SQT Blog

Many service related process (e.g. Insurance claims, processing a tax return) can be quite complex and cumbersome. It is only when these processes are mapped and clearly understood that opportunities to eliminate NVA steps and simplify a process become apparent. In many cases, the ‘As-Is Process’ involves navigating, through a maze of screens of different databases and information sources.

To redesign the ‘To-Be Process’, the Sort and Store elements of the 5S can be very useful to improve the process layout and flow e.g.

• Simplify work screens in a software application or database by removing the unused or non-required work fields (Sort)
• For those that are left over, create icons or shortcuts that are easy to see and access (Store)

Submitted by Éamon Ó Béarra, SQT Lean Six Sigma tutor

Let’s be honest about it. In many cases, collecting data (especially when done manually) can be tedious and viewed by some as a ‘pain in the backside’. This is understandable to a degree but imagine a situation where after spending 6 weeks collecting data we find out that it is inaccurate, it can’t be used and is in effect a waste of time. This issue can be due to the fact that we put no thought or effort into how we defined the metric in question.

E.g., a Food Processing Company was trying to baseline the Cleaning in Place (CIP) Process. In order to understand if here a difference in the CIP time by shift, product type, CIP types, etc. they set about collecting data over a 6 week timeline to answer some of these questions.

When the Project Team examined the data after the 6 weeks, they found there were some major differences by shift and the other aforementioned factors. Importantly though, this was not due to a difference in performance but by how the Metric was being measured.

• Shift A was interpreting the CIP time as ‘from the time the equipment was stopped until it was started again with the CIP complete’
• Shift B was interpreting the CIP time as ‘from the time the equipment was stopped until an acceptable micro test result for the CIP was back from the Lab allowing the equipment to be restarted’.
• Shift C had another interpretation altogether

Unfortunately, it was then back to the proverbial drawing board!

The morale of the story is to agree on a very specific Operational Definition for a metric, include it on the Data Collection Sheet and even go as far as to give the Data Collectors a fictional pre-completed data collection form to use as a guideline.

 

Submitted by Éamon Ó Béarra, SQT Lean Six Sigma tutor

 

In order to evaluate and compare Lean Six Sigma course offerings it is important to understand the various certification options associated with them. Whilst course curriculums may appear similar, the requirements for certification can often vary dramatically.

CLASSIFICATION OF CERTIFICATION

There are three general classifications of programmes in terms of certification:

1. Non-Certified Courses
2. Academically Certified Courses
3. Professionally Certified Courses

 

1. Non-Certified Courses

Non-certified courses do not carry national recognition, however, there are advantages if gaining a qualification is not a key motivation for completing the training programme. For example:

• The course can be tailored around a client’s specific training needs which may result in the removal of certain elements of a prescribed curriculum or body of knowledge (BOK) related to a specific belt
• There is no assessment requirement. However, if a learner is motivated to complete additional self-study they could achieve professional certification by successfully completing relevant examination(s) from one of the professional accreditation bodies such as the ASQ (American Society for Quality) or IASSC (International Association for Six Sigma Certification).
• Another advantage of non-certified training is that team based projects may be used as a means of assessment, this is generally not acceptable in academically or professionally certified training courses.

 

 

2. Academically Certified Training Courses

Many academic institutions such as technical colleges and universities provide certified Lean Six Sigma programmes within their post-graduate or life-long learning course offerings. The advantage of pursuing such programmes is that they have been validated against prescribed award standards and have undergone a significant element of peer review and oversight by the external awarding body.

The Bologna Process has ensured comparability in the standards and quality of higher education qualifications across European countries. For example, in Ireland, the National Framework of Qualifications (NFQ) has been designed for the development, recognition and award of qualifications based on standards of knowledge, skill and competence acquired by learners. The Framework consists of 10 levels, from basic learning (level 1) to Doctoral awards (level 10). SQT have agreed Quality Assurance Procedures with QQI, the national agency responsible for the external quality assurance of further and higher education and training and validates programmes and makes awards for certain providers in these sectors. SQT Offers QQI Certified Special Purpose Awards at Levels 6, 7 and 8 on the National Framework of Qualifications.

Another major advantage of perusing an academically certified programme (particularly those utilising real projects in the learner assessment) is that there is a deadline for project completion. Sponsoring companies can therefore expect significant benefits to be accrued by the learners in the short term during the course of the delivery and assessment period alone.

There are two main arguments against academic certifications. The first is that academic training providers may be far removed from industry and may tend to focus too much on theory rather than giving practical insight and guidance to learners. Against this argument there are some academically certified training courses which are delivered by private training organisations, such as SQT Training, which have trainers that are in fact current industry Lean Six Sigma experts. The other argument often used against academic certification is that the assessment is purely based on the learners’ knowledge of the theory rather than competency in its application.  In reality this argument doesn’t hold true in many cases as many QQI (formerly HETAC) accredited programmes use real project submissions in the assessment of the leaners. Project management, leadership and change management skills are also assessed. For example, the assessment of SQT’s QQI Certified Green Belt programme is based on the successful delivery of a real work project through all stages of the DMAIC methodology while correctly selecting and applying tools appropriate to the project. Therefore, while academically certified, the actual course delivery has a very practical focus.

 

A Word of Caution…..

If you are considering perusing academic certification be sure to do the following:

1. Compare the training curriculum against the ASQ body of knowledge to ensure that no shortcuts have been taken
2. Check to see what level of recent practical experience the tutor(s) have
3. Establish if there is a project requirement as part of the assessment
4. Understand what level the programme has been validated at and the number of credits allocated (Further information relating to levels and credits are available here)

 

3. Professionally Certified Training Courses

Prior to 2010 there was only one accepted source of professional certification for Lean Six Sigma practitioners, namely ASQ (American Society for Quality). The ASQ has been at the forefront of professional certification for quality practitioners for over 65 years. It has worldwide recognition and charters all over the globe. Former chairs of the ASQ include some of the who’s who of quality gurus of the past century, including Armand Feigenbaum and Philip Crosby.  Since the emergence of Six Sigma as a global phenomenon in the late nineties, ASQ has been to the forefront in identifying a standardised body of knowledge (BOK) for Six Sigma belts.  In 2010 a new organisation, namely IASSC (International Association of Six Sigma Certification) emerged as an independent third-party certification body.  Both the ASQ and the IASSC rely on knowledge assessments (exams) to determine if learners demonstrate the capacity to be professionally certified.

 

The two main ASQ exams are the CSSGB (Green Belt) and the CSSBB (Black Belt) exams.  While project based assessment is not included in either of these certifications, the CSSBB does require that a project has been successfully completed, with an affidavit to that effect. It is widely held that the CSSBB is a very challenging exam due to the statistical requirements of Six Sigma. IASSC on the other hand do not require the submission of any project or affidavits, and while the exam format and BOK are almost identical to the ASQ, the IASSC exam is likely to have less statistical and more lean content.

 

Both the ASQ and IASSC offer certification options to suitable training providers on a fee basis. The ASQ do so in a partnership model to ensure the training is consistent across providers (there are a small number of ASQ partners).  IASSC remain an independent certification body and therefore do not provide training.  Both the ASQ and IASSC exams are open to any applicant regardless of the source of training.

 

In Summary…

When evaluating a Lean Six Sigma Programme it is wise to remember the following:

 

1. Academically certified programmes have undergone a significant level of independent review and oversight by an external awarding body, however, there is still much variance in courses offered. It is vitally important to examine the curriculum, understand the level of the qualification and associated credits, establish the practical experience held by the tutor(s), and finally whether the practical work (in most cases a project) actually forms part of the assessment.
2. There may be valid reasons to opt for an uncertified programme, this is particularly the case where team based projects and significant customisation is required by the company seeking the training. Large corporations such as Honeywell and GE self-certify according to their own requirements.
3. Professional bodies such as the ASQ provide the best source for what a ‘belt’ should know (body of knowledge), the exams are open to all learners but a successful result will not verify if the learner has the ability to be a good Green or Black Belt, as no practical work is examined. The professional body IASSC is relatively new and is therefore a little too early to compare with ASQ. Certification for both bodies is exam based only.

 

Having delivered all three types of programmes described here it is SQT’s experience that the best option both for personal development and company delivery is to choose an academic certification which assesses learner capability via project delivery. This will ensure a win/win for the learner and his/her organisation.

I think that because of the emphasis in the literature on “Root Cause” analysis some teams working on problem solving tend to believe that they are expected to identify a single root cause of the problem. I don’t believe that they should expect that outcome. Over the years I have trained and consulted with more than 100 teams undertaking root cause analysis, and it is a rare occasion in my experience in which a team will be able to identify a single cause of a problem. Indeed, if a team tells me they have managed to isolate a single root cause, I will question whether they have considered all of the possible causes in sufficient depth.

It is much more usual that the team will identify a number of possible causes of the problem. These causes may well have complex interactions, which will be difficult to disentangle, without substantial data gathering and mathematical analysis, most likely beyond many teams undertaking root cause analysis.

I believe that the best that can be expected is that the team will undertake a thorough analysis of all possible causes and identify a short list of causes, on which corrective actions can be taken. I don’t think that there is merit in teams at the point of identifying a short list, devoting time to try and find the single root cause of the problem, which I see teams attempting to do. If the team is successful in identifying the potential short list of causes, and corrective action is implemented on this short list, and is effective, then the problem will be eliminated. It is a key responsibility of the team to identify the causes on which action is to be taken.

Learn more about the techniques of problem solving by attending our Root Cause Analysis Control training course.

All manufacturing organizations nowadays have a comprehensive measurement instrument calibration process in place, and pay a lot of attention to ensuring that the calibration work is carried out in accordance with procedures. Failing to calibrate the measuring instruments in accordance with requirements would result in the organization falling foul of auditors of the various standards such as ISO 9001, ISO 13485, and FDA regulations. It is greatly surprising then, that so many people, including many of the aforementioned auditors, have so little understanding of the importance of studying the variability of the measuring process. The lack of emphasis in standards such as ISO 9001 and FDA regulations on the need to study measurement system variability, is also a surprise.

Operating a calibration process without Gauge R&R (Gauge Repeatability & Reproducibility) leaves a critical gap in assessing the health of the measuring process; Measurement system variability is not assessed during the calibration process. Routine calibration is, of course, very important. However, I have seen people responsible for the measurement process, shocked when Gauge R&R studies were completed, to learn that the information provided by their carefully calibrated measuring instruments is of little practical use, because the real variability in the manufacturing process that they are attempting to study is smothered by excessive variability in the measuring process.

There are well established methods under the general heading of Gauge R&R available to facilitate the study of measurement system variability. Also, the analytical work can be undertaken with the Gauge R&R modules available in most statistical software packages. Personnel responsible for the measurement process can be readily trained to use modern computer software to design and analyse Gauge R&R studies, which will enable them to see whether the instruments are fit for purpose, and to indicate the direction of corrective actions, should the need arise.

Many organizations now undertake Gauge R&R studies on their measuring instruments. However, the facilities now available for undertaking similar studies for non-measurement type instruments, such as Go/No-Go gauges, are less well known. Strictly speaking, the term “Gauge R&R” applies only to the study of instruments which measure characteristics on a continuous scale, such as force, length, viscosity, pH, etc. When the performance of the gauge or procedure being studied is used to make assessments on a non-continuous scale, such as Pass/Fail or a rating, then it is more usual to refer to the study as Attribute Agreement Analysis.

The Attribute Agreement Analysis study can be set up in much the same way as a regular Gauge R&R study. A number of parts are selected from the process, and are assessed by two or more operators. It will be possible from the study to determine how consistent the operators are within their own assessments, as well as the degree of consistency between operators. If it is possible to set a standard for the assessment of each part, then the performance of each operator can also be compared to standard.

The Attribute Agreement Analysis study doesn’t just apply to Pass/Fail type assessments, such as those used with Go/No-Go type gauges, but can also be used to test the consistency of operators where they make assessments on a rating scale.

Modern statistical software, such as Minitab, can be used to collect the study data and undertake the analysis. Graphical output and Kappa statistical values can be used to study the effectiveness and accuracy of the operators in carrying out their assessments.

Learn more about Gauge R&R and Attribute Agreement Analysis by attending our Measurement Systems Analysis training course.

Continuing his series of tips for people implementing Continuous Process Improvement, our tutor Albert Plant writes:

There is a natural tendency for experimenters to hurry to look at the effects graphs, ANOVA’s and other analysis, when they have completed their hard work running experiments, and they may forget to check the residuals. Residuals are unexciting, but contain crucial evidence on the validity of the experimental outcome. I have seen experimenters writing up lengthy reports on the outcome of their experiments, only to discover later that the condition of the residuals indicates that they have drawn incorrect conclusions. For example, an analysis of the residuals may indicate that it is necessary to transform the data to obtain the most appropriate model.

All DOE computer software, such as Minitab, provides extensive analysis of DOE residuals. Design Expert DOE computer software has no less than eleven separate graphs plus additional text output analysing the residuals. Clearly, the people who develop the mathematical tools for us to design and analyse our experiments are in no doubt as to the importance of the residuals in DOE.

Learn more about how design and analyse experiments and how to get the most information from the residuals, by attending our Design of Experiments training courses.

I think that some experimenters believe that you should always be a case for using blocks when designing experiments. This is not so. There will be circumstances when it will be necessary to consider using blocks, but blocking doesn’t come cheaply. For example, four blocks will require three of your precious degrees of freedom, using up three experimental runs. Furthermore, blocking greatly reduces the range of randomization that can be used. In a blocked experiment randomization of runs can only be carried out within the blocks, instead of across the full set of experimental runs, as is the case in an un-blocked experiment.

Blocking can be useful when you are concerned that factors not included in the design may add variability to the design outcome. Such concern may arise, for example, where you need to divide the experimental runs among several operators, or you need to use two or more different pieces of equipment.

Try to avoid the need for blocking by ensuring that the running of the experiments is carried out with minimum changes among the factors not included in the design. For example, try to have the experiments carried out by just one operator; use the same piece of equipment for all runs; use the same piece of measuring equipment for all measurements from all runs, etc.

Learn more about the correct use of blocking designs by attending our Design of Experiments training courses.

Some experimenters believe that you must always include centre points when designing experiments. This is not the case, and in particular, it doesn’t make sense to include centre points when designing screening-type experiments, with large number of factors.

Centre points have an important role in testing for curvature in the appropriate circumstances, but will not provide an aid in the search for factors with significant main and/or interaction effects , which is the main objective when screening.

Centre points can be added to the design, if desired, when we have completed our screening work, and we are contemplating proceeding to optimisation using Response Surface Methodology (RSM).

I sometimes see people including a single centre point when designing screening experiments. This makes no sense. A single centre point is completely useless and is simply a wasted experimental run.

There is a view among some experimenters that adding centre points gives you a three-level design. This is not the case. Centre points are located at the centre of the design space and have no role in determining effects of the factors at the middle value of the factor levels.

Learn more about the correct use of centre points by attending our Design of Experiments courses. We present two Design of Experiments courses; a three day Design of Experiments on both a public and in-house basis and a six day Design of Experiments for R&D on an in-house basis.

Some experimenters are of the view that it is always necessary to replicate designs in order to test for significant effects. As a consequence, some experimenters will replicate fractional factorial designs. This doesn’t make sense.

Consider the case of a quarter fraction, 2-level design of 6 factors in 16 runs. There is considerable aliasing among the two factor interactions, about which we will be concerned. See the alias structure here reproduced from Minitab:

Alias Structure

I + ABCE + ADEF + BCDF

A + BCE + DEF + ABCDF
B + ACE + CDF + ABDEF
C + ABE + BDF + ACDEF
D + AEF + BCF + ABCDE
E + ABC + ADF + BCDEF
F + ADE + BCD + ABCEF
AB + CE + ACDF + BDEF
AC + BE + ABDF + CDEF
AD + EF + ABCF + BCDE
AE + BC + DF + ABCDEF
AF + DE + ABCD + BCEF
BD + CF + ABEF + ACDE
BF + CD + ABDE + ACEF
ABD + ACF + BEF + CDE
ABF + ACD + BDE + CEF

Replicating this design will require 32 runs, but the additional 16 runs won’t assist in breaking up the aliases among the two factor interactions. After the replication we will have a more powerful test for significance of effects, but we will continue to have the same degree of aliasing as before the replication. Instead, if there are resources available for 32 runs, it makes much more sense to use the 32 runs to design a half fraction, in which, in this particular case, there won’t be aliasing among the two factor interactions. The power to test for significant effects will be much the same as that achieved from the replicate on the quarter fraction, because the additional insignificant effects that are likely to be produced using a half fractions can be converted to sums of squares, and will perform much the same role as the replicate on the quarter fraction. That is; you will have much the same ability to detect significant effects, while avoiding aliasing among the two factor interactions, for the same 32 experimental runs.

Learn more about the correct use of fractional designs by attending our Design of Experiments courses. The next public Design of Experiments course is a three day course scheduled for 5th – 7th June in Dublin. This course can also be run on an in-house basis. We also have a six day Design of Experiments for R&D course. The latter is run as an in-house course.