How Much Data do I Need?
That is an excellent question that can either have an exacting answer or one that is ambiguous. Formulas exist to calculate an exact answer to the question except that until you collect some data you won’t know the values for some of the variables in the equation. In both cases, either continuous data or discrete data, we assume an infinite population that we are sampling from.
First, take a look at the case for continuous data. With continuous data we can describe a data set with the mean and the standard deviation. The mean provides the distribution’s location and the standard deviation provides the measure of the variability in the data set. If the estimate of concern is the mean of the data set the minimum sample size can be calculated from the following formula.
Where n is the estimate for the required minimum sample size, s is the standard deviation of the data we are sampling which is unknown, and d is the +/- confidence interval we want to have about the estimate of the mean based on our sample size. Our confidence interval is 95% if we are within +/- 2s of the mean, which is why we have 2s in the formula. The only problem with the formula is we have to guess at the standard deviation, s.
To handle the ambiguity of the unknown standard deviation the rule of thumb is to collect about 20 data points and then calculate an estimate of the standard deviation. Use that standard deviation in the above formula. If you need more than 20 data points then collect the additional data. With the proper amount of data points collected based upon the formula you can now estimate the mean with the confidence interval d that you have specified.
The following example will illustrate the use of the formula. We want to estimate the mean of our process, but how much data do we need? The confidence interval, d is +/- 0.5 and the standard deviation s=2.0 as calculated from our initial sample of 20 data points.
Based on the calculation above the minimum sample size is 64 so an additional 44 data points are required to estimate the process mean with a 95% confidence.
Second is the case for discrete data. With discrete data we can describe a data set with frequency of occurrence or percent of occurrence. We often view a process by percent yield (goodness) or percent defective (poorness). This can also be classified as percent agreement or percent disagreement with a point of view such as a poll question. The estimate of concern is a proportion, or percentage at a 95% confidence within a delta of some +/- percentage points.
In the case of discrete data the minimum sample n required to estimate a proportion with a 95% confidence can be calculated using the following formula. As with continuous data we specify the confidence interval d, but with discrete data it is a +/- percentage point spread about the proportion we are estimating.
Where n is the minimum sample size, p is the proportion we are trying to estimate which is unknown, and d is the +/- percentage point spread we are specifying about the estimate of the proportion. The 2 in the formula is the factor that provides the 95% confidence in the estimate of the proportion based upon the minimum sample size from the formula.
The unknown that drives the equation is the value of the proportion we are trying to estimate. Until we collect some data we really don’t know the value of the proportion. Make an educated guess at the proportion and use the formula. Collect the data and then calculate the proportion. Plug that proportion into the formula and determine is more data is required.
The following is an example. We would like to know the percentage of registered voters who are dissatisfied with the performance of the United States Congress. The confidence interval is specified as +/- 3%, or d=0.03. Because we are uncertain the proportion to use to begin with is 50%, or a p=0.5.
From the formula above we require a minimum sample of 1122 registered voters. If it turns out the actual proportion is either greater or less than p=0.5 we already have an adequate sample. Next time you see a poll in the newspaper check out the sample size and the value for d. Now you know where those numbers are coming from.
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The Importance of Planning Data Collection
Whenever we are trying to improve a process, a product, or the delivery of a service information, or data, is required to zero in on the root causes of the problems that are getting in the way of success. Generally, there are numerous questions that we need to answer. The answers are within the data that we collect.
Planning the data collection narrows our focus to assure we get the right data that will answer the burning questions we have about the problems facing us and the root causes of those problems. A data collection plan is comprised of the following:
Before collecting any data make sure there is a well defined purpose for gathering the information, which is supported by a detailed plan. The reasons for collecting data are the following:
The Importance of Planning Data Collection is to prepare us to Efficiently Take Actions that will Solve the Problems facing us.
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What is the difference between Process Sigma and Process Capability?
In both cases of Process Sigma and Process Capability we are talking about performance relative to the Customer’s requirements. Is there a difference? Is one measure better than the other? These are good questions which you will be able to answer by reading on.
The difference between the Six Sigma metric of Process Sigma and Process Capability relates to the definitions of these performance metrics. In both cases the Customer Specifications are compared to process performance. For metrics that can be measured on a continuous scale the process mean and standard deviation must be calculated. If the metrics from the process are discrete, or attributes, then the percent defective is calculated. In either case the process performance as measured on a control chart should exhibit only natural random patterns of variation, or what is often called common cause variation over time.
Process Sigma
Process Sigma is defined by numeric levels that are related to a process’s output of defects per million opportunities. Defects are defined as any failure to meet the customer’s specifications. Process yield is used to look up the Process Sigma level from a table. Yield is based on Defects (D), Units Processed (N), and the number of Opportunities (O) for a defect to occur. Once the yield is calculated the Process Sigma can be found in the table below.
Find the Process Sigma level in the table above.
If the process output can be measured on a continuous scale the process average and standard deviation are used in a formula that compares the average to the closest specification, whether it is the Upper Specification or the Lower Specification we choose the one closest to the process average. Basically we are making this a one sided specification. The standard normal distribution is used to estimate the defect rate which can then be converted to yield and use the above table to look up the Process Sigma level.
The other method is to calculate the Z statistic which estimates the number of standard deviation units the average is away from the closest specification which is based on the standard normal distribution. We then add a 1.5 sigma shift to the calculated Z statistic which gives us the Process Sigma level directly. The 1.5 sigma shift has been a matter of contention, but it is part of the definition of Process Sigma. The following is the formula.
The definition of Process Sigma is that at a level of 6 on the scale there will be only 3.4 defects per million opportunities. The process is assumed to remain stable over time and if it drifts in one direction at a time of 1.5 sigma there will be no more than 3.4 defects per million under the tail of the distribution outside of the Customer’s Specification. When we calculate the Z statistic we add the 1.5 sigma shift to give us the Process Sigma level directly.
Process Capability
Process Capability assessment begins with control charts to evaluate the stability over time for the process. For the Process Capability study to be meaningful the process must exhibit only common cause variation, which are natural patterns of random variation.
In the case of Attribute, or discrete variables, data the Process Capability becomes the centerline of the Control Chart, or the average p, c, or u depending upon the chart being used. In the example below the average p, or fraction non-conforming, is 0.076, or 7.6% defective. The chart is in statistical control so the capability is the overall process average of percent defective.
In the case of continuous variables data the mean and standard deviation from the stable, or in statistical control, process comes into play. We will describe 2 of the many Process Capability Indices. The first is the Cp which compares total process variation to the total width of the specification. The second is Cpk which takes into consideration centering of the process within the specification so we look at ½ of the process variation compared to the closest specification limit.
The Cp formula below divides the specification width by the measured process spread of 6 standard deviations. A fully capable process has a Cp = 1.33 or greater.
The Cpk formula below divides the absolute value of the difference between the process average and the closest specification by ½ of the measured process spread of 3 standard deviations. A fully capable process has a Cpk = 1.33 or greater.
The Cp value indicates how capable a process can become if it is perfectly centered. The Cpk value indicates how much work is needed to get centered. In the following table notice that for a Cpk = 1.33 the Process Sigma = 5.5. A process that operates at a Process Sigma = 6 has a Cpk = 1.50. The Z Value is the number of standard deviation units away from the specification when the data is converted to a standard normal distribution.
Table of conversions from Z to Cpk and Process Sigma.
Both measures of performance use that same statistics to be computed. Whether you prefer Process Capability or Process Sigma the key is to apply the metrics consistently. To be valid your process must be in a state of statistical control and exhibit only common cause variation.
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SIPOC Approach to As Is Analysis
SIPOC is an acronym that stands for Suppliers, Inputs, Process Steps, Outputs, and Customers. This high level process mapping method is ideal for diagnosing and analyzing the current state, or As Is condition of any business process. The following outlines the SIPOC approach to As Is analysis and improvement solution identification.
The first task is to assemble a small team, 3-5 people, who play roles within the process under analysis that are familiar with the activities that take place within the process. You may find that this team collectively knows the process from end to end even if some of the team members individually do not. The team identifies the one key performance metric that defines success or failure for the process.
SUPPLIERS are the functional groups that provide the essential ingredients for the process to perform. Create this list may include actual supplier companies that are contracted for goods and services as well as the internal groups and individuals that provide the things that make the process tick. You may miss a few initially, but you can always add suppliers during the process mapping activity when they are identified. If the process needs something, which Supplier provides it?
INPUTS are the essential ingredients provided by the Suppliers. Those ingredients could be materials, goods, services, data, instructions, decisions, documents, analyses, or whatever the process requires. For each Supplier make a list of all the inputs they provide.
PROCESS STEPS are the 5 high level steps from the beginning to the final outcome for the process. Start with Step 1 and Step 5. That defines the end to end scope of the As Is process analysis. You can think of the process steps as the 5 key value stream steps. Fill in Steps 2 through 4 after Step 1 and 5 are defined. The performance metric should be measureable at Step 5 to determine process success or failure.
OUTPUTS are the result of each individual step in the process. After completing Step 1 what is the thing that is generated by this process step. In some cases a process step may have multiple outputs. When you list the outputs they are in groups associated with their respective process step and should flow in order from 1 through 5.
CUSTOMERS are the recipients of the process outputs. The process step generates something and it goes to a functional group which is either internal to the process or external to the process.
With the SIPOC Process Map completed the team collectively has a better overall understanding of the process from end to end. Now is the time to challenge the process to identify where the issues are. What are the causes of either success or failure based on the key performance metric? Brainstorm using the Cause and Effect, or Fishbone Diagram. This technique identifies the potential causes of the process issues.
To develop solutions the SCAMPER brainstorming technique is very useful. In this structured method the SCAMPER acronym is used to challenge the process for improvements by asking what could be Substituted, Changed, Adapted or Amplified, Modified, Put to other uses, Eliminated, or Replaced / Removed / Rearranged. Now you know the SIPOC approach to As Is analysis and improvement solution identification.
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Designing Environmental Sustainability Programs using Process Design for Six Sigma
Environmental Sustainability is in the best interest of the Global Economy and Global Corporate Citizenship. More and more companies are global in nature especially when you consider their supply chains and the total life cycle of their goods and services. Environmental consciousness is increasing in importance and a key for competing in the global economy.
Customizing your company’s approach to defining and applying environmental best practices requires a systematic approach for process design. Process Design for Six Sigma (DFSS) follows a five phased approach, namely Define, Measure, Analyze, Design, and Validate (DMADV).
The Define Phase is the Development Project Definition. In this phase the scope, or depth and breadth, of the Environmental Sustainability Program is defined. Here is where the resources are committed to the project, the project is planned, and the review points with specific deliverables are defined.
The Measure Phase is Requirements Definition. In this phase the Voice of the Customer (VOC) is captured which in turn is translated in the requirements for the Environmental Sustainability Program. The VOC comprises your suppliers, your company, your customers, and the countries where you conduct business. This comprehensive view of the VOC requirements drives the right sizing of the program. A Functional Model of what Environmental Sustainability has to accomplish and provide is aligned and prioritized using the VOC. At this stage requirements are fully defined.
The Analyze Phase is better described as the Conceptual Design. What must be accomplished to meet the VOC has been documented with the prioritized functional model. To meet the functional requirements processes are designed at the conceptual level using the SIPOC (Suppliers, Inputs, Process Steps, Outputs, and Customers) Process Mapping method. In this method only five process steps are addressed which keeps the maps at a high level. Information System requirements are defined to support each SIPOC Process that has been designed. Performance Metrics are also defined. Estimates of cycle frequency, cycle time, and staffing for each SIPOC all also determined. Organizational requirements are conceptualized as well.
The Design Phase is Detailed Design. The conceptual designs are now converted into detailed process maps. This is the future state design of the Environmental Sustainability Program’s processes for execution. Pilot testing takes place at this stage along with finalizing organizational requirements. Work instructions, procedures, and policies are documented for efficient execution of the processes that will drive the Environmental Sustainability Program.
The Validate Phase is the final phase of DMADV. In this phase the new processes go through final testing and debugging of support systems, validation of performance metrics, and implementation on a full scale.
Process Design for Six Sigma is the structured approach for any organization to determine just what Environmental Sustainability means for them. Process DFSS guides the development of the processes that will meet your Environmental Sustainability goals and objectives.
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Going Green with Six Sigma
The efficient approach to Going Green for any company is to follow the five phased DMAIC improvement process of Six Sigma. Going Green can mean different things to different organizations. You will have to define what Going Green means for your organization.
Defining your problems with waste management and setting your goals for waste reduction and savings is the first step. The structured approach of the Define Phase in a Six Sigma project is the best approach to accomplish this. The deliverables are your Problem Statement, Goal Statement, Constraints, Assumptions, Guideline’s for the Team, Green Requirements, and a Project Plan. Types of waste that are often tackled are:
For these types of waste establish the baseline of Total Pounds of Waste, the Good is Recycled Pounds, the Bad or Defect is Land Fill Pounds. Use this to set your process yield and process sigma level in the Measure Phase.
The Measure Phase for Going Green is all about establishing how you will measure your waste as previously defined and establishing your organization’s baseline performance. Without the baseline performance you won’t be able to measure the green impact and savings from your Going Green Six Sigma Project. One approach is analogous to the Mass Balance equation where Mass is conserved between inputs and outputs which must remain equal.
The Mass Balance Equation
Total of Material and Consumables Input = Output of Saleable Goods and Services + Waste to Atmosphere + Waste to Water Supply + Waste to Landfill + Hazardous Waste + Recyclables
Your organization will have to determine the extent to apply this equation.
In the Analyze Phase the Team evaluates the waste categories and how and where they are created. Is it a process issue, a policy issue, or just that we have always treated waste that way? Once you understand where it is coming from then you can develop the creative solutions that will transform landfill waste into recyclable waste. Depending upon the scope of the project the team may also be addressing other waste components of the mass balance equation.
In the Improve Phase the Team investigates alternatives for how to handle the multiple waste categories and how to minimize their creation. Many items that were always sent to the landfill if separated can become recyclable. In many cases recyclables can generate revenue. The costs of storage and hauling can then be reduced. This in turn changes the ratios of Total Waste to Recyclable Waste and to Landfill Waste. Going Green has cost reduction and revenue enhancement benefits.
In the Control Phase the Team implements the policies, procedures, and work instructions to handle the waste categories. Arrangements are made with waste and recycling organizations to handle the waste categories per the improvements identified. Ongoing measurements are put in place to continue to drive landfill waste reduction and increase recyclables. Similarly if the project scope was larger the other categories of waste would be included as well. This is how Going Green is accomplished with the Six Sigma DMAIC approach to solving problems.
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Tracking Performance over Time
Tracking performance over time can be accomplished using either one of two methods which are Run Charts or Control Charts. Both methods use time as the baseline and the performance measure as the measurement that is tracked over time. The major differences in the methods relate to the measures of demarcation on the charts. Run Charts have a center line that represents the mid point of the measurement that is being tracked. Control Charts have a center line that represents the average of the measurement that is being tracked and lines above and below the centerline that are estimates of 3 standard deviations about the average of the measurements.
In both of the charts time is the X axis. The data is plotted in succession over time. The charts can be segmented by specific time periods such as setting the baseline and after improvements have been implemented. You will easily see the before and after results. We can measure the difference between the before average and the after average to calculate the magnitude of the improvement.
Tracking of performance is also a control for maintaining the gains from the improvements. The Run Chart or the Control Chart provides a method to continuously evaluate performance and point out when corrective actions should be taken or when the process should be left alone.
Whenever we contemplate improving a process the baseline for the critical performance metric(s) must be established before any corrective actions are taken. By doing this you will always be able to evaluate the impact of the improvements by measuring from the baseline to the future state of process performance. In the rare case where the corrective action doesn’t work the tracking charts will point that out quickly so an alternative action can be taken. I am sure that you can remember when a great idea just did not work so it is always best to track performance to validate the actual impact of our improvement efforts.
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Performance Measurement
Measuring performance is the key for driving dramatic improvements in any organization. Choosing the right performance measures, or metrics, is essential to hone the focus of improvement project teams.
The key to honing the focus for the project team is to select measurements that are relevant to the level of the organization that is being impacted by the improvement project. If the team is working on improvements at the work cell, or production line level within the organization having measurements that resonate with the Corporate Level of the organization won’t make much sense. The measures must be selected that are appropriate for the level of the organization where the improvements are being made.
Examples of performance measures and the level within the organization follow:
Corporate Level
Division Level
Business Unit Level
Work Cell / Production Line
A rating scale can be used to help with selecting the Key Performance Metric, or the CTQ (Critical to Quality) Metric, that will drive the improvement at the given level within the organization. The rating scale follows:
Rating Scale for Performance Measures
Relevance
Usefulness
Understandability
Availability of Data
Overall Average Score
The metric with the highest overall score should be used to drive, monitor, and maintain the gains form the improvement efforts. Choosing the right performance measures is the key for driving dramatic improvements in any organization.
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Control Strategy that Works
Maintaining the gains from operations improvements is challenging, but not insurmountable. It all starts at the beginning of your improvement project before you have made any changes. The following steps are a guide to make your improvements stick!
You can now run your process and maintain the gains from your improvements. The Control Strategy that Works starts and ends with the measurement system. We know where we are before we make changes and then validate the gains after implementation of corrective actions. Keep on measuring to maintain those gains!
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Design of Experiments Process
The objectives for conducting a designed experiment are the following:
The Elements of an Experiment
The Design of Experiments Process
Benefits
Scrap
Delivery Cycle
Properly allocate resources
Key Strengths
To get started we offer a Design of Experiments Basics Course
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Functional Cost Modeling
There are two pieces to a Functional Cost Model. The first is the Functional Model of the product or process. The second is a comprehensive cost analysis which includes material, labor, allocated expenses, allocated assets, production processing and supporting processes, outsourcing, shipping, installation, service, warranty, and end of life. After these two components have been created blending them together yields the Functional Cost Model.
The Functional Model
A functional model comprises the Prime Function, Tier 1, Tier 2, and in some cases Tier 3 sub-functions. The Prime function for a product, or a service, describes in 10 to 15 words or less just what the product or the service is supposed to do. Once that “What” for the prime function has been nailed down then the question becomes “How” is the “What” achieved.
The Tier 1 functions provide how statements required to achieve the prime function. Then the Tier 1 functions become the “What” has to be achieved and the Tier 2 functions provide the how statements. This “What” “How” relationship continues until the functional tree is completed.
For processes the functional tree usually gets to the third tier where the process steps are reached. For products the tiers may go beyond the third tier, or as far as needed until the BOM elements, production process steps, systems, installation processes, or servicing processes are reached. The BOM elements can be a system, sub-system, or an individual component.
Comprehensive Cost Analysis
If a process is being analyzed each step in the process requires a complete cost breakdown from the customer demand for the service to final delivery of services and any remedial activities.
If a product is being analyzed the data includes all of the costs from product conception to end of product life. The complete supply chain must be considered. All outsourced activities must be considered.
Functional Cost Model
The final step in Functional Cost Modeling is gluing the Functional Model and the Comprehensive Cost Analysis together. Costs are assigned to the Functional Model at the appropriate level whether BOM, process, system, sub-system, or other activity.
Once the functional cost model is complete, roll up the costs, look for the high cost functions, and then attack the high cost functions for possible ways to reduce the costs. The functional requirements for a product or a service are driven by the voice of the customer. How we choose to deliver those functions is where the creativity comes into play with the design and thus the cost.
The functional cost model is the basis for the Product Cost Reduction Process and Process Design for Six Sigma.
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Ten Different Kinds of Errors
Poka Yoke five best practices for Process and Design are:
Process
Design
Utilizing the best practices of Poka Yoke can lead the way for your work environment to achieve zero defects.
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SMED for Setup Reduction
SMED is an acronym that stands for Single Minute Exchange of Die. The systematic approach to reaching this nirvana was developed by Shigeo Shingo in the spring of 1950 at Toyo Kogyo’s Mazda plant in Hiroshima, Japan. He defined a new way of looking at the structure of production by focusing on the relationship between processes and operations. Here is how to do it.
Shingo defined Processes as:
Shingo defined Operations as:
SMED for Setup Reduction is comprised of four phases.
Phase 1
This is the as-is, or current state, situation for the production process where both internal and external setup operations are considered to be the same. There is no separation. To understand how to make the distinction between internal and external setup operations requires the capture of baseline information and data.
To get the data and information you can:
Phase 2
Now that the baseline has been established the task is to identify and separate the internal and external setup operations.
Definitions:
Internal Setup Operations
External Setup Operations
The key to achieving SMED is the ability to distinguish between internal and external setup.
Phase 3
Now that the setup operations have been classified as either internal or external operations it is time to convert internal to external aggressively. Baseline setup times can be reduced by 30% to 50% or more and this is just the beginning. Go for it and get even more reductions!
How?
Phase 4
Streamline all aspects of both internal and external setup operations. In this phase look at each of the operations for setup and look for ways to improve them and reduce the time required to get them done. Nirvana is Single Minute Exchange of Die. You might not get there that that is the goal!
Phases 3 and 4 can be worked concurrently. Shigeo Shingo developed this method, SMED, over a period of nineteen years through examining the theoretical and practical aspects of setup improvement. He developed the cookbook. It is up to you to apply it.
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Kano Model – Classifying Your Customer Requirements
The Kano Model was developed by Noriaki Kano who is renowned for his expertise in quality management. Through practical application experience he developed his model for classifying and understanding customer requirements. There are three classifications, namely Must Have, More is Better, and Delighters.
Capturing your Voice of the Customer (VOC) requirements is paramount to driving continuous improvements within your organization. The Kano Model provides a tool for discussion to classify the requirements into one of the three categories. Customer requirements are dynamic so the initial classifications may change over time. The initial classifications provide guidance for what must be done to meet and exceed your customer’s wants and needs.
Let’s use the television as an example for understanding the classifications.
Must Have
To compete in the marketplace these requirements have to exist in the product, or the service. In our television the Must Have requirements are things like connections for cable, stereo, and DVD players. Don’t forget the remote control, which not too many years ago was a Delighter, which is now a Must Have.
More is Better
These requirements by definition are all about continuous improvement. Things like picture quality, sound, size and weight ( larger, but lighter).
Delighters
These are the requirements that win over the new customers and also keep your existing customers from changing brands. Delighters are things like HD and now HD 3D, HDMI connections, WIFI, and internet with services for movies and more.
With your requirements classified you will be in position to prioritize your total improvement program across the supply chain. Don’t forget that the Delighters of the recent past become the Must Haves of the future!
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Supply Chain Optimization by applying Lean and Six Sigma
A company’s supply chain is its lifeblood. The opportunities for improvement and change are numerous once identified. To dramatically improve the speed, flexibility, and cost of the supply chain requires a structured and systematic approach. Our proven approach follows the DMAIC Six Sigma operations improvement process, integrates Lean principles, and includes the DMADV Design for Six Sigma process as well.
The following outline describes at a high level how to achieve Supply Chain Continuous Improvement.
Process Predictability Management has applied this process to numerous supply chains with dramatic results. We can both teach and guide your team to apply these methods to your supply chain. If you are serious about dramatic and continuous improvement contact us. We are here to help.
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Multiple Regression Analysis
Multiple Regression Analysis can be a powerful tool for evaluation of multiple process, or product inputs versus a single output characteristic which is often a balanced scorecard element. The results can provide insight into what makes a difference, what doesn’t make a difference, and unfortunately the just can’t tell.
The keys to the analysis boil down to the structuring of your data set for the multiple regression. A common stratification factor that aligns all of the data elements is paramount and the most common one is the date that is associated with the data elements, both the input X’s and the output Y. If some data is missing for a particular date the best practice is to remove all of the data elements for that date from the model. Maintaining balance is critical.
Before throwing all of the data into a multiple regression analysis using a statistical analysis package such as JMP or Minitab look at the data graphically with a matrix scatterplot. Look for relationships, either positive or negative, that relate an input X with the output Y. Also, be wary of highly correlated X to X relationships. That could be colinearity, which can lead to misinterpretation of your regression model.
The graphic look at the relationships will point out the X variables that don’t need to be included in the model to start with. During the multiple regression analysis multicolinearity can be dealt with using variance inflation factors (VIFs) which require values of 5 or less. Depending on the statistics software package during the first iteration of your regression model you will be presented with T statistics or F statistics. These statistics are used to generate the all important p values.
Pooling is the elimination of the non-significant variables. The first step in reduction of variables is to identify the X’s that have either F<=1 or absolute value of T<=1. These must be removed from the model. Now re-run the regression model and check the VIFs. Typically any VIFs that are greater than 5 can be removed based upon having a large p value. The caution here is to take them out one at a time and rerun the model. I forgot to tell you, that is why it is called multiple regression analysis, you will be doing this multiple times.
Now that your regression model has been reduced to the significant X’s you should have the following.
The next question after all of this work has been completed is, “Do the relationships in the model make sense?” Use your process and product knowledge to give the results a sanity check. If it makes sense than you are ready to use the mathematical model developed to make predictions. The conservative approach is to stay within the minimum and maximum values for each of the significant X variables that were used to develop your model, remember that is the only data that you have and is the basis for the model.
Here is why. There was a study done in Oslo. The population of storks was increasing along with the number of babies being born which is a strong positive correlation. Therefore, we can declare that Storks bring Babies! The truth is, storks nest near warm chimneys so as the population was growing more houses were built, more chimneys, and thus more nesting places for the birds. Remember, you’re a priori knowledge about products, processes, and services should be used to give your multiple regression model results a sanity check.
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Test Your Knowledge Quizzes
New Test Your Knowledge Quizzes Posted.
Just what do you know? Take the quizzes to find out. Our courses will teach you all of the topics covered in these interactive quizzes. The following are the courses related to these new Test Your Knowledge Quizzes.
Sign up for an online eLearning course today!
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New -Process Design for Six Sigma Course
The Process Design for Six Sigma Course will teach you the DMADV five phased approach to new process development. The five phases of the DMADV approach are Define, Measure, Analyze, Design, and Validate.
To facilitate the course we follow an actual new process being developed, a Quality Management System, step by step using the Process Design for Six Sigma method. You will see first hand how the deliverables lead to the creation of the new process.
You will be provided a set of tools that can be used to develop a new process following the DFSS, Design for Six Sigma methodology. The course features animations, videos, interactive quizzes, and a final exam. The course takes about 3 hours and 10 minutes of total seat time. No module within the course is longer than 30 minutes. There are 20 modules in this course which includes 6 quizzes and a final exam.
Course Outline
This course is priced at $200. A bargain for learning how to develop new processes that will meet and exceed customer requirements. Register Today!
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Measurement System Analysis – Is it important?
Just how reliable is your data? Everyday, devices are used to measure things and give us the data. Colleagues evaluate things and make decisions about goodness and badness. Then, all this information ends up in a computer where the data can be crunched and plotted using a software tool. We can then evaluate the results and make those all important data driven decisions. That’s what we want to do, but what if the data isn’t reliable?
To answer that question the often overlooked Measurement System Analysis (MSA) comes into play. We can all agree that using data and statistics to guide our decision making process makes sense. Assuring that our data has integrity is therefore highly important. That is just what a MSA can do for you.
MSA can be applied whether the measurements are taken with a device or assessed based upon some criteria. For the data to be considered reliable for use in a statistical analysis for making decisions it must be captured by a process that is repeatable and reproducible. Repeatable means that if I evaluate the same item multiple times I will get the same answer each time. Reproducible means that if you, me, and your best friend were to evaluate the same item multiple times we would all get the same answer each time.
The reliability of our data is very important because taking corrective actions or making data driven decisions can make or break an organization. It gets back to the old saying, “If it ain’t broke don’t fix it.” The MSA study does take a little time and some planning. It is basically a hypothesis test to determine if the measurement system can determine if there is a difference or not. We want to prove that all the variation in the data is associated with the differences in the items being evaluated, not in the measurement system that is being used for the evaluation.
Time and again we have found that the first place to take corrective action in a process is the measurement system itself. Be wary of data that came from the information system. Just how did that data get into the system in the first place? Make sure that time is aligned with the data. Measurement System Analysis is important, especially if you want to make good business decisions based upon data.
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Building a Performance Based Strategy
To begin some definitions are required so we are all on the same page. The definitions that support our premise are the following.
Objectives = Things and Activities that you do
Goals = Milestones and Business Metrics that can be Measured
The objectives, which are the things you want to accomplish in the coming year, lead to the goals you are trying to achieve which are measured by milestone attainment and business metrics. To achieve the objectives and the goals requires a strategy for the business.
The strategy is the focus for each individual business leader within the organization. You must ask yourselves what are the strategic components that each business leader is responsible for and which are collaborative, or co-owned. Your leadership team also needs to look at the tactical aspects that are required to deliver the strategy, the objectives, and the goal attainment.
The tactical activities become the business plan that drives the strategy which focuses all the business leaders on the objectives to accomplish which are measured by the goal attainment.
Steps for Building the Performance Based Strategy
Your operating plan is now tied to your performance based strategy. Complete your objectives, achieve your goals, and the strategy is fulfilled.
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Sustaining Your Lean Six Sigma Gains
Sustaining the gains from your improvements is always a sticky point, but it doesn’t have to be. It begins with the structure of your Lean Six Sigma initiative and the application of performance measurement. These two things are the keys to sustaining your gains.
The organization of the Lean Six Sigma program requires a high level, senior management, sponsor who has program success tied to compensation and bonus. The sponsors of projects must be held accountable for success of the project teams. The teams must be held accountable to complete projects in a timely manner and implement the solutions. Time must be provided for the teams to work on their projects so they can be successful. The people most critical to the organization must be involved.
During the measure phase of a project key performance metrics were established to set the baseline to measure the improvements from. This is essential for sustainment. In the control phase we compare the new performance after the solutions are fully implemented to the baseline to validate that the project was successful. Then we can celebrate that success.
To maintain the gains and continue to improve the critical performance metrics must continually be tracked to validate that everything is still working. If slippage occurs corrective actions can be taken quickly to get back on track with the improvements from the Lean Six Sigma project.
Some high level performance metrics are required for overall program tracking. The Delphi method is often used to cascade corporate metrics all the way down to individual production processes so the roll up is easily understood. The general high level performance metric that should always be put in place that drives sustainment is Cost of Quality. When all is said and done it is the Cost of Quality that shows the reductions in cost from the improvements.
Monthly reporting of the key performance metrics that each Lean Six Sigma project has made an impact on is imperative. If the metric slips you can see that the improvement needs to be re-implemented, training is required, or some other form of corrective action must be taken. Controls must be in place to continue to monitor the improvements, which means performance metric tracking. If baselines were never established before improvements were implemented it becomes impossible to validate the magnitude of improvement and the gain we are trying to maintain is a mystery. Records of completed projects need to be archived and easily retrievable for future project teams to use as reference and replication of solutions.
Sustaining the gains from Lean Six Sigma improvements requires an organization structure that is held accountable and a performance measurement system for maintaining control and fostering continued improvement.
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Sales and Marketing Six Sigma - If not now, then when?
Sales and Marketing is the process of converting the efforts of the company into revenue. The Process should create value in the market place by differentiating the products and services of the company versus your competitor’s alternatives resulting in prospects becoming customers. The Six Sigma approach to improving any business process follows the DMAIC Improvement Process namely Define, Measure, Analyze, Improve, and Control. Organizations that are embracing this approach have realized productivity increases, cost reductions, and revenue stream enhancement. Success has been proven in operations, production, and service delivery. It is now time to rollout Six Sigma DMAIC in Sales and Marketing.
Ask yourself if your organization is facing any of the following issues:
Any of the above issues can be tackled as a Six Sigma DMAIC improvement project.
Six Sigma DMAIC can be applied whether the improvement project scope can be accomplished in days, weeks, or months.
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Keys to Successful Operations Improvement
Operations improvement is not magic, but strategically focused hard work. The keys to successful operations improvement include these five elements:
Include these elements sequentially in the operations improvement plan as your strategy for success.
1. Performance Metrics tied to business goals and objectives
The leadership team reviews the goals and objectives for the business and selects the performance metrics that measure success. Often, Quality Function Deployment (QFD) or a Prioritization Matrix is used to align and rank the performance metrics with the business goals and objectives. The marching orders are now set for the improvement projects.
2. Leadership agreement on, and support for, select improvement projects
The leadership team reviews the processes across the supply chain from the beginning to the end. Where are the issues? Where do we fall short of expectations? Where is the waste in the system? These questions drive the brainstorming to identify potential improvement projects. A list is made of the projects and incorporated into another QFD or Prioritization Matrix to align and rank the improvement projects with the rated and ranked performance metrics. Using the matrices keeps the prioritization of the projects aligned with the business goals and objectives.
3. Project teams staffed for success
The leadership team focuses on the top four or five projects based upon the priorities they have agreed on using prioritization matrices. The teams for these projects are selected from the best and the brightest personnel within the organization that have knowledge or skills relevant to the project. Often these individuals are considered too important or too busy to work on improvements. That is the reason for their selections. These personnel decisions answer the question, “Are you serious about making improvements?”
4. Project centric training
Each project and its assigned team progress through the Lean Six Sigma training and apply the tools and techniques directly on their improvement opportunity. As quickly as methods are learned they are applied to make progress toward the completion of the project following the train and do philosophy. If the opportunity is a process improvement the methodology follows DMAIC (define, measure, analyze, improve, and control). If the opportunity is a design improvement the methodology follows DMADV (define, measure, analyze, design, and validate), or Design for Six Sigma.
5. Accountability for timely successful project completion
Success requires the leadership team and the improvement project teams be held accountable to complete the projects in a timely manner. Deliverables based project plans focus the effort of the teams to move through the DMAIC, or DMADV, process phases successfully. Phase exit reviews are held between the project and the leadership teams. Phase exit reviews assure that all parties involved are held accountable to achieve successful strategically focused operations improvement.
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Choosing Hypothesis Tests
A question my students often ask is, “Which hypothesis test should I use and when?” In this article we will address some guidelines to answer the question. The available hypothesis tests are:
The following examples will address which test to use given a certain set of circumstances. In hypothesis testing we are faced with answering the question, “Do the variables in my process make a difference, or not, if they are changed?”
Continuous Variable Outcomes
The output, or outcome, in the process is measured on a continuous scale. We will refer to the outcomes as the “Y”. The input variables, or the things we will be changing, are varied between discrete settings, or levels. The variable could be continuous, but the settings are specific and can be considered discrete.
Case 1: T Test
The T Test allows testing of two items only, or two level settings only. Let’s say we want to improve our gas mileage. The output Y is miles per gallon. The inputs for the T Test are gasoline additives. The level settings could be Yes (use the additive) and No (plain gasoline without additives). The sample size can be small using the T Test. Run 5 tanks of fuel under each condition and measure the miles per gallon. The null hypothesis for this test is regardless of whether or not we use the additive the gas mileage will remain the same evidenced by p values much greater than 0.05. The alternative hypothesis is that there is a difference between Yes and No which is evidenced by p values that are less than or equal to 0.05.
Case 2: Paired T Test
In the Paired T Test only two items can be tested, but the tests are run concurrently, or in pairs of both items. We use the pairing technique when environmental factors may influence the outcomes. We want that “noise” to have an equal chance to affect the test subjects so running the test concurrently assures this equality of noise distribution. In this case, we will test two hull designs for nautical speed. Testing will be carried out over several days so the conditions in the ocean will definitely be changing such as wind speed, wind direction, wave height, and currents. Both of the hull designs will be subjected to the same conditions when we conduct the tests simultaneously in pairs. The plan is to conduct 5 races over the course of one week. If the p values in the Paired T Test are less than or equal to 0.05 than the hull design with the greatest nautical speed can be declared the winner because the test shows a significant difference. If the p value is much greater than 0.05 then we need to go back to the drawing board because there is no difference in the hull designs.
Case 3: ANOVA
Analysis of Variance, or ANOVA, is very powerful because there is essentially no limit to the number of items, or level settings that can be evaluated during the testing. We are limited only by practicality. In this case we want to determine if there is a difference in the distance a golf ball can travel. The outcome Y is the distance in yards. We will test Pinnacle, Nike, Titleist, Srixon, Bridgestone, and Callaway. A robot with one type of golf club will be used to launch the golf balls. Swing speed and force will be the same for each test subject. Twenty of each ball will be launched and the driving distance will be measured. As in all of these hypothesis tests, the p value is the measuring stick for declaring if a difference exists or not. When the p value is < or = to 0.05 we have a 95% confidence that a significant difference exists. When the p value is much, much greater than 0.05 we declare that no significant difference exists between the test subjects.
Case 4: Test for Equal Variances
In the three previous cases the concern was a difference in the average value of the outcome based upon the level setting of the input variable. With Test for Equal Variances the evaluation is the variability of the outcomes about the average. The standard deviations are evaluated to test for differences in variation. In this case we will use the data from Case 3, the driving distance of the golf balls. Which golf ball is most consistent in driving distance? If I buy a dozen of these golf balls can I expect the same results? The Test for Equal Variances provides the answer. If the p value is low than the null must go, but if the p value is high the null applies. The null hypothesis is always “There is no difference.” Two tests are used, one is called Bartlett’s Test which requires the distributions to be normally distributed and the other is Levene’s Test which requires only that the data is continuous.
Discrete Variable Outcomes
The output, or outcome, in the process is measured by counting occurrences which is a discrete variable. We will refer to the outcomes as the “Y”. The input variables, or the things we will be changing, are varied between discrete settings, or levels.
Case 5: Chi Square
Chi Square testing compares discrete Y’s and discrete X’s. In this type of analysis categories, or groups, are compared to other categories, or groups. For example, “Which cruise line had the highest customer satisfaction?” The discrete X variables are (RCI, Carnival, and Princess Cruise Lines). The discrete Y variables are the frequency of responses from passengers on their satisfaction surveys by category (poor, fair, good, very good, and excellent) that relate to their vacation experience. Conduct a cross tab table analysis, or Chi Square analysis, to evaluate if there were differences in levels of satisfaction by passengers based upon the cruise line they vacationed on. Percentages are used for the evaluation and the Chi Square analysis provides a p-value to further quantify whether or not the differences are significant. The overall p-value associated with the Chi Square analysis should be 0.05 or less. The variables that have the largest contribution to the Chi Square statistic drive the observed differences.
Now you should have a good understanding of which hypothesis test to use and when it is most appropriate. Remember that it is just as important to determine that there is no difference as well as that there is a difference. Sound business decisions depend on making choices based on significance.
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Where do we apply Statistical Methods?
Before starting any type of analysis classify the data set as either continuous or attribute, and in many cases it is a blend of both types. Continuous data is characterized by variables that can be measured on a continuous scale such as time, temperature, strength, or monetary value. A test is to divide the value in half and see if it still makes sense.
Attribute, or discrete, data can be associated with a defined grouping and then counted. Examples are classifications of good and bad, location, vendors’ materials, product or process types, and scales of satisfaction such as poor, fair, good, and excellent. Once an item is classified it can be counted and the frequency of occurrence can be determined.
The next determination to make is whether the data is an input variable or an output variable. Output variables are often called the CTQs (critical to quality characteristics) or performance measures. Input variables are what drive the resultant outcomes. We generally characterize a product, process, or service delivery outcome (the Y) by some function of the input variables X1,X2,X3,…Xn. The Y’s are driven by the X’s.
The Y outcomes can be either continuous or discrete data. Examples of continuous Y’s are cycle time, cost, and productivity. Examples of discrete Y’s are delivery performance (late or on time), invoice accuracy (accurate, not accurate), and application errors (wrong address, misspelled name, missing age, etc.).
The X inputs can also be either continuous or discrete. Examples of continuous X’s are temperature, pressure, speed, and volume. Examples of discrete X’s are process (intake, examination, treatment, and discharge), product type (A, B, C, and D), and vendor material (A, B, C, and D).
Another set of X inputs to always consider are the stratification factors. These are variables that may influence the product, process, or service delivery performance and should not be overlooked. If we capture this information during data collection we can study it to determine if it makes a difference or not. Examples are time of day, day of the week, month of the year, season, location, region, or shift.
Now that the inputs can be sorted from the outputs and the data can be classified as either continuous or discrete the selection of the statistical tool to apply boils down to answering the question, “What is it that we want to know?” The following is a list of common questions and we’ll address each one separately.
That’s enough questions to be statistically dangerous so let’s begin by tackling them one at a time.
What is baseline performance?
Plot the data in a time based sequence using an X-MR (individuals and moving range control charts) or subgroup the data using an Xbar-R (averages and range control charts). The centerline of the chart provides an estimate of the average of the data overtime, thus establishing the baseline. The MR or R charts provide estimates of the variation over time and establish the upper and lower 3 standard deviation control limits for the X or Xbar charts. Create a Histogram of the data to view a graphic representation of the distribution of the data, test it for normality (p-value should be much greater than 0.05), and compare it to specifications to assess capability.
Minitab Statistical Software Tools are Variables Control Charts, Histograms, Graphical Summary, Normality Test, and Capability Study between and within.
Plot the data in a time based sequence using a P Chart (percent defective chart), C Chart (count of defects chart), nP Chart (Sample n times percent defective chart), or a U Chart (defectives per unit chart). The centerline provides the baseline average performance. The upper and lower control limits estimate 3 standard deviations of performance above and below the average, which accounts for 99.73% of all expected activity over time. You will have an estimate of the worst and best case scenarios before any improvements are administered. Create a Pareto Chart to view a distribution of the categories and their frequencies of occurrence. If the control charts exhibit only normal natural patterns of variation over time (only common cause variation, no special causes) the centerline, or average value, establishes the capability.
Minitab Statistical Software Tools are Attributes Control Charts and Pareto Analysis.
Did the adjustments made to the process, product, or service delivery make a difference?
To test if two group averages (5W-30 vs. Synthetic Oil) impact gas mileage, use a T-Test. If there are potential environmental concerns that may influence the test results use a Paired T-Test. Plot the results on a Boxplot and evaluate the T statistics with the p-values to make a decision (p-values less than or equal to 0.05 signify that a difference exists with at least a 95% confidence that it is true). If there is a difference choose the group with the best overall average to meet the goal.
To test if two or more group averages (5W-30, 5W-40, 10W-30, 10W-40, or Synthetic) impact gas mileage use ANOVA (analysis of variance). Randomize the order of the testing to minimize any time dependent environmental influences on the test results. Plot the results on a Boxplot or Histogram and evaluate the F statistics with the p-values to make a decision (p-values less than or equal to 0.05 signify that a difference exists with at least a 95% confidence that it is true). If there is a difference choose the group with the best overall average to meet the goal.
In either of the above cases to test to see if there is a difference in the variation caused by the inputs as they impact the output use a Test for Equal Variances (homogeneity of variance). Use the p-values to make a decision (p-values less than or equal to 0.05 signify that a difference exists with at least a 95% confidence that it is true). If there is a difference choose the group with the lowest standard deviation.
Minitab Statistical Software Tools are 2 Sample T-Test, Paired T-Test, ANOVA, and Test for Equal Variances, Boxplot, Histogram, and Graphical Summary.
Plot the input X versus the output Y using a Scatter Plot or if there are multiple input X variables use a Matrix Plot. The plot provides a graphical representation of the relationship between the variables. If it appears that a relationship may exist, between one or more of the X input variables and the output Y variable, conduct a Linear Regression of one input X versus one output Y. Repeat as necessary for each X – Y relationship.
The Linear Regression Model provides an R2 statistic, an F statistic, and the p-value. To be significant for a single X-Y relationship the R2 should be greater than 0.36 (36% of the variation in the output Y is explained by the observed changes in the input X), the F should be much greater than 1, and the p-value should be 0.05 or less.
Minitab Statistical Software Tools are Scatter Plot, Matrix Plot, and Fitted Line Plot.
In this type of analysis categories, or groups, are compared to other categories, or groups. For example, “Which cruise line had the highest customer satisfaction?” The discrete X variables are (RCI, Carnival, and Princess Cruise Lines). The discrete Y variables are the frequency of responses from passengers on their satisfaction surveys by category (poor, fair, good, very good, and excellent) that relate to their vacation experience.
Conduct a cross tab table analysis, or Chi Square analysis, to evaluate if there were differences in levels of satisfaction by passengers based upon the cruise line they vacationed on. Percentages are used for the evaluation and the Chi Square analysis provides a p-value to further quantify whether or not the differences are significant. The overall p-value associated with the Chi Square analysis should be 0.05 or less. The variables that have the largest contribution to the Chi Square statistic drive the observed differences.
Minitab Statistical Software Tools are Table Analysis, Matrix Analysis, and Chi Square Analysis.
Does the cost per gallon of fuel influence consumer satisfaction? The continuous X is the cost per gallon of fuel. The discrete Y is the consumer satisfaction rating (unhappy, indifferent, or happy). Plot the data using Dot Plots stratified on Y. The statistical method is a Logistic Regression. Once again the p-values are used to validate that a significant difference either exists, or it doesn’t. P-values that are 0.05 or less mean that we have at least a 95% confidence that a significant difference exists. Use the most frequently occurring ratings to make your determination.
Minitab Statistical Software Tools are Dot Plots stratified on Y and Logistic Regression Analysis.
Are there any relationships between the multiple input X’s and the output Y’s? If there are relationships do they make a difference?
The graphical analysis is a Matrix Scatter Plot where multiple input X’s can be evaluated against the output Y characteristic. The statistical analysis method is multiple regression. Evaluate the scatter plots to look for relationships between the X input variables and the output Y. Also, look for multicolinearity where one input X variable is correlated with another input X variable. This is analogous to double dipping so we identify those conflicting inputs and systematically remove them from the model.
Multiple regression is a powerful tool, but requires proceeding with caution. Run the model with all variables included then review the T statistics (T absolute value <=1 is not significant) and F statistics (F <=1 is not significant) to identify the first set of insignificant variables to remove from the model. During the second iteration of the regression model turn on the variance inflation factors, or VIFs, which are used to quantify potential multicolinearity issues (VIFs <5 are OK, VIFs > 5 to 10 are issues). Review the Matrix Plot to identify X’s related to other X’s. Remove the variables with the high VIFs and the largest p-values, but only remove one of the related X variables within a questionable pair. Review the remaining p-values and remove variables with large p-values >>0.05 from the model. Don’t be surprised if this process requires a few more iterations.
When the multiple regression model is finalized all VIFs will be less than 5 and all p-values will be less than 0.05. The R2 value should be 90% or greater. This is a significant model and the regression equation can now be used for making predictions as long as we keep the input variables within the min and max range values that were used to create the model.
Minitab Statistical Software Tools are Regression Analysis, Step Wise Regression Analysis, Scatter Plots, Matrix Plots, Fitted Line Plots, Graphical Summary, and Histograms.
This situation requires the use of designed experiments. Discrete and continuous X’s can be used as the input variables, but the settings for them are predetermined in the design of the experiment. The analysis method is ANOVA which was previously mentioned.
Here is an example. The goal is to reduce the number of unpopped kernels of popping corn in a bag of popped pop corn (the output Y). Discrete X’s could be the brand of popping corn, type of oil, and shape of the popping vessel. Continuous X’s could be amount of oil, amount of popping corn, cooking time, and cooking temperature. Specific settings for each of the input X’s are selected and incorporated into the statistical experiment.
Minitab Statistical Software Tools are DOE, Factorial Plots, Pareto Effect Plots, ANOVA, Histograms, and Response Optimizer.
You are now ready to tackle some data, answer some questions, and become statistically dangerous.
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Six Sigma and the Bottom Line
Many companies have gone down the path of continuous improvement only to be discouraged by the lack of “breakthrough” results. All of the texts on Total Quality harp on the need for strong commitment from senior management for these initiatives to be successful. What is it that motivates these business leaders? The answer is straightforward; Business Leaders are motivated and driven to achieve bottom line results and increase value to shareholders. Six Sigma provides a structured and rigorous approach with a customer focus that drives benefits to the bottom line.
What is Six Sigma?
The Structured and Rigorous Improvement Process is called DMAIC and is comprised of the following five phases.
Teams utilizing the five phases of DMAIC can deliver breakthrough improvements to business processes. This is the road to the Six Sigma stretch goal of 3.4 defects per million opportunities.
How Do Six Sigma Efforts Impact the Bottom Line?
Reductions of 10%-30% in both Cost of Goods Sold and Operating Expenses are common, with most companies averaging 20% reductions.
How Do Six Sigma Efforts Do It?
The DMAIC Process comprises the following:
Six Sigma is not just another “Quality” program like TQM, or Quality Circles, but one that has teeth rooted in the financials of the business. Projects are only undertaken if they meet strict business case guidelines set by the senior management team. Not all projects make the cut. Some typical minimum thresholds for a green belt project are $150k cost or expense reduction, $500k cost avoidance, or $1000k cash flow depending on the nature of the business process. The first wave of 5 to 7 projects, completed within 12 to 14 weeks after green belt training, should yield $1 to $2 Million in annualized benefits. That’s bottom line “breakthrough” results.
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DMAIC or DMADV, Which Process to Choose?
To choose the proper process for the improvement project requires a brief understanding of each of the acronym based processes of DMAIC and DMADV. DMAIC is namely Define, Measure, Analyze, Improve, and Control. Its five phases are geared toward breakthrough improvements in products, processes, and service delivery. DMADV is namely Define, Measure, Analyze, Design, and Validate. Its five phases are geared toward development of new products, processes, or service delivery.
Choosing which process to follow can be a little confusing at the outset of an improvement project where the names of the first three phases for each of the processes is exactly the same. To clarify the differences in the first three phases in DMAIC it’s clearly spelled out as Define, Measure, and Analyze with no hidden meanings. In DMADV the definitions need clarification:
If the project’s goal is to develop a new product, process, or service delivery than clearly the process to follow is DMADV, which is a five phased development process. If the project’s goal is to improve an existing product, process, or service delivery than clearly the process to follow is DMAIC, right? In most cases it is, but if the existing situation is so broken and just fixing it won’t provide the ability to meet or exceed customer requirements a course correction to DMADV from DMAIC is in order.
Was time wasted by starting off with DMAIC? The Define and Measure phases in the DMAIC process generate deliverables that will be essential in the development project. The Analyze phase provides great detail about the failure modes that a new product, process, or service delivery will have to mitigate to be successful. Time wasn’t wasted, but the conversion from DMAIC to DMADV should take place during the Analyze phase at the latest. Often after the Measure phase it is clear that a new development is required to meet customer expectations.
From the outset if the project is a new development it is DMADV. Otherwise, start with DMAIC and course correct if it is determined that redesign is required for the product, process, or service delivery. Time will not have been wasted unless you don’t course correct.
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Quality Cost Performance Measurement
Quantifying the benefits from operations improvement projects is simplified when Quality Cost is one of the key performance measures of an organization’s balanced scorecard. Quality Cost is actually a financial accounting activity that is often overlooked or goes undone. The question for your organization is "Why are Quality Costs Not Managed"?
Quality Cost accounting comprises four general categories, namely Prevention, Appraisal, Internal Failure, and External Failure. To capture details in all four categories is often a daunting task. Prevention and Appraisal activities are generally percentages of some departments’ overall activities, but not necessarily a specific function, which makes quantification inexact. Internal and external failures are generally easier to quantify.
Quality Cost management is most effective when it is kept as simple as possible. Quantify what is generally easy to capture and have the information come directly out of the general ledger. If the measurement can be simplified the organization gains a powerful performance metric. How can this be done?
First, focus on just the internal and external failure categories. Examples of internal failure are the following:
These costs disappear if the process, or service delivery output is defect free.
Examples of external failure are the following:
These costs disappear if the process, or service delivery output is defect free after the customer receives it.
The next step is tedious, but only needs to be done one time. Obtain the general ledger and review the definitions for all line item entries. The general ledger often contains thousands of entries which makes this a tedious exercise, but one that is eye awakening. Now use the definitions to identify and classify the ledger line items that fit the categories of internal and external failure.
The final step is to track the internal and external failure ledger line items in a monthly Quality Cost Management report. Successfully completed improvement projects drive the Quality Costs down. When the performance measurement tracking is simplified then there is no reason for an organization not to manage their Quality Cost.
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Communication, the Key for Implementing Change
Human beings are inherently curious. We want to know what is going on and why. Social networks are booming because they fill the curiosity void through communication. It doesn’t have to be a book. The communication can be as little as 140 characters. Implementing change without communicating the Why, What, and the How can lead to failure.
When teams are formed to tackle issues and implement change everyone outside of the inner core team becomes curious. Why are they getting together? What are they doing behind those closed doors? How will this affect me? The rumor mill will fill the curiosity void with answers to all of these questions. Proactively getting out ahead of the rumor mill through communication is the key to successful implementation of change. If we haven’t communicated proactively even the best technical solutions will be difficult to implement and sustain.
A study was conducted by John P. Kotter and published in the Harvard Business Review entitled “Why Transformation Efforts Fail.” The following are the key points from the article.
It is clear from this list that communication that was proactive, easily understood, and delivered in a timely manner would circumvent all of the above. Even though we often think of communication as a soft skill it is the key for implementing change.
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Where is Lean Six Sigma Applicable?
Lean Six Sigma is a five phased improvement process that employs tools and techniques to meet, or exceed customer requirements. If your organization has external customers, internal customers, or suppliers then opportunities exist to apply Lean Six Sigma.
In every case, the improvement team defined the issues that were getting in the way of satisfying customer requirements. Measurements were used to establish a baseline of the current state of performance. Analysis was conducted on the measurement information to isolate the root causes of the issues. Creative solutions were then developed to improve performance beyond the current state. To assure that the issues resolved remained resolved controls were implemented. The preceding illustrated the Lean Six Sigma process of Define, Measure, Analyze, Improve, and Control. The following are examples from numerous organizations.
Lean Six Sigma is applicable for any organization’s operating processes, delivery of services, or their production of products.
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CTQ Tree – It’s All About What to Measure
The CTQ Tree (Critical to Quality Tree) is all about what needs to be measured to drive improvement in the eyes of the customer. Our customers come in three categories with the first being the external customer, the second is the internal customer, and the third is the vendors who provide the goods and services that fuel our supply chain.
During the define phase of the DMAIC improvement process we study our customer requirements.
These are the multiple voices of the customer and the requirements we must define.
The CTQ Tree provides a graphic to tie the progression of the requirements together. The progression starts with the Want or Need, which leads to the Drivers within the Process we must control, and then to the Critical to Quality Characteristics (CTQs) that we measure to control the Drivers.
Once we know what to measure we must define what means good or bad, which is also called the specification. Typically our multiple levels of customers let us know what they want or need, but rarely do they define the CTQs and the specifications.
View an example of the CTQ Tree In the measure phase of DMAIC we will measure our current state performance to establish the baseline.
We measure our improvements from this baseline. The CTQ Tree not only defines what to measure but also whether or not the performance is good or bad.
We offer online courses that will help your organization improve. To review our course selections visit EducateVirtually.com
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Gemba Kaizen – Are We Patient Enough?
Gemba Kaizen is the philosophy of small incremental improvements every day, every week, and every month. It all adds up to significant benefits at the end of the year, but are we patient enough? Gemba in Japanese means “Real Place”, which is where products are produced and where customers meet service providers.
Kaizen means “continuous improvement” which is defined as small, incremental improvements, where if we spend any money it is minimal, and the improvement results are measured in hard cost savings, higher quality, and better productivity. There are three cornerstones that support the Gemba Kaizen philosophy of continuous improvement.
1. Eliminate Waste
2. Housekeeping
3. Standardization
Eliminate waste is based upon the seven wastes as defined by Taichi Ohno, the father of the Lean Philosophy, which was originally known as “Just in Time”.
1. Overproduction – minimize producing more than is necessary to meet customer demand.
2. Inventory – minimize the quantity of raw materials, components, semi-finished, and finished goods on hand at any time.
3. Scrap, Rejects, and Repairs – minimize the time and cost associated with evaluation, disposition, repair and materials, scrap disposal, material handling, storage, expedited shipping, overtime, and non standard labor
4. Motion – minimize the motion and wear and tear on the employees through workplace design, ergonomics, and safety
5. Processing – minimize processing steps, travel distance, cycle time, and inventory levels
6. Waiting – minimize the downtime of equipment, changeover and set-up time, lack of parts, and synchronization between process steps and processes.
7. Transport – minimize transportation of materials and components on trucks, forklifts, and conveyors
Housekeeping is based upon the philosophy that order, organization, and cleanliness fosters pride and efficiency in the workplace.
1. Sort the Tools and Objects that are Used from those Not Used
2. Straighten the needed items so they are easy to find, use, and put away
3. Scrub and clean the work area, which includes shelves and cabinets
4. Systematize the times and frequency for cleaning and putting all items back in order
5. Standardize your efforts and report progress on the work group’s Visual Management System
Standardization is based upon repeatability and reproducibility of the best, easiest, and safest way to do a job or provide a service. Repeatability means that I will be consistent time after time and reproducibility means that the entire work group will be consistent time after time. To drive small incremental improvements every day, every week, and every month is best accomplished following a simple four step cycle. This cycle, often referred to as PDCA, was originated by Walter Shewhart and promoted in Japan by W. Edwards Deming. PDCA is the acronym for Plan, Do, Check, and Act.
The following is the continuous cycle.
1. Study the Process (Check)
2. Determine Corrective Actions (Act)
3. Plan and Prioritize the Actions (Plan)
4. Implement the Improvements (Do)
It all adds up to significant benefits at the end of the year, but are we patient enough?
Gemba Kaizen training is available at EducateVirtually.com complete with all the tools needed for the continuous improvement journey.
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Statistical Experimentation History
Experimental design was developed in the 1920s by Sir Ronald A.Fisher of England. His techniques were first applied in agriculture.
Dr.Genichi Taguchi has been one of the primary contributors to upgrading these experimental design methods for use in industry and design applications. Dr.Taguchi, an engineer, has developed a very powerful way to help improve the quality of products while simultaneously lowering costs. Between 1950 and 1970 Dr. Taguchi's methods of experimental design were developed at the Electrical Communication Laboratories (E.C.L.), the Japanese counterpart of Bell Laboratories. A notable application of these techniques was the development of a switch relay device. In 1971, the E.C.L. beat Bell Labs to market with this device, completing the project with one- fifth of Bell's personnel and one-fiftieth of its budget. Bell Labs invited Dr.Taguchi to explain his methods in 1972. A few years after the switch relay's introduction, Western Electric stopped production of the device and now imports them solely from Nippon Telephone and Telegraph.1 Ford Motor Company embraced Dr.Taguchi's methods in 1980 and formed the American Supplier Institute, where Dr.Taguchi is based, in 1981. Since that time, companies in many different industries, including ITT, Hewlett Packard, 3M, AT&T, Texas Instruments, and Sheller Globe, have begun to use these methods. Dr.Taguchi is considered to be one of the leading engineers of this century. He has received four Deming Prizes for his contributions to Japanese Quality. As a consultant to industry, he was awarded the Willard F. Rockwell, Jr. Medal at the 1986 International Congress on Technology and Technology Exchange. This prestigious award is given to only two individuals each year, with no more than one award per continent.
Among the explanations suggested for Japan's post-war industrial success are Japanese methods of management, the use of statistical process control, and the application of Just-In-Time manufacturing techniques. However, the real key to their success has been designing the quality into the product using Dr.Taguchi's System of Experimental Design.2 The method is best described as an engineering tool with a statistical base. This approach is concerned with gains in productivity. Cost effectiveness is stressed, rather than statistical rigor. In the world of manufacturing, the classical assumptions of a detailed hypothesis, normality, or homogeneity of variance are generally impractical.
In manufacturing, cost savings are realized by the reduction of scrap, lowering of inspection costs, and minimizing rework losses. These savings are achieved through process improvements and variation reduction. Design cost savings are realized by reducing the delivery cycle and minimizing engineering design changes. Reducing total Product cost is the ultimate goal.
Industrial experiments consist of three different groups of key elements. First are factors such as time, temperature, and speed. Second are the levels for these factors, such as one minute versus two minutes, or 100 degrees versus 200 degrees. Third, is the outcome or quality characteristic being measured, or evaluated, like surface finish or cost. Selection of each of these elements is an important step in developing a well-designed experiment.
The experimental design process flows through the following steps:- 1. Define the problem
- 2. Determine the objective
- 3. Brainstorm
- 4. Design the experiment
- 5. Conduct the experiment and collect the data
- 6. Analyze the data
- 7. Interpret the results
- 8. Verify the predicted results
These steps do not guarantee a successful experiment, but they do force the experimenter to proceed in a logical manner. All experiments conducted in this manner provide useful information, although some of them require a second experiment to achieve the desired improvement. Designing a successful experiment often requires a team of people familiar with the process or design. The members of the team contribute "a priori" knowledge, which helps to facilitate a well-designed experiment.
Dr.Taguchi's methods provide a means for minimizing the effect of factors that can't be controlled, by controlling the factors that are controllable. Thus, the process or product is made robust in the face of uncontrollable factors. Dr.Taguchi calls these uncontrollables noise factors. A noise factor causes definite variation, but can't be eliminated from the design or the manufacturing process.
Dr.Taguchi's experimental methods provide required information in a cost-effective manner for sound engineering decisions. Also, factors are identified which do not impact the quality of the process or product but can provide additional cost savings. Reproducible results are the key strength of Dr.Taguchi's methods. These techniques can improve quality without incurring capital and material cost increases. An important benefit is the separation of the vital few from the trivial many.
Industrial experiments have too many variables with different characteristics for the cost-effective use of the classical experiment methods. Dr.Taguchi modified these experiment methods for manufacturing and design applications for cost effectiveness and efficiency. By doing so, he has provided the engineering community with a powerful, applicable, and useful tool for making sound engineering decisions.
Process Predictability Management and EducateVirtually.com have assisted numerous companies over the past 26 years in the design and successful application of design of experiment methods. We have been directly involved in more than 450 experiments. These experiments ranged from new product development to solving product and process issues that were deemed impossible to resolve.
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Course Results Lean Six Sigma Green and Black Belt Training
The Lean Six Sigma Green and Black Belt Course taught at Missouri State University's Management Development Institute finished last week. The results from the students are very impressive.
The course comprises 4 weeks of training in a workshop format over a 4 month period of time spanning 13 weeks.
Four Lean Six Sigma projects were completed during this time and 15 students achieved their Green Belt Certifications and have already begun their second projects to achieve their Black Belt Certifications.
The total savings for the completed projects exceeded $750,000.
We congratulate our students for their excellent project work and the successful application of what they learned.
Three additional projects from other students are nearing completion. Your project teams can achieve similar results at the upcoming MSU MDI Lean Six Sigma Course offered this Spring on campus.
The Black Belt training covers the full 4 weeks and the Green Belt training is the first two weeks of the course. The Spring dates are 2/22 to 2/25, 3/29 to 4/1, 4/26 to 4/29, and 5/24 to 5/27.
4 Week Black Belt Certification Training: $4995
2 Week Green Belt Certification Training: $2995
Ask about team discounts
Contact the MDI Course Manager with any questions or to Bulk Register a Team Click here for Belinda Davis
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Process Design for Six Sigma (DFSS)
Designing a new process, or dramatically improving an existing one, is handled best by following a structured process. Design for Six Sigma (DFSS) has a five phase development process with the DMADV acronym. This acronym stands for Define, Measure, Analyze, Design, and Validate, but what does it actually mean with regard to a development project?
The Define Phase is best described as the Development Project Definition. Where the keys to success are:
The Measure Phase is best described as the Requirements Definition. Where the keys to success are:
The Analyze Phase is best described as the Conceptual Design. Where the keys to success are:
The Design Phase is best described as the Detailed Design. Where the keys to success are:
The Validate Phase is best described as Test, Validate, and Implement. Where the keys to success are:
Process Design for Six Sigma requires collaboration among the process stakeholders. Often being face to face for the collaborative meetings is not possible from a logistics, or cost standpoint, which is where on-line Webinars provide the solution.
At EducateVirtually.com we provide the facilitation and coaching required for extremely productive Webinars that accelerate the DFSS process. We have found that communication is enhanced and documentation is more complete using internet based meetings. New processes have been developed and implemented in as little as 12 weeks starting from a clean sheet of paper.
For additional information, or to contact us visit EducateVirtually.com
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SIPOC, The Key to Process Design for Six Sigma
Designing new processes is a complex task especially if your desire is zero defects. The Design for Six Sigma (DFSS) process is geared toward identifying the failures in the process that must be eliminated. The premise is the new design will mitigate all risks and potential failures because they were identified and then designed out of the process.
The question becomes, “Just how do you do that?” The DFSS process has the acronym of DMADV, which stands for Define, Measure, Analyze, Design, and Validate. Let’s clarify these terms and make them understandable.
Process DFSS is a development project that should follow the typical development phases as named previously. During the requirements definition phase the voice of the customer and the business are defined. Potential failure modes are identified and the process functions that mitigate the risks are developed.
The tricky transition is coming up with the conceptual design following the completion of the requirements definition. Here is where the SIPOC process mapping method comes into play. SIPOC stands for Suppliers, Inputs, Process, Outputs, and Customers.
The conceptual process design is at a high level. To begin, identify who supplies inputs into the process and what those inputs are. Next, where does the process start and where does the process end. The process steps from the beginning to the end are limited to five steps. Then the outputs from each step, 1 thru 5, are defined along with the customers both internal and external who receive those outputs.
The SIPOC process map becomes the conceptual design of the new process. The suppliers who provide inputs into the process are defined. The beginning and ending points are defined. The process outputs and their customers are defined.
The next questions to answer are what volume will flow through the process and how often does the process cycle. These answers lead to the staffing requirements. Information technology solutions are identified at this point as well. The SIPOC process map facilitates timely completion of the new process conceptual design.
At EducateVirtually.com we offer Webinar on Demand to coach and facilitate Process Design for Six Sigma. Visit us today.
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Webinar On Demand Provides Live Training and Coaching
Live Training and Coaching support for your Operations Improvement projects is available through Webinar On Demand
If you answered “Yes” to any, or many, of the above questions then EducateVirtually.com can help. We provide Webinar On Demand Live Training and Coaching sessions whereYou Pick the Topic and the Schedule.
There is no limit to the number of attendees.
Up to 30 computer connections at a time!.
Visit EducateVirtually.com for Details, Pricing, and Topics Supported
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QFD Process Design Roadmap
The task of designing a new process or dramatically improving an existing process can seem overwhelming. Quality Function Deployment, QFD, provides a collaborative tool to guide decision making and trade off evaluations. QFD contrasts “What” the customer wants with “How” the “What” will be satisfied. The Process Design Roadmap assures that the newly designed process will successfully meet customer requirements.
We begin with capturing the Voice of the Customer requirements for the process being designed. Often, the VOC comprises External Customers, Internal Customers, and the suppliers to the process. A series of criteria are applied to collaboratively weight and rank the requirements.
The first QFD matrix defines the functional requirements for the process. Just what is it that the process is supposed to do? Each function is then evaluated for its relationship to the delivery of each customer requirement. They are scored only where relationships exist. The outcome is the list of prioritized functions that drive meeting VOC requirements.
The second QFD matrix designs the process. To meet the functional requirements we need processes and IT support systems. These are input into the QFD and compared against the prioritized list of functions. We then assess if our process design can deliver the functions that will satisfy the Voice of the Customer. During this iterative QFD analysis the outcome is the new process design, with a ranking of processes and systems that are critical to success.
The last QFD matrix designs the organization that is required to run the processes. Roles, responsibilities, skill sets, structure, and staffing are determined.
The results from the QFD roadmap are:
That’s the Quality Function Deployment Process Design Roadmap click here to Take the Free Nano Course
At educatevirtually.com we offer e-learning, on demand webinars, and on site training, coaching, and facilitation to support your operations improvement needs.
Contact us for training, coaching, or facilitation support at EducateVirtually.com
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Lean Root Cause Analysis
If you are in this situation then finding the root causes in your processes is the answer. Lean Root Cause Analysis begins with mapping of the current state, which is where you are now. All it takes is a walk through review of your current process, a digital camera to capture actual evidence, some sticky notes, sharpies, and a roll of brown paper.
Depicting the current state of your process on a Process Map, whether it is a Value Stream Map or a Deployment Process Map, gives you a visual representation from the beginning to the end of your process. You will be able to take a step back and review what’s actually going on objectively.
Photographs provide reminders of what is actually taking place. Sometimes the photos show some pretty scary things taking place in the process.
Information that should also be captured are cycle times, processing times, lead times, quantities of good and bad process outputs, travel distances, and inventory. The list is not all encompassing, but a good start.
The next step is to classify and quantify the Value Adding activities and the Non Value Adding Activities. We define Value Adding activities as ones that
(1) the customer considers to be important and would be willing to pay for them,
(2) the “THING” that travels through the process is physically changed, and
(3) the process activity is done correctly the first time through the process.
All three requirements must be met or the process activity is considered to be Non Value Adding.
To reduce or eliminate the Non Value Adding activities in the process requires an understanding of the root causes that created the need for them. We use Cause and Effect Diagrams, Failure Mode Effects Analysis, and 5 Why Brainstorming to uncover the root causes.
Devise your corrective actions and implement the improvements. Sounds pretty easy and it can be. Just follow the 5 step DMAIC improvement process. Define your current state, measure what is actually happening, analyze the information to uncover the root causes, develop creative solutions to improve the process, and then implement the solutions and install controls to maintain your gains.
If you need some help getting started then take our course Lean Root Cause Analysis
The Lean Root Cause Analysis course teaches practical application tools for uncovering the root causes in your processes. Lean concepts are demonstrated with a simulation. You will then learn how to define your current state and uncover the root causes that are the impediments to your future state success. Budget friendly at only $69.95
Register for a Course Today at EducateVirtually.com
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Test Your Knowledge of Continual Improvement
Test your knowledge of Continual Improvement Methods
Each Knowledge Test has 5 Interactive Questions
Your Score is provided at the end of the Test
Follow this link Test Your Knowledge
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How does Lean Six Sigma work?
Lean Six Sigma is all about the integration of practical application tool kits. Lean is the synchronization of process output to customer demand.
There are seven key principles in the Lean philosophy.
The Six Sigma philosophy has four key principles:
The glue that holds it together is the DMAIC Improvement Process. The duration of a project is driven by the scope of the issue that must be resolved.
The Lean Six Sigma philosophy can be applied in a day, a week, or multiple months. It just depends on the depth and breadth of the issue.
See Lean Six Sigma in action with The Sailboat Company Simulation!
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Why Lean, then Six Sigma, and now Lean Six Sigma?
Lean has its roots in the Just in Time and Continuous Flow days of the 1970s. Supporting the implementations and solving problems back then was the job of the TQM folks using one of many multi-step problem solving processes. We had the TQ people and the JIT people, who generally talked, but walked down different paths.
The programs were considered to be separate, but complimetary. So when the Six Sigma bandwagon started up in the 1980s and Lean started its rebirth in the 1990s we once again had two separate initiatives. This generally put a strain on resources. There was also a misconception that Lean was easier than Six Sigma and we better start with Lean first. Once we clean up our act maybe we will be ready for Six Sigma.
Once again, organizations were missing the point. Six Sigma is only two things (the zealots may disagree, but hear me out)
The tools to support the problem solving process come from many other disciplines and previous improvement programs and initiatives.
Six Sigma did not invent Statistics, SPC, DOE, Process Mapping, Brainstorming, Kano Modeling, Voice of the Customer Analysis, Benchmarking, and so on.
Lean has a toolkit that comes from the Toyota Production System, JIT, Continuous Flow, SMED, 5s, Value Stream Mapping, Kanban inventory management, and so on.
It gets back to my earlier premis that Six Sigma has a structured improvement process and a universal performance measure and uses tools to tackle issues from many resources. So, add the Lean Toolkit as well.
We should always utilize the "Best Practice Tools" that are required to tackle the issues we are facing! Who cares what discipline they came from. Integration is the key to success and Lean Six Sigma is the new buzzword.
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How do you resolve any issue with a product, process, or service?
That is a great question, but the answer is straight forward.
We can use the DMAIC Improvement Process Regardless of the Tool Box we are going to employ.
DMAIC can be applied whether the improvement project scope can be accomplished in days, weeks, or months.
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