But sometimes using these toolkits can feel insurmountable due to their complexity or the lack of examples for specific applications.
This article’s focus is on the 7 Basic Tools of Quality – 7 different analytical statistical models that you can use as a beginner without needing to break out any heavy math.
Each of the 7 basic tools is described briefly below and accompanied with a relevant manufacturing example.
1. Check Sheet
A check sheet is a visual indicator of data collected while a process unfolds. It can be as simple as a numbered checklist to be followed sequentially, or as complicated as the task requires.
It operates much like a kanban board, which is a visual display of jobs currently in progress or work to be done. It differs from a kanban board in that it tracks a single job’s progression as well as information about the workers, specs, and location of the job.
Pro Tip : The manufacturing principle of Just-in-Time stresses the importance of monitoring processes in real-time as they occur. This allows for optimal response time for changes in workflow according to the most up-to-date insight. Check sheets are great for real-time insights because they continually collect data over a particular time period.
Question to Solve: How do we monitor and evaluate a process Just-In-Time?
Manufacturing Example: You want to measure process control over the whole of a production line, in this case in your motor assembly workstation. There are many variables but you’re not worried just yet about root causes or cause-and-effect – just the basics, like how many defects are caused throughout a regular week, including what types of defects and on which days they occur.
Using this tally on a check sheet, you can collect data as a beginning step for further analysis.
For example, take a look at the Motor Assembly Check Sheet below. A quick glance at the tallied data shows that the most frequent defects are with rusted supply parts, and that this issue most commonly occurs in the first half of the week.
Therefore, a keen manager would do best to see if there is any issue in the supply chain, such as:
Parts arriving on location already rusted;
Parts arriving on location late in the week and improperly stored over the weekend, causing them to rust;
Parts assessed for rust more rigorously by the Monday & Tuesday shift leads than by other shift leads;
Check sheets are incredibly versatile. See the below video for more creative insight on using check sheets within software applications, such as the VKS platform:
2. Ishikawa Diagram
Fishbone diagrams were first created by Kaoru Ishikawa, a quality control engineer in the Japanese shipbuilding industry during the 1960s. Also known as Ishikawa diagrams, they are meant to be used as a collaborative approach to root cause analysis.
Ishikawa diagrams, like other management charts and methods of inquiry, are valuable tools for teams looking to solve a variety of problems. The benefits of fishbone diagrams include:
Practicing using frameworks adjacent to Total Quality Management (TQM)
Simple enough to be drawn without software or equipment
Quick approach to brainstorming
Foster collaborative team building
Question to Solve: What do we hypothesize is the root cause of an issue?
Manufacturing Example: Your manufacturing leadership team has noticed an occurring defect that has a major impact on quality, but you’re not quite sure exactly where the problem stems from.
So, you lead a brainstorming session to identify possible causes and contributing factors in every area throughout your production process in order to find the root causes.
You can easily replicate this exercise with only a pen and paper or even a whiteboard. Look at the diagram below: The first step will be to write down what the main issue is – in this case, the problematic defect.
Next, gather the appropriate team members aware of this defect and have everyone write down a potential cause of this problem. Write these broad causal areas along the main spines of the fish where it says “Step #2”.
Finally, dig a little deeper as a team and ask questions about why those causes happen. Don’t worry too much about being exactly accurate, since this is a brainstorming exercise. Write down these underlying root causes underneath the categories you’ve just identified in Step #2.
3. Control Chart
Control charts are line graphs that can be used to see product deviation at a glance. The fluctuating line part of the chart hovers around a median “control line” that represents a specific quality standard.
The purpose of the control chart is to detect process errors and anomalies and also to model prospective improvements. They are commonly used in industrial manufacturing and process management especially when it comes to continuous improvement.
Question to Solve: Is the amount of defects we see within a normal range or is something causing more errors than usual?
Manufacturing Example: Upon opening your manufacturing execution system (MES), you can see historical data about the number of defects produced on average by your manufacturing team per month.
However, you’ve just switched to upgraded machinery and want to know if your defect rate is still within acceptable limits or if your process control is no longer predictable.
Therefore, you set up a control chart with two control limits represented by horizontal lines to see at a glance if the data falls in the desired range.
The above image is a template to understand what the basic elements of a control chart are. Here is an example of a control chart within our software and the way it looks on the shop floor within a manufacturing operation:
The data for the absorption levels of color grading within a vacuum mixer machine is plotted here by number of batches. The data points fall within the two red dotted lines which represent the Upper and Lower Control Limits.
In this case, therefore, the absorption levels are within the red lines, which means that the process is “in control.” In other words, the defects that are caused by the vacuum mixer are within expectations for normal wear and tear, and the machine is probably calibrated correctly.
Histograms are great because they don’t just point out the “deviations” or “outliers” in datasets, they also show the frequency of those deviations.
If a factory produces an immense volume of items with millimeter-precision measurements, it can be difficult to find areas which can be continuously improved.
A histogram chart can easily identify if there’s one machine among dozens that is error-prone or set up erroneously. This makes it an invaluable tool of quality when producing at scale.
Question to Solve: Are certain defect types becoming more or less frequent than others?
Manufacturing Example: Similar to the control chart example above, let’s say you have just upgraded several new machines on your shop floor, and they run alongside some legacy equipment.
The production rates differ between the new machines and the old ones, so it’s hard to compare them against each other.
You want to analyze the old machines’ rate of production over time to see if and when defects become more frequent. In this way, your histogram chart will indicate when you should replace the old machinery.
5. Pareto Chart
Pareto charts are commonly used for quality control where organizations tally the number of defects and determine which defects are causing the greatest amount of problems and/or lost revenue.
The magic of the Pareto Chart is that it is both a line and bar graph, allowing organizations to get an accurate view of the number of defects while understanding their cumulative value next to each other.
The distribution does not need to be a strict 80/20 split but this is the most commonly observed distribution under the Pareto principle.
Question to Solve: What is the most common or impactful subset of data?
Manufacturing Example: You manufacture a product of exceptionally high quality but as with any production process, there are inevitably unexpected defects that arise. You make a Pareto chart to determine which type of defect is the most impactful to your bottom line due to its frequency and effect.
The above example is a basic Pareto Chart, showing that approximately 80% of all defects that occur are due to dents and paint errors. Therefore, the factory manager knows which areas to focus on for maximum overall decrease in defects.
For a more detailed walkthrough of using pareto charts through manufacturing software platforms, check out our video on creating pareto charts with VKS:
As described in the video above, Pareto charts like ones that track defects need the following four columns: defects, frequency, cumulative frequency, and cumulative percentage. By exporting the data to excel, you’ll easily be able to assemble your own pareto chart.
6. Scatter Diagram
A scatter diagram is one of the simplest charts, also known as an X-Y value chart. These charts are used when you have paired numerical data, with one variable of the pair along the X-axis and the other variable along the Y-axis.
They are used when you suspect there may be a relation between variables. This is why scatter charts are often used when determining cause and effect relationships.
Depending on the presentation of the dots in the scatter chart, you may be able to draw a line of statistical significance (meaning there is indeed a causality or correlation) across the chart through the groupings of dots.
Question To Solve: Do two variables impact each other?
Manufacturing Example: You wonder if your manufacturing process could be optimized to lead to higher quality products by changing the amount of a raw material, iron. You measure two variables to see if they affect each other: overall product purity, and the presence of iron molecules in the product.
Once the data points are plotted (i.e. “scattered”), you draw two lines to represent the median value along each axis. Since there is no easily identifiable trend (i.e. the plot points don’t fall evenly across the lines), you can conclude that the two variables – iron and product purity – have little to no effect on each other.
Therefore, it’s not a good strategy to try and boost quality by changing the iron levels.
Stratification is a mode of data analysis where data is grouped into homogenous groups – called strata – for visual graphical representation. It is not as in-depth, statistically or mathematically-speaking, as a control chart.
Each stratum is from a different data source, and is represented differently on the stratification chart according to a visual legend.
The purpose of a stratification chart is to allow someone to see patterns between and different sets of homogenous data.
Question To Solve: Are there any patterns here to explore more deeply?
Manufacturing Example: You have 3 separate machines working on your shop floor simultaneously. They are for manufacturing separate components and thus have different data points depending on which machine you are looking at, but you want to compare all 3 machines' overall equipment effectiveness (OEE) against each other to see if any has an identifiable pattern in defect quantity.
In this example of a stratification chart, it appears that machines 1, 2, and 3 all have similar curves or lines connecting isolated data points. Machine 3 has by far the highest number of defects per date on the X-axis.
This could mean a couple things:
If all 3 machines are the same type of equipment, then Machines 2 & 3 are underperforming and need to be checked for maintenance and setup.
If all 3 machines are different – for example, Machine 1 is a robotic assembly arm, Machine 2 is a torque tool, and Machine 3 is an automated load bearing test – then it’s okay for each of the 3 lines in the chart to be separated like they are.
Either way, a stratification chart will help the viewer to assess problem areas and the impact those separate areas have on the overall production process.
Using The 7 Tools of Quality For Continuous Improvement
The 7 Tools of Basic Quality Control are different statistical methods of analyzing data sets that are useful in manufacturing contexts. Despite their mathematical basis, they are simple enough to be used by those without a heavy analytical background, especially when combined with work instruction software that stores, sorts, and processes data throughout your facility.
Using these 7 tools can sometimes be more of an art than an exact science, as you can pick and choose which tools are best suited for your contextual situation in manufacturing. Feel free to use several at a time or only a couple – just let data-driven insights emerge in order to lead your next steps toward continuous improvement.