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hey guys andy here with cqe academy and in today's video i want to talk about a really important topic which is the seven qc tools now whether you just want to get better at work and use these tools in your everyday job or you're preparing for something like the green belt exam or the black belt exam or the cqe exam today's lecture is for you all right let's head over to the computer get started all right let's go ahead and jump in right into the agenda so we're gonna start with a brief intro of the seven qc tools kind of talk about all of them and how they fit into the problem solving process or the the improvement process and then we're gonna go through each one we're gonna start the flow chart the check sheet the pareto chart the cause and effect diagram scatter diagram histogram and then the control charts and then along the way as we go through this we're actually gonna work a problem using all seven tools and we're gonna reduce the number of defects associated with our toaster all right let's go and get started so the seven qc tools i love this quote from kerou ishikawa who said as much as 95 of quality problems can be solved with seven fundamental tools and i absolutely agree with that i think these tools are probably the seven most powerful tools whether you're talking about green belt or black belt or quality engineering it doesn't matter these seven tools are incredibly powerful for solving problems and making improvements and and this is a really important topic by the way as we go through this i'll make sure to talk about where we're at in something like the plan do check act or the domain cycle we're gonna solve a problem with our toaster and we'll we'll use either the domain or the plan do check out process to do it all right let's get into it all right so the very first tool is the flow chart and what a flowchart does is say it's a visual tool that helps you depict the flow or the sequence of a process this could be things like the flow of information or the flow of tasks or material or people or decisions it doesn't matter the reason that a flowchart is so incredibly valuable is it makes a really complex process simple and it promotes a common understanding of a process anytime you get more than one person in a room to talk about a process there's likely going to be disagreement about how the process works and i love using this analogy often times when when we sit down to analyze a process there's what management thinks is happening there's what the procedure says is happening there's what's actually happening on the production floor and then there's what could be happening and the beauty of a flow chart is it it does just that it gets everyone on the same page about what's actually happening and i love this quote from dr deming who said if you can't describe what you're doing as a process you don't know what you're doing and the best way to describe what you're doing is to use a flowchart and that's why this tool is so powerful if you're in the planning phase of the define phase it's really good to use a flow chart define your process and then use that flow chart to plan out your experiment and plan out how you're going to make an improvement so let's do just that let's say we're talking about our toaster and we want to make an improvement right and so the first thing we're going to do is we're going to start with the boundaries we want to analyze a process but we want to start with our boundaries first so we're going to go from receiving a work order to completing a workload that's the boundaries of our flow chart now i've got the team here because all of these activities all these tools are all team based so imagine you're sitting down with your team and the first thing you're going to do is brainstorm all of the steps in the process right talk to the experts how does the process work use post-it notes right don't try to do this in some software use post-it notes write down all the activities and then once you're done brainstorming organize those thoughts into that logical flow that logical sequence of activities for your process and now that we have our process here we're in that planning phase and we want to create a target right what sort of improvement are we going to make and we want to reduce defects by 25 now we can't make an improvement and we can't solve a problem without data and we know that most of our defects happen during final testing so now we need to collect a little bit of data and this is where the check sheet comes into play so the check sheet is a very simple tool for collecting organizing and analyzing data every problem you solve or every improvement you make should be based on data and the check sheet is probably the most powerful tool for collecting data now there's something wrong with the check sheet that i'm showing you here on the screen and that problem is is it doesn't have any metadata if you're collecting data and you want to make a high quality decision using that data you also need metadata so when you're creating your check sheet don't forget to include things like who and when and where all those key elements of data integrity and data accuracy are really important for making high quality decisions okay so we've got the team together and again we did a little bit more brainstorming we said okay at final testing we have eight defects that we want to collect some data on so we create this check sheet we've got our metadata here we hand this off to the team and they come back to us a week later with a bunch of data now this is fantastic we finally have some data that we can analyze and the question is which defect do we focus on i want to improve our target so we originally said we want to reduce defects by 25 percent well now that we have a little bit of data we can actually create a target so we have 145 defects across a whole week that's seven days that means we're averaging about 20 to 21 defects per day now if we can reduce that by 25 percent we will eliminate five defects per day now we obviously can't focus on all these defects so the real question is how do we know what to focus on and that's where the pareto chart comes into play so the pareto chart is another qc tool that allows you to analyze your data in search of the pareto principle so what it what is the pareto rule what is this 80 20 rule so this this is a a natural phenomenon that was discovered by a guy named vilfredo paredo he's an italian researcher who was studying land ownership and wealth distribution in italy and in europe and what he found is that 80 percent of the land was owned by 20 percent of the people and this 80 20 rule in this 80 20 phenomenon was also experienced by a guy named joseph duran now he gave credit for the tool to wilfredo praeto but he was the one who popularized this idea of the 80 20 rule and this idea of the pareto chart and what he told us and what he taught us is that a pareto chart helps you separate the vital few from the trivial many now what did joran mean what he means is when you're solving a problem there's often one or two key issues key root causes or key defects that you need to focus on to have a major impact on that particular situation and that's exactly what you see here when we take our data from the check sheet and we put it into this pareto chart we see that control pcb issues accounts for nearly 40 of our defects you can see if we come across here we've got 40 percent of our defects coming directly from control pcb now there's two things happening on this graph obviously there's the blue bars which are simply just the frequency or the count of defects that occurred throughout the week and then this black bar is actually the cumulative line so this first defect accounts for 40 percent and then we go up and up and up all the way to 100 now that we have this pareto analysis we know that control pcb is our primary issue it tells us what to focus on now we still don't understand why these issues are happening and this is where something like the cause and effect diagram can be incredibly useful so this is the the fish bone diagram or the ishikawa diagram there's all sorts of different names for it but it is a cause and effect diagram and the way this works is we start with the effect that's over here on the right that's the head of the fish here in orange this is our effect and so step one of the cause-and-effect diagram is to start with a really well-written problem statement so i've put in pcb failures but in reality you want to have a much more descriptive problem statement than this and once you have this effect you can start working through the the fish bone process to analyze all of the potential causes and failures now i'm showing here what's called the 8ms and this is the beauty of the fishbone process is that it's a well-structured approach to root cause analysis it forces you to think about all of the potential different categories or scenarios or causes that might be contributing to your problem now along with the cause and effect diagram are a number of tools that you should be using so i would recommend you get out your flow chart look at your process use your flow chart and and ask yourself how might each step in the process fail and contribute to the the effect that we're seeing teamwork is also a must here you're not going to be a subject matter expert in all of those eight m's and you need people from operations and engineering and quality and r d and marketing and maintenance to really do a thorough analysis in each of those areas to truly understand the root cause and then of course brainstorming you know you're going to have to creatively think about and talk about and discuss potential root causes that maybe you're not even aware of and then the five-way analysis i love the five wise it really helps you go from a high-level symptom down to the true root cause and really ask why why why to truly get to those those real root causes that you need to address and then as you have that team discussion and you you go through the process you can identify potential root causes and contributing factors to the problem you're trying to solve now obviously again it's we have to go back to that parade of principle we can't focus on everything we have to talk about the most likely root causes and the most likely contributing factors so again at the end of your cause and effect diagram you might identify three or four issues that you need to study further now i wanna i wanna talk about this one high humidity during assembly now as we were working through the cause and effect diagram process the engineer who was helping us looked at our check sheet and noticed an interesting pattern what they noticed here and i've highlighted here in yellow is that sunday monday tuesday we we only had a few defects right six and four and one whereas on wednesday thursday friday you'll notice that our defect rate jumped up a little bit and what the engineer remembered is that we had a rainstorm come through on tuesday night and the humidity level in the facility really jumped up and so what the hypothesis here is that humidity is affecting our defect rate so i've created this little table here to show the days of the week along with the defects and the humidity now to truly understand this relationship we have to create a scatter diagram so here's exactly what that scatter diagram looks like what we do here is we're plotting pairs of data so for example on sunday we had six defects and 18 humidity you can see that right here that's this data point right here we had six defects 18 humidity now the way this scatter diagram works or you might hear this called an xy scatter plot is here on the x-axis we put our controllable variable our independent variable and then on the y-axis we put our response variable so here we believe that relative humidity is the the independent variable that is affecting our response variable which is defects and you can see here that there appears to be some relationship between pcb failures and humidity now it's really important when you're looking at the scatter diagram not to assume that this relationship is a causal relationship right there's this really important concept that you can have correlation without causation two parameters or two variables can correlate without having a cause and effect relationship so let's assume though let's assume that we've done a doe here and we've proven that humidity has an effect on our pcb defects we could come back to the scatter diagram we could say okay our target for pcb defects is five or less let's call it let's call it five or less and so we come down here to humidity and say okay we wanna control humidity to around 20 to keep our defects low does that make sense and that's a this is a great way a scatter diagram is a great way to understand the relationship between two possible variables now once you've done your scattered diagram you can quantify the relationship between those two variables so what i'm showing here is the pearson correlation coefficient and this coefficient ranges from positive 1 all the way over here on the left to negative 1 all the way over here on the right and that ranges from a perfectly positive correlation here you can see that as x changes y changes identically and then same thing here with r equals minus one this is a perfect negative correlation now as we get closer to zero we start to lose that relationship so an r value of zero means there's no correlation between those two parameters as x changes y basically does whatever it wants there's no relationship between those two variables now the next thing we could do in our analysis is to look at relative humidity over time so let's say we go out we talk to our facilities engineers we say okay give us the relative humidity within our environment you know every six hours for the last six months and we can take that data and we want to plot it because we need to understand how relative humidity is changing within our facility and one of the ways you could analyze that data is with a histogram so a histogram is just a very simple bar chart that graphs the frequency of occurrence of continuous data and again this is a great way to talk about your process every process or every product or every quality attribute out there has some level of random normal variation that will often occur in a pattern and as engineers we need to understand what is the pattern associated with with our outputs or our process and a histogram is a great way to understand the pattern or the variation in your process now you might grab this data and you might get like a skewed distribution or maybe a bimodal distribution or exponential distribution there's all sorts of distributions you might get but it's great to know how your process is behaving now the other beautiful part about a histogram is you can take this data and let's overlay some some specification limits right now what we have is the beginnings of process capability so the histogram is a fantastic tool to quantify and understand how your process behaves and if you compare that against the specification limits we can now start talking about process capability okay so we're on to the very last and final qc tool let's assume we now control for humidity and we want to make sure that that change has been effective over time a control chart is the right tool or the perfect tool to do that so what is a control chart it is essentially a tool that allows you to confirm that your process is in control now when i say in control what i mean is that you're only experiencing normal variation when your process is experiencing normal cause variation your data should fall with within those control limits by the way if you're new to spc i have a whole separate video on control charts you can go check it out i've got both the x bar on our chart as well as attribute data and a control chart is a fantastic tool to use at the end of a project to monitor and control your process and make sure that your changes were effective and let's take a look at what this looks like for our particular process so here's our process right the first week of data you can see we're really all over the place and our control limits are really wide because we're not controlling for humidity and we've got all this data and you can see on average we have about eight defects per day right we're really jumping around here and then let's say on day nine we start controlling for relative humidity and we've got our our control chart we're collecting data and you can see that for the next you know 20 plus days our defect rate has dramatically fallen in fact our new mean defects per day is around three so essentially we've gone from eight defects per day down to three defects per day and we've hit our target of reducing defects by 25 percent we've gone from 20 plus defects a day down to about 15 all by controlling relative humidity in our process all right that's it for today i hope you enjoyed it if you did hit that like button also if you're serious about becoming a cqe i've got a free course go check it out it's at cqe academy.com free course where i cover the top 10 topics on the cq exam and i also give you a bunch of great free practice exams to help you on that journey all right i hope you enjoyed it thanks so much i'll see you again bye