Monte Carlo, Lean Six Sigma & DFSS

In a recent report The Innovation Imperative in Manufacturing - How the United States Can restore its edge, produced jointly by The Boston Consulting Group, the National Association of Manufacturers and the Manufacturing Institute, The United States ranked #8 out of 110 countries in innovation leadership.     

The Top Ten List (overall)

1. Singapore
2. South Korea
3. Switzerland
4. Iceland
5. Ireland
6. Hong Kong
7. Finland
8. United States
9. Japan
10. Sweden

The first half of this report painted a pretty bleak picture for the US.  Among the depressing  statements:
“The United States is losing its distinction as an innovation leader and may be under-investing in the future.”

 “The United States is disadvantaged in several key areas, including workforce quality and economic, immigration and infrastructure policies.”
Some companies are even “moving R & D centers abroad to capitalize on leading-edge talent and lower cost scientists and engineers or to better meet local market needs . . .”

This information is difficult to swallow, but it clearly makes the point:

It’s time for change.

In the article, they actually used the quote “innovate or die!”

Even without the current economic crisis, we know we have issues and challenges at many levels, and if this report helps revitalize our companies and government into action, then all for it.

The second half the report does a nice job detailing what they considered to be the Four Key Factors for Success, which they felt are:

• Idea generation
• Structured Processes
• Leadership
• Skilled Workers

 
The research feels the US Government plays a vital part in encouraging innovation, and their role is to boost Company payback on innovation through consistent programs, like supporting innovation activities through government-funded laboratories and research labs. Tax credits are also a common way, but this deemed to be more of “thank you” then a motivator, and is often inconsistent from year to year.

Although they stated that some recommendations were beyond the scope of the report, they suggest the US make concrete improvement in six areas:

1. Strengthen the workforce
2. Lead by example
3. Make innovation easier
4. Maintain a strong Manufacturing base
5. Improve the pay back
6. Be Consistent

To reiterate, we know that innovation, quality and jobs in the US have been on the decline for past years. It’s time to act, Adopt an innovation,  product development,  or quality program such as Design for Six Sigma (DFSS), Design for Lean Six Sigma (DFLSS), Critical Parameter Management, (CPM)or whatever you want to call it, deploy it and stick to it! As I have may have mentioned before, many companies I have worked with who indicated successful deployment really never got off the ground floor.
 

You can read the entire report at www.nam.org/~/media/AboutUs/ManufacturingInstitute/innovationreport.ashx

Next week, one of the industry’s largest and best events will take place in Orlando, Florida. That is IQPC’s Lean Six Sigma Summit. Every year it is one of the few events that I look forward to attending. The IQPC does a great job gathering industry experts from around the world to share their thoughts, experiences, and wisdom.

This year will be a challenging year for all, so it is particularly necessary to spend travel and education funds wisely, should you be lucky enough to still have a travel budget. If you do, I would highly recommend considering this event.

There will be a cross-functional group of experts such as  William Kimbrell of Blue Ocean Strategy, who will share his growth strategies in today’s over-crowded industries. Tina Huesing of Motorola and JD Sicilia, Director, Department of Defense, Lean Six Sigma Program Office will address the what I call “ Doing more with less. “ Ms. Huesing’s focus will be streamlining processes, timely execution, and waste elimination. Mr. Sicilla’s focus will be on Strategic Alignment, Consistency of Approach, Integration and Human Capital. Also, George Turner of Rock-Tenn will be speaking on professional development.

These topics have always been at the root of Lean Six Sigma but now more than ever they are very important in today’s business climate. I am looking forward to learning from these experts next week at the Summit.

Palisade will be demonstrating the latest and best in Monte Carlo Simulation for Six Sigma. If you are attending, please stop by and say hello! If you don’t attend, please contact me and I will be happy to provide you with a free trial and web demo.
 

In a recent article called Six Sigma Speak in iSixSigma Magazine, Craig Gygi does a very nice job articulating one of the barriers to Six Sigma, and that is the terminology that we use. We are laden with terms from other languages such a poka-yoke, “same letter lists,” as well as synonyms and acronyms galore.  As he describes, this is caused because each organization that adopts Six Sigma then adapts words to reflect the specifics to their organization’s culture and priorities.
 

To name of few of the more common synonyms in Six Sigma:

• Output = response = effect = key metric = CTQ (critical to quality) = CTC (critical to customer) = KPOV (key process output variable) = Y
• Input = variable = factor = KPIV (key process input variable) = X
• Average = mean = central tendency = expected value
• Spread = variation = dispersion = range = variance = scatter
• Variable data = continuous data
• Attribute data = categorical data = discrete data

The one thing he doesn’t address in the article head on, are the sheer number of acronyms we use. Below I have named just a few:

• DMAIC = Define, Measure, Analysis, Improve and Control
• DFSS = Design for Six Sigma
• GB, BB, MBB, LSSBB etc… which represents some of the levels  of certification one can achieve from GB (Green Belt) to MBB (Master Black Belt) and LSSBB (Lean Six Sigma Black Belt) please don’t forget some of the others such as LSSMBB, DFLSSBB etc…
• VOC = Voice of the Customer
• DOE = Design of Experiments
• DFMEA = Design Failure Mode and Effect Analysis
• MSA = Measurement System Analysis

To add to the list are the process capability and statistical acronyms such as Cp, Cpk, Pp, PpK, ANOVA, ANOM, DPPM etc . . . As we all know this is a very small example of the special terminology that we have either created or adopted over time. It’s no wonder that the people outside of the Six Sigma world are intimidated and bewildered before they even get into learning the tools to develop or improve a process.
 

Craig Gygi is also the co-author of Six Sigma for Dummies which I have to admit was one of the first Six Sigma books I purchased when trying to make sense of it all.
 

Lastly, I learned a new acronym on iSixSigma.com while writing this post, BAU (Business as usual.) I don’t know if it’s truly a LSS term but what the heck!
 

Just a couple of months ago when gas cost over $4/gallon, Americans were scrambling maximize their fuel economy and buying hybrid electric vehicles to save money, cut our dependency on foreign oil, and to save the earth. At that time, a very wise friend said, “Wait and see, gas prices will come back down and we (Americans) will forget all about it.” What happened over the next few months is exactly as he predicted. If it were not for the domestic automakers being on the brink of economic collapse, there would not have been any recent talk of innovation, reform, or the need for more efficient automobiles.

As we all know and feel, the gas crunch as been replaced by a much larger and serious worldwide economic down turn that threatens all. There are many speculations to how we got here and even more on how we get out. Lean Six Sigma is a proven methodology to remove waste and variation from our business processes and is receiving a lot of attention to do just that. Many companies are - but many more should - be scrambling to implement a Lean Six Sigma program to save what is left of their businesses. I hope that they will be successful.

Now let’s look ahead 2, 3 maybe 5 years . . . once our economy has stabilized and “returned to normal.” Have the experiences of the past changed us for the better? We will continue to apply sound Lean Six Sigma principles and risk analysis to our business, making them stronger and more efficient. Or will this too fade as a distant memory until the next time?

I do not feel there is not a business or process that would not be benefited by applying the tools  and principles of  Lean Six Sigma in many  aspects of their businesses. Let us use this opportunity as a catalyst for business and government reform and increased efficiencies in order to provide the best products and services for our customers, and to avoid a repeat of the current state of affairs.

First of all, what is Monte Carlo simulation?
Monte Carlo simulation is a computerized mathematical technique that allows people to account for variability in their process to enhance quantitative analysis and decision making. The technique is used by professionals in such widely disparate fields as finance, project management, energy, manufacturing, engineering, research and development, insurance, oil&gas, transportation, and the environment. (read more)

Where did it come from?
The term Monte Carlo was coined in the 1940s by physicists working on nuclear weapon projects in the Los Alamos National Laboratory.


How Monte Carlo simulation works:
Monte Carlo simulation performs variation analysis by building models of possible results by substituting a range of values—a probability distribution—for any factor that has inherent uncertainty. It then calculates results over and over, each time using a different set of random values from the probability functions. Depending on the number of uncertainties and the ranges specified for them, a Monte Carlo simulation could involve thousands or tens of thousands of recalculations before it is complete. Monte Carlo simulation produces distributions of possible outcome values.

Advantages
Monte Carlo simulation provides a number of advantages over deterministic, or “single-point estimate” analysis:

• Probabilistic Results. Results show not only what could happen, but how likely each outcome is.
• Graphical Results. Because of the data a Monte Carlo simulation generates, it’s easy to create graphs of different outcomes and their chances of occurrence. This is important for communicating findings to other stakeholders.
• Sensitivity Analysis. With just a few cases, deterministic analysis makes it difficult to see which variables impact the outcome the most. In Monte Carlo simulation, it’s easy to see which inputs had the biggest effect on bottom-line results.
• Scenario Analysis. In deterministic models, it’s very difficult to model different combinations of values for different inputs to see the effects of truly different scenarios. Using Monte Carlo simulation, analysts can see exactly which inputs had which values together when certain outcomes occurred. This is invaluable for pursuing further analysis.
• Correlation of Inputs. In Monte Carlo simulation, it’s possible to model interdependent relationships between input variables. It’s important for accuracy to represent how, in reality, when some factors goes up, others go up or down accordingly.

A few examples of Monte Carlo Simulation for Six Sigma and Design for Six Sigma for you to explore.

In the midst of the holiday season, I want to bring up the subject of applying Six Sigma to food preparation, mainly baking.  I am not implying that you try to apply Six Sigma variation reducing techniques to anyone’s holiday baking as it could cause negative unintended consequences, like boxed macaroni and cheese dinners for the New Year.

As a child, I recall sitting around the dinner table after consuming a huge holiday meal, listening to the discussions about my grandmother’s homemade cheesecake and lemon meringue pie.  Statements such as “the cheesecake was the best ever”, “this year’s lemon meringue pie wasn’t a tart as last year’s”, “the crust came out perfect” etc . . . To be honest as a 10 year old, I was not able to discriminate such subtleties. Now that I am an adult, I question whether they really could either, particularly after such an eating event, not mention comparing samples 12 months apart. With that said, the deserts were always phenomenal.

Now, onto present day . . .  why not apply Lean Six Sigma to baking? Well, some do! A few years ago a regional supermarket chain in the mid Atlantic region hired a Lean Six Sigma consultant to optimize their chocolate cake for ultimate customer satisfaction, taste, pricing and of course profitability.  Using taste tests,  QFD,  Kano models and a little DOE, they were able to identify the characteristics that were most important, then worked on reproducing those characteristics every time with little variation.

The project was a success for both the customers and company producing ultimate chocolate cake experience. Going back to my statement of unintended negative consequences, the Black Belt may have gained a few extra pounds during that assignment.

What’s next? If we can apply Lean Six Sigma to baking cakes to maximize profits and customer satisfaction, doesn’t it make sense to apply it to all food industries?

This morning I received a couple of interesting emails. The first being from Vijay Bajaj, WCBF’s Founder&CEO. The second being from Michael Cyger, Founder of iSixSigma.  For those of you who may not be familiar with either organization, iSixSigma is one of the premier commercial organization that provides information and networking to the Six Sigma and Design for Six Sigma Communities through their iSixSigma Magazine, networking events and through isixsigma.com. The WCBF- Six Sigma Solutions (Worldwide Conferences and Business Forums) focuses exclusively on Six Sigma and related quality Conferences & Events.

The WCBF is giving a limited number of complimentary conferences passes, which can be used at any of their Lean and Six Sigma Conferences and Summits in 2009. In order to “win” one, you must complete a survey. The survey asks for the latest burning, need-to-know issues that should be addressed at events, and asks you to submit your recommendations for cutting-edge, provocative and perspective-shaking speakers. These survey results are certainly going to be used to decide future conference agendas. A bit complicated, but they seem to be gathering the VOC to ensure their future conferences meet the needs of their market, additionally they seem to be positioning themselves to bolster event attendance if needed, knowing travel budgets have been slashed in many organizations.

iSixSigma is offering huge discounts for their upcoming iSixSigma Live! Summit & Awards in January, and throwing in two (2) free 3-hour Master Class Workshops. Additionally, they also give suggestions “pinch pennies” while traveling.

These actions are extraordinary, knowing both organizations are commercial firms and have called the shots over the past years. The current economic crises seems to be taking a toll. I applaud them for their efforts and hope they continue to gather and evaluate the VOC even after these challenging times to are behind us.

Recently, I compiled a list of Lean Six Sigma and related events for 2009. Currently, there are 47 national tradeshows and events scheduled. As I indicated in a past post, I wish each of these organizations would hold one to two per year instead of a dozen or more. This would allow better idea and best practices sharing and potentially reduce costs for the event organizers.

Today, I stumbled over a blog article Six Sigma and Simulation Part 2 written by Jeff  Joines, who is an Associate  Professor in Textile Engineering at NCSU.   He identifies the areas he feels that Monte Carlo simulation can be used in each of the phases of the DMAIC process.

Define
*Estimate cost savings for each project by indicating variability instead of using single-point estimates; provide a more reliable estimation as well as a confidence level of achieving the estimated savings.

Analyze and Improve
*Design of Experiments (DOE) (Full, Fractional, Mixed, etc.) is the most common tool utilized that provides a base line to illustrate improvement when changes are made, as well identifying factors of interest to control or change.
*DFSS - If the product or process does not exist as is the case in a Design for Six Sigma, simulation models can be used to ascertain capability of a new process and product before implementation.
*Cost reduction of performing a DOE (i.e., raw material cost, cost of shutting down current process). You can determine the process capabilities and ascertain the potential improvements while minimizing time and expenditures.
*Multiple processes that feed one another. Transfer functions can be generated from a traditional DOE on each individual process but not the entire system. A simulation model can be used to combine each individual transfer function into determining the capability of the whole system as well as testing a wider range of values.

Control
*Simulation can also be used as a process control aid as the process is being implemented to determine potential problems.

I don’t know Jeff’s background and experience with Six Sigma, but he did a very nice job in both blogs explaining the Six Sigma methodologies. To read either posting in their entirety, visit Success in Simulation. I am looking forward to the third and final posting in the series.

There seems to be some low level confusion about Cpk and Ppk and what they actually represent. Very basically, Cpk is short term “noiseless” process capability where Ppk is long term “noisy” process capability. Some of the confusion regarding these two metrics may be because the automotive industry had at one time used Cpk to represent long term process capability and Ppk for short term, the complete opposite of the rest of the Six Sigma community. They have since conformed to the industry’s standard definitions. Another potential source of confusion is that Ppk is a much underutilized metric for what it measures. When developing a process performing at six sigma level, wouldn’t we ultimately want be focused on Ppk instead of Cpk? That is another subject that hopefully will receive more attention in the future.

In an excellent and soon to be published whitepaper, Cpk; What is its “Capability?” Rick Haynes' of Smarter Solutions dives into using Cpk as a capability metric, to provide insight on performance to a requirement if the process data used in the calculation comes from a normal distribution.  If the process data is non-normal or it is the result of a combination of processes then it provides an underestimation of the true non-conformance capability. He evaluates the impact of the non-normality on Cpk by using a Monte-Carlo software, @RISK. 

On Thursday, December 11th, Mr Haynes' will present the topic in a 1 hour webinar, which promises to be time well spent. I look forward to seeing you there!

One of the misperceptions about Monte Carlo Simulation is that it is only useful in reducing the number of “experiments” or “test runs” in conjunction with Design and Analysis of Experiments (DOE). Please don’t get me wrong it is a very powerful and useful tool for this use and is becoming more and more popular particularly during these economically challenging times.

What I’d like to touch on today, is using Monte Carlo software (@RISK) for a Lean project where you have virtually no data. When I was looking for my Lean Six Sigma Black Belt certification project, I contacted an aerospace company in the hopes they would have the need of process improvements in areas where there was much data so that I could apply all the wonderful tools I had learned about in my studies. To make a long story short, they had the need for process improvements in both manufacturing and engineering but their true needs were in the project management processes upstream of manufacturing. They regularly quoted a 12 month delivery time, but usually delivered in 17 months, with OTD rate of 22%. The only data we had to work with were, order date, date released to production and date the finished goods were shipped to the customer.  The catch was, most orders were already late by the time manufacturing received all the parts to build the unit.

In order to identify the “problems”, we took one afternoon created a Process Flow Map by interviewing each of the departments and employees who were in the value stream, identified all the steps, assigned a best case, worse case and mostly likely time duration for each step, assigned distributions for each and created a Monte Carlo simulation model.  The output of the model indicated a 20% OTD Rate and an average delivery rate of ~16.5 months. Not exact, but close enough to validate our model to allow the team to use @RISK’s sensitivity graphs to pinpoint which of the 80 plus process steps were contributing to the most variation. This also gave the team a starting point to hit the ground running. They are currently working on the suggested process improvements, which should save them ~$500k/year and reduce the average lead time to ~10 months.

If you would like more information, please let me know, I’d be happy to share more about the project with you.

Believe it or not, 2009 is upon us. The introduction of @RISK - Palisade Corporation’s Monte Carlo simulation add-in for MS Excel - into the Six Sigma and DFSS communities has been has been incredible! Many large and small corporations have standardized on @RISK for their quality, process improvement or development projects, as well as in their Six Sigma Black Belt and DFSS training courses.

If you are interested in sharing your success stories at an upcoming event or through a white paper, by all means please do. Between the almost countless events put on by ASQ, IQPC, ISSSP, iSixSigma and Marcus Evans, there is bound to be an event near you. Please let me know about your project and how you used @RISK and if there is any way we can support you in your desire to share your story. 

You can contact the event organizers directly, or contact me and I can help point you to the proper venue to present at. Also, please feel free to contact me at any time should you have any questions about Palisade Corporation, @RISK, or the advantages of simulation to save time and money in improvement and development projects, or if you would simply like a free trial.

Steve Hunt
Shunt@palisade.com
607-277-8000

Last week, Palisade Corporation held its North American User Conference; it was a very successful event that brought together @RISK Users from around the world. Presentations and discussions touched on topics such as the subprime mortgage crisis, financial risk management, modeling flu, project risk management and of course, the ways to Monte Carlo simulation in Six Sigma.

It was great to see such a high level of interest in the Six Sigma related presentations and buzz they created in both the social networking opportunities as well as the feedback forms that were submitted after the conference.  This shows despite the economic difficulties and the natural tendency to eliminate all unessential spending, Six Sigma and Design for Six Sigma is rightfully viewed as part of the solution.

SigmaFlow’s president Jay Holstine, presented Process Mapping for Knowledge Transfer: Doing More with Less. A very pertinent topic in today’s economic times, which will be presented live as an ISSSP Focused Session on November 25 at 2pm EST. Please join us.

Ed Biernat from Consulting with Impact led a presentation on the use of Six Sigma in Process Industries. If you are interested in viewing his presentation, Lean Six Sigma Applicatin of @RISK Part I, it can be viewed online.  Part II will be live on December 12, 2008 at 1pm EST where he will dive deeply into the use of @RISK in this case study. Please join us.

A recent article, Executives Switch to Survival Mode, in the Wall Street Journal indicates that two of the top issues in crisis management can be managed with a strong Lean Six Sigma program, these were:

• Excellence in Execution – Whether on the shop floor or in administrative processes, there is no longer room for inaccuracies or waste.
• Speed, flexibility and adaptability to change is another area where a strong Six Sigma program mitigates the effects of crisis.