to do so well that they will want to see it again and bring they're friends become advocates ken blanchard causes raving fans and ps the net promoter scoring system calls them promoters these air folks out there just so wowed by whatever it is you're doing they're talking about your services and your your your value proposition at cocktail parties that lunch is on airplanes and hotels and yeah I know exactly who used to do business with I get and they're actually handed out your car your website or your numbers for you and your phones ringing and you're like wow uh thank you so that coupled with a lot of the online and social media type opportunities we have today is a great way to use creativity before capital all right I mean I used to have printed brochures they weren't free you know and then you had to mail him out that wasn't free I would be out I would be mount fifty thousand brochures and one mailing that's like dropping brochures out of an airplane it seems like ...

you know who's the people you're going to read these things and now they're going to sign up for the course you know these days a lot of that stuff paper listen a lot cheaper so it was a take a big gulp to do that kind of an investment to get people to come to seminars fifty thousand grocers at once is not cheap now toe print it tio bulk mail it okay and then toe into them to deliver on it all right so the purpose of this session whoopsie is to go back to the make and focus again on improved so we're going to do another point guys in this case we're gonna take that wobbly catapult when we're going to figure out a way to stabilize it try about the variation improve the customer experience because right now the customer's not getting up a precise timely accurate delivery think of the catapult to rep represent perhaps the diamond department around one or two where was we were getting variation in our and our diamonds all right same same idea prove our accuracy precision on our performance in the results and again this in this case use the status post bring the lessons to life so that's where we're going we're going to shift back to the point kaizen from the flow kaizen not great after this particular point in the process and uh and nail it all right so that's where we're going to go with this so we pull out our domestic and we looked we now we go to the analysis phase and we say well let's start with the top beauties those top undesirable effects what is it we're tryingto to eliminate or reduce we could pull out our aipo diagram to learn that transfer function the input process output diagram what's uh what's going on with that, we could pull out a tool like our fish bone charter ishikawa diagram and say, all right, uh, let's, put it the head of the fish, the the variation in our distance, and then let's, go back and explore the the different causes through the different fish bones. All right, let's, do it let's, do a thorough process analysis to understand her voice of process and what's going on there. Let's. Take that then, and then find the leverage points let's, let's. Find those, uh, those major leverage points. Stabilize those and see if we can't measurably improve our performance. All right, so there's, our wise, a function of x transfer function. When we say, you know, you know, let's, take this catapult, and, ah! All right we've got shooters is everybody shooting the ball the same way you know when we couple it with methods and procedures we all following a very standardized procedure and answer is no we people shooting at different ways loading the ball differently you know, pulling it back differently so we had variation in our car are we had variation and our people because not one person was shooting at the whole time we had six different people shooting it so I said well we could reduce that variation john by just getting rid of the other five and have encouraged 00:04:00.543 --> 00:04:03. you with all the shots so kate shoot all the shots 00:04:04.32 --> 00:04:06. that's not gonna work business today infact I fly 00:04:06.98 --> 00:04:07. I fly every week 00:04:08.94 --> 00:04:11. pretty much so it's interesting because every time 00:04:11.06 --> 00:04:13. I get on a plane I see a new pilot that's like I'm 00:04:13.6 --> 00:04:16. not the guy that flew gloomy last week the week before 00:04:16.91 --> 00:04:20. that so I'm seeing different pilots all the time that 00:04:20.13 --> 00:04:22. might scare some people doesn't bother me because 00:04:22.95 --> 00:04:25. I know they have very rigorous standard operating 00:04:25.9 --> 00:04:30. procedures and methods to take the variation out all 00:04:30.87 --> 00:04:34. right and operated above seven sigma in the airline 00:04:34.68 --> 00:04:39. industry on safety not on luggage but safety all right 00:04:39.44 --> 00:04:41. they operated a very high signal level because it's 00:04:41.8 --> 00:04:42. tze life 00:04:43.96 --> 00:04:46. all right? So we've got to make our process robust 00:04:46.95 --> 00:04:48. to the fact that we got different people shooting 00:04:48.67 --> 00:04:51. it material what do we do about that beginning material 00:04:51.4 --> 00:04:55. issues here the rubber band the the ball okay the 00:04:55.9 --> 00:04:58. equipment what about the catapult itself all right 00:04:59.2 --> 00:05:02. any variation going on with that any variation and 00:05:02.51 --> 00:05:05. policy and procedure what's this you know removed 00:05:05.03 --> 00:05:06. the rubber man and we hook it every time thing all 00:05:06.93 --> 00:05:10. about we really need that interrogate that you know 00:05:10.5 --> 00:05:13. our pullback method or load the ball method are released 00:05:13.12 --> 00:05:17. method our measurement method ok uh the environment 00:05:17.31 --> 00:05:19. itself does that have anything to do with it the carpet 00:05:19.58 --> 00:05:22. the wood the floor of the table the temperature the 00:05:22.31 --> 00:05:26. humidity we interrogate our process to the point where 00:05:26.14 --> 00:05:29. we're finding those critical ex variables that are 00:05:29.58 --> 00:05:33. propagating to the y variation so like we talked in 00:05:33.46 --> 00:05:36. the last segment identify the variation in your business 00:05:36.44 --> 00:05:40. wherever it is and it's everywhere to a large extent 00:05:40.64 --> 00:05:42. but sort of short sort through it and figure out where 00:05:42.95 --> 00:05:46. do we want to start think about it from the customer's 00:05:46.04 --> 00:05:48. perspective first so value in the eyes of the customer 00:05:48.29 --> 00:05:51. we've talked about that repeatedly and value as ah 00:05:51.79 --> 00:05:54. it's a moving target but what are they value on time 00:05:54.53 --> 00:05:57. performance is there variation in the time of delivery 00:05:57.64 --> 00:06:00. right first time was the variation in the quality did they get what they expect so there's variation and we want to make sure we pay attention to that starting with the customers I always start with a customer and work back all right by the way what kind of variation is the customer experiencing it's, not just about variation from us handed off to them. What kind of variation are they experiencing, and are we contributing to that? Are we actually making their lives more variable and putting them at risk? That's a great question. Think about your customers, variation, and south for that. All right, that's, a gonna that's, a real partnership kind of question. How do we find a win win? So we mapped the current state, and we did that earlier, and we said, wow, as we look at that map. You know we got variation all over the place we get variation in the way we're set up on the ball pulling it back we get variation in our release we get variation in our measurement system it could even propagate to our documentation our interpretation of that our whole system's loaded with variations all right so let's map that out let's call it out to be brutally honest with it. Okay, well if we have ah statistical process control and play let's use that as a tool toe to evaluate where we're experiencing the variation by the way this is you know if you're an automotive um if you're not a motor in the nineteen eighties you had to have this teo even bid on work this is nothing new you want to do business in the automotive industry you have to have everyone on your company trained in spc a minimum of eight hours you have to have documentation and proof that you're using it and your facilities or you can't even bid you can't even get business new business if you haven't got the simply that's how fundamental it isthe so if the big best in class cos they're using this stuff maybe there's a reason why maybe we should pay attention to that and even if we're running a little water to person show okay are we measuring our pro performance on a regular basis and learning from that to get better and better and better at whatever we do, it's it's. Critical. All right. Those owns all right so we've got our mean our average we've got you know zone c zone b zone a those air each typically those each represent one standard deviation so you've get six standard deviations okay across your three on each side of the main basically we know that ninety nine plus percent of our data is going to fall within those control limits inside those owns but we can now start to look at symptoms if you will that our systems not in control so we could certainly we could do this we could populate it control chart with our data from around one of the of the catapult and we'd start looking at things obviously if one or more points is outside the control in it we're out of control something something funky is going on but there's other tests we can run here and now the clever software have these built in so you can actually run that will run the test for you and then throw a flag up when there's a when there's something that requires your attention all right but if there's six seven consecutive points on the same side of the center line we talked about that last segment um that's that's the likelihood of seven heads and a tail flipping a coin that's not that's not normal so check it out all right seven consecutive intervals and either side increasing are entirely decreasing the probability of that's less than one percent check it out so this thes tests are basically saying to us check it out there's something funky going on in your in your system you might want to check it out and know about it okay, so this is again this is where this is world class performance where we really get to know our system so well that we can predict with a high degree of confidence and accuracy what's gonna happen before it does and our customers will trust us when we were with that smart about whatever it is we do we're that reliable okay? And then you know when we we have out of control and play some of the most common reasons for that are listed here we have inadequate s o p standard operating procedures that we don't have him at all when we haven't but nobody's following them we have noise variables which we talk just a little bit about early we have variables that are beyond our control that we haven't learned howto deal with or protect ourselves from that cannot that could certainly hurt like I said earlier we might have s o p is that we don't follow all right or we could actually have made true process improvement now think about that you mean we made things better and now we're statistically out of control and the answer's yes, what that saying is that we need to now re calculate our control limits with the new data to read to now create new control limits because the new process improvement in relation to the old controlling that says we're way off we're we're way out of control so I mean that could look something like this you know where we reduced our cycle time from this average to something less we reduced r variability from something that looked up like that to that that that could say what we're no longer in control using the same control limits but we're better than we were before so it's statistically in control or out of control doesn't necessarily mean good or bad it really means stable are not stable and we just want to know that so we want to know okay this is again this is like checking out your your heart rate this is like checking out your your body in some way it's an indication of are we are we healthy? We're in good shape we're every at risk so it's again running running your life with good data personally and professionally right so these are just some symptoms and some visuals if you will of out of control performance so here's an example where it says I don't take action now because something weird is going on we've got you know we've got two out of three right here and zone air be so something weird's going on that's not supposed to happen the probability that's very low and check it out all right, simple tests the questions for out of control these would be very important as you is you think about your business and your process and your customers and everything um and you've identified variation that's important sir donnelly think about variation from the customer's perspective again what where they experiencing it and then start to ask yourself you know their differences in our measurement tools or measurement systems accuracy something statistically we call a messa measurement systems accuracy is something weird going on there if we go back to our catapult example we had a real problem with measurement system we really didn't know exactly where the ball was landing so some of the variability and our process was was coming from our measurement system you know what if we're driving down the road and the police officer you know pulled you over and uh and says you know you were speeding got my speedometer said I wasn't well I got john radar and you were but my measurement system says I wasn't our measurement systems are inaccurate all right we could be operating with lousy data and uh putting ourselves at risk about methods are there any differences in the methods people using the pullback to release is the process affected by the environment in any kind of way humidity temperature things like that other differences in the machinery the tools equipment being used now think about this this should start to look like a pattern we've seen before and that would be that fish bone. Remember the six m's so? Measurement, method, mother, nature, machinery. Man, man, power things like that. So this would be a great way to start to say okay, well, let's, let's pull out a tool to go after variation reduction and gain knowledge. And this is where we could pull out the tool we talked about earlier that the issue cow in the fish bones. This is a great little six sigma type of cause and effect tool. We've got one ut not ten like we had earlier that we're really going to go after here. That one happens to be this variable distance where the ball's going? We're out of spec customers sending it back, telling us it's no good. So we have a high pasta, poor quality that causes all kinds of other things. So has it got anything to do with material? We're using the rubber band, the ball. Anything to do with the catapult itself. Anything to do with the different methods we use the people shooting, training, things like that. What about our measurement system? We just talked about that a little bit and in miscellaneous or mother nature, we get into, you know, whatever else we way think of. So where you gonna discipline method now to drill into what you know what's, causing this variation and shifting that again from the I think I know. So back teo improve now we've been through this once before this is just another little cycle to use it in a different kind of way we got the problem statements we've got the ideas and the solutions along with solution specs things like that the risk analysis and I'll introduce you to a new tool in this section I've talked about it of reference that thie f mea the failure mode in effect analysis is great little too especially if your business start up for an entrepreneur solo aunt entrepreneur with an idea and you want to be a six run a successful business you get you have to know the f mea that's it's absolutely key to oh I'd call it a top ten give not a top five tool in the whole toolbox it's it's it's that important and then kaizen will come back to hk eyes in which we've learned about and well actually won a little simulated guys in here with her you know with our catapult so ah an example problem statement well could be something like this the problem is that we have excessive variation our process this causes miss deliveries lost revenue excess inventory piling up I cost too poor quality high carrying cost and negative customer feedback the cost of this problem is x and this illustration a million and a half dollars annually so this variable process that we have whether it's the catapult, it's what we've experienced earlier with the lean sigma game is it's a trigger the whole lot of beauties beauties in this case would be the miss deliveries the mist hits no which we saw yesterday as well if we miss the delivery we missed the target we don't get revenue for it in fact we cost us more money now to try to fix it or scrap it or whatever we do with it meanwhile we get inventory piling up so it's the same kind of problem statement we had before and we'd say to the guys in team I was pretty clear on what our mission is here the problem is what we're trying to do yeah okay we're good so we go to solution specs all right what kind of solution specs do we have well the customer could give us some customer could say you've got it you've got to get your range down to twelve inches that's a must that's a must have and you're twenty nine now so you've got a lot of work to do basically you need you cut that range by more than half incidentally you have to do it by the end of the week or I'm going to another vendor so time could be a factor we gotta have this done fast we gotta really blitzen oh and by the way you have to do it without any money so creativity before capital we're not gonna just throw a lot of money at this tell you what we will do we've. We found a few things that we might be. That might be helpful. We've we found some duct tape, you can't travel without duct tape you so you gotta have some duct tape on your handy, you know, we found a sandbag that could be useful. We found things like aluminum foil, okay, so we didn't go out and spend a whole lot of money, but we we have a few basic things around here that we could get creative with, and but you can't go out by new catapults, and you can't drill it to the floor and things like that. All right, so, uh, we've got the customer specs. We've got the time, the budget, things like that, uh, and we have a way, even have a bottle cap. So what do you know well you know we're going to do with that thing but we'll figure something out okay so uh we got some uh we got some resource is handy this is creativity before capital if you ever saw the movie apollo thirteen there's a wonderful illustration of this where it's like we dump a bunch of parts out on a table and say you got this much time to figure out how to solve this problem or people are going to die if you've ever seen that if you haven't seen that movie you're seeing that scene it's it's amazing because that's kaiser and that's amazing it's amazing what creativity khun khun do sometimes so we brainstorm alternatives all right let's get our post its out let's start writing down what you know what could we do? How could we stabilize that catapult you know what we could do with that sandbag what what what what about the duct tape what are we gonna do with that all right so let's start toe let's start to brainstorm alternatives and then a finito eyes those if you will so we start to brainstorm and we get these ideas you've seen this before so it's a great way in five ten minutes to get a whole lot ideas from the team to say let's just open brainstorm let's not try to get to over analytical here let's not judge just let's just write down with whatever comes to mind let's capture it and then that's a finito ize it and put it in grouping so now we say well you know what one thing we could do is is partition and control those x variables let's sort through him and let's put him in tow uh categories it's partition um so some are going to be noise variables and some are going to be constant or controllable variables and some might even be what we call experimental variables I'll come back and cover that so we could we could start by doing that we could figure out a way to mistake proof the pull back well what do you mean by that? Well what if we could figure out a way to pull it back to the same one seventy seven every time even with our eyes closed but if we poke a yoke to pull back to the point where we didn't even have to look we designed it into our process well that's pretty clever we could do something like that. What about improving our measurement system? I mean, we really had a lot of very was probably had at least a six inch very, very variation right there. So that's not going to help. What could we do to improve the the measurement system? And maybe one of these ideas is you know what if you what if we came up with a way to put something on the floor so when the ball landed it ping did it made a dent you know it told us that's exactly where it landed we don't have to guess there's a big dent I wasn't dead in the carpet wasn't dead in the the wood but I'm sure there's something around here that we might be be able to use that would show up like a ping in it I'm saying, well, well we'll have to look around and see if we can find something oh there is we could probably stabilize that catapult you know what? We know how we're going to do that but one of those post its might uh say duct tape on it you know or sandbag or something like that and standardized that work we might wantto have ah, standard process to that everybody follows to make sure that we're all using the same pull back same release man technique, that kind of thing same load the ball technique matter of fact we might even look at our process map here and we might discover that we don't even have things in the right order necessarily necessarily okay, because I'll tell you what if we uh we load the ball and then we pull back what's a potential failure mode there you dropped the ball blown up sir so you know what if we simply pulled back first all right poky okay, this whole thing and then we loaded the ball that might outsmart the system so to speak so we started looking at our process and say we might not even have a curtain I talked about this at lunch time in a major project he was involved with some years ago where well the way we sequence the work we might have things out of sequence that might be things we can d'oh in parallel or in a different sequence that would make the process a lot smarter okay so those are those are some thoughts that they could show up in our brain storming all right so uh we've got all these ideas this is the fun part of kai's in because I think that people sometimes people say well I'm not creative I'm not a creative type what was that one of the takeaways this you know earlier was what we're whole brain we're not one or the other so so yeah you're creative he just might not realise it or nurture it or tap it yeah kurt uh you know one thing you taught me john boy this morning was to really think outside the box when you taught me about diamonds actually being circles I'm thinking that way now and I'm looking at the variables that we have here and I'm wondering if getting rid of the most number of variables would actually be moving away from a catapult entirely having a person take the ball and place it at that point they're still using the same inputs on you're getting extremely controlled output that's what I was thinking subtraction why don't we just get that whole catapult right out of the way you know well we've had some wonderfully clever when we do this in a workshop that's um you know where we have teams and we like to compete set up little competitions we'll have the teams go out and I'll actually brainstorm all kinds of stuff we have to set certain rules to keep the game on us so to speak because we've got people trying to shoot the ball up against the wall and drop it into a funnel which goes into the cup it's like watching miniature golf or something it's like well clever creative it's beautifully it's brilliant but no that's not allowed yeah is more long lives and not so much of a question is more of a thank you for getting me to think really different oh good yeah good that's the idea of this whole thing this whole course thank you appreciate that kurt could good feedback so here's mory partitioned the variables this is where we're staying all right you know we've got that why output but we've now taken the ex inputs and we've partitioned them so we've got variables that we can control and then we've got variables that we cannot control all right the noise variables and then we could also start to identify some very variables that we could actually then use for experimentation so this is where we run designed experiments, so the key here is if we're going to run a designed experiment, which is what we do when we intentionally mess with certain variables to see how it impacts the output. So this would be like if I'm if I'm bacon pumpkin pies and I'm trying to reduce my cycle time to get more pies out, what might I be able to do to reduce my cycle time but still maintain good quality? And we might start saying, well, what if we turn the oven to temperature up five degrees and reduce the amount of time in the oven by three minutes? With that correlation of variables, get the pie out sooner and still still with the same integrity and efficacy and taste everything that we want look so we could start to manipulate variables to see what happens. All right, we could start to manipulate pin heights and pull back toa teo to predict with great accuracy where the ball's going to land because in this, in this round of the catapult, we have much more advanced grounds. In this round, all we're trying to do is stabilize the process and get the range down to six inches from twenty nine, but in in future events I might say well I haven't told you where I want the bottle land what if I told you I want you to hit one thirty five with your first three shots then I want you to hit one twenty two with your next three shots then I want you to hit ninety five and one twenty five oh man how we're going to hit those numbers well you're gonna have to run a little regression modeling or design of experiment toe to figure out what to pull it back to it what pin height t hit that target and then so you're you're purposefully changing your acts variables to hit your white targets that's that's really intelligent business management and systems and you know and that's that's what we're seeing in the military and that's what we're seeing in a lot of ah world class companies is they know if I tweak this and I do this it's going to do this they know that's the knowledge transfer transfer function they know they're not guessing they know so at what speed think about this the next time you're on a on a flight the pilot comes on and says all right we're taking off now um our flight time we should be arriving at two thirty eight this afternoon 00:28:09.33 --> 00:28:11. that's based on the headwinds that's based on the 00:28:11.99 --> 00:28:13. course that we're going to have to take his there's 00:28:13.56 --> 00:28:16. a storm up there and we have to go around it and when 00:28:16.57 --> 00:28:20. you landed it's two thirty eight because they know 00:28:20.31 --> 00:28:23. that they weren't winging it now they might wing it 00:28:23.81 --> 00:28:25. at the counter when you walk up there and there's 00:28:25.32 --> 00:28:27. no plane and they say you're leaving in ten minutes 00:28:28.4 --> 00:28:29. but I'm talking about once you're on that plane and 00:28:29.98 --> 00:28:33. your taking off that that's tightly controlled 00:28:34.1 --> 00:28:35. so this is what we're doing with partitioning the 00:28:35.73 --> 00:28:38. variables and this is what we'll need to do of course 00:28:38.51 --> 00:28:40. with our their exercise 00:28:41.93 --> 00:28:46. incidentally, we don't mess with these until we have 00:28:46.0 --> 00:28:49. a stable process until we have a stable pumpkin pie 00:28:49.92 --> 00:28:52. coming out of the oven every time if we start messing 00:28:52.69 --> 00:28:54. around with I've been temperature and things like 00:28:54.57 --> 00:28:58. that, we don't know we're confounding things we don't 00:28:58.03 --> 00:29:00. know if it's the oven temperature we're messing with 00:29:00.77 --> 00:29:03. their some other variable that way we don't have control 00:29:03.38 --> 00:29:07. over that's causing the change in the output so step 00:29:07.69 --> 00:29:10. one is always stabilized the process so that it's 00:29:10.0 --> 00:29:12. predictable it's repeatable and then you can start 00:29:12.76 --> 00:29:14. to interrogate it a little bit tweak it to figure 00:29:14.7 --> 00:29:18. out a way to reduce your lead time, reduce your 00:29:19.99 --> 00:29:22. loss rate and prove your wind raid if you're selling 00:29:22.67 --> 00:29:25. something solid, you know selling more that type that 00:29:25.52 --> 00:29:25. content 00:29:26.93 --> 00:29:30. it's running your system, your process, your business 00:29:30.41 --> 00:29:34. with intelligence and knowledge, not harder work and 00:29:34.22 --> 00:29:34. more effort 00:29:37.0 --> 00:29:39. so we find our best option hey maybe it's a combination 00:29:39.82 --> 00:29:43. maybe we should stabilize the catapult and pokey yoke 00:29:43.7 --> 00:29:47. the pull back not one or the other and we should probably 00:29:47.34 --> 00:29:49. do a little measurement systems analysis improvement 00:29:49.75 --> 00:29:54. exercises well so this isn't just a single point it 00:29:54.21 --> 00:29:56. might be a point in the process but there's several 00:29:56.85 --> 00:29:59. caissons we want to make we want to make good change 00:29:59.87 --> 00:30:01. to our measurement system make good change 00:30:03.0 --> 00:30:06. to our procedure and our method and at the end of 00:30:06.19 --> 00:30:10. the day hit the target shoot the ball in a cup ping so what are the forces for all of these things where the forces against it you know this forces against could be well budget we don't have any money so we're going to have to come up with some something else okay? We can't drill the catapult to the floor there's policy all right? The creative fly folks don't want us drilling the catapult to the floor and screwing it down that could certainly help but not the stage so uh what? You know what? What kind of forces against against us we have and this is a key takeaway I urged people to because we always want to know what are we up against to be successful if I want to know if I want to write a book but I don't know how to write a book that ignorance, that lack of knowledge is a force against me I got to figure out oh I do that back to our top of girl there j k I bought a book on how to write a book I read a book I read a book on how to write a book great book by the way two but it walked me right through everything I needed to know and then I continue to make a lot of mistakes but I went to a conference actually and orlando florida this was after my second book was published and this was a conference on book publishing I wanted to learn more about book publishing and copyrighting and legal and distribution and all of this all the the book industry I didn't know anything about it so I signed up for conference it was down at disney was very cool I went down there and I brought brought down by the two books that I had published and I learned in that conference that I did just about everything wrong except I sold out all my books eso the experts were telling me about everything I did wrong the only thing they couldn't explain is how did you sell out all your books okay because they were my second printing already at things like I didn't have bar codes on my books on the covers but you can't sell a book without a bar code on it so they had the label them all and things like that and uh you know I couldn't distribute through certain channels without barcodes groups toe miss that one. And, uh so it's, a lot of mistakes. But you learned from those mistakes and then that and the reality is that the book's all sold out and the first book pulling together has been reprinted now by four different publishers and a couple different languages and sold hundreds and hundreds of thousands of copies. So something worked, you know, but ignorance was a big factor for me, and I had to figure out ways to overcome that this where our countermeasures come in. So you know what? The forces against us back to that tool let's, come up with the best solutions these could be separate projects are all one project. So the project would be we need to stabilize that catapult doing measurement systems analysis, tighten up the measurement system, implement all these changes to a kai's, an event to prove that out on demonstrate that it's better. So here's the f m e and I said this was a top ten tool in my in my mind. So in the f mea, we list the component or the step. Where there could be an error. So this could be, you know, a jet engine, what's, the failure mode. What could go wrong? This is where we at what could go wrong and stops working, you know, catches on fire, something like that, what's, the effect of that. Well, okay, that's, not pleasant, and could be very serious. So this isthe severity to pickle fm. A. Use a scale of one to ten or one, two, five, aye, like one to five, and so we rank this. The five would be someone's going to die, or somebody's going to prison. It's very, very, very serious. No one would be it's, it's, noticeable, it's, it's, aggravating, but it's, it's it's. Not really that serious what's likely to cause that can. We did dig into the cause of it. Oh is oh no currents so how likely is it toe happen is this a five it's it happens frequently it happens all the time is it a one it hardly ever happens what's the occurrence detection is how likely are we to know when it happens like right away so in this case we weigh reversed the numbers a little bit because the higher numbers are high risk so if it's going to escape us maybe it's going to get into the market end up in a recall and that's going to cost us millions of dollars or lawsuits are all kinds of very pain ok it got past us we didn't detect it that's a high risk that's a five if we detected instantly we've got a visual control system that kicks it out to something like that we're going to know instantly that it's it's not right that's a lower risk because now we can least kick it out and deal with it so this is a risk analysis and mitigation tools so our is risk sometimes referred to his r p and risk priority number and I risked priority number is our s times r o times are d it's a number and it just helps us prioritize all the different things that could go wrong with whatever it is we're doing so drop the ball blown up sir that's serious that's gonna that's gonna crush us in terms of our process or performance with our customers that's a serious thing likelihood we didn't see it happen in round one but let me tell you something happens a lot happens in almost every workshop I do when we have multiple teams out doing this it's almost always some team that drops the ball once or twice it does happen a lot I didn't do it on purpose it just happens all right detection well if we dropped the ball we're going to go right away so that's a low lower number but the key then is to come up with a foolproof plan and the fool proof pants says well how do we make sure that doesn't happen can we design in or polka yoka ah a solution and this might be where we say you know what why don't we do this why don't we have the operator pull it back get it to the pokey poke the one seventy seven and I have someone else actually load the load the mesler load the ball so to speak that takes a lawyer a ah huge potential for air could we do that? Sure a fact you'll notice a you know a lot of times in the military uses that strategy where it's a team approach it's not one person loading their own ammo a lot of times things like that right so here's a scale on this is in your materials where you can see that one to five scale and run fm is it's nothing, nothing, teo too tough fact, we used sometimes a very simple version of f mea, which is just three questions. What could go wrong? So I urge anybody at home to just release run a simple f emma and your business, what could go wrong list there? All the different potential failure months? What could go wrong? I don't I don't I don't get sales, okay? I get sales, but I can't deliver. I'm making promises I can't keep because my process is messed up. So what? What could go wrong in your business? There's a lot of kinds of things, okay, and what's the risk if it does go wrong, how serious is it? And then the most important question really is what are you going to do to make sure it doesn't? What kind of ah, what kind of a solution are you going to come up with? It helps mitigate or really reduce that risk you can't make your business is completely risk free, and every entrepreneur knows that, but you could certainly take out a lot of the risk, and wise business leaders do that and central preneurs.