Customer-Centric Risk Management Via Hoshin Planning

The new millennium brought a great deal of change in the marketplace. As the financial scandals of the 1990s came to light, new regulations were drafted and put into place to avoid such occurrences in the future. U.S. financial institutions must comply with the Sarbanes-Oxley Act, which came on the heels of the Enron scandal, and the portions of the Basel II accords, which address the global impact of a credit crisis.

Most companies are already reviewing and documenting what processes to implement in order to meet the requirements of Sarbanes-Oxley regulations. Compliance is required this year. Financial regulators in the United States are adopting only some of the Basel II accords – but the adopted areas are very complicated. Institutions with $250 billion in assets or more than $10 billion of total foreign exposure on their balance sheet are required to comply. The compliance date for Basel II is 2007.

Applying Six Sigma to These Regulations

So how does Six Sigma apply to understanding and managing risk and complying with these regulations? Should the compliance effort be simply an audit task or should it be a best practice strategy?

Companies which do not have a framework in place to execute continuous process improvement are likely to treat Sarbanes-Oxley and Basel II requirements as a one-time project (a project which just integrates the minimum requirements of the regulations) or as one more just-in-time audit activity. But if a company makes compliance a best practices strategy, then that means alignment across the organization for people, processes and technology. Hence, Sarbanes-Oxley and Basel II become a part of the risk management philosophy that uses the best aspects of Lean Six Sigma.

Risk management is a discipline historically thought of as a way to avoid problems, not create new solutions. In a Lean Six Sigma culture, risk management issues become a way of improving processes and transcending functional tasks to allow for an improved customer experience.

The illustration below depicts how a company can best address its risk management capabilities. At the lowest levels of the continuous process improvement curve, companies are simply monitoring compliance requirements and being reactive at best. As a company moves up the curve, its ability to utilize Lean Six Sigma tools to address risk management capabilities increases. From a cultural perspective, the organization changes from one that is event-driven and constantly putting out fires, to one that utilizes an effective and proactive approach in a “project way of life,” and finally reaches the highest level where a Six Sigma culture attains a “process way of life.”

Continuous Process Improvement Curve

Treating Risk Management Like a Process

So what are the Lean Six Sigma tools that can best be applied to the risk management arena and help a financial institution gain competitive advantage in the process? There are just as many tools in the Lean Six Sigma tool kit as there are applications to the risk management process. The reason: To be successful risk management needs to be treated like a process just like mortgage loans and item processing. A great place to start making this happen is giving risk management an equal place in the strategy of the company. Some of the most successful customer-focused companies, such as Hewlett Packard, Xerox and Bank of America, do strategy in a Six Sigma way through Hoshin Kanri.

As the Hoshin planning process has evolved in financial services organizations utilizing Lean Six Sigma, it has developed into a business tool that influences best practices across numerous lines of business. Stemming from the utilization of operational risk dashboards with the definition of the proper operational risk metrics, the Hoshin planning process can be enhanced to track and improve performance in managing the aspects of risk that affect associates, customers and shareholders.

Hoshin planning plays a key role by allowing the operational risk planning team to concentrate on the most meaningful metrics that lead to successful strategic execution. The data collection process becomes more efficient through many software products now available to deal specifically with Sarbanes- Oxley and Basel II. As recently as three years ago, the process of collecting metrics and consolidating them into a functional scorecard could take weeks. Now, this process takes a couple of days, and allows all associates to access a scorecard via the company’s intranet. This timely enhancement allows teams to update strategic goals regularly, aligning them in accordance with changes in the business environment.

One Company-wide Six Sigma Operation Risk Plan

Also, lines of business progress towards established operational risk goals within the Hoshin plan can be presented through a vast amount of information. Reporting spreadsheets, a balanced business scorecard, and performance plans – once considered separate items – can all be captured under one company-wide Six Sigma operational risk plan. An operational risk Hoshin plan becomes the foundation of the review systems required by Sarbanes-Oxley and Basel II. The manageable, actionable metrics of the Hoshin plan provide structure and discipline in a highly dynamic environment. The flexibility of the Hoshin plan also is important, because metrics may take on new meaning if external risk factors shift a current business environment.

An operational risk management leadership team also should use Kanri as the second step to incorporate Hoshin planning into its regular management routines. While a Hoshin plan captures a team’s strategy, Kanri is a management process that focuses on the Hoshin plan and its tactical execution. Significant improvements in key process indicators around operational risk management occur with the inclusion of the Hoshin plan in monthly business reviews. The Hoshin plan should act as a means to judge business goals and drive operational risk decisions in the everyday, business-as-usual environment. By utilizing Kanri within an operational risk Hoshin plan, each of the strategies will be linked to their corresponding metrics, so financial institutions in a process-oriented and proactive-way can easily see how they are measuring up to the requirements of Sarbanes-Oxley and Basel II.

Conclusion: Reduce Risk/Be More Competitive

In summary, the Sarbanes-Oxley Act and Basel II are all about managing risk (financial, fraud, credit market or operational). Executives should understand that reducing risk will make their institutions more competitive. Banks that lower risks will be the winners in the marketplace. Risk management should become a “best practices strategy,” aligning data, technology, people and processes across the organization, and not an “audit compliance task.” Ultimately, companies should adopt a risk management philosophy that evolves from discrete events and projects into a risk management process that can become a key breakthrough strategy in a Hoshin plan and can leverage all the best aspects of Lean Six Sigma.

Continue reading

Making the Best Use of Process Improvement Consulting

How often do organizations that have a dedicated team or group working full-time on quality initiatives under the name of Six Sigma, or Lean, or Business Excellence or Service Quality and at the same time the strategic major projects like business reengineering, restructuring, revenue enhancements are outsourced to big consulting organizations? The answer is: all too often.

This is an obvious gap that some managers who are responsible for quality in their organization try to ignore. Meanwhile they try to convince themselves that they are making the most positive impact possible in their current roles. Experience indicates these are the most likely causes of this situation:

1. Inappropriate Choice of Methodologies and Consultants

Most of the methodologies are positioned by the consultants as being the only solution to the organization’s problems. Using an example of a fictitious consulting organization called Super Consultants, which is into marketing and training of a particular methodology for services, say Six Sigma. The consultants make a presentation to a bank management about how it should deploy Six Sigma through capability route and invest in Black Belt and Green Belt training programs over a three-year period.

The reality is that the bank’s key businesses are in the red so the bank needs a couple of projects to immediately get into the black. Despite knowing this, the marketing head of Super Consultants convinces them to launch the Six Sigma movement starting with a few Black Belt and Green Belt projects. The training is launched, the trained candidates struggle. A company in the red will be firefighting every day so team members do not turn up for meetings set up by the Black Belt candidates. And management cannot force them because they are need in the daily firefights, which seem essential just to keep the bank’s nose above water. So in the end, money is spent, resources are allocated yet nothing works out. The collective conclusion reached is “Six Sigma does not work!” The consultant gets away by saying that management did not ensure that people attended the meetings and so on…

2. Issues with the Methodology Experts

The are two key issues with internal resources as compared to external consultants:

◈ Facilitation misused as a hands-off approach – During a interview for a senior project manager’s position, a candidate was asked: “What is your role?” He said that he was a deployment leader, which means meeting business heads, understanding their problems and suggesting training programs for their resources to resolve problems. So is he taking ownership of providing a solution and impacting the business? The answer was, “No, my job is to facilitate and not get involved in the day-to-day project working.” Facilitate here meaning “review, advise, escalate, etc. – all of which fall into the category of a hands-off approach. This is where business consultants might get the job of solving the problem since their proposal gives the aforementioned deployment leader a sense of security that at least the external consultant will take the ownership and recommend a solution; some consultants might even implement.

◈ Stuck to methodology and tool usage – Often it has been observed that internal resources are trained by external trainers to follow the “rigor” of the methodology which ends up as “rigor mortis” for the project. Since every project requires flexibility and common sense in tool application, newly trained Green Belts or Black Belts are not helped by “rigorous” mentors who insist on tool usage not because the project requires it, but as a demonstration to meet certification criteria. Too often trainees carry forward this false tendency along with an over emphasis on a PowerPoint tollgate presentations. While Champions or stakeholders are expecting fact-finding, smart conclusions and pragmatic and effective action plans; they can get unnecessary and sometime irrelevant data analysis and fancy presentations.

What then is the best way forward?

A Model of Internal Business Consulting

The model of internal business consulting that works best is one that judiciously combines internal and external resources. The overall strategy is outlined by the internal consulting group, whether is called Service Quality, Quality Initiatives, Six Sigma, or whatever. Here are three key points about the model:

1. The organization should have a set of senior managers who are trained in problem solving (DMAIC, DFSS, etc.) with the experience of having led at least three or four large scale projects. They are the equivalent of senior consultants. They are given a team of junior Black Belts and Green Belts designated as project managers for the field work.

2. Project managers can be sourced from external consultants as per requirement and can be placed under supervision to internal senior project managers to carry on additional projects. This takes care of additional unplanned projects as well as keeps internal costs lean.

3. One of the senior project managers is given the responsibility of internal capability building. This resource is an expert in all the methodologies being used and is proficient in training skills.

So one is looking at the possibility of external project managers being placed round the year as part of the internal consulting team. This option has the following obvious advantages:

◈ Optimizing costs for the organization as well as any consulting firm. For the organization, it get twice the output for the same dollars paid to the consultant.
◈ Increasing accountability and tightening control over the quality of deliverables by the external consultant.
◈ Crunching project turn around times by 30 percent to 50 percent, since internal resources are closely monitoring the project as well as ensuring proactive removal of roadblocks/delays.
◈ Assuring the quality of solutions.

This model makes a business impact and allows an internal consulting team to gain the trust of management as the solution provider. It is an all around win-win solution.

Continue reading

Seven Basic Tools of Quality

The Seven Basic Tools of Quality (also known as 7 QC Tools) originated in Japan when the country was undergoing major quality revolution and had become a mandatory topic as part of Japanese’s industrial training program. These tools which comprised of simple graphical and statistical techniques were helpful in solving critical quality related issues. These tools were often referred as Seven Basics Tools of Quality because these tools could be implemented by any person with very basic training in statistics and were simple to apply to solve quality-related complex issues.

7 QC tools can be applied across any industry starting from product development phase till delivery. 7QC tools even today owns the same popularity and is extensively used in various phases of Six Sigma (DMAIC or DMADV), in continuous improvement process (PDCA cycle) and Lean management (removing wastes from process).

The seven QC tools are:

1. Stratification (Divide and Conquer)

Stratification is a method of dividing data into sub–categories and classify data based on group, division, class or levels that helps in deriving meaningful information to understand an existing problem.

The very purpose of Stratification is to divide the data and conquer the meaning full Information to solve a problem.

◈ Un–stratified data (An employee reached late to office on following dates)

5-July, 12-July,13-July, 19-July, 21-July, 26-July,27-July

◈ Stratified data: (Same data classified by day of the week )

2. Histogram

Histogram introduced by Karl Pearson is a bar graph representing the frequency distribution on each bars.

The very purpose of Histogram is to study the density of data in any given distribution and understand the factors or data that repeat more often.

Histogram helps in prioritizing factors and identify which are the areas that needs utmost attention immediately.

Chart 2.1

(Histogram: Defects day wise)

3. Check sheet (Tally Sheet)

A check sheet can be metrics, structured table or form for collecting data and analysing them. When the information collected is quantitative in nature, the check sheet can also be called as tally sheet.

The very purpose of checklist is to list down the important checkpoints or events in a tabular/metrics format and keep on updating or marking the status on their occurrence which helps in understanding the progress, defect patterns and even causes for defects.

Table 3.1

(Check Sheet: Defect types with their occurrence on day of the week)

4. Cause-and-effect diagram. (“Fishbone” or Ishikawa diagram)

Cause–and–effect diagram introduced by Kaoru Ishikawa helps in identifying the various causes (or factors) leading to an effect (or problem) and also helps in deriving meaningful relationship between them.

The very purpose of this diagram is to identify all root causes behind a problem.

Once a quality related problem is defined, the factors leading to the causal of the problem are identified. We further keep identifying the sub factors leading to the causal of identified factors till we are able to identify the root cause of the problem. As a result we get a diagram with branches and sub branches of causal factors resembling to a fish bone diagram.

In manufacturing industry, to identify the source of variation the causes are usually grouped into below major categories:

◈ People

◈ Methods

◈ Machines

◈ Material

◈ Measurements

◈ Environment

Figure 4.1

(Fishbone Diagram: Missed deadline in manufacturing of product)

5. Pareto chart (80 – 20 Rule)

Pareto chart is named after Vilfredo Pareto. Pareto chart revolves around the concept of 80-20 rule which underlines that in any process, 80% of problem or failure is just caused by 20% of few major factors which are often referred as Vital Few, whereas remaining 20% of problem or failure is caused by 80% of many minor factors which are also referred as Trivial Many.

The very purpose of Pareto Chart is to highlight the most important factors that is the reason for major cause of problem or failure.

Pareto chart is having bars graphs and line graphs where individual factors are represented by a bar graph in descending order of their impact and the cumulative total is shown by a line graph.

Pareto charts help experts in following ways:

◈ Distinguish between vital few and trivial many.

◈ Displays relative importance of causes of a problem.

◈ Helps to focus on causes that will have the greatest impact when solved.

Chart 5.1

(Pareto Chart: Causes for defects in supplied items)

6. Scatter diagram

Scatter diagram or scatter plot is basically a statistical tool that depicts dependent variables on Y – Axis and Independent Variable on X – axis plotted as dots on their common intersection points. Joining these dots can highlight any existing relationship among these variables or an equation in format Y = F(X) + C, where is C is an arbitrary constant.

Very purpose of scatter Diagram is to establish a relationship between problem (overall effect) and causes that are affecting.

The relationship can be linear, curvilinear, exponential, logarithmic, quadratic, polynomial etc. Stronger the correlation, stronger the relationship will hold true. The variables can be positively or negatively related defined by the slope of equation derived from the scatter diagram.

Figure 6.1

(Scatter Diagram: Types of correlation in Scatter Plot)

7. Control Chart (Shewhart Chart)

Control chart is also called as Shewhart Chart named after Walter A. Shewhart is basically a statistical chart which helps in determining if an industrial process is within control and capable to meet the customer defined specification limits.

The very purpose of control chart is to determine if the process is stable and capable within current conditions.

In Control Chart, data are plotted against time in X-axis. Control chart will always have a central line (average or mean), an upper line for the upper control limit and a lower line for the lower control limit. These lines are determined from historical data.

By comparing current data to these lines, experts can draw conclusions about whether the process variation is consistent (in control, affected by common causes of variation) or is unpredictable (out of control, affected by special causes of variation). It helps in differentiating common causes from special cause of variation.

Control charts are very popular and vastly used in Quality Control Techniques, Six Sigma (Control Phase) and also plays an important role in defining process capability and variations in productions. This tool also helps in identifying how well any manufacturing process is in line with respect to customer’s expectation.

Control chart helps in predicting process performance, understand the various production patterns and study how a process changes or shifts from normally specified control limits over a period of time.

Figure 7.1

(Process Control Chart)

Continue reading

Six Sigma: Three Deadly Sins In Strategic Planning

When clients are not getting the results they expect from their strategic plans, I often see three overlooked causes – incomplete planning, inadequate links between strategy and action, and poor communications. This is not to say that there are not other problems. But these three seem to be the most common, and better, something can be done about them!

The First Deadly Sin: Incomplete Planning

Almost all strategic plans cover marketing/sales, product plans and development/ acquisitions. But can a business really think about the future without considering a strategy for its human capital? Or its operations strategy? All too often I see those aspects relegated to the status of mere tools, interesting only insofar as they enable meeting the sales or product plans, or the financial targets. The 1990s taught us about core competencies and thinking about our people as assets: let us make our strategic plans complete by considering our strategic competencies in at least those realms. Bring their leaders to the table early in the strategic planning process and challenge them to tell you how they can become competitive advantages.

The Second Deadly Sin: Missing Links To Action

It is obvious that without deliberate, careful linkages from strategy to actions, strategic plans seriously jeopardize their chances of implementation. I often see lots of actions happen, but the strategic change is left for the next year’s plan. What is wrong is that the strategic plan process can’t just be about the content of the strategy. The strategic plan process needs a vital feedback loop between tactical planning and strategic planning. The goals should be to unearth the necessary and sufficient tactics to reach strategic success and also to refine the strategy to be realistic.



A simple procedure can link your strategy to actionable objectives and to projects that people will “sign up for.”

◈ Develop strategy statements. Strategy statements are “big picture” statements that direct the organization toward achieving competitive advantage. “Cut costs” and “increase sales” fail because they aren’t specific enough. Where should the Ops leader turn to cut costs? Or the Sales leader to increase sales? Instead, “increase market share by growth of sales channels in EU and pricing advantage in the USA” is a better strategy statement because it points towards specific actions that can lead to making a difference competitively. Keep these short, simple, and clear in their direction. And end this step with a test: do we have the needed statements to constitute “success” for the business?

◈ Translate strategy statements into strategic objectives. “Introduce at least six new flavors of toothpaste in 2000, all of which will be profitable in their first year” might be a useful strategic objective. Strategic objectives should immediately suggest projects (specific actions) that will both achieve the strategic objective and clearly link to the strategy statements. And they need be quantified, so they define “success” clearly enough to evaluate if subsequent projects are “enough.” End this step with a test: do we have the necessary and sufficient set of strategic objectives to meet each strategy statement?

◈ Prioritize the strategic objectives. For example the resources may not be there this year to “move licorice toothpaste from R&D into production in 2000.” Which ones best meet the overall financial objectives? Which establish the desired market dominance? Which have overarching legal, political or environmental imperatives? What criteria will you use to prioritize?

◈ Define projects to meet the important objectives. Once the important strategic objectives are chosen, we can define the specific major projects necessary to achieve each one. Then plan resources and determine the time frames needed to meet the objectives, therefore the strategy statements. End this step with a test: do we have the necessary and sufficient set of projects identified to meet each strategic objectives?

Is it a linear process, going from step one to four? Hardly. The feedback loop that refines the strategy and finds breakthrough projects is inherently non-linear. The steps overlap, and executives have to listen carefully as ideas and choices about tactics boil up. This approach needs to involve larger number of employees, for two reasons: better and more ideas, and gaining the support of those who are central to implementation of the projects. But reaching that larger group makes effective communications vital.

The Third Deadly Sin: Poor Communications

I have seen too many strategies hidden behind complicated prose, making them incomprehensible to employees and difficult to get excited about. We know the equations for effectively leading change: simple and accurate expressions of where you want the business to go, or what you want employees to do brings broader understanding; understanding brings buy-in; buy-in brings commitment of the many employees who have to bring to life the strategies and tactics; commitment plus aligned goals, measures and rewards make it happen.

I saw in a division of one of the world’s largest and best-regarded companies where their strategic plan was 13 volumes in length. Their tactics seemed to float like many dense fog-banks but they didn’t surround any particular strategic mountaintops! Yes, it is harder to write a short message than a longer one. But an idea that can’t be communicated readily will not go far. Let us make sure that our strategic plan can be conveyed in words and paragraphs, rather than pages and books.

Strategy statements and strategic objectives, crisply linked to action projects, which consider all the assets of the business, can help you avoid the effects on these three deadly sins.

Continue reading

Is Your Organization Ready to Implement Six Sigma?

Many companies have jumped on the Six Sigma bandwagon hoping to emulate the success of such industry leaders as AlliedSignal, GE and Motorola. However, 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.

Conclusion: Then the Deployment Plan

Simply kicking off a Six Sigma program does not guarantee results. However, a company can increase its chances for success by objectively evaluating whether the necessary factors for success currently exist in the organization. Then, the company can start spending time preparing a well-thought-out deployment plan.

Continue reading

Making the Business Case for a Six Sigma Deployment

Many quality managers have read about and seen the benefits of Six Sigma but are unsure how to approach their senior leadership about the opportunity because they do not have a concise package of information to convey. Fortunately, there is a series of tried and true steps which can be taken to sell management on the benefits of Six Sigma. These are the steps blazed by pioneering quality professionals who successfully sold management on the methodology and eventually deployed an effective Six Sigma program.

The Importance of Leadership Buy-in

Without leadership buy-in, there is little hope for Six Sigma adoption. A company’s executives must believe and support Six Sigma’s potential with dollars, words and actions just like any other corporate objective or goal. Executives are looking for a return on investment (ROI), risk mitigation and competitive advantage. Therefore, to convince them of the value Six Sigma will bring to the organization, it is important to present the benefits as a business case. The major steps to developing and presenting the business case are:

1. Identify and evaluate your audience.
2. Research and summarize successful launches at other organizations with similar functions; include the ROI and a sample project.
3. Document critical success factors.
4. Define deployment requirements.
5. Define a pilot project.
6. Calculate and display the potential financial savings range and ROI including “soft” elements such as corporate image and competitive advantage.
7. Present and sell Six Sigma to the executives.
8. Get ready for deployment!

The Audience

The first step is to determine the perspective of your audience. This involves evaluating their appetite for new initiatives and reviewing their previous messages to the company. Useful resources include mission statements, competitive or objectives cascades, and presentations made to various levels of the company. If appropriate, initiate an informal interview with the audience members in advance to determine their current challenges or passions and evaluate their previous success in finding tools to manage those challenges. From these sources, identify areas on which to focus the business case, including appropriate examples and pilot project ideas.

Benchmarking

To demonstrate the potential of Six Sigma, it is necessary to provide examples of successful deployments in other organizations. Focus on similar-sized organizations within the same industry if possible. The purpose is to show an investment of similar scale and the resulting performance improvements realized within one or two years of deployment. Many success stories exist in quality management publications or on quality Internet sites. A few hours spent using an online and/or library periodical search tool should yield a good list of appropriate examples as well. Identify themes that made Six Sigma a success for those companies and look for examples that include some of the messages gleaned from the executive research to align the message to currently accepted viewpoints.

An important message to stress during the presentation to the executives is that simply training a number of Black Belts will not transform the organization into a “Six Sigma company.” Leading consulting firms have proven the critical success factors through years of Six Sigma launches. These factors include true leadership support, a data-driven culture, proof of concept through the use of a pilot project, alignment to corporate/functional objectives, and full integration in the business environment. To strengthen the significance of the message, reference examples of these factors found in the success story articles. The critical factors are a critical take-away from the meeting.

Deployment Plan

The purpose of presenting a deployment approach is to show the scale and timing of the undertaking. To address concerns about resource and training cost risk, include a stair-step approach to deployment. This approach uses approval tollgates to obtain executive signoff of the deployment’s effectiveness before rolling it out to a larger group of employees. The first step is the selection of a pilot project with one employee and an external consultant or Master Black Belt. If the project proves successful, the next step is Black Belt training and project management for a pilot department. This logic continues until all functional areas have 10 percent of their employees trained as Black Belts and 100 percent trained as Green Belts. This scalability also allows the training of internal, experienced Black Belts to become Master Black Belts, which reduces the need for expensive consultant-based training. One successful way of presenting this is through the use of a one-page timeline showing two years of anticipated benefits, the goals of training, expected project savings and external consultant needs diminishing. Also highlight a continuous line representing ongoing executive support.

Pilot Project Idea

The selection and presentation of the pilot project idea is another place where it is critical to link the project idea to a known “hot topic” of the executives. It will peak their interest if one of the company’s current challenges is addressed. But be careful not to promise “world peace.” One of the necessities of an individual project is a scope small enough to allow for real, measurable and sustainable improvement within a realistic time frame.

The pilot overview outlines the resources required and the process of evaluating its effectiveness. First review the objective (hot topic improvement) and the scope (one major contributor to poor performance). Next review the resources needed, including recommending an experienced, quality-driven employee for Black Belt training. Then outline the expected external consulting needs (a Master Black Belt), including a cost range. A natural next step is to review the timeline of the pilot project, which will explain the Master Black Belt costs over the term, including training and project oversight. The timeline should follow the DMAIC project steps with signoff tollgates to assure each step passes corporate and Six Sigma evaluations. The final evaluation should follow a few months after the project is closed to assure that the improvement is sustained according to the control plan implemented as a result of the project.

The Bottom Line

The return on investment is the focus of many executives. This section of the presentation should include a five-year cost/benefit analysis outlining the hard costs and savings as well as a list of soft issues that also affect the decision. The hard costs need to include the offset to department staffs when Black Belts are assigned fulltime to Six Sigma. Other costs include training, consulting, possible travel, software (statistical, flowcharting), and hardware (additional laptops). The cost offsets will increase over time as training increases. They will represent the average savings per Black Belt for your industry based on three to four projects closed per Black Belt annually. Although the five-year return data will interest the leadership, the softer issues communicate the real objective – competitive advantage. Here it is important to show Six Sigma as an enabler of the organization’s goals. Include appropriate illustrations such as “proven approach,” “fact-based management,” “process of continuous improvement,” “sustained improvements versus fix and forget” or other selling points that fit the executive mindset. The bottom line is not to oversell the benefits, but to convince management of the potential so that they approve the pilot project and continue their support as Six Sigma integration continues.

Presentation Format

A lot of time and effort will have been put into the Six Sigma proposal by the time it is ready for presentation. Despite this, the purpose of the presentation is to gain buy-in. It is wise to follow a concise format, coinciding with similar material the executives are used to reviewing. The best approach is to use a successful presentation example unique to the organization. In the absence of a such proven example, the following format is a good bet:

1. Executive summary
2. Concise presentation of the highlights
a. Industry examples
b. Critical success factors
c. Deployment requirements
d. Pilot project
e. Return on investment
3. Detailed report including copies of crucial references (to leave with them for further information)
4. Question-and-answer session
5. Schedule a follow-up decision point meeting

Now for the Real Work

When the executives are convinced that Six Sigma is the key to the future, they will likely view the person who sold them on the idea as the subject matter expert. They will typically select that person as their deployment leader to direct the integration of Six Sigma within the organization. The deployment leader’s next steps will include selecting a consulting/training/deployment company, managing the pilot project, developing Six Sigma overview training, and attending Black Belt training. With the executives engaged in the deployment, the organization is ready to reap the rewards of Six Sigma.

Continue reading

Which one is a better course, PMP or PMI-ACP?

PMP (Project Management Professional) & ACP (Agile Certified Practitioners) are two certifications offered by PMI in two different areas i.e. PMP is more towards traditional project management with a bigger horizon whereas ACP with an agile focus. Even though these are two different dimensions aspirants are confused which one to take between two.

Precisely when an aspirant is asking about which one to take, s/he is actually trying to prioritize that which one should be taken first. Reason I am saying this is very simple, both the certifications in combination works really well and completes the cycle of traditional as well as agile knowledge. Both should be done from a longer run prospective, so the question should be which one to do first.

In this article we shall discuss on the multiple dimensions of these two certifications which will help one take a call which one to go for.

Dimensions

There are multiple aspects one should look at while choosing from one certification on place of other. Let’s look at these aspects one by one to understand the dilemma in a better manner.

Future Aspirations

One driver for any certification is the future aspirations and career goals. E.g. a team leader aspires to become Project Manager after a while and want to equip himself for the same with certification. Similarly a developer wants to move towards scrum master role.

It’s very important to find out whether you want to be a Project Manager or Agile practitioner. Based on that first preference of the certification will make sense. It doesn’t mean that a Project Manager can’t go for agile and vice versa both are better but preference should be evaluated as per aspiration.

Current Organization Needs

Current organization is the first preference always to grow for anyone as you already have a good track record to show and you have known people around who have seen your work. The organizations also have needs. If you organization is having need for qualified Project Managers then the preference should be given to PMP as PMP gives full overview of all it takes to manage a project end to end but on the other hand if your organization is has agile environment and roles like managers are either not there or getting reduced, you should go for ACP so that you can grow in agile practitioners track

Knowledge Horizons

PMP definitely has bigger horizon from knowledge prospective as it covers all dimensions of Project Management where as ACP concentrates only on agile. To put it in nutshell, ACP is subset of PMP where details of agile is discussed. Interest of an individual towards these knowledge horizons will help in selecting one out of other. It’s important to keep in mind that if you work what you love to do, you will be most efficient in it.

Mind-set changes

Traditional Project Management and agile way of working is a mind-set change from managing to leading/coaching. From controlling to self-organizing. Choosing a path between two will definitely depend on the mind-set of the organization you are in or target to go in, customer and your own self. Few people have mind-set that work can’t be executed without a central controlling body whereas another set says more you give responsibility and accountability to individuals they perform better. Before concluding a career path it’s important for one to check his thought process towards one of these and accordingly take a call.

Market Dynamics

The type of technological work you are into, if it calls for quick changes by your customers and ask you to be flexible, knowing agile will help you more than traditional way of working where change is not that easy to accommodate. If your project of a kind where market is more or less stable and customer requirements are more or less defined, traditional ways of working will suffice the needs. In that case selecting PMP will help you in getting yourself ready for your growth

Eligibility Fulfilment

As per PMI, both the certifications have different eligibility criteria i.e.

PMP

1. The candidate must have a total 4500 hours of unique experience (in case of Bachelor’s/Equivalent University Degree) or the candidate must have a total 7500 hours (in case of High School Diploma/Equivalent Secondary School).

2. The candidate must have 35 hours of education by any R.E.P (Registered Education Provider of PMI®)

ACP

1. 2,000 hours of general project experience working on teams. A current PMP® or PgMP® will satisfy this requirement but is not required to apply for the PMI-ACP.

2. 1,500 hours working on agile project teams or with agile methodologies. This requirement is in addition to the 2,000 hours of general project experience.

3. 21 contact hours of training in agile practices

If in case you haven’t worked on agile projects till now, ACP can’t be targeted at this moment and you may need to do PMP first. Similarly if you have little experience in project management and eligibility requirement for PMP is not fulfilled, you should target ACP

Continue reading

A Study of Estimates of Sigma in Small Sample Sizes

This paper looks at some of the methods of estimating standard deviation (which I will usually refer to as ‘sigma’). Additionally, I propose a new formula for estimating sigma for small sample sizes and also present a means to mathematically evaluate these competing estimates of sigma.

The question was posed to me: “I have five samples to test from my population. From that data, how can I estimate capability against our specifications?” Of course, the brutally honest answer is, “Poorly.”

But Black Belts do not survive by being sarcastic, so a better answer might be: “Any estimate of sigma from a small sample will have very large confidence intervals, giving you little knowledge of the actual population.” The question, however, pointed me toward an interesting avenue of exploration into the various ways that we may estimate the sigma in small sample sizes.

Ways of Estimating Sigma

First, let’s review some of the more common methods of estimating sigma (or standard deviation, SD):

A. Using the average difference between an observation and the mean adjusted by sample size (the classic formula for sigma).

B. Using the range of the data divided by a factor, C, where C varies with sample size. Common values of C include 4 and 6, depending on sample size.

SD = Range/C

C. Using the moving range (MR) of time ordered data (where we subgroup on successive data points), divided by a factor. This is the method used in individual moving range control charts.

SD = MR/1.128

D. Using the interquartile range (IQR) of the data divided by a factor (D) where D = 1.35 is the most commonly proposed value.

SD = IQR/D

E. Using the mean of successive differences (MSSD) to estimate variance.

F. Using the mean absolute deviation (MAD) , absolute deviation (AD) is an estimate for SD.2

G. Also, you can use the data minimum, maximum, and median to calculate an estimate of variance for small sample sizes.

 For the purposes of my study I chose to evaluate all of the methods except for F. I chose methods to study that were easy to calculate (B, C, D), were available in Minitab (A, E) and sparked my interest (G).

Criteria for an Improved Sigma Estimate

A successful search for a better estimate of sigma, when the sample size is N ≤ 10, would meet the following two criteria:

1. The center of the estimates would be equal to the true population sigma.
2. The variation of the estimated sigma would be tighter than the variation observed when using the classic formula for standard deviation.

In other words, if I repeatedly take random samples from a normally distributed population and calculate sigma, then all my samplings of sigma should begin to form a distribution with the average estimate of sigma equaling the true sigma of the data. Improved estimates of sigma will have a tighter distribution of estimates from this repeated sampling than other methods.

(Note: I will be primarily using a visual analysis of dot plots of the distribution of estimates of sigma to evaluate each method of estimating sigma.)

I also calculated the absolute deviation from true sigma (ADTS) using a formula similar to the mean absolute deviation (Method F) in order to numerically gauge each method’s performance against the criteria using the formula:

where Xi is each individual estimate of sigma generated in the simulation and sigma (σ) is the true population standard deviation from which the random normal data was calculated. In this case, N is the number of total estimates in the simulation. This formula sums the absolute value of the difference between each estimate of sigma and the true sigma of the population and divides by the number of estimates. The higher the ADTS factor, the worse the estimation of sigma. (Note: The distribution of standard deviation is a chi-square distribution and is, therefore, not evenly balanced about the mean. This unbalance can be seen in some of the simulations, but was not always clearly observed.)

Method of Evaluation

Minitab’s Random Data calculator was used to create random normal data for testing of the various methods. I decided to study only normally distributed data sets as calculating sigma for the purposes of this article.

I used Minitab to create thousands of random normal data points. These thousands of pieces of data were then sampled using varying sample sizes, from 5 to 50 samples. In most of my explorations that follow, I used 5,000 pieces of random normal data.

Out of the 5,000 random normal data points, I used the following:

◈ 1,000 five-piece samples

◈ 500 10-piece samples

◈ 200 25-piece samples

◈ 100 50-piece samples

From all these samplings of the random data, I could then calculate the sigma using each of the methods. This gave me a set of data for each method of calculating sigma. A dot plot clearly shows the spread and frequency of each of the estimates of sigma. I could have used other displays of this data such as the classic histogram, but I felt that the dot plot improved the visual clarity of the data.

For example, the dot plot shown below in Figure 1 was created from selecting 1,000 five-piece samples out of 5,000 pieces of random normal data with mean = 0 and sigma = 1. The sigma was calculated for each of these 1,000 five-piece samples using the classic formula (Method A).

Figure 1: 1,000 Five-Piece Samples, Mean = 0, Sigma = 1

We observe from the dot plot the range of all 1,000 estimates of sigma. As the population was created using a sigma of 1, we can add that line to the dot plot and observe how all of the 1,000 estimates of sigma gathered about the “true” value. In real life we would never know that true value unless we had some previous knowledge from a larger sample. What this shows us is that if we take five random samples from a population where the actual sigma is 1.0, we might get a sigma anywhere from as low as 0.2 to as high as 2.3.

Calculating the ADTS factor for this set of sigma estimations gives us a value of 0.28. Values of ADTS can be compared to each other as long as the estimates of sigma come from the same population (same population mean and sigma).

Evaluation Methods for Estimating Sigma

A randomly generated set of 5,000 normally distributed data points with mean = 0 and SD = 1 was used to evaluate the various candidates for estimating sigma.

Moving Range Evaluation (Method C)

Using the moving range (average MR/1.128) for an estimate of sigma is problematic as it requires that the data is in time order. If we have a small sample size of data and if the order of this data is either unknown or not relevant, then using MR to estimate sigma is not valid.

N < 10, the MR is a better estimate of sigma than the classic formula. I performed a test of this using a smaller subset of my normal population dataset and calculated sigma using both the classic formula and the MR. Figure 2 below shows that sigma calculated from MR gives no obvious advantage over the classic formula. The calculated ADTS values support this conclusion.

Figure 2: SD Versus an Estimate of Sigma from MR

MSSD Evaluation (Method E)

From my 5,000-piece random normal data set, I calculated sigma using the classic formula (Method A) and compared that estimate to the SQRT (MSSD) method. Using Minitab’s Store Descriptive Statistics function along with the calculator function, I compared these two methods for sample sizes of 5, 10 and 50.

As can be seen in the dot plot comparison and ADTS values in Figure 3, the two methods have nearly identical centers and spread. Note that the value of ADTS becomes smaller as the sample size increases. This is as expected since the estimate of sigma improves as sample size increases. Given that the MSSD method provides nothing new over the classic formula, I dropped MSSD from the rest of my evaluations.

Figure 3: SD and Estimate Using MSSD by Sample Size

The “Hozo” Method (Method G)

In their article, “Estimating the mean and variance from the median, range, and the size of the sample,” mathematicians Stela Pudar Hozo, Benjamin Djulbegovic and Iztok Hozo proposed Method G for estimating variance and, thereby, SD involving sample size, minimum value, maximum value and the median.3

The authors used simulations and “determined that for very small samples (up to 15) the best estimator for the variance is the formula” shown here as Method G.

I tested this formula using my 5,000-piece population of random normal data and compared to the classic sigma formula as shown in Figures 4 and 5.

Figure 4: SD Versus Hozo Method, Sample Size = 5

Figure 5: SD Versus Hozo Method, Sample Size = 10

When the sample size is five, the Hozo method shifts the range of estimates to the left. Even though the spread of estimates is better for the Hozo method, the ADTS values show the classic standard deviation formula is slightly better. At the sample size of 10, the shift is not as great and the ADTS values indicate little difference between the classic sigma and Hozo methods.

However, in fairness to Hozo et al, their study parameters were different from mine. Instead of a mean of 0 and sigma of 1 (which I used to create normal data), they “drew 200 random samples of sizes ranging from 8 to 100 from a normal distribution with a population mean [of] 50 and sigma [of] 17.” This is a large sigma when compared to the mean (17/50). I am not sure that is practical in engineering.

Therefore, I created 5,000 random normal data points using mean = 50 and SD = 17 to see how the Hozo method compared to the sigma formula for a sample size of five and 10. This simulation still shows that with N = 5 the estimate of sigma is shifted away from the actual population sigma of 17. For N = 10 the shift is less but still present. The ADTS values bear out this conclusion.

Figure 6: SD Versus Hozo Method, Sample Size = 5, Mean = 50, SD = 17

Figure 7: SD Versus Hozo Method, Sample Size = 10, Mean = 50, SD = 17

In their study, Hozo et al found that “when the sample size increases, range/4 is the best estimator for the sigma until the sample sizes reach about 70. For large samples (size more than 70)[,] range/6 is actually the best estimator for the sigma (and variance).”

Range/C (Method B) and IQR/D (Method D) Evaluation

While all the other formulae are definitive in their variables, the range and IQR methods require some way to decide what to use for the values of C and D.

SD = Range/C

SD = IQR/D

From Hozo et al I found that commonly used values for C are 4 and 6, and that the most commonly used value for D is 1.35.

I decided to test these values against alternate values of C and D in the hopes of finding an improved range or IQR formula for small sample sizes (e.g., <10). After preliminary modeling using a wide selection of values for C and D, I settled on testing the following factors:

◈ Range (R)/C where C = 2.5

◈ IQR/D where D = 1.35 and 1.55

Once I determined the values of C and D to be evaluated, I used the 5,000 pieces of random normal data to calculate the spread of estimates of sigma using values of C and D as defined above and compared them to the classically calculated formula for sigma along with their ADTS factor.

In the dot plots that follow I left out the plots of the classic sigma formula as I am focusing on the centering and spread of the various estimates using R/C and IQR/D.

The code of labeling is as follows:

◈ Range 5/2.5 means range data from the sample size = 5 group divided by 2.5

◈ Range 10/2.5 means range data from the sample size = 10 group divided by 2.5

◈ IQR 5/1.35 means IQR data from the sample size = 5 group divided by 1.35

◈ And so on

Range Evaluation

Figure 8: Estimates of Sigma Using Range/C for N = 5, 10, 20, 25 and 50

Figure 8 shows that R/4 is greatly left-shifted when N < 10 and that R/2.5 is centered – although the spread of the R/2.5 data is large. At values of N > 10, using R/4 centers around the true sigma much better than R/2.5.

Plotting all the ADTS factors by sample size and sigma calculated by R/C and classic standard deviation shows how R/2.5 and R/4 change with sample size (see Figure 9). We also can see how these estimates of sigma compare to the classic standard deviation formula.

Figure 9: Estimate of Sigma Using R/C Versus SD

IQR Evaluation

In Figure 10, the IQR/D estimates show that when the sample size is 5 or 10, then IQR/1.55 is more centered and has less spread of estimates than when compared to IQR/1.35. With sample sizes greater than 10, however, this pattern shifts and IQR/1.35 slightly improves.

Figure 10: Estimate of Sigma Using IQR/D for N = 5, 10, 20, 25 and 50

As with the range data, I plotted the ASTS values for each IQR/D method and compared them to the classic formula for standard deviation (Figure 11).

Figure 11: Estimate of Sigma Using IQR/D vs. Standard Deviation

A summary chart of these two methods is shown in Figure 12. This chart shows that all methods are almost equal when N = 5. When N = 10, the IQR/1.55 is better than R/2.5 but not as good as the classic formula for standard deviation.

Figure 12: Summary Data of R/C and IQR/D Findings

Taking a Final Look

Given that the best estimates for sigma appear to be IQR/1.55, R/4 or R/6 (depending on sample size), I created a new set of 5,000 pieces of random normal data and re-ran all of the calculations of ADTS for each combination.

The graph in Figure 13 is interesting in that it shows how IQR/1.55 is actually pretty robust over sample size. The IQR/1.55 method would be a good choice if picking a method for estimating sigma (that was not the classic formula).

Figure 13: Best Estimates of Sigma by Sample Size

The IQR/1.55 method has another advantage. Both the R/C method and the classic sigma method are prone to outliers, especially with small sample sizes. The IQR/1.55 method is not affected by an extreme outlier in a small sample of data.

For example, let’s look at a set of seven data points to see how an outlier affects our estimates of sigma. Below are two seven-piece random and normal samples of data from a population with a known sigma of 1.0. The first set of data does not have an outlier; the second set of data does have an outlier (2.0) as confirmed by Grubb’s test for outliers.

Table 1: Sample Data

Data 1	Data 1_1
0.229762	0.22976
0.370426	0.37043
0.402137	0.40214
0.589118	0.58912
0.776588	0.77659
0.845852	0.84585
0.969874	2.00000

From this data we can calculate the following estimates of sigma and see how the IQR method is robust to an outlier. The classic method and the R/2.5 method change significantly with the presence of an outlier.

Table 2: Comparing IQR and R to Classic Sigma

	Classic Sigma	IQR/1.55	Range/2.5
No Outlier	0.76	0.307	0.296
With Outlier	0.596	0.307	0.708

For ease of calculations, if you are given a choice, a sample size of N = 7 allows the IQR to be easily solved. For N = 7, the third quartile is the sixth data point in ordered data, and the first quartile is the second data point in the ordered data. When N = 11, then the third quartile is the ninth point and the first quartile is the third point.

Continue reading