Six Sigma Training Updates

Six Sigma is a Disciplined, Data-Driven approach and Methodology to reduce variation in the process which in turn leads to an increase in performance of the system and significant financial gains.

A Metric:

• Focuses on reducing defects, so that high quality is delivered.
• The sigma level defines the robustness of the process – its ability to provide superior, consistent output

A Methodology:

1.A clearly defined structured approach in solving the complex problem

2.Proven methodology DMAIC or DMADV to deliver results

A Journey:

While traditional Quality initiatives have focused on detecting and correcting defects, Six Sigma focuses on creating robust processes so that defects are not produced in the first place.

Many studies have focused on the relationships between particular quality dimensions and organisational outcomes.

Customer Satisfaction
Customer satisfaction has been addressed in quite an extensive literature in the field of marketing (Churchill and Surprenant, 1982; Oliver and DeSarbo, 1988; Anderson and Sullivan, 1993). Although empirical evidence is limited, increases in customer satisfaction are generally believed to shift the demand curve upward, reduce marketing costs, increase marketing costs for competitors (satisfied customers are more difficult for competitors to take away), lower transaction costs, reduce customer turnover, increase cross-selling (more products, larger accounts), lower employee turnover, enhance reputation, and reduce failure costs.

Competitiveness
Some claim that quality has a positive association with increased demand (Abbott, 1955) and with inelastic demand (Porter, 1980). Porter claimed that organisations differentiate themselves from their competitors mainly by providing more durable or reliable products, adding desirable features, providing high levels of customer service, and having an extensive dealer network—all aspects of TQM. The Profit Impact of Market Strategies (PIMS) analyses confirm this competitive advantage by showing perceived product quality to be the most potent predictor of corporate financial success when compared with market share, productivity, low-cost production, diversified product mix, and other common predictors of performance (e.g., Buzzell and Wiersma, 1981).

Productivity
Although a number of quality gurus have been writing extensively since the early 1970s that ''Quality is Free" because high-quality eliminates the costs associated with lost customers, rework, excess time, indirect engineering, modified specifications, data collection and analysis, field service, reinspection, and waste (Crosby, 1979; Deming, 1982; Schonenberger, 1982; Imai, 1986; Ferdows and DeMeyer, 1990; Cole, 1993), empirical evidence remains sparse. Ittner (1992) found that nonconformance costs go down simultaneously with reductions in conformance costs, thus enhancing productivity.

All of the 'Belts' play various roles in the Six Sigma project. Green Belts and Black Belts are allowed to commence, expand and lead projects in their functional areas.

i. Project Implementation:

Green Belts are those employees who are trained to implement Six Sigma projects under the guidance of the Black Belts to achieve the desired goals. They carry out these activities along with their regular responsibilities. Green Belts spend around 25-50% percent of their time involved in the Six Sigma projects. They are aware of all the important activities that they have to carry out.

They should be able to explain the importance of the y=f(x) formula to the business and the processes. They are trained in the various tools needed to carry out the data collection and for validating the measurement system. They are also well-trained on DMAIC methodology and statistics.

The Black Belt coach imparts detailed instructions on the statistics and creation of histograms and pareto diagrams. This experience is valuable, and their prowess with Six Sigma methods helps them in getting promotions and incentives within the organization.

Upper management also reviews the Six Sigma Green Belts' projects and provides the necessary feedback on developments and shortfalls in the project path.

ii. Monitor Project Progress:

Six Sigma Green Belts are selected by the management team to receive either Six Sigma online or classroom training, or a combination of both on Six Sigma methods, tools and techniques. However, the Six Sigma training they receive on these tools is generally less than what Black Belts receive. The Black Belts carry out cross-functional responsibilities.

Green Belts carry out the project for their own areas of operations. Each company has its own set of Six Sigma certification requirements, which may include the completion of Six Sigma online training, written or online exams and completion of a project. Green Belts may be given incentives by companies to complete a project or even Six Sigma certification.

However, another company most likely will not recognize the certification of one company. Some companies require one project to be carried out to maintain the certifications. They need to carry out meetings with Black Belt coaches to review the project progress and seek improvements.

iii. Understanding Benefits:

With the Green Belts training, they are able to understand the benefits and gains out of the project success. They understand the relevance of the project to make the processes easier and the overall effect of it on the team and the organization as a whole.

Timely completion of the Six Sigma project is necessary before the tollgate review. Green Belts have to plan properly and stay in control of the progress of the project. The project should be strictly based on data; if Green Belt has no knowledge of finance, the Green Belt project will give them an opportunity to learn it, know about the issues faced by management, and to quantify the benefits.

iv. Carry Out Tests:

Six Sigma Green Belts know that tools exist, though they may not be using them. They will be able to calculate the average and standard deviation from the set standards of the various metrics; however, they are business professionals and not quality controllers. They will be able to carry out statistical tests using software such as Minitab and JMP.

Green Belts should be aware of the importance of preparing the Fishbone diagram to understand the basis of process defects. They will be able to help their bosses and co-workers by preventing defects. This will also help them in winning support for the Six Sigma project. Green Belts should be able to form a base for change with less resistance from co-workers, if they have shown visible improvements in the processes and the output.

Green Belts should start their projects only after top management commits to the necessary resources for improvement. They should share information with others above them, their co-workers and others who have a stake in the growth of the company.

Let's take a closer look at the tools for numerical survey data analysis. The graph below shows the tools that are available to you and their objectives in each case. These methods are often used to group numeric variables according to similarity, they may also be useful in studying how individuals are positioned according the main groups of variables in order to identify user profiles.

Let's look a bit more closely at the tools we can use for analyzing categorical survey data. Again, the diagram below shows the tools that are available to you and their objectives.

It’s usually not a good idea to rely solely on a single statistic to draw conclusions about your process. Do that, and you could fall into the clutches of the “duck-rabbit” illusion shown here:

If you fix your eyes solely on the duck, you’ll miss the rabbit—and vice-versa.

Cp: A Tale of Two Tails:

Cp is a ratio of the specification spread to the process spread.

The process spread is often defined as the 6-sigma spread of the process (that is, 6 times the within-subgroup standard deviation). Higher Cp values indicate a more capable process.

When the specification spread is considerably greater than the process spread, Cp is high.

When the specification spread is less than the process spread, Cp is low.

By using the 6-sigma process spread, Cp incorporates information about both tails of the process data. But there’s something Cp doesn’t do—it doesn’t tell you anything about the location of the process data.

Process A Vs Process B:

Obviously, Process B has a serious issue with its location in relation to the spec limits that Cp just can't "see."

Cpk: Location, Location, Location!

Like Cp, Cpk is also a ratio of the specification spread to the process spread. But unlike Cp, Cpk compares the distance from the process mean to the closest specification limit, to about half the spread of the process (often, the 3-sigma spread). When the distance from the mean to the nearest specification limit is considerably greater than the one-sided process spread, Cpk is high.

When the distance from the mean to the nearest specification limit is less than the one-sided process spread, Cpk is low.

Notice how the location of the process does affect the Cpk value—by virtue of its being calculated using the process mean.

Yet there's something important that Cpk doesn't do. Because it's a "worst-case" estimate that uses only the nearest specification limit, Cpk can't "see" how the process is performing on the other side.

For example, the following two processes have the about same Cpk value (≈ 0.9):

                      
Notice that Process X has nonconforming parts in relation to both spec limits, while Process Y has nonconforming parts in relation to only the upper spec limit (USL). But Cpk can't "see"any difference between these two processes.

To get the two-sided picture of each process, in relation to both spec limits, you can look at Cp, which would be higher for Process Y than for Process X.

What Is Monte Carlo Simulation?

The Monte Carlo method uses repeated random sampling to generate simulated data to use with a mathematical model. This model often comes from a statistical analysis, such as a designed experiment or a regression analysis.

Regression Equation:

With this type of linear model, you can enter the process input values into the equation and predict the process output. However, in the real world, the input values won’t be a single value thanks to variability. Unfortunately, this input variability causes variability and defects in the output. To design a better process, you could collect a mountain of data in order to determine how input variability relates to output variability under a variety of conditions. However, if you understand the typical distribution of the input values and you have an equation that models the process, you can easily generate a vast amount of simulated input values and enter them into the process equation to produce a simulated distribution of the process output

How Can Monte Carlo Simulation Help You?

With Companion by Minitab, engineers can easily perform a Monte Carlo analysis in order to:

• Simulate product results while accounting for the variability in the inputs,

• Optimize process settings,

• Identify critical-to-quality factors,

• Find a solution to reduce defects.

Choosing the right type of subgroup in a control chart is crucial:

In a rational subgroup, the variability within a subgroup should encompass common causes, random, short-term variability and represent “normal”, “typical”, natural process variations, whereas differences between subgroups are useful to detect drifts in variability over time (due to “special” or “assignable” causes). Variation within subgroup is therefore used to estimate the natural process standard deviation and to calculate the 3-sigma control chart limits.

In some cases, however, identifying the correct rational subgroup is not easy. For example, when parts are manufactured in batches, as they are in the automotive or in the semiconductor industries.

Batches of parts might seem to represent ideal subgroups, or at least a self-evident way to organize subgroups, for Statistical Process Control (SPC) monitoring. However, this is not always the right approach. When batches aren't a good choice for rational subgroups, control chart limits may become too narrow or too wide.

Control Limits May Be Too Narrow:

Since batches are often manufactured at the same time on the same equipment, the variability within batches is often much smaller than the overall variability.

In this case, the within-subgroup variability is not really representative and underestimates the natural process variability. Since within-subgroups variability is used to calculate the control chart limits, these limits may become unrealistically close to one another, which ultimately generates a large number of false alarms.

Control Limits May Be Too Wide:

On the other hand, suppose that within batches a systematic difference exists between the first two parts and the rest of the batch. In this case, the within-batch variability will include this systematic difference, which will inflate the within-subgroups standard deviation. Note that the between-subgroup variability is not affected by this systematic difference, and remember that only the within-subgroup variance is used to estimate the SPC limits. In this situation, the distance between the control limits would become too wide, would not allow you to quickly identify drifts.

For example, in an injection mold with several cavities, when groups of parts molded at the same time but in different cavities are used as subgroups, systematic differences between cavities on the same mold will necessarily impact and inflate the within-subgroup variability.

Use an I-MR-R/S chart to monitor the mean of your process. Use this to find the variation between and within subgroups when each subgroup is a different part or batch:

i. I chart: The I chart monitors the process mean. The control limits of the I chart are calculated using the between-subgroup variation, but not the within-subgroup variation.

ii. MR chart: The moving range chart plots a moving range of the subgroup means. Therefore, the MR chart only monitors the between-subgroup component of variation, not the within-subgroup variation.

iii. R chart or S chart: The R or S chart monitors within-subgroup variation.