Q. 189  Pareto Analysis is one of the most used tools in the search for significant contributors to a problem. What are some of the common misuses of Pareto Analysis? Explain with examples.

 

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In a pareto analysis, if we take value stream where 5 major different departments are there. Lets say department 1 is having high rejection in terms of number lets assume 100 piece but value of part at that process  per piece is 10 rs.   similarly department no 5 is having highest value addition with per piece cost at that process is 100 rs but rejection is 50 % of the department 1 in nos that is 50 parts.

so overall loss at department 1 is 100 x 10 = 1000 rs and loss at department 5 is 50 x 100 = 5000 rs. 

so if we make pareto with department wise pareto in terms of rejection pieces it will lead to wrong direction as showing department 1 as important in terms of nos of pieces rejected. if we reduce department 1 rejection, the effect will not be much as compared pareto analysis. This is the draw back of pareto.

Question	Pareto Analysis is one of the most used tools in the search for significant contributors to a problem. What are some of the common misuses of Pareto Analysis? Explain with examples.
Common Misuses	100% data not considered while plotting the Pareto chart and applying 80-20 principle
	Applying Pareto to non standarized data
	incorrect data points providing wrong lead points
	Appropriate timeline (having sufficient data) not considered for analysis e.g. seasonal fluctuations may lead to incorrect intrepretation
Example	Poor training leading to negagtive customer feedbacks. Given below the available sample data for Pareto analysis
	Training issues noted	Challenges Faced								No. of instances
		Jan-19	Feb-19	Mar-19	Apr-19	May-19	Jun-19	Jul-19	Sub-Total	Jan-19	Feb-19	Mar-19	Apr-19	May-19	Jun-19	Jul-19	Sub-Total
	Late reception of input of new joiners details	Yes		Yes		Yes	Yes	Yes	5	2		4		5	3	2	16
	Training plan not received on time	Yes	Yes		Yes		Yes		4	5	6				9		20
	Training plan not updated	Yes	Yes	Yes		Yes	Yes		5	4	3	8		6	6		27
	Assessment not cleared but put in live production	Yes	Yes	Yes	Yes	Yes	Yes	Yes	7	2	1	1		1	1	1	7
	Assesment questionnaire not updated	Yes	Yes	Yes	Yes	Yes	Yes	Yes	7	2	4	1		6	1	1	15
	Trainer not available for training	Yes		Yes	Yes		Yes	Yes	5	4		6			7	1	18
	Training material not available or incomplete	Yes	Yes		Yes	Yes	Yes	Yes	6	5	3			3	2	8	21
	No hands on training given	Yes	Yes	Yes	Yes		Yes	Yes	6	6	4	2			5	7	24
	No dedicated system for hands on training	Yes	Yes			Yes	Yes	Yes	5	4	2			4	7	3	20
	No. of new joiners faced training issues	12	8	8	13	14	15	6
	Lists of challenges that can lead to incorrect Pareto Analysis
	Using "Challenges faced" data for Pareto analysis cannot give ideal or top challenges faced
	In "No. of instances" table, data for month of Apr-19 is not available. This can lead to incorrect analysis
	Also "No. of new joiners faced training issues" faced training issues does not match with "No. of instances"

Data which can be well interpreted in bar graphs, people use pareto for the same. e.g. If a company wants to know which state contributes more to revenue generation and customer complaints(at the same time) may use Pareto instead of bar graph. Without considering the business contribution of a state, identifying complaints contribution is of no use. Complaints are directly proportional business numbers. 

 

 

On 8/27/2019 at 3:06 PM, Vishwadeep Khatri said:

Q. 189  Pareto Analysis is one of the most used tools in the search for significant contributors to a problem. What are some of the common misuses of Pareto Analysis? Explain with examples.

 

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Misuse of tools and techniques is a very common phenomenon. Misuse of a tool primarily happens because of two reasons

1. Intentional Misuse (it is better to call it as Misrepresentation)

2. Unintentional Misuse (due to lack of understanding of the concept)

 

Pareto analysis or the 80/20 rule is a prioritization tool that helps identify the VITAL FEW from TRIVIAL MANY. 80/20 implies that 80% of problems are due to 20% of the causes.

 

Intentional

1. Top 20% causes might not be the ones leading to bigger problems - usually it is observed that causes with smaller effects occur more often. Applying the Pareto principle will divert the focus of the team to the causes that have a smaller effect on the customer while the actual cause might be languishing in the trivial many
2. Prioritization without keeping in mind the goal - Pareto will help if the significant contributors identified help us achieve the goal. However, it is seldom checked whether the VITAL FEW will help us achieve the goal or if there is a need to take a larger number of causes. As an example, if our goal is complete defect elimination, we will need to consider all causes. If our goal is elimination of 95% defects, we will need to cover more of the cause.

 

Unintentional

1. Going strictly by the 80/20 rule - some people take the 80/20 principle in the literal sense. They will do a Pareto plot and blindly apply the 80/20 principle. What needs to be noted is that 80/20 is a rule of thumb and it is not necessary to always have 80/20 split. It could also be 70/30 or 90/10
2. Keeping the total to 100 = 80+20. This is one of the most common misunderstanding of the 80/20 rule where one beliefs that the sum should always be 100. It could be 80/15 or 75/25 as well
3. Unclear about the purpose of using a Pareto Analysis. Pareto can be used while defining afocus area and also in Root Cause Analysis to identify significant contributors. In the former, data is for problems and their occurrence while in the later, it is causes and their occurrence. Due to lack of clarity of purpose, if problems and causes are clubbed together in the same Pareto, then meaningful inferences cannot be drawn.
4. Treating Pareto as a non-living tool - Pareto is usually done once and the same result is treated as sacrosanct for a long period of time. Pareto chart only provides a time snapshot. Over a period of time, the defect categories or causes and their occurrence numbers might also change and hence if Pareto Analysis is done at different points of time, it might yield different results

 

Some that could fit in both categories

1. Small data set - Pareto Analysis will help if you want to prioritize vital few from a big data set. Doing a Pareto analysis on 4-5 categories will seldom yield a good result
2. Completely ignoring the trivial many - Pareto analysis helps identify the vital few but it does not say that one should ignore the trivial many. It simply states that first fix the vital and then move on to trivial. However, most people consider that if they fix the top 20%, they do not need to work on the remaining. Pareto can be used to continuously improve the process by repeatedly prioritizing the causes that you need to focus on
3. Doing Pareto at a high level only - Like most of the tools in Root Cause Analysis, Pareto can also be used to drill down. E.g. Pareto can be done first to identify the top defect categories and then a second level Pareto can be done for the top defect categories (using the causes)

Pareto Analysis is used to separate Vital few from Trivial Many parameters. Vital few contributing to 20% and trivial many 80%. This principle is otherwise called as 80-20 Rule.

 

It simply says, majority of the results come from minority of causes.
In numerical terms,
20% of inputs are accountable for 80% of output

80% of productivity comes from 20% of associates
20% of causes are accountable for 80% of problem

80% of sales comes from 20% of customers
20% of efforts are accountable for 80% of Results

 

Example Dataset:

Metric	Freq	Percentage	Cumulative
Demand Exceeds Supply	232	24.12%	24.12%
Incorrect Memory and CPU Usage	209	21.73%	45.84%
Bandwidth Constraints	203	21.10%	66.94%
Network Changes	64	6.65%	73.60%
Fatal Bugs in Production	59	6.13%	79.73%
Poor Front-End Optimization	52	5.41%	85.14%
Integration Dependencies	39	4.05%	89.19%
Database Contention	34	3.53%	92.72%
Browser Incompatibility	23	2.39%	95.11%
Device Incompatibility	14	1.46%	96.57%
Hardware Conflicts	13	1.35%	97.92%
Inadequate testing	9	0.94%	98.86%
Too much code	6	0.62%	99.48%
Exception handling	5	0.52%	100.00%

Classification: Public

 

Pareto Chart:

Classification: Public

 

Some of the common misuse include below scenario’s:
Working only on Vital few parameters:

There could be other potential parameters were the frequency is less and which falls on one of the trivial many factors, however when criticality or the severity of the potential parameter is high, since the frequency is low it is not considered and underestimated.
 

For the referred example, Inadequate testing can be critical, if there is insufficient test case or when the test review is poor it can lead to multiple production issues, which is not factored when focusing only on Vital Few.

 

On a ideal situation, 80% of the resource should focus on reducing the vital few and 20% of the resource working on minimizing trivial many parameters.

 

Using pareto for defects belonging to multiple categories:

Another misuse of pareto analysis is when combining defects from multiple categories. We need to clearly understand that categories must be Mutually Exclusive.

 

Using Pareto when parameters are not collectively exhaustive:
What is collectively exhaustive?

Collectively, all the failures in the list should cover all the possible failures for the problem., that is, there should not be any gap.

 

Definition:

Events are said to be collectively Exhaustive, If the list of outcomes includes every possible outcomes.

 

Performing analysis on small data sets/few data points:

For statistically significant analysis, we will have to use relatively large data sets rather than working on small data points.

At the same time number of categories need to be practically large enough.

above pareto analysis, does not make sense, when the data set is relatively small.

 

Inaccurate measuring:

Visually looking in the pareto chart and selecting the Vital Few rather than considering cumulative % < (less than) 80%

 

Analyzing defects only once:

Pareto Analysis should be performed before the problem is solved and

during the implementation period to see the trend and

Post improvement.

 

It is repetitive and iterative process, rather than running only once and focusing on the defects that were identified during the early stages of the analysis.

 

80 + 20 should be 100; and not 75 - 20 or 90 - 40

 

Considering 80 in the Left Axis:

Left axis displays frequency and right axis the percentage, some time when people consider 80 in left axis leading to selecting wrong vital few could lead to poor problem solving.

 

Flattened Pareto Analysis:

If there is any bias in data collection methods, we might end up with bars being flat, this happens mainly when we are separating / breaking vital problems into small problems. It does not make sense to proceed with Pareto Analysis. Rather work on action plans based on the severity and criticality.    

 

Considering defects as Root Cause:

Considering Vital defects identified during the analysis as Root Causes, and not analyzing further/deep dive to understand the root cause. This will not potentially stop the defect in occurring rather it would be applying band-aid scenario for the identified loop holes.

Pareto analysis is a statistical technique used in decision making mostly where there is competition amongst the root causes, contributors to the cause and we need to identify the number of such causes or contributors, which has a significant overall impact.

 ‘‘In the late 1940s Romanian-born American engineer and management consultant, Joseph M. Juran suggested the principle and named it after Italian economist Vilfredo Pareto, who observed that 80% of income in Italy went to 20% of the population. Pareto later carried out surveys in some other countries and found to his surprise that a similar distribution applied.’’ – as mentioned in the website By Duncan Haughey

 

Hence, we use the Pareto Principle (also known as the 80/20 rule) which means focusing on 20% of the work/cause/contributor; one can impact 80% of the overall job/problem/benefit. Hence Pareto Diagram is one of the essential tools used in total quality control and Six Sigma.

 

While performing Pareto Analysis there could be some common misuses as follows –

 

a)     All problems/causes/ factors may not be included before doing the analysis.

For example; if we are doing a Pareto of delay in booking of goods receipt. If I consider only few plants for the analysis the Pareto Diagram will only generate basis the data hence decision making would be inaccurate in case some of the major plants contributing to this problem has been left out of the analysis.

 

b)     Inaccurate, insufficient, or inconsistent data collection. For example, if the causal factors are not measured accurately and consistently for the same period and the 80/20 principle will also give inaccurate results.

 

c)     Only those probable root causes are included in the analysis that we assume might be the contributors and not the actuals. This means if we have not identified all root causes, which is actually affecting the problem it could lead to misleading output hence the problem would not get resolved or would be resolved for a temporary period.

 

d)     If the data points have interdependence, the outcome of the Pareto Analysis will not help. For example both data points must be addressed if they are interlinked even if one of them do not fall under the 80% contributing bucket.

 

e)     Some of the lower contributing factors could be quick wins however they get ignored again because these do not fall under the 80% contributing bucket.

 

f)       If weightage is added to the attributes considered for Pareto Analysis the outcome of such would be biased and misleading.

 

*Some errors having chance of giving huge loss may don't get highlighted because of less occurrence. Example: In automobile industry Brake issue giving severe loss may don't get highlighted as it occurred less *   Pareto may show less impact defect as important one just because its multiple occurrence. Example: Pareto may highlight seat cover defect which has less impact  * Same kind errors may classified as different which lead to miscalculation. Example: If Seat and seat cover defect calculated separate it lead to miscalculation

The Pareto Principle or the 80/20 rule essentially means 80% of the Output comes from 20% of the input.  This no doubt is a very important tool in Six Sigma Analyze phase to identify those critical root causes which can make some significant changes to my output Y. 

 

Given most of our Six Sigma projects are time bound, we need to work with the approach on "Not to Boil the Ocean". As per Pareto Principle, we identify those top 20% of the inputs which when addressed can bring about the desired changes for my Output Y i.e. either shift the mean or reduce the variation. We then try to develop solutions during our Improve Phase for those identified X's. 

 

However, Pareto Principle comes with it's limitations. To name a few...

• Pareto Principle holds good when the frequency of occurrence of any issue is of significant. However it doesn't cover the severity of the issue.
• The 80/20 rule shouldn't be comprehended to add to a 80+20 logic. This is not a 100% pie that we are working on to split into a 80% and 20%. It only shows that 20% of the inputs has a significant impact on 80% of the output. In-fact based on the business the inputs % can be changed from 20% to 30% or 40% as long as the inputs identified can bring about the desired changes basis project requirement. 
• 80/20 doesn't shouldn't stop you looking other X's which can  have it's impact. As the X's which fall under 20% are those critical X's which when addressed brings the desired changes to Y, the one which falls outside can still make a difference when addressed. These X's outside the 20% bracket can have a "Just Do It" solution or Low hanging fruits which still can be worked out while we take a statistical approach to address the critical Xs in Improve phase.
• A basic cognizance of when and where to apply Pareto principle should also be considered. For example if I look at my talent building capabilities or saving money, while I identify 2-3 potential capabilities which can enhance my skill sets or investment potential, we shouldn't ignore the other aspects which brings in those incremental benefits. Bottom line is to stay Agile - while you focus on your core X's to bring about the desire changes, don't ignore the small X's which can give you incremental benefits.

Despite these limitations, for most of our regular projects Pareto is still a good tool to Identify our Critical X's. 

 

Benchmark Six Sigma Expert View by Venugopal R

‘Priority Consciousness’ is one of the key topics discussed in Management. Sometimes we do hear people saying the ‘Everything is equally important’.. however, in reality it becomes difficult and even inefficient if we do not prioritize our tasks.

 

Principle of Pareto Analysis would not require any explanation for most members in this forum. Pareto principle, though named after the Italian economist Vilfredo Pareto, was popularized and adopted in the field of Quality Management by Joseph Juran.

 

All the seven Quality tools are excellent methods to provide guidance to problem solving, but teams have to apply their minds, process knowledge and situational requirements for the best decisions. This applies for the usage of Pareto analysis as well. There could be many ways by which the Pareto analysis may not be done to get its best benefits, and some misuse as well.

 

1.     Not considering severity

We may use Pareto analysis to classify the defects of a product based on the frequency of occurrence for a period of time… for example, take the case of an electrical home appliance. Top most occurring defect could be a scratch on the panel, and the least occurring could be an insulation breakdown. Obviously, if the priorities are judged based on frequency of occurrence alone, without considering severity, it could be disastrous! It will be a good practice to perform FMEA as well, so that the priorities are not decided just based on the occurrences alone.

 

2.     Using Pareto charts only as a presentation tool

Pareto charts are meant to be tools used as part of causal analysis, but they also serve as good presentation method. If we draw up the pareto charts just for project presentation, and do not build them during the appropriate phase of the problem solving, it is a misuse.

 

3.     Labeling ‘stratification’ as ‘cause’

Pareto analysis can be used for stratification of data as well as for causal analysis. For example, the sales figures of a particular product across 12 cities can be depicted using a pareto, as a stratification exercise. However, if you drill down to 10 reasons for poor sales and depict them using pareto for each city, then you are using the tool for causal analysis. Sometimes, the failure to differentiate between the two, could result in labelling ‘stratifications’ as ‘causes’

 

4.     Improper Grouping

The purpose of pareto is to identify a pattern of “Vital few and Trivial many”. If one type of grouping is resulting in a flat pareto, you may have to try some other type of grouping. For example, if you are working on improving the productivity of processing invoices and you develop a pareto of the productivity by grouping them vendor wise… assume you get quite a flat pareto. This does not allow you to differentiate productivity levels across vendors, so, you may try to group the data based on types of invoices, irrespective of vendors and develop a pareto. Similarly, different types of grouping need to be tried to identify a pattern of ‘vital few’.

 

5.     Making ‘Others’ too tall

Lack of adequate grouping can result in a very tall ‘others’ bar. We have seen pareto charts where the ‘others’ bar come up as the tallest! Certainly, the thoughts and efforts for grouping have not been adequate.

 

6.     Missing out on ‘Quick wins’

Many times, an occurrence with lower frequency could have an easy solution, with less efforts. You should not just keep putting efforts only as per the pareto sequence, failing to notice the quick wins.

 

Pareto analysis finds application in all phases of DMAIC phase. However, this tool has to be applied with some logical thinking and subject matter knowledge. It is a tool that helps in giving a broad level of prioritization, which has to be used along with other considerations.

Pareto principle is an effective root cause analysis (RCA) tool which help us separate the vital few factors from the trivial many without having to conduct deep research into each of causes of key factors, however, if not applied well, we may run into oversimplification of critical factors which may not resolve the issues or take us in a different direction from the real factors which actually affect the business outcomes.

 

There are some inherent limitations of Pareto principle and if these are not taken into account wrong conclusions may be drawn while applying the principle:

 

•  Historical Data: Based on historical data & does not account for the changed dynamics of the present. If for example 20% of the customers currently contribute to 80% of revenue, incorrectly applied Pareto conclusions may lead the business to ignore the others which may have greater untapped revenue potential.
• Does not take into account the future possibilities : Pareto Principle is descriptive & not prescriptive, so it will be  a mistake to use it for forecasting future patterns.
• May not apply to all business phenomena: In some cases it is entirely possible to have evenly distributed causal factors or may have only one significant cause with others being more or less equally distributed. e.g. 80% of system downtime may not be directly linked to in 20% of the computers.
• Based on quantitative data only & ignores
  • human factors which may have considerable influence on the outcomes. E.g. in a ten member team, while there may be significant productivity variations, it is unlikely that 2 people will contribute to 80% of the work.
  • Data independence: While we take care to have mutually exclusive & collectively exhaustive (MECE)data as far as possible, interrelationships between the causal factors may not be ruled out as in real life, they may not be totally independent of each other.
  • Pareto works well on a large data sample & may not apply well in those with few samples & causal factors
  • Short period data may be lopsided & may not reflect the true behavior of the process when observed for a long period & this may lead to incorrect conclusions
  • Very long period data may not account for changes in the nature or attributes of the causal factors over the time period. E.g. while looking at response times, the fact that information processing system may have undergone key upgrades and changes over time may be overlooked.
  • Data collected from unstable processes or outlier events may lead us to draw incorrect conclusions
• 80 & 20 do not necessarily equal to 100: the 80% & 20% do not refer to the same elements & hence do not necessarily total to 100%. The 80%  represents the result / outcome or Y  whereas 20% represents the causal factor/ input or X. It is not correct to take them together for making a 100% as may be mistakenly done.
• Mistaking the 20% drivers for root causes: The 20 % causal factors are only the categories that contribute to the 80% of out outcome, these are NOT the root causes, which will need to be understood further by conducting further analysis. The mistake here is to stop at the vital few identified & not go deep into the looking at the root causes.
• Not reviewing the "Others" category: Typically we tend t ignore the "others" category while looking at Pareto, however, if this category has key drivers, it would not be a wise decision to ignore them completely as there could be significant business implications , especially if business environment & other factors change.

While most of the answers very well highlight the misuses of Pareto Analysis, the most comprehensive answer is that of Natwar Lal; thereby marked as the best answer. His idea of mentioning intentional vs non-intentional misuses is interesting. 

 

Benchmark Expert View has been provided by Venugopal R.