Six Sigma Tools: The Definitive Guide (35 DMAIC Tools)
Choosing the right six sigma tool to use is an important skill to master. If you get it right, these tools can help you understand the problem you are trying to fix, analyse the data, and point to a viable solution. However, a poor choice of tool can lead to wasted time, effort and resources collecting unnecessary and potentially inaccurate data.
With such a rich history, there are plenty of tools to choose from.
When in doubt, it is always helpful to refer back to the basic six sigma methodology of trying to reduce variation. Reducing variation in a process is the way to achieve six sigma quality levels. Therefore select the tool that will measure, analyse, improve or control the variation most efficiently.
The tools covered in the following chapters are not an exhaustive list of six sigma tools, but they are the ones most commonly used. Every six sigma project is different and therefore the tools that are utilised will vary from one project to another. You don’t have to use all of these, and you probably shouldn’t. Think of it more as a choose your own adventure story. Use the tools that make the most sense, and ignore the others.
You should also know that it is possible for the same tool to be used in different phases of the DMAIC cycle. However, they have been presented here in the phase where they are most commonly used.
Please note that if you are using the lean process of PDCA, instead of DMAIC, then these tools can still be used. Choosing the right tool for the job is a key element of process improvement. There is no rule that says lean manufacturing tools and six sigma tools can’t be used together on the same project.
Six Sigma Tools
During the Define phase of a DMAIC six sigma project the team has to explore exactly what the project is and isn’t about. Tools such as project charters and plans are created during this early phase of the project.
During the measure phase the project team needs to determine the baseline level of performance. Six sigma calculations are used to determine how many defects per million opportunities exist in the current state process. Measurement systems analysis is also used to ensure the measurement process itself is not influencing the results significantly. Therefore data collection tools are the order of the day during the measure phase.
During the analyse phase the project team needs to identify the root cause of the problem. Therefore the focus is on using tools to statistically analyse the data collected in the measure phase. In this post we will look at six sigma tools for root cause analysis.
During the improve phase the team generate, refine and select solutions to improve the process and reduce output variation.
The control phase is about sustaining the gains made in the improvement phase. This is achieved through process documentation such as standardised work, training and policies and procedures.
Tools used in the Define Phase of a Six Sigma DMAIC Project
In this chapter, we are going to look at some of the six sigma tools used during the define phase of a DMAIC project. The tools described are the ones that are most commonly used. However it is not essential to use every tool outlined below. It’s also entirely possible to use other tools if they help the team move closer to their goals.
The define stage is the starting point for any six sigma project. Many of the tools used in the define phase of a dmaic project are to document the project. What it is the team is responsible for improving, how they will do that, when it will be done by and who will be involved. When it comes to fixing a problem in an existing business process, six sigma uses the DMAIC methodology.
A project charter is a single document that contains the six sigma project definition. In other words, all of the information about the project. The project charter provides a single reference point for anyone wishing to find out about the particular project. The format varies from one organisation to another but the key fields will be very similar, including:
- Project Title
- Project Leader
- Other Team Members
- Project Start Date & Anticipated
- Cost of Poor Quality
- Voice of Customer
- Process Problem
- Process Start/Stop Points (Scope)
- Project Goals
- Project End Date
Fundamentally, the project charter “helps team members distil the critical elements of the business case” (RASIS et al., 2002). By combining the customer requirements and the business case together, the project charter ensures that the project results have a significant impact.
During the define stage of dmaic, the black belt will create a project plan. This is a formal document which states when a project’s objectives are to be achieved. It usually takes the form of a Gantt chart, and shows the major milestones, activities and resources required. It is an extremely useful tool for the black belt, as it helps manage the project and keep it on track. The plan will also allow:
- Identification of:
- tasks (work breakdown structure)
- task relationships and dependencies
- schedule constraints
- Optimisation of:
- resource allocation
- scheduling (SLACK et al., 2001)
A process map is a visual representation of how a product or service moves through a process. Three types are commonly used in six sigma, each of which is now discussed in turn.
A high level process map is useful when establishing the project scope and boundaries. The most commonly used type is a SIPOC diagram as shown on the right.
SIPOC stands for Supplier, Input, Process, Output, Customer. The benefit of using this diagram is that it allows the project team to identify the customers who they need to satisfy. Working backwards they can then identify which parameters are considered critical by the customer (price, weight etc.) and the processes that have the biggest impact on those parameters. It is also necessary in some cases to trace the problem back further and look at the inputs to the process, or even the supplier as this may be where the root cause of a problem lies.
2) Process Flowcharts
Flowcharts are widely used to create a detailed process map, which can have many uses within a project:
- They allow the project team to see if the flow is logical and to identify potential bottlenecks.
- They represent a snapshot of the process at any particular point in time. As a result they can be drawn for the ‘as-is’ state and future state to highlight the difference the project could make. In the case of streamlining a process, there would be far less steps on the future state diagram.
- They allow identification of delays and non-value adding steps.
- They can also aid brainstorming for problems in the process.
One advantage of flowcharts is that if standard symbols are used (diamond, oval, rectangle etc.), it becomes possible for an outsider to the process to understand what is going on. This is obviously very useful for six sigma project teams, where certain members will have had no previous experience with the process. However, if the flowchart is not accurate, the advantages they present are diminished. This can occur when people working in the process are afraid to admit to what actually happens, or when the project team does not include a representative from the process.
3) Functional Deployment Diagrams
A functional deployment diagram is simply a flowchart that has been organised into ‘lanes’. The advantage of this approach is that each lane can be used to represent individuals or departments, which makes it possible to see, at a glance, who is responsible for each step in the process. Here is an example of a simple functional deployment diagram.
Surveys are a simple method of collecting data directly from an individual, and are typically used to gather the voice of the customer in a six sigma project. The advantages of a survey are the low cost per response, the good ability to reach a dispersed population, the lack of potential for interviewer bias and the high ability to ascertain the truth on sensitive questions. The disadvantages are the slow and often poor response rate, lack of flexibility in questions and the high potential for misunderstanding a respondent (BRASSINGTON & PETTITT, 2003). As a result, surveys tend to be used in conjunction with other market research tools such as interviews, focus groups and observational research.
Kano analysis is a quality measurement tool used to facilitate better understanding of the value that customers place on a product or service. Improved understanding in this area helps align products with customer requirements and reduce the risk of over-emphasising features that have little importance. The advantages of using Kano analysis are:
- It is a good ‘first cut’ technique to evaluate relative importance of customer requirements.
- It allows identification of segments by the type or level of quality that customers expect.
- It helps determine if there are requirements that:
- were not explicitly stated by customers.
- were included in previous offerings and are still valued by the customer.
- It helps shape VOC data-gathering plans. (GEORGE et al., 2005)
The analysis is based on Noriaki Kano’s model shown in the diagram. The horizontal axis represents product functionality and the vertical one customer satisfaction. Traditionally, the relationship between the two was assumed to be linear. An example would be battery life or warranty period for a digital camera, the longer they last, the more satisfied the customer will be. Kano identified this type of requirement as ‘one-dimensional’ and proposed that it was actually one of three types, the other two being ‘delighters’ and ‘must-haves’.
A must-have requirement occurs where customer satisfaction decreases as product functionality decreases, but where customer satisfaction never rises above neutral, no matter how functional the product becomes (WALDEN (ed.), 1993). For instance, having poor reception on a mobile phone will cause dissatisfaction; having good reception, however, does not raise the level of customer satisfaction – it is expected. A delighter occurs where customer satisfaction increases as product functionality increases, but where satisfaction does not decrease if functionality decreases (WALDEN (ed.), 1993). Basically, delighters are luxury features that can be useful, but would not be missed if they were omitted, such as a rear window wiper on a car. A company should primarily focus on ensuring all must-have requirements are satisfied. If they are not, then it will not matter how well they do on other features. Satisfying must-haves is a basic market entry requirement. Providing delighters offers a competitive edge and the chance to excel, assuming all basic requirements are satisfied.
Quality Function Deployment (QFD)
QFD is “a structured method in which customer requirements are translated into appropriate technical requirements for each stage of product development and production” (DANIELS et al., 2002). It was pioneered by Yoji Akao and Shigeru Mizuno and aims to ensure that customer satisfaction is achieved first time, every time. It makes use of a number of other tools and combines them in a framework known as the House of Quality, shown below. “These tools are a series of tables and charts to then systematically disaggregate customer needs into prioritised product requirements, functions, technology, systems, subsystems, parts, reliability, cost, manufacturing, production, equipment setup, operator training and process controls”
The benefits of using QFD are that it:
- Can improve a company’s processes, products or services.
- Requires fewer resources than other quality tools.
- Can produce a faster outcome than other methods.
- Provides definition to the design process.
- Helps a team stay focused.
- Helps present information graphically, which allows for easy management and peer review of activities.
- Leave a team well positioned if they need to improve upon results for future processes, products or services.
(REVELLE et al., 1998)
However the disadvantages are that the whole analysis depends upon the strength of the method used to collect the VOC. If that is done poorly then QFD can not make up for it. It can also be a time consuming process; it takes time to fill out all the matrices correctly and therefore makes adapting to rapidly changing market needs more complex.
The House of Quality contains six major components:
- Customer requirements: Lists requirements derived from the VOC.
- Technical requirements: Lists potential product features.
- Technical correlation matrix: Highlights product features that compliment each other and also potential clashes.
- Interrelationship matrix: Displays the relationships between product features and customer requirements. The relationship is usually rated on an appropriate scale to aid prioritisation.
- Planning matrix: Displays relative importance of customer requirements as well as company and competitor performance in meeting these requirements.
- Priorities, benchmarks and targets: Displays the priorities assigned to product features and levels of performance of competitive products. The final output is a set of targets for each requirement.
The storyboard acts as the main communication tool in a six sigma project. It is updated by the project leader as the project progresses and is presented to stakeholders at the end of each DMAIC phase. It contains information about the progress of the project and keeps enthusiasm for the project high. It also provides a review opportunity since the team can check that every step has been completed. Consider the define phase as an example. One of the key objectives of this phase was to identify and validate the improvement opportunity. Once this has occurred, a storyboard will be presented to upper management to explain why the project is worth pursuing. It may also be the case that the storyboard is used to explain why the project is not feasible. The result is that management become aware of the plan and has an opportunity to give the go ahead. After all it is they who will have to authorise the resources to implement the solution.
Summary of Define Phase Tools
Once the define phase of dmaic has been completed, the team is ready for the gateway review. This is an opportunity to present the project to the main stakeholders, and to gain consensus around the problem being solved. If the team has done it’s job well and the stakeholders agree, they are ready to move into the measure phase.
Tools used in the Measure Phase of a Six Sigma DMAIC Project
In this chapter we are going to run through the main tools used during the measure stage of a six sigma DMAIC project.
The main purpose of the measure phase in dmaic projects is to establish a baseline for the current process performance. Once the existing level of variation is understood, the project team can analyse which inputs and process controls affect the output most significantly. This allows them to more efficiently improve the process than if they had taken a trial and error approach. One of the other measure phase deliverables is a measurement systems analysis. This is critical to ensure the observed variation is not caused by the measurement equipment or operator. Lets have a look at some of the tools used in the measure phase of dmaic.
Data Collection Plan
A data collection plan is used to manage the dmaic measure phase. It starts off by stating what needs to be measured, which is usually derived from Voice Of the Customer data gathered during the define phase. The next step is to define ‘operational definitions’. These are understandable, unambiguous descriptions of what is to be measured or observed to ensure consistency (PANDE et al., 2000). A simple example of the importance of tight operational definitions is asking a group to go and count how many cars they can see of a certain colour. Unless the colour is properly specified, it is highly likely that the observers will arrive at different results. The next stage is to develop a data collection form. It should be simple and easy to use to avoid mistakes occurring as it is completed. This requires stratification of data. For example, it might be necessary to measure how many customer returns are received. This would be a simple measurement. However, later on it might be necessary to break the number down to show where the returns were from or the reasons for their return. If this data is not collected in the first place then it creates problems later. Therefore an important aspect of the data collection plan is to predict what layers of data will be required.
Gauge Repeatability & Reproducibility (GR&R)
This is a test of the effectiveness of a measuring system and determines the proportion of variability contributed by the measurement system to the total variation. It involves repeating a measure in various environments to test against four criteria (PANDE et al., 2000):
- Accuracy. How precise is the measurement?
- Repeatability. If one person or piece of measuring equipment measures the sameitem more than once, will the same value be returned?
- Reproducibility. If two or more people or machines measure the same thing, willthey get the same results?
- Stability. Over time, will accuracy or repeatability deteriorate or shift?
One of the outputs of a GR&R study is the operator-part graph similar to that shown above. It is used to determine if there is a relationship between the operator taking the measurement and the part being measured. If the lines connecting the plotted points diverge significantly, indicating that operators are consistently measuring differently, then action needs to be taken.
A check-sheet can take many forms but is fundamentally a tool that aids a person to capture data. Some common types of check-sheets include the following (PANDE et al., 2000):
- Defect or Cause Check-sheet. Used to record types of defects or causes of defects.
- Data Sheet. Captures readings, measures or counts quantities.
- Frequency Plot Check-sheet. Records a characteristic of an item along a counted scale or continuum.
- Concentration Diagram Check-sheet. Features a picture of the item or document being observed; data collectors then mark where problems, defects or damage are seen on the item.
- Traveller Check-sheet. A ‘traveller’ is any kind of check-sheet that ‘moves’ with the product or service through the process. Data about that item is then recorded in an appropriate place on the form.
The advantages of using check sheets include (GEORGE et al., 2005):
- Faster capture and compilation of data.
- Consistent data from different people.
- Captures essential stratification factors that might otherwise be overlooked.
Control charts are easy-to-use charts which display graphically both special and common cause variation in a process. They are sometimes called Shewhart charts, after their inventor, Walter Shewhart. A control chart consists of a centerline, typically the mean, as well as upper and occasionally lower control limits. The data is then plotted over a period of time. An example of a control chart is shown in the diagram.
It is possible to use control charts to identify special cause variation using several ‘rules of thumb’:
- 1 data point falling outside the control limits indicates a shift in the mean, an increase in the standard deviation, or a single aberration in the process.
- 6 or more points in a row steadily increasing or decreasing indicates a drift in the process mean (or standard deviation if they only increase).
- 9 or more points in a row on one side of the centerline indicates a shift in the process mean or standard deviation.
- 14 or more points alternating up and down indicates systematic effects, such as two alternately used machines, vendors or operators.
Summary of Measure Phase Tools
The six sigma measure phase is the second step after the define stage. During the six sigma measure phase tools such as control charts, check sheets, Gage R&R and data collection plans are used. One of the most important deliverables of the measure phase is a measurement systems analysis. This is to ensure that the variation being measured in the process is indeed caused by the process and not variation in the measurement process itself. Once the measure phase is complete, another gateway review is held with the project stakeholders. Then it is time to move onto the analyse phase.
Tools used in the Analyse Phase of a Six Sigma DMAIC Project
In this chapter we’re going to be looking at the tools used in the analyse phase.
The analyse phase in six sigma is where the project team take a detailed look at the data they collected in the measure phase. The analyse phase deliverables are to identify the vital few inputs and process variables they need to adjust to properly control the outputs of the process. Once this relationship is understood they can start applying that knowledge to reducing variation in the outputs during the improve phase. Lets have a look at some of the tools used in the analyse phase of DMAIC.
Pareto charts are bar charts in which the horizontal axis is split into categories such as defects. The chart then shows the frequency of each defect occurring in descending order of magnitude. Often a line graph is superimposed to show the cumulative frequency. The benefit of using a Pareto chart is that it clearly displays where to focus improvement efforts by separating the few problems of high importance from the many problems with low importance.
5 Whys is a tool that is used to find systematic causes of a problem so that an appropriate corrective action can be implemented. It simply involves asking why at least five times until a root cause is established. It requires taking the answer to the first why and asking why that occurs and so on (LIKER, 2004). In the video The 5 Whys, Eric Ries, entrepreneur-in-residence at Harvard Business School, explains how to find the human causes of technical problems using 5 Whys.
A scatter diagram is a graphical representation of the relationship between two parameters, typically an input and an output variable, in a process. In some cases the points will be scattered all over the graph with no visible trends. This data shows no correlation, meaning that no relationship exists. Otherwise a line of best fit is plotted to show what type of relationship exists between the two parameters:
- data sloping from the bottom left to the top right indicates positive correlation (as one variable increases, so does the other).
- data sloping from the top left to the bottom right indicates negative correlation (as one variable increases, the other decreases).
- data that displays a visible trend that is not linear usually indicates some other factor at work that interacts with one of the other factors. Multiple Regression or Design of Experiments should be used to discover the source of these patterns.
(GEORGE et al., 2005)
However, correlation does not always imply causality. Just because two factors display a relationship does not prove that one variable is causing the other. It is possible for a ‘lurking variable’ that was not included in the analysis to be the underlying cause.
Ishikawa diagrams are so named after their inventor Kaoru Ishikawa. They are also commonly called cause and effect or ‘fishbone’ diagrams.
The line along the centre represents the problem with all the possible causes branching off it. This makes it possible to group similar causes around the different branches. The advantage of using this tool is that causes are arranged according to their level of importance. Typically the main branches used for a manufacturing process are the 4 M’s: man, machine, method, material. Sometimes an additional two M’s are added which are measurement and mother nature. In the case of a service process, the four main categories used are usually equipment, policies, procedures and people.
Non-Value Added (NVA) Analysis
A tool used to distinguish the process steps that add value to the product compared to those that do not. A value added step is one for which customers are willing to pay. Its objective is to identify and eliminate the steps in which no value is added. This reduces hidden costs, process cycle times and process complexity, which in turn leads to a reduced risk of an error occurring.
A t-test is used to test whether a statistical parameter such as the mean is significantly different to another value. For example a one-sample t-test could analyse if the mean diameter of a sample of gears is significantly different to the target value. A two-sample t-test could analyse if the mean diameter of gears from one supplier is significantly different to that from a second supplier. In the example, the null hypothesis would be that the samples from each supplier do not differ significantly. The alternative hypothesis would be that they do differ significantly. The results show that the calculated value for t is greater than the tabulated value for twenty degrees of freedom at the 5% level and therefore suggest that the two samples differ significantly.
A chi-squared test is used to test the validity of a hypothesis when both the input variable and output variable are discrete. An example would be “does the operator affect whether the product passes on the test rig?” In this case the null hypothesis would be that the two variables are independent and therefore the choice of operator makes no difference to the test rig outcome. The alternative hypothesis is that the choice of operator has an effect on whether or not the unit passes or fails on the test rig.
Analysis of variance, or ANOVA for short, is used to compare three or more samples to see if their means differ significantly. To do this, a one way ANOVA considers three sources of variability, total, between and within. Total refers to the total variability between all observations. Between refers to the variation between subgroup means and within refers to the random variation within each subgroup or ‘noise’ (GEORGE et al., 2005). A one-way ANOVA, involves just one factor and tests whether the mean result of any alternative is significantly different. An example would be the fuel consumption of three different cars. A two-way ANOVA uses the same principles as a one-way, but tests how different levels of two factors affect the output variable. An example would be the fuel consumption on the three cars being driven in say two different locations. The two-way ANOVA has the added benefit of showing the impact that the location has on the output variable. It may not be significant, but if one location was much hotter than the other then it is likely the drivers would use the air conditioning system and therefore increase fuel consumption.
Design Of Experiments (DOE)
Design of experiments is used to test and optimise a process. It uses tests of statistical significance, correlation and regression to enable understanding of process behaviour under varying conditions. It differs from empirical observations because rather than just observing, it allows control to be taken of the variables. It is useful for:
- Finding optimal settings.
- Identifying and quantifying the factors that have the biggest impact on the output.
- Identifying factors that do not have a big impact on quality or time (and therefore can be set at the most convenient and/or least costly levels).
- Quickly screening a large number of factors to determine the most important ones.
- Reducing the time and number of experiments needed to test multiple factors. (GEORGE et al., 2005)
Traditionally, to understand a system, experiments are carried out that test only one variable at a time. This can be time consuming and expensive as many ‘runs’ can be required. DOE is much more economical and efficient:
“DOE takes the art out of experimentation and substitutes science in its place.”
~ HOCKMAN & JENKINS, 1994
The other advantage that DOE has over the one-at-a-time approach is that it will highlight interactions between factors. The basic steps are as follows:
- Identify factors to be evaluated.
- Establish levels of factors to be tested.
- Create an experimental array.
- Conduct the experiment under the prescribed conditions.
- Evaluate the results. (PANDE et al., 2000)
Taguchi methods are similar to DOE in that they are both tools used to analyse a process that has multiple input variables that affect the output variable. The main difference is in the way they handle interactions between input variables. DOE starts off by assuming all inputs interact with all other inputs, whereas Taguchi Methods assume that some knowledge of the process already exists, which is used to make the experiments more efficient (CESARONE, 2001). By not investigating interactions that are known not to exist, the number of runs can be reduced and therefore results arrived at more quickly.
Another difference is that Taguchi Methods distinguish between factors that are controllable (control factors), and those that are not (noise factors). Due to the reduced number of tests, Taguchi Methods manage to test each combination more than once, typically with different levels of noise factors:
“For example, we might test a particular set of inputs at high temperature and high humidity, high temperature and low humidity, low temperature and high humidity and low temperature and low humidity…to inject maximum variability into experimental designs.”
~ CESARONE, 2001
The reason for doing this is that it not only determines which combination produces the highest output, but also which is the most repeatable or ‘robust’.
Regression is used in conjunction with scatter graphs to define a model that links the input variables to the output variable, which can then be used to predict future performance. Linear Regression is used for one input and one output variable. Multiple Regression is used when there is more than one input variable and is particularly useful because it quantifies the impact of each input on the output and shows how they interact.
Summary of Analyse Phase Tools
The six sigma analyze phase is the third of the five stages of a DMAIC project. This is where a number of statistical analyses are used to identify the variables which consistently control the process. Every project is different of course, but tools typically used during the DMAIC analysis phase include linear regression, design of experiments and Pareto analysis. Once the analyse phase deliverables are complete, the next step is the improve phase!
Tools used in the Improve Phase of a Six Sigma DMAIC Project
We’ve already looked at tools used during the Define, Measure and Analyse phases, so we’re ready to look at the Improve phase in this chapter.
During the improve phase in six sigma, the project team use tools to generate, refine and select solutions. Data from the analyse phase is used to improve the process by reducing variation in the outputs. Lets look at some of the tools used in improve phase:
Brainstorming is a tool used to encourage creative thinking. Creativity is encouraged in a group situation by not allowing comments or criticisms to be voiced until everyone has run out of ideas. All ideas are considered legitimate no matter how radical they are and get listed on a diagram. Once everyone has had their input it is possible to go back and add more information to the diagram such as advantages and disadvantages of each idea. This helps start the process of turning the raw list into a more useful prioritised one. However a more effective tool for ranking ideas from a raw list is the Affinity Diagram.
The affinity diagram is a technique used to discover related groups of ideas from those in a raw list. As a result, affinity diagrams are commonly used to process the outcome of a brainstorming session. Ishikawa recommended that these diagrams be used in situations where information about a problem is not well organised.
A good tip here is to conduct the brainstorming process using sticky post-it notes. Everyone writes down their ideas, one per note. Everyone’s notes get stuck on a wall or board as per the normal brainstorming process. However, because of the use of post-it notes, it’s really easy to move them around and group them into an affinity diagram without having to write them out again.
5S is a cycle used in Japanese industries to eliminate the wastes that contribute to defects and also injuries in the workplace. The term 5S refers to the five stages of the cycle which all begin with the letter S in Japanese. They are seiri, seiton, seiso, seiketsu and shitsuke, which when translated into English mean:
- Sort – sort through items and keep only what is needed while disposing of what is not.
- Straighten – “A place for everything and everything in its place”.
- Shine – The cleaning process often acts as a form of inspection that exposes abnormal and pre-failure conditions that could hurt quality or cause machine failure.
- Standardise – develop systems and procedures to maintain and monitor the first three Ss.
- Sustain – maintaining a stabilised workplace is an ongoing process of continuous improvement.
Implementation of the 5Ss is a simple and quick way to make improvements to a process. Over a period of time wastes can build up and hide problems to such an extent that they become accepted. By performing 5S on a process it may make identification of root causes much easier.
Poka Yoke (Mistake Proofing)
Poka-yoke is a concept created by Shigeo Shingo to reduce the risk of a human error turning into a defect. It is typically a device that is used to either detect or prevent defects from occurring in the first place. The benefits to the business are that less energy, time and resources are wasted. Poka-yokes are a cost effective alternative to full automation:
“Simple fail-safe methods are the low-cost route to parts-per-million error rates.”
~ Hall, 1987
The best way to understand the concept is through an example. Check out this video.
Simulation is used to test the impact that a solution will have without actually implementing it. It usually takes the form of a computer model that can emulate real world conditions. The advantages offered by conducting a simulation are that it is usually much cheaper and faster than building multiple prototypes for testing. Another advantage is that a computer simulation can offer a level of detail that is difficult to measure with current technology. An example of this would be surface interactions on an atomic level. There are also drawbacks to simulations which usually result in misleading results. These are generally down to a poor model being used that does not accurately represent the real world situation.
Benchmarking is the process of identifying, understanding, and adapting best practices inside and outside the organisation. These are then applied to processes to improve performance.
“Many Fortune 500 companies and other large organisations have embraced benchmarking as an important, systematic methodology for achieving the organisation’s strategic objectives.”
~ APQC, 1995
The Pugh matrix is a decision making tool used to compare solutions based upon customer requirements and functional criteria as shown in the image. It identifies pros and cons for each solution allowing the team to capitalise/improve upon them in successive iterations, until an optimal solution is arrived at (GEORGE et al., 2005).
One of the benefits of using a Pugh matrix is that the criteria can be weighted based on their importance. This helps improve the scoring so that the most appropriate solution – the one that best satisfies the most criteria – comes out on top.
Using this tool can also help remove the emotion from the selection process. It’s not uncommon for individuals to become so invested in their own ideas that they can’t appreciate those of others. The Pugh matrix helps take the emotion out of the selection process and instead focuses on facts, data and the consensus of the team.
Cost Benefit Analysis
A cost benefit analysis lists all the possible solutions that have been suggested by the project team, and considers the benefit they offer against the cost of implementation. It is a useful tool for selecting a solution from a shortlist, whilst keeping in focus the needs of the business. A highly technical and therefore costly solution is not always the best if the project only affects a minor proportion of customers or products. A cost benefit analysis can also highlight areas where more analysis is required before a solution can be selected.
Summary of Improve Phase Tools
The improve phase is the fourth in the five steps of six sigma. It is the stage in the project where the team applies what they learned in the analyze phase to reduce variation in the process. The process is improved and this is verified by comparing to the benchmark set in the measure phase. The final step after the improve phase deliverables have been signed off at the gate review, is to move onto the control phase in dmaic.
Tools used in the Control Phase of a Six Sigma DMAIC Project
So we’ve finally reached the final chapter in this six sigma tools guide! In the previous chapters, we’ve looked at the tools most commonly used during each phase of a six sigma DMAIC project. It’s now time to consider the control phase.
In the control phase of a DMAIC project the team focuses on sustaining the gains made in the improve phase. The aim is to control the process to prevent results reverting back to how they were before. The new way of working needs to become the standard for future continuous improvement efforts. This is similar in some respects to the check and act elements of the PDCA cycle. It’s about continually monitoring progress so that small incremental changes can be made to keep on track. The control phase gate review should not be passed until everyone is confident the process is indeed in control! Lets have a look at some of the control phase activities:
A control plan is used to facilitate the transition of control from the six sigma project team back to the process owner. It contains documentation of the new process including work instructions, answers to frequently asked questions and new process maps. It also defines the measures that are to be used to monitor process performance and usually a control chart to visually display the information. Another element of a control plan is the contingency plan, which informs the process owner how to tell when corrective actions are required to maintain stability, and quick-fixes they can use in that situation. The final element is a continuous improvement plan, which lists opportunities for further improvement that were discovered during, but were outside the scope of the project. This closes the loop in the DMAIC cycle by offering starting points for future projects.
Standardised work is defined as work in which the sequence of job elements has been efficiently organised, and is repeatedly followed by a team member (DENNIS, 2002). It represents the best way to do things and prevents the need to reinvent the wheel with every new project or manager. Best practices are documented and a copy is available at every work station for reference by the operator. An important part of standardised work is the opportunity for the operator to make suggestions about how the process might be improved. These are tested and where they prove to be successful get incorporated into the documentation. Standardised work is one of the fundamental elements of the highly acclaimed Toyota Production System, as it maintains predictability, regular timing and regular output of processes (LIKER, 2004).
Failure Modes & Effects Analysis (FMEA)
An FMEA is a systemised group of activities intended to recognise and evaluate the potential failure of a product or process, identify actions that could eliminate or reduce the likelihood of the potential failure occurring and document the entire process (CHRYSLER et al., 1998). The benefits that accrue from using this tool are:
- Identification of failure modes.
- Estimation of risk associated with the failure modes.
- Produces prioritised actions to reduce risk of failure.
- It evaluates the current control plan.
(GEORGE et al., 2005)
Summary of Control Phase Tools
The control phase in six sigma is really a safety net. It allows the project team to review the project as a whole and ensure that the improvement that has been made is going to be sustained. Unfortunately the control phase in project management circles is the least well understood and is often implemented poorly. It is less exciting than the improve phase and generally involves paperwork exercises like updating policies and procedures and rolling those out to under resourced managers who don’t take the time to understand what has changed. The last thing the company wants is for the problem to recur and have to cover old ground again. Sadly this is often the case. They would be much better to ensure all of the control phase deliverables have been completed thoroughly. That way the benefits of the improvement can be claimed, the process owner can take control and the project team can be disbanded or better still, move onto the next improvement opportunity!
Having considered the DMAIC cycle as a structured application of quality improvement techniques, as well as the tools and techniques themselves, it can be seen that six sigma draws from a diverse range of subjects including project management, team working and statistics. One of the main problems associated with the large toolbox is that it is easy to lose sight of the target and end up using tools for the sake of it. It is therefore important to be able to judge which tool to use at each point in the project. Hopefully this post has helped with that task!