Keep your finger on the pulse: automate data crunching

It is becoming more important than ever to swot up on your data analysis skills. It’s more than just working the numbers and reporting endless statistics to clients and stakeholders. Large data sets can be invaluable for active decision making throughout the project.

As we head deeper into the 4th industrial revolution, project software is becoming smarter, and the trend for automation is snowballing. Harnessing technology to automate data crunching will allow you to keep your finger on the pulse of your project. Here’s how:

Easier management with tailored alerts

Project management tools have the ability to collect and collate large amounts of information. Scheduling software alone will gather vast levels of data from your team during a project, such as time spent on a task, completion rates, any issues or incomplete assignments. Project software can also track budget, use of resources, and much more. How you then use this data is critical.

Smart dashboards offer a live, to-the-minute view of your project on a very user-friendly interface. Often this data visualisation will be customisable, with widgets that allow you to see an overview of your project at a glance. It is worthwhile taking some time to set up your dashboard to show what is important to you; or if you haven’t edited this in a while then have a quick review. By seeing your project in this way, you can very easily navigate to manage the areas of the project needing your attention.

Take this a step further, and set up tailored alerts. Most commonly used tools now offer some form of digital assistant with customisable features such as alerts (although their exact capabilities will vary depending on the tool or app). These could be in the form of a notification if a project gets close to budget or runs a certain percentage over time. Cleverly enabling alerts will allow you to step in and take action ahead of time. Having a smart setup makes great use of your project software’s data crunching ability. It improves efficiency and allows you to focus and manage in the right areas.

Better forecasting with project performance data

Beyond being invaluable for the daily management of a project, data has the ability to optimise your forecasting. This benefit is twofold – at a project’s start it offers you the intelligence to reflect on information from previous projects and is hugely beneficial for project planning. Additionally, data analysis can be an ongoing resource for forecasting throughout a project, allowing you to keep your finger on the pulse and react and respond in an agile way.

Data technology can better predict project outcomes, and has the power to assist with forecasting everything from duration to project profitability. What’s more, thanks to AI developments and machine learning, project technology is increasingly able to make recommendations too. These may be; how to make best use of resources, areas where costs can be reduced, or tasks which can be performed more intelligently - for example, by drawing on the strengths of a particular team member.

In terms of project planning, a data-driven approach allows managers to make more accurate predictions and forecast completion dates and expenditure more successfully. Working in this way allows you to set goals with greater precision and can inform your decision making.

Avoid standstill with new features

Technology is continually developing to assist us in our daily work, so be sure to avoid standstill. Whatever software you use, it is worth being in touch with your contact at the company to see if there are any updated features you should know about. Many offer free screen-share style training sessions you and the team could undergo. Alternatively, shop around - it is worth switching up the applications you use from time to time if they are not best serving you, or offering updates and new features.

Project tools are already saving us painful hours of data collection and analysis, meaning the role of the project manager is evolving to focus increasingly on interpreting data smartly. Do this well, and utilise data analysis automation, and you are sure to see your project performance boosted.

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Using Data Analysis to Identify Six Sigma Sales Projects

Six Sigma is a useful methodology for identifying sales improvement opportunities. The data-driven approach facilitates correlation of the factors involved in the sales process and helps isolate specific inputs to expand the potential for increasing sales. Analysis of existing sales practices and various statistical sales results provides a good source of project ideas.

As an example, here is sample analysis involving a fictitious company that sells computers and computer peripherals. The analysis begins with the formula Y = f(x1, x2, x3,…xn), where Y is the output and f is the function of all inputs (x‘s). In this particular case, Y (sales) = f (marketing campaigns, salesperson skills, information source, word-of-mouth, etc.)

To begin, the Black Belt works with the project team to determine all possible reasons for a sale. The cause-and-effect diagram is a useful tool in this effort (Figure 1). The reasons identified include the following:

◉ Marketing campaigns

◉ Skill of the salesperson

◉ Various sources of information about the product

◉ Resellers

◉ Delivery options

◉ Long-term relationship criteria

◉ Loyalty programs

◉ Cost of the product and cost of service

◉ Competitive advantage knowledge

◉ Customization and value-added features

Figure 1: Cause-and-Effect Diagram (Fishbone)

Once the inputs are identified, they are correlated with the outputs in a cause-and-effect matrix (Figure 2). The matrix reflects the key process input variables (KPIV) and key process output variables (KPOV), which are subsequently analyzed in Pareto charts (Figures 3 and 4).

Figure 2: Cause-and-Effect Matrix

Figure 3: KPIV Pareto Chart

Figure 4: KPOV Pareto Chart

Once the KPIVs and KOPVs are identified, a detailed analysis is conducted for each variable to determine the best project opportunities for increasing sales. Listed by variable, the steps taken to complete this analysis are:

Marketing Campaigns (First Design Input)

The project team gathers data to identify the areas with the highest potential for achieving a successful marketing campaign. It uses the following steps:

Step 1 – Obtain data of personal computer ownership density by:

◉ Region

◉ Customer age

◉ Income

◉ Gender

◉ Employment type

◉ Education

◉ Computer usage

Step 2 – Determine the type of marketing strategy needed:

◉ High PC density in one region will require a low-profile marketing campaign associated with value-added services.

◉ A maximum number of non-users below the age of 15 requires a marketing focus on individual tastes to draw attention to the product.

◉ High income in one region requires a marketing focus on high-end costly products.

◉ Create opportunity maps for potential new business by different products.

Different Sources of Information (Second Design Input)

Step 1 – Gather data on all available sources of detailed information for customers to learn about products:

◉ Call center

◉ Web sites

◉ Word of mouth

◉ Mailers

◉ Brochures

◉ Seminars/exhibitions

Step 2 – Determine:

◉ Which source has the maximum number of inquiries.

◉ If that source covers all relevant information.

◉ Which source has the maximum number of confirmed orders.

◉ Which location to target for a seminar on printers, monitors or PCs.

Salesperson Skills (Third Design Input)

Step 1 – Gather data on the best-selling techniques and performance data according to:

◉ Tenure of agent

◉ Gender of agent

◉ AHT of agent

◉ Communication skills of agent

◉ Reporting supervisor

◉ Performance incentives

◉ Training

◉ Customer-profiling ability

Step 2 – Identify the best combination of the above qualities that would increase sales.

Knowledge of Competition (Fourth Design Input)

Gather data based on the performance of competitors offering similar products:

◉ Which competitor products have a better market share?

◉ What critical-to-quality (CTQ) characteristics of the product are instrumental in giving the competition an edge?

◉ What opportunities and risks are identified through SWOT analysis?

◉ What is the voice of the customer saying through the Kano analysis?

Personalized Service (Fifth Design Input)

Gather data based on:

◉ The number of existing customers who have received value-added service.

◉ The various ways personalized service is provided.

◉ The MTTR between complaints.

◉ The various methods used to identify purchasing patterns.

◉ The efficiency with which customers’ future needs are anticipated.

Follow-up on Usage (Sixth Design Input)

Gather data based on:

◉ The methods employed to assure customers of a long-term relationship.

◉ The number of customers who have gone to the competition after their first purchase.

◉ The number of customers who have upgraded to a higher cost or quality product.

◉ The percentage of customers who have received a follow-up call after delivery of the product.

◉ The level of impact (positive or negative) of the follow-up process on customer satisfaction.

◉ The number and type of incentive programs offered to customers who refer new customers.

Once all the design input information is obtained, it is used to create a quadratic or polynomial equation to uncover correlations between the sales data.

Y = A0 + ax1 + ax2 + ax3…+axN

Where Y = sale, A = constant from a regression analysis, a = a numerical value which, if high equates to the x being considered more important, and x = input variables.

Benefits from Hypothesis Testing

Additional benefits are gained through hypothesis testing. Examples of test statements for the sample company include the following:

A larger, more substantial marketing effort provides a more intimate customer relationship than a smaller effort. The hypothesis test results drive marketing methods used to ensure a sense of accountability among the sales representatives. The salespeople serve as either sales consultants or aggressive marketers.

An indirect sales model is more efficient than a direct sales model. The hypothesis test results determine whether direct mailers for customers are more effective than incentives to resellers for good promos.

An educated customer equals a successful sale. The hypothesis test results, which incorporate an assessment of the number of customers who call in the first time for information versus the number who purchase a product, determine the strength of the existing information dissemination process.

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Process Capability – Surface Finish Example: Part 2

This is Part 2 of a two-part article on process capability. Part 1 addressed the concept of process capability and how to calculate it, including what to do with data that is normal and non-normal. Part 2 looks at failure rates and material conditions for a surface finish example.

Sometimes, a process has specifications that are one-sided such as flatness, perpendicularity, surface finish and roundness. When assessing process capability in those cases, you will actually be looking at the process capability for one half of the distribution – the half that’s not cut off by the boundary specification. Develop the control chart (ImR), the normal probability plot and then assess process capability. Here is an example of a capability study of surface finish.

The control chart is stable and in control (Figure 1).

Figure 1: Stable and in Control

There seems to be an issue with the normal probability plot (Figure 2). The data is non-normal. Investigating the data a little further reveals that the shape appears to be asymptotic as it approaches zero; and the right side of the data follow a line fairly well. The probability density function (PDF) of this data would look like half of a normal PDF.

Figure 2: Normal Probability Plot

In this case, there is no value in estimating the Cpk(lower) as zero is a physical limit and the most desirable condition. For example, surface finish cannot get better than zero. In this case, report only Cpk(upper). When analyzing this data, the 0/lower spec is selected as a boundary. This tells the software to only estimate Cpk(lower).

Figure 3: Estimating Cpk(upper)

Estimating Failure Rate from Cpk

If there is a process that:

◉ Has an average of 9.02 ml

◉ Has a standard deviation of 0.5 ml

◉ Has an upper specification limit (USL) of 12 ml

◉ Has a lower specification limit (LSL) of 8 ml

◉ Is stable and in control

◉ Has data that follows a normal distribution

then process capability can be calculated.

Figure 4: Calculating Process Capability

Assess the Cpk to see that the number is .79 as Cpk(lower) (.79) > Cpk(upper) (2.30). The Z score, sometimes called sigma level, can be used to predict failure rate. Z score can be calculated easily. Z is similar to Cpk where Xi is the specification limit.

where by observation it is shown that Z is 3 times Cpk.

Z(upper) is 5.95 and Z(lower) is 2.03. Look up these values on a standard normal table to get the probability of exceeding these values. The probability to fail on the upper side is 0 as shown in Figure 5 below. The probability to fail on the lower side is 2.095 percent. Convert this to parts per million (PPM) and get a 20,950 PPM fail rate.

Thus far, the focus has been on potential capability or short-term capability. Over time, in each instance, a batch of material is sampled; the sample generates a distribution that is shifted from the previous sample distribution. When all this data is pooled, the distribution is wider. This leads to Pp and Ppk. Ppk is long-term process capability. It is sometimes called process performance. It includes all the data from a number of samplings. The only difference in the math between short-term Cpk(upper) and long-term Ppk is how standard deviation is estimated. Cpk(upper) uses a control chart method to estimate standard deviation   as with an ImR chart and Ppk uses the sum of squares estimate,   as with a    chart.

Figure 5: Value Versus Performance

Both Cpk and Ppk require the process to be stable and in control to be valid. If the process is stable and in control, it makes little numerical difference which estimate of capability is used. What is most important is that that the data is sampled such that it includes expected variations and noise conditions. Many organizations require short term Cpk’s to be higher than long term Ppk’s – usually by a factor of 1.5 standard deviations – to guard against missed variation and normal shift and drift. It is common to expect the average of samples to vary by ±1.5 σ.

Maximum Material Condition or Least Material Condition True Position Capability

The traditional process capability measures run into trouble when there is a maximum material condition or least material condition true position specification.

Figure 6 shows the Geometric Dimensions & Tolerancing (GD&T) feature control frame. (Refer to ASME Y14.5-2009 for details.) This defines the size, location for the hole in the surface finish example.

Figure 6: Geometric Dimensions & Tolerancing (GD&T) Chart

In a situation like this, the diameter and position are not independent. This is an issue because independence is a requirement for Cpk, Cp, Ppk and Pp. This requires the use of multivariate analysis.

Rather than using an ImR chart to test for stability, use a T2 – generalized variance chart. The generalized variance chart is treated like the range chart in terms of assessing stability and the T2 chart is used to assess control. This process is stable and in control. In this example, the data is given in the form of diameter, true position.

Figure 7: T2-Generalized Variance Chart of Diameter, True Position

Table 1: Example Data

X nom	Y nom	Dia nom
95.6000	113.9000	4.6500

Table 2: More Example Data

Variable	Count	Count	StDev
X	30	95.595	0.0144
Y	30	113.88	0.0249
Diameter	30	4.5226	0.0341
True Position	30	0.06300	0.03168

Data Is in the Form of (X,Y) Coordinates

If the metrologist reports the sample data in X, Y coordinates, the process is highly simplified. Even better, Minitab has a macro for release 17 to do the analysis called POSCAP.MAC.

Figure 8: Related Equations

Which leads to a PCpk of 7.13.

In order to use Minitab you must obtain the macro POSCAP.MAC. Contact Minitab help to do this. Then save this macro in your macro folder. Once the macro is stored in the correct location, be sure to enable commands as shown in Figure 9.

Figure 9: How to Enable Commands

The total diameter tolerance was calculated to be 0.51.

To run the macro, type the following at the command prompt (MTB>)

MTB > %poscap c1 c2 0.51;

SUBC> Nominal 95.6 113.9

The default nominal for the X and Y coordinates is (0, 0). A different nominal value can be entered for the X, Y coordinates as in this case (95.6, 113.9). Even if only one of the nominal values is nonzero, both nominal values must be entered.

The macro provides two outputs based on two analyses. It gives a traditional capability based on the location from nominal. It is a one-sided capability with 0 being the boundary. Please note that this is an analysis of location only. It does not include size or bonus tolerance. In essence, it does the calculation shown in the equations and Figure 10 below. It is a boundary condition at 0 so it only reports a Cpk(upper). The macro reports this as offset.

Figure 10: Process Capability Report for Offset

The second output from the macro is the PCpk result along with a X, Y scatter plot. It overlays two circles: one centered at nominal and representing the tolerance diameter, the other centered at the mean (X, Y) coordinates which represents the process variation. This calculation includes size, position and bonus tolerance. It is a true representation of process capability for a MMC or LMC callout.

Figure 11: Scatter Plot of Positional Coordinates

If your data is in form of diameter, true position then the calculation becomes cumbersome and is not supported by a macro. It is highly beneficial for your metrologist to report the data in X, Y coordinates. The following are the calculations for diameter, true position format:

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Process Capability – The Basics: Part 1

What is process capability? From a conceptual view it is a measure of the relationship between the voice of the process and the voice of customer (VOC). It is essentially a ratio of the customer requirement (specification) and the expected process variation.

Process capability = Voice of the customer / Voice of the process

It is an expression of how well your process performs relative to the VOC. And it is a prediction of how well your process will meet customer requirements in the future. A capable process is one in which almost all measurements of a feature produced by the process fall inside specification limits. There are several indices that are commonly used.

Cp

Let’s use a car and garage example to drive home the concept of Cp. The garage defines the specification limits. The car size represents the process limits.

Figure 1: Cp

If the car is smaller than the garage, it means Cp > 1; the car will fit inside the garage. When you find that your data (car) is smaller than the specification limits (garage), your process is capable. It is, therefore, safe to conclude that you will not have problems meeting the specifications. In other words, you will not have problems parking the car in the garage. Cp is the specification width divided by the process width.

Cpk

In the Figure 1 illustration above Cp > 1, which as we just learned means the car should fit in the garage. . .if the car is always centered.

Figure 2: Cpk

However, when the car isn’t centered, you are at risk of damaging the car as well as the garage – or, in process terms, falling outside of the specification limits and not meeting customer requirements. Another process measure is needed to address the centering of the car in the garage. Cpk to the rescue! Cpk tells how much clearance can be expected from the side of the car to the nearest edge of the garage. Look at the distance from the center of the car to the nearest edge of the garage, then divide that by half of the width of the car.

Figure 3: Customer Expectation

Consider CE as customer expectation. The customer expects the car to fit inside the garage and that the car will be reasonably centered in the garage. The customer of your process has similar expectations. For a process:

USL = upper specification limit

LSL = lower specification limit

 = estimate of the process’s standard deviation

Conceptually, the standard deviation is the average spread of the data about the mean.

Quality guru Dr. Walter Shewhart taught that a process is behaving normally when it varies by no more than ±3. (for a total spread of 6σ). Therefore, the denominator of the Cpk calculation is 3 (6σ divided by 2). Cp is always a positive number as it is the ratio of two positive numbers. Cpk can be positive, zero, or negative.

Cp and Cpk

The relationship between Cp and Cpk is shown in Figure 4.

Figure 4: Relationship Between Cp and Cpk

Process Capability Assumptions

When calculating process capability Cp or Cpk, there are three key assumptions:

◉ Large sample size

◉ Stable process

◉ Normal distribution

When these assumptions are not met, the values are not valid.

Most capability index estimates are valid only if the sample size used is “large enough,” which is generally thought to be about 30 or more independent data values.

Below, within the steps of a process capability analysis, we discuss how to determine stability and if a data set is normally distributed.

Steps for a Capability Analysis

To assure valid results when performing a capability analysis, follow these steps.

1. Generate I and mR charts.

◉ Start with the range chart and determine stability. Are all points inside of the control limits? If yes, the process is stable and the analysis can move forward. If no, the process is unstable and this must be addressed before moving on.

2. Look at individuals chart.

◉ Are there any indications of out-of-control data per the rules of process control? If yes, the process is out of control and this must be addressed before advancing in the capability analysis. If no, then the process is in control and analysis may proceed.

3. Generate a normal probability chart and test for normality

◉ If a distribution is close to normal, the normal probability plot will be close to a straight line. Minitab and other common software packages report the Anderson-Darling statistic. The null hypotheses for this test is that the distribution is normal; thus, to conclude that the data is normal, the p-value must be greater than 0.05 (typically).

◉ If the data is normal, then assess capability. Otherwise the data may need to be transformed. A popular transformation is the Box-Cox transformation. (Alternatively, you could use a process capability index that applies to non-normal distributions. One statistic is called Cnpk [for non-parametric Cpk]. For additional information on non-normal distributions, see the book Process Capability Indices.)

4. Assess process capability.

Figure 5: P-values

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How to be prepared for when projects don’t go to plan

If the current global pandemic has taught companies anything, it is how to continually adapt. As government guidelines for business change as a response to threat levels and external factors, organisations have had to be proactive in implementing change. This has affected management at every level who have undoubtedly had to make revolutionary changes either to meet guidelines, or as a knock-on effect of working business to business.

Also Read: PRINCE2 Foundation

Unplannable business situations don’t just occur during pandemics though. Everyday business and projects can be hit by unplanned events. Here we take a look at how to prepare for when projects don’t go to plan, and reflect on the lesson already learned from the COVID-19 situation.

Learn to continually adapt

Perhaps in light of recent events, you and your company feel better equipped to react to seismic shake-ups. For others however, it may be an ongoing struggle. Perhaps you are already reflecting on how you could have reacted better? By mastering agile practices businesses will find themselves better equipped to react to change.

Agile practices enable business to be responsive. Teams who are accustomed in reacting to evolving landscapes are well-equipped to handle project disruption. Agile training teaches candidates the practices they can adopt in order to be responsive and proactive when things don’t go to plan. Whereas in traditional methodologies, a considerable amount of re-planning may have to take place when hit with an unplanned event. An agile approach is flexible and adaptive and enables teams to deal with uncertainty.

You can find out more about our PRINCE2 Agile training here. We offer e-learning courses which allow your teams to upskill from home, at their own pace.

Take a closer look at your risk management strategy

Every project has potential risks which will impact the smooth running of the project and outcomes. By evaluating and understanding those risks, you will be better prepared for when things don’t go to plan. Now is the perfect time to take a better look at your risk management strategy. In fact, it is the ideal time to make a habit of regularly reviewing risk.

By routinely monitoring and reevaluating significant risks during a project you will be better equipped to respond in a timely manner, and in a way that is advantageous to the project and your business.

Though unforeseeable risks are near impossible to prepare for, it is always worth having a ‘worst case scenario’ or ‘in case of emergency’ plan on the backburner. This should detail measures you can put in place as a reaction to the business disruption of unprecedented events. A further ‘unplannable’ factor often overlooked in traditional risk management is human error. A recent article by APM states that ‘managing human error is crucial if you want to succeed.’ Human error is, on the whole, an unplannable factor, but their article offers some great tips for mitigating the risk by addressing it in your risk management planning.

Factoring in ‘outside of the box’ risks beyond those traditionally accounted for in risk management strategies can be of great benefit for keeping projects on course. When there are external factors knocking projects off track, pairing agile working with improved risk management is a recipe for success.

Build a team who pull together

Finally, if life during a global crisis has taught us anything, it’s the value of pulling together. As a nation we’ve committed to the cause by staying at home during lockdown, and developed a great sense of community, coming together to help out neighbours, friends and strangers; with the shared goal of staying well.

This is of great value in our work lives too. We’ve talked before on this blog about the importance of encouraging a culture of ownership amongst your project team. Essentially, how committed team members problem solve and unite in order to prevent failures. The key takeaway here being that there are incredible benefits of a team devoted to project success; and that by working together during challenging times and when things don’t go to plan, we can achieve better outcomes.

Source: prince2.com

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Practical Examples Enhance LSS Training

When I was a teenager, I took a SCUBA diving certification course. For the first few lessons, I sat in a classroom with the other students and learned a lot about critical concepts like nitrogen in the bloodstream, proper equipment usage and emergency procedures. We eventually progressed to a shallow swimming pool where we tried the basics of breathing underwater and how to handle hazardous situations that may arise while diving, most of which involved variations on not having air available to breathe.

Finally, we proceeded to the open water test of our skills. In one of my first demonstrations of applying this knowledge, I was asked to take off my mask and then remove my regulator from my mouth and toss it behind me. Next, I had to successfully recover both – while remaining under 40 feet of water. In my first attempt, I managed to get my removed mask tangled with the regulator and snagged on another piece of equipment behind me. In the classroom, I was trained to reach for my second regulator or signal to my diving buddy that I needed assistance.

In practice, I attempted to bolt for the surface and had to be held down by the instructor to save me from a dangerous consequence of rapid ascent.

Classroom Versus Reality

Learning statistical tools in a Lean Six Sigma (LSS) course more often than not follows the same fate: what seemed perfectly reasonable and logical in the classroom fails to account for real-world complexities. Learning those tools has little value if the student is inclined to immediately abandon them when the situations they face do not look like those the instructor showed. As a result, Belts gravitate to simpler tools within their comfort zone that often fail to deliver breakthrough results on complicated problems.

Critical to Statistical Analysis

To bridge this gap, consider the elements necessary for effective statistical analysis as shown in Figure 1.

Figure 1: Elements Necessary for Effective Statistical Analysis

This is not an exhaustive list, but I consider these five categories to be fundamental to doing things correctly. Now consider where the majority of time is spent during Belt training: tool selection and analysis. With little-to-no formal training in collecting and preparing data for analysis or presenting it to an audience once completed, is it any wonder Belts don’t feel confident?

Statistical Tool Training Requirements

To make training more effective, I recommend several practices as part of training in statistical tools.

1. Use real data. Clean training datasets that avoid the problems of real-world data may make it easy to focus on the tool being taught, but they avoid the realities of using that tool in practice. By using real data sets in class, data manipulation tools and quality checks are incorporated into the analysis. Instructors can further gain enormous credibility for the power of the tools by allowing students to bring their own data and then working through it “live fire” in front of the class.

2. Formal training in data collection and presentation. You are probably aware of the catastrophic results of engineers working on the Challenger space shuttle not being able to clearly communicate the results of their data analysis. Converting technical information to understandable graphical presentation and verbal communication is a skill; if Belts are to be successful, they need to be taught – and have the opportunity to practice – that skill. The same goes for data collection: understanding how databases work and best practices for manual data collection is a hard skill that should be taught extensively – not glossed over.

3. Test with open-ended problems. An exam, especially at the Black Belt level, should reflect expected competency following the course. In that spirit, questions asked should not direct the student to the appropriate tool to use or ask specific questions with obvious answers. Rather, providing a dataset (preferably real data) or situation and requiring the candidate to determine the best course of action will best determine whether they can apply knowledge outside of strict tool usage.

4. Practical application. Incorporating an actual problem to solve in the course provides context and an opportunity to go through the end-to-end process of using the tool. This is additional work for the instructor, who must identify an opportunity ahead of time and line up resources and backing to attack the problem as part of class. But the potential value to students, who now work together through all five elements of statistical analysis to achieve a real result, is potentially the most significant of all activities in the course.

As an example, consider using these practices as part of learning design of experiments (DOE). The five elements, learned and applied to a real designed experiment performed as a class, might look like what is shown in Figure 2.

Figure 2: Statistical Analysis in a DOE

The Value of Real-World Experience

The questions posed in Figure 2 are not comprehensive but provide an idea of the kinds of problems and considerations a student should learn to respond to when they are executing their own project. By teaching mostly tool selection and analysis in class, instructors aren’t preparing students for what to expect after class and how to handle it. Thus, the student ends up lacking confidence when their project doesn’t seem to produce “clean” data analysis opportunities as were presented in class. The students then gravitate away from using them.

Excuses for why statistical analysis was not appropriate for a project include statements like “my data was not normal,” “the process was not in control” and “there wasn’t even data on this problem.” These are followed by face-saving statements intended to make the practitioner sound like they did the right things by not using data, such as “this was more of a process-oriented project” and “the company isn’t really ready for those types of tools and is more Lean-focused right now.” Of course the company isn’t ready to be using these tools – that’s why they trained only specific individuals in those skills! And you can be sure that the organization is in full support of problem-solvers using the tools that get the best results.

My dive training experience taught me valuable lessons that I have taken with me on more than 50 dives, including one in which another diver knocked my mask off in 80 feet of water. Having undergone a real-world test on my training, I was able to stay calm and take appropriate action that resulted in not only retrieving my mask but continuing the dive.

There is no replacement for real-world experience, but these practices begin to incorporate that experience in the learning process. By ensuring better balance across all elements of statistical analysis, instructors can better prepare students to stay calm in the face of unexpected obstacles and not “bolt for the surface.”

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Six Sigma: It’s Not Just a Company’s Processes That Vary

A good implementation of Six Sigma covers a lot of territory, from improving existing business processes to designing new ones and then to managing them for the long term.

Organizations that understand begin by applying Six Sigma to improve current processes, using specific statistical tools to identify and reduce the causes of unsatisfactory results. Later, Six Sigma provides ongoing monitoring and managing of these processes to ensure that they continue to produce the desired results. As process-oriented thinking becomes established throughout the organization, a true system of process management starts to take shape.

This approach is the ideal, taking advantage of the full potential of Six Sigma.

However, the reality is that there are variations in the way Six Sigma is implemented and trained, some of them actually limiting its potential. These variations are caused by a number of factors that Six Sigma practitioners need to recognize.

Key Sources of Variation in Implementing Six Sigma

The variations in the way an organization chooses to deploy Six Sigma come from many places. Here are some typical ones:

1. Different definitions of Six Sigma based on previous experience: First is the “There is only Six Sigma” crowd. These are often people who learned to appreciate data analysis and statistical method for the first time through participation in a Six Sigma program and are genuinely enthusiastic. For them, Six Sigma was their first contact with quality and process management, and the tools employed are forever associated with it. They are eager to share what they know and deploy it in the classic fashion by emphasizing project structure using DMAIC (Six Sigma applied to improve existing processes in five project phases – Define, Measure, Analyze, Improve, Control) and monitoring financial payback. But the drawback is that this group tends to overlook the position of Six Sigma within the longer history of process management and treats it as an isolated cure-all, thus potentially alienating people.

Then comes the “toolkit” crowd. This group consists primarily of engineers and traditional quality professionals who recognize the tools and methods from their own experience, but then often reduce the definition of Six Sigma to just that – a toolkit. Due to their awareness of process management in the past, they also are more likely to reject the enthusiast’s perception of Six Sigma as a completely new phenomenon. Implementation focuses on training others in the tools to improve technical processes, usually limited to manufacturing, with the potential to over-emphasize the statistics and ignore Six Sigma’s top-down, bottom-line driven, strategic application. However, if an organization already has a program in place to promote strategic process management, this definition may make sense.

2. Different approaches to problem solving based on cultural differences: Businesses in English-speaking countries tend to be driven by an MBA culture of professional management, accentuated by a strict bottom-line focus and extremely pragmatic approaches to problem solving. In this environment, training in Six Sigma is viewed as similar to obtaining a driver’s license: “Just tell me what I need to know to get safely and efficiently from A to B.” Six Sigma training and projects will emphasize speed and the attainment of financial goals. The downside could be insufficient or sloppy data analysis.

Contrast this with many parts of continental Europe and elsewhere where managers are more likely to have engineering backgrounds that stress theory and the exploration of detail. Here, the attitude is much like saying: “It is not enough to know how to drive, you also need to be a certified auto mechanic before you are allowed to turn on the ignition.” Expectations from Six Sigma tend to include the intense training of statistical theory while undervaluing, or even ignoring, the strategic contribution of Six Sigma to business results and long-term cultural change.

3. Different learning traditions: Approaches to learning new content are of two basic types – top down and bottom up. This is partly driven by the cultural differences cited above.

Top-down learning is pragmatic and dictates that one start with a specific goal and first aim for a basic understanding of content, gradually working down toward the detailed level through multiple learning cycles. This approach focuses on knowing enough to get the immediate job done (i.e., attaining the specific goal) while theory and detail are absorbed over time through practical experience. It is the most common approach to Six Sigma training, whereby a subset of statistical knowledge is used initially to achieve immediate project results and promote a broad appreciation of statistical process control within the organization. Doing multiple projects with varieties of data deepens this knowledge and identifies people with the talent and interest for even more detail.

Bottom-up learning, on the other hand, already assumes a capacity for broad detail and is more suited to a technical culture. A base of theoretical knowledge is first established. Once the full theory has been completely explored and digested, subsets of the knowledge are applied to specific situations at higher levels. This approach is thorough, but time consuming and works best for developing a team of operations-level data analysis experts in technical environments. However, it may slow projects down considerably and also does little to promote a broader appreciation of process management throughout all areas and levels of the business because non-technical people will quickly lose interest or never start in the first place.

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Six Sigma Certification, Who Can Do It?

Six Sigma certification questions continue to roll into my inbox. While I can’t find time to answer each email with a tremendous amount of detail, I can and do save them for answering in an article such as this.

Can My Company Certify Six Sigma Yellow/Green/Black/Master Black Belts?

Six Sigma certification is a funny concept. Everyone wants to be certified, but nobody really understands what it means in the industry and how it might enhance your resume outside your current company.

There isn’t a single certification body for Six Sigma. Because of this, you’ll find certification options from consulting companies (like the one that probably trained and certified the first wave in your business) and ASQ, as well as from businesses like Motorola, Allied Signal, GE, and many others. Each of these businesses has different certification criteria. And so to answer this question, the answer is Yes. Yes, you can certify anyone you want to any standards you think are appropriate. Provide instruction, test for knowledge and hand out certificates.

But the question should not be “Can I certify Green or Black Belts in my company?”, the real question should be “What is the true value of all these certifications?”

◉ What is the value of the content being instructed?
◉ How well is the content being instructed?
◉ Does the content cover all pertinent aspects of Quality and management that every change agent should know?
◉ Has the content not only been learned, but been put into practice and successfully utilized?
◉ Has the knowledge of individuals been tested to acceptable levels?
◉ Have processes been defined, measured, analyzed, improved and controlled for the better of the organization?

Ok, now that I have that idealist thinking of my system, let’s get to what I feel is the true question that people should be asking, “What is the street value of the certification?” Why do I say street value? Because that’s where the rubber hits the road and you show what you know and what you don’t.

For example, no business is going to hire a graduate of Wharton (arguably the most prestigious business school in the U.S.) without asking her to explain business concepts or to apply a model to a hypothetical business situation. Sure she comes with a pedigree from a top 5 institution, but these types of questions help the hiring manager evaluate the candidate’s level of understanding. Similarly, a Quality manager interviewing a Black Belt or Master Black Belt candidate is going to ask how she facilitated a difficult team meeting, to explain what a Z value is, and to differentiate and explain the p values associated with a recent project. That is the true test of a person’s Six Sigma value to the organization.

Finally, wouldn’t it be nice if everyone was valued for the contributions they have made or will make to the business? The fact is that pedigrees are important to people — which is exactly why this certification question came up in the first place. Since there aren’t any independent firms determining the “value” of certification from the many available sources as is the case with MBA schools, the question becomes more difficult to answer. In my opinion, the closer you are to professional instruction coupled with rigorous application the more value it has.

There are a handful of original consultants teaching Six Sigma from the early Motorola days. Would you rather have one of these consultants help you certify your organization, or someone who read 4 books on the subject and is knowledgeable of the topics? Ok, you caught me. I oversimplified the subject and there are always exceptions to the rule, but I think you understand my point with respect to the professional instruction. Now let’s look at rigorous application: Would you feel more comfortable with a candidate that improved and controlled a process at GE or one that did the same at a $5 million dollar company? Probably the GE one, because you know that large companies have more rigor around their application and certification processes, and failures at a GE are not as easy to sweep under the carpet. Alright, before someone can jump over to the forum to post hate mail to me, there are exceptions to the rule and I can cite 5 instances that will fail my test also, but in my opinion the general rule holds. After all is said and done, Motorola and GE certifications are pedigrees and company XYZ is not as valuable. Don’t blame me, blame the perceptions of the Quality community. I’m just the bearer of the news.

So, to sum up this question and answer…Can you certify people within your own business? Yes, of course you can. Is it worth much in your company? Probably, if your management team values it and rewards those who attain certification. Is it worth much outside your company? Maybe so, maybe not. It all depends how knowledgeable the person is and how the pedigree is perceived by the interviewing company.

What Are The Legal Implications Of Six Sigma Certification?

Many people like to ask me legal questions about Six Sigma. Here’s what I know:

◉ Motorola has registered and trademarked the phrase “Six Sigma”
◉ Each consulting company owns the copyrights to their own intellectual property and training materials
◉ Certification is in the eyes of the beholder

Given the limited information I know :), here are my pearls of wisdom:

◉ Don’t use the trademarked terms of other companies without their permission
◉ Don’t steal, reprint, or use other people’s copyrighted materials without their written permission
◉ Set and use Six Sigma certification standards that are appropriate for your company

Aside from that, I don’t know of any other issues that would prevent your company from certifying Six Sigma Green Belts, Black Belts or Master Black Belts. If someone knows of any other reasons, post them to the forum.

Can Non-Certified Master Black Belts Mentor Black Belts?

The answer is yes, but let me ask you a question. Would you want a non-certified dentist with years of experience doing your root canal? Or would you want a non-certified mechanic working on your car when you know that most mechanics are certified with biggest certification company in the U.S.? The fact is that both the dentist and the mechanic can probably do the job correctly without certification if they are properly educated, trained and practiced.

But would you want them working in your mouth or on your car? Not me. The fact that some company (outside or inside) has set forth criteria, and that this person has taken and passed a test reassures me (to some degree). We know that not all certifications are equal, but I personally would prefer to be mentored by someone who has learned and used the Six Sigma DMAIC methodology before. Call me old fashioned.

Are There Other Certification Companies Besides Those Listed On iSixSigma?

Every time I visit the Consultants > Six Sigma category of links, I find a new consulting company that has been added to the list. Although the list is very comprehensive and includes all of the big Six Sigma consulting firms, it probably isn’t exhaustive. New consulting companies are formed daily and I’m sure a few of them deal with Six Sigma, and even certify.

So the answer to this question is also Yes. There are other companies that are not listed on iSixSigma that would certify your company and employees. But you should refer back to my previous answer to determine the differences between certifications.

What Are The Requirements For A Consulting Company To Provide Six Sigma Certification?

The answer is none. Any person or persons can form their own consulting company, develop their own materials, and begin training and certifying others. There is no certification body that each consultant must visit to be granted rights to certify. It’s not like ISO 9000 where a registrar might be involved to provide certification of meeting requirements. Caveat emptor – let the buyer beware.

It’s one of the problems of Six Sigma, and also one of the greatest aspects of Six Sigma. You use Six Sigma as your company determines is most appropriate. What…you need to do work to successfully implement Six Sigma in your company (note the sarcasm)? Yes. While the Six Sigma methodology is for the most part standard, Six Sigma deployment catches many people off guard; they’re not used to the lack of structure or rules around deployment within their company. Each company is different: structure, culture, reward systems and size. But if you ask me, it’s the lack of structure that allows each company to maximize their benefit from a customized deployment.

Source: isixsigma.com

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Is Your Organization Ready to Implement Six Sigma?

Deployment of Six Sigma is not an easy task and does not necessarily translate into instant results.

A successful implementation depends on many factors, such as a company’s culture, and the commitment and conviction of its leadership. Without the necessary pieces in place, a Six Sigma deployment can falter. Identifying a few simple factors can help determine whether an organization is ready for a successful deployment.

Top-Down Support

One of the biggest determinants of success is the company culture. If Six Sigma or any formal process improvement program is a foreign concept to a company, then overcoming organizational resistance can be significant. Even if a business has had a quality initiative in the past, introducing something new can be seen as another “flavor of the month.” Although people are naturally resistant to change, establishing a lasting process is possible by changing the mindset of the individuals who make up the organization, starting at the top. The leaders of an organization largely influence company culture. If they don’t buy in and commit themselves to Six Sigma, no one should expect the rest of the employees to do so. Six Sigma, or any initiative for that matter, is unlikely to survive as a grassroots campaign.

It is almost predictable that some people in the organization will not accept the new initiative and may never be converted. However, support from the top will make it possible to institutionalize the initiative as a part of the business. The resistance that Jack Welch, former chief executive officer of GE, initially received from managers when Six Sigma was launched in 1996 is recounted in Mikel Harry’s book, Six Sigma: The Breakthrough Strategy. When managers assumed that Six Sigma was simply a passing company fad, Welch reacted by informing employees that advancement into senior management positions would be dependent on whether they received Green Belt of Black Belt training prior to the beginning of 1998. Managers quickly realized that Welch was serious and Six Sigma was not a fleeting policy. Had Welch not responded with such conviction, it is arguable that GE’s experience with Six Sigma would have been quite different.

Dedicated Resources

In order to make Six Sigma work, management must commit dedicated personnel to lead projects and mentor others who are working to make process improvements. This seems obvious, but there are still companies that are trying to do Six Sigma “part-time.” That is, management is using existing operational resources and adding the title “Black Belt” to the job description. If an employee is expected to do an operational job and act as a Black Belt, then Six Sigma will be short-lived. An employee with such a dual assignment will normally focus on day-to-day activities rather than process improvement projects. That is because daily actions and firefighting are more likely to grab the attention and win the approval of the boss. As a rule, employees and supervisors have learned that immediate business needs come before long-term solutions. Six Sigma requires a dedication to focusing on process improvement so that day-to-day problems are solved once and for all.

Employee Incentive

Assuming the necessary support from the organization’s leadership exists, what is going to motivate the rest of the population to accept and internalize this new initiative? Whether it is a financial incentive or it is tied to each employee’s performance evaluation or advancement (as with the case of GE), a system must be established to ensure that employees remain engaged. Again, this is linked to the company’s commitment to Six Sigma. Management should not expect to simply throw a new initiative at their staff and have it readily adopted. Six Sigma can be a significant paradigm shift for many companies. Overcoming inertia and changing the culture of a company is never easy. With apologies to Sir Issac Newton, one must consider the Second Law of Management: “Organizations in motion will stay in motion unless acted upon by another force.” That force is management’s incentive or reinforcement policies. It may be debatable which method is best, but if management does not employ any tactics, then it should not be surprised if employees do not quickly embrace the new concepts.

Business Aptitude

Although the tools and methodology apply to any process regardless of industry, the speed at which Six Sigma is accepted and understood by an organization may be a function of the type of business or industry. The early adopters of Six Sigma (as with most quality initiatives) were manufacturing firms. Only in relatively recent history has the service industry begun to follow suit.

It seems intuitive that manufacturing firms, with their experience in past quality initiatives – ranging from statistical process control to total quality management – in addition to their familiarity with statistics, mathematical modeling and metrics, could quickly assimilate Six Sigma into their businesses. This is not to say that service-based firms do not stand to gain the same benefits as manufacturing firms. They do. It only suggests that the best approach to Six Sigma implementation differs from one industry to the next. A transactional company should avoid implementing a generic training program without considering the need for the program to relate to its specific business needs.

Essentials of Training

Clearly, training is an essential part of a Six Sigma deployment. Several aspects of the training plan must be considered. For example: What are the training objectives? Who will get trained and in what order? Will everyone receive the same training? If not, what are the criteria for who receives each level or type of training? How will the training be structured and what areas will receive the most attention? What will be the duration of training? What methods, case studies, format, training aids will be used during the training session? Who will conduct the training – internally trained personnel or consultants? What are the selection criteria for choosing instructors?

These are just a few questions that should be taken into account when evaluating the training plan. There are numerous ways to conduct training depending on the objectives or needs of the business and there is no one-size-fits-all solution. However, answering these questions up front and identifying potential issues before training begins will factor into the success of the implementation.

Project Alignment

After Six Sigma is initiated and personnel are trained, numerous ideas for projects may be generated. The number of projects launched is far less important than their impact to the bottom line. A handful of successful, completed projects outweighs a multitude of never-ending works in progress.

Maximizing involvement on Green Belt and Black Belt projects may initially help facilitate the culture change within the company if two common pitfalls can be avoided.

◉ The first is when employees find themselves on so many project teams that they can barely manage their time. This results in Six Sigma overload and translates into unmet project deliverables.

◉ The second is when projects are selected without key stakeholder support or buy-in from the organization’s leadership. This can doom the project from the start.

Even before launching Six Sigma, it is important to assess how projects are selected. Projects should be directly tied into the key business indicators and metrics. “Feel good projects” (those that may be highly visible but with no positive financial outcome) will ultimately erode support for Six Sigma. Management should be able to agree on the importance of each project, regardless of whether it truly grasp the concepts and tools of Six Sigma. There are more questions to answer relative to project selection: Does management select and staff projects or do the Black Belts identify areas of opportunity and petition for support? What are the selection criteria for projects? What is the process to obtain project sponsorship? Ensuring that the organization feels the impact of projects and keeping management engaged are essential to creating a lasting quality program.

Communication Processes

Finally, a commitment must be made to the internal processes to communicate the progress of the Six Sigma projects as well as the lessons learned and results. More questions to answer: How often do different parts of the organization interact with one another? Does the company communicate well with respect to other business initiatives? Does each business function or company department work in its separate silo? To maintain momentum, project successes also should be broadcasted through the organization. Is there an effective medium already in place to carry this out? Effective internal lines of communication will not only promote Six Sigma but also will accelerate improvements through best- practice sharing and benchmarking.

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