Pareto Analysis for Tasks: The Formal Method for Finding What Matters

Most People Know the 80/20 Rule – Almost Nobody Runs the Actual Analysis

You’ve probably heard that 20% of your work produces 80% of your results. But there’s a difference between nodding along with a principle and sitting down with your own data to prove which 20% that is. The formal method – Pareto analysis – gives you a structured, repeatable process for identifying your highest-impact work with evidence rather than gut feeling. Joseph M. Juran, the quality management pioneer who formalized this method, showed that applying the Pareto principle as a data-driven analytical tool consistently separated the “vital few” causes from the “trivial many” across industries [1]. This article teaches you the actual process: collecting data, counting frequencies, building a Pareto chart, and using the results to restructure how you spend your time on tasks every week.

Last Updated: April 2026

What You Will Learn

• The history of Pareto analysis – from Vilfredo Pareto’s income observations to Juran’s quality revolution
• How Pareto analysis differs from the 80/20 rule as a concept
• The complete step-by-step Pareto analysis method for task prioritization
• How to build a Pareto chart from your own task data
• The Cause-Frequency-Impact Score (CFIS) Framework – a goalsandprogress.com method for personal task analysis
• Using Pareto analysis for root cause analysis of recurring productivity problems
• How Pareto analysis applies across industries – manufacturing, healthcare, project management, and personal work
• Common mistakes and how to avoid them

Key Takeaways

Pareto Analysis History: From Income Data to Your Task List

Pareto analysis originated from Italian economist Vilfredo Pareto’s 1906 observation that 80% of Italy’s land was owned by 20% of the population, and was formalized into a quality management tool by Joseph Juran in 1951. Pareto documented similar patterns across multiple countries – a small share of the population consistently held a disproportionate share of the wealth [1].

That observation might have stayed buried in economic literature if not for Joseph M. Juran. In 1941, Juran – then Head of Industrial Engineering at Western Electric – visited General Motors and came across Pareto’s work. He had a realization: this same lopsided distribution appeared everywhere in manufacturing quality data. A small number of defect types caused the vast majority of quality failures. A few root causes drove most of the customer complaints [1].

Juran formalized this into a working method. In his 1951 Quality Control Handbook, he introduced what he called the “Pareto principle” and coined the terms that still define the method today: the “vital few” and the “trivial many.” The vital few are the small number of causes producing most of the effect. The trivial many are everything else – the long tail of minor contributors that, individually, barely move the needle [1][6].

Vital few: The small number of causes – typically two to five categories – that account for the majority of a measured effect, such as defects, time spent, or outcomes produced. Trivial many: The remaining categories that each contribute a relatively small share of the total effect. Both terms were coined by Juran to replace the original Pareto terminology and are now standard across quality management [1].

“The vital few and trivial many” distinction that Juran introduced became the foundational concept of Pareto analysis – the recognition that a small number of causes are responsible for the large majority of problems, and that identifying those causes through data separates effective action from scattered effort [1].

In 1954, Juran traveled to Japan at the invitation of the Japanese Union of Scientists and Engineers (JUSE), where he taught these quality methods to manufacturing executives. His influence helped shape what became the Japanese quality revolution – and Pareto analysis became a standard tool in every quality improvement program worldwide [6].

There’s an honest footnote here. In a 1974 paper titled “The Non-Pareto Principle; Mea Culpa,” Juran admitted he’d given Pareto too much credit. The distribution pattern Pareto described was real, but the broad application to quality management was Juran’s own contribution. Pareto never intended his economic observation to serve as a universal analytical tool [7]. Juran built that bridge himself – and it held.

Pareto Analysis vs. the 80/20 Rule: Why the Method Matters More Than the Concept

Pareto analysis is a structured analytical process with defined steps and data requirements, while the 80/20 rule for daily tasks is a conceptual heuristic for rough estimation. The 80/20 rule is a concept – a mental model that helps you think about effort and output in rough terms. You look at your to-do list and ask yourself, “Which of these items would produce the most results?” That’s useful, and we have a full guide on applying it to your daily planning.

Pareto analysis is a structured analytical process with defined steps, data requirements, and a specific visual output called the Pareto chart. The concept tells you that imbalance exists. The method tells you exactly where it exists in your specific situation.

Think of it this way: saying “most of my results come from a few tasks” is the principle. Tracking your tasks for two weeks, counting completion rates and impact scores, building a frequency chart, and discovering that client proposal work and product research account for 78% of your measurable output – that’s the analysis.

The table below shows the key differences:

Dimension	80/20 Rule (Concept)	Pareto Analysis (Method)
Type	Mental model / heuristic	Formal analytical process
Data required	None – intuition-based	Minimum 30 data entries recommended
Output	General awareness of imbalance	Ranked list with specific percentages
Visual tool	None	Pareto chart (bar + cumulative line)
Precision	Approximate (“roughly 80/20”)	Exact (could be 73/27 or 90/10)
Repeatable	Subjective each time	Consistent process, comparable results
Best for	Quick daily prioritization	Strategic analysis of recurring patterns

Both have a place in your productivity system. The 80/20 rule is your daily filter. Pareto analysis is the quarterly or monthly audit that keeps your filters accurate.

Now that you understand what makes the formal method different from casual 80/20 thinking, here’s how to run Pareto analysis on your own task data.

Pareto Analysis Step-by-Step: The Complete Method for Tasks

Pareto analysis for tasks follows six sequential steps: define your question, collect data, calculate frequencies, identify the vital few, run a parallel impact analysis, and restructure your time. Mohammad Alkiayat’s practical guide to Pareto charts in healthcare quality improvement lays out a process that translates directly to personal task management [2]. I’ve adapted the standard quality management approach into six clear steps for individual use.

Step 1: Define What You Are Measuring

Before you collect any data, get specific about what effect you’re trying to understand. In manufacturing, this might be “defect types causing product returns.” For your tasks, it could be:

• Which task categories produce the most completed deliverables?
• Which task types consume the most time?
• Which activities lead to the most revenue or client satisfaction?
• Which recurring problems eat the most hours each week?

Pick one question. Running the analysis on a vague prompt like “what matters most” produces vague results. A tight question produces a tight answer.

Step 2: Collect Your Data (Minimum Two Weeks)

You need raw data – real numbers, not guesses. Track every task you complete over at least two weeks. For each task, record:

• Task category (e.g., email, client calls, content creation, admin, meetings, research)
• Time spent (in minutes or hours)
• Outcome type (deliverable completed, problem solved, revenue generated, or no measurable outcome)

Statistical quality control principles recommend a minimum of 30 data entries for meaningful Pareto results [3]. In our experience, two weeks of task tracking for most professionals produces roughly 50 to 100 entries, which is more than enough.

Use whatever tracking method works for you – a simple spreadsheet, a time-tracking app, or even a paper tally sheet. The format doesn’t matter. The count does.

Step 3: Count Frequencies and Calculate Percentages

Once you have your raw data, tally the totals for each category. Then calculate what percentage of the total each category represents. Here’s an example using task time data from a hypothetical marketing manager’s two-week tracking period:

Task Category	Total Hours (2 Weeks)	Percentage of Total	Cumulative %
Meetings (internal)	18.5	23.1%	23.1%
Email / Slack responses	14.0	17.5%	40.6%
Campaign content creation	12.0	15.0%	55.6%
Client reporting	10.5	13.1%	68.8%
Strategy / planning	8.0	10.0%	78.8%
Admin / scheduling	7.0	8.8%	87.5%
Social media posting	5.5	6.9%	94.4%
Training / learning	4.5	5.6%	100.0%

Sort your categories from highest to lowest. Then calculate the cumulative percentage by adding each row’s percentage to the running total. A cumulative percentage is the running total of individual category percentages, showing exactly how quickly the top categories accumulate toward the 80% threshold – and this column is what makes Pareto analysis work.

Step 4: Identify the Vital Few

Look at where your cumulative percentage crosses approximately 80%. In the example above, the top five categories (meetings, email, content creation, client reporting, and strategy) account for 78.8% of all time – just above the threshold. These are the “vital few” categories as Juran defined them [1].

But here’s the part most guides skip: you’re not done yet. This table shows you where your time goes. You need a second analysis to figure out where your time should go. That’s where Step 5 comes in.

Step 5: Run a Parallel Impact Analysis

Now run the same process, but instead of measuring time spent, measure output value. For each task category, count the number of meaningful deliverables, completed goals, or measurable outcomes it produced during the same period.

Task Category	Meaningful Outcomes	% of Total Outcomes	Cumulative %
Campaign content creation	11	28.9%	28.9%
Strategy / planning	8	21.1%	50.0%
Client reporting	7	18.4%	68.4%
Social media posting	5	13.2%	81.6%
Email / Slack responses	3	7.9%	89.5%
Training / learning	2	5.3%	94.7%
Meetings (internal)	1	2.6%	97.4%
Admin / scheduling	1	2.6%	100.0%

Now you can compare the two tables side by side. Meetings consumed 23.1% of total time but produced only 2.6% of meaningful outcomes in this Pareto analysis example. Content creation used 15% of time yet generated 28.9% of outcomes. The gap between where time goes and where value comes from is the actionable insight in Pareto analysis for personal productivity.

Step 6: Restructure Your Time Based on the Data

The final step is making decisions. With both tables in front of you, you can create clear action items:

• Protect and expand: Categories that score high on outcomes relative to time (content creation, strategy). Give these more hours.
• Reduce or delegate: Categories consuming lots of time with low outcome ratios (meetings, admin). Cut these back.
• Investigate: Categories where the ratio seems off – maybe email takes a lot of time and produces few “outcomes,” but the outcomes it does produce are relationship-building that matters in ways you haven’t captured.

This six-step process is the core Pareto analysis method adapted for individual task management. Run it monthly or quarterly to keep your time allocation aligned with your actual results.

Pareto Analysis Charts: A Practical Construction Guide

A Pareto chart is a combination bar-and-line graph that displays categories in descending order of frequency alongside a cumulative percentage line, providing immediate visual identification of the vital few. The American Society for Quality (ASQ) defines it as one of the seven basic quality tools [4]. Here’s how to build one from your task data.

Pareto chart (also called a Pareto diagram): A dual-axis combination graph that plots categories in descending order of frequency or magnitude as vertical bars against a left axis, while a connected line on a right axis tracks the running cumulative percentage. The point where that cumulative line crosses approximately 80% marks the boundary between the vital few causes on the left and the trivial many on the right [4].

Anatomy of a Pareto Chart

A properly constructed Pareto chart has five parts:

1. Left vertical axis (Y-axis): Shows the raw count or measurement (hours, frequency, cost)
2. Right vertical axis: Shows the cumulative percentage scale from 0% to 100%
3. Horizontal axis (X-axis): Lists categories in descending order from left to right
4. Bars: Represent the individual value of each category, tallest on the left
5. Cumulative line: A line graph plotted against the right axis showing the running total percentage

Step-by-Step Chart Construction

Using the time-tracking data from our earlier example, here’s the data prepared for charting:

Category (Left to Right)	Bar Height (Hours)	Cumulative Line Point
Meetings	18.5	23.1%
Email/Slack	14.0	40.6%
Content Creation	12.0	55.6%
Client Reporting	10.5	68.8%
Strategy	8.0	78.8%
Admin	7.0	87.5%
Social Media	5.5	94.4%
Training	4.5	100.0%

To create this chart in a spreadsheet:

1. Enter your sorted categories in column A and their values in column B
2. Calculate cumulative percentages in column C
3. Select the data and insert a combination chart – choose “Clustered Column” for the values and “Line” for the cumulative percentage
4. Move the line series to the secondary axis
5. Set the secondary axis scale from 0% to 100%
6. Draw a horizontal reference line at the 80% mark on the secondary axis
7. Everything to the left of where the cumulative line crosses 80% represents your vital few

In most spreadsheet applications (Google Sheets, Excel, LibreOffice Calc), this takes about five minutes once you have the data prepared. The chart gives you an immediate, visual answer to the question: where is the concentration?

Pareto Analysis Enhanced: The Cause-Frequency-Impact Score (CFIS) Framework

Standard Pareto analysis works on a single dimension – usually frequency or time. That’s fine in manufacturing, where you’re counting defect types. But personal task management has more complexity. A task might happen rarely yet produce enormous results. Another task might happen constantly but contribute almost nothing.

The Cause-Frequency-Impact Score (CFIS) Framework is a three-dimensional adaptation of Pareto analysis built for personal productivity analysis at goalsandprogress.com. It scores each task category across three dimensions and produces a composite ranking that accounts for both how often something happens and how much it matters when it does.

The Three CFIS Dimensions

For each task category in your tracking data, assign three scores on a 1 to 5 scale:

1. Cause Score (C): How directly does this task category cause or contribute to your primary goals? (1 = no direct connection, 5 = directly produces goal outcomes)
2. Frequency Score (F): How often does this task occur in your tracked data? (1 = rarely, 5 = daily or more)
3. Impact Score (I): When this task produces an outcome, how significant is that outcome? (1 = trivial, 5 = major deliverable or milestone)

Calculating the CFIS

The formula weights Cause and Impact more heavily than Frequency, since a high-impact task that happens occasionally is often more valuable than a low-impact task that fills every day:

CFIS = (C x 2) + F + (I x 2)

Maximum possible score: 25. Minimum: 5.

Here’s the CFIS applied to our marketing manager example:

Task Category	Cause (C)	Frequency (F)	Impact (I)	CFIS Score
Campaign content creation	5	4	5	24
Strategy / planning	5	3	5	23
Client reporting	4	3	4	19
Social media posting	3	5	3	17
Training / learning	3	2	4	16
Email / Slack responses	2	5	2	13
Meetings (internal)	2	5	1	11
Admin / scheduling	1	4	1	8

The CFIS ranking often tells a different story than raw time data alone because it accounts for goal alignment and outcome significance alongside frequency. Notice how training and learning – which ranked near the bottom in time spent – scores higher on CFIS than email or meetings. That’s the three-dimensional view revealing what a single-dimension analysis would miss.

After calculating CFIS scores, run a standard Pareto analysis on those scores. The top categories (those accumulating to roughly 80% of the total CFIS points) become your vital few – the task categories that deserve the largest share of your schedule.

The CFIS Framework optimizes which tasks deserve more time. But Pareto analysis can also diagnose why your current task allocation keeps going wrong – and that requires a different kind of data collection.

Pareto Analysis for Root Cause Diagnosis of Productivity Problems

So far we’ve used Pareto analysis to understand where time goes and where value comes from. But the method is equally powerful for diagnosing why things go wrong. Root cause analysis: A systematic process of identifying the underlying causes of problems or defects rather than addressing their symptoms. When applied with Pareto analysis, the method ranks which root causes are responsible for the most incidents – allowing teams to target the causes that will eliminate the most problems with the least effort.

Instead of categorizing tasks by type, categorize your interruptions, failures, and time-wasters over a two-week period. Every time you get pulled off track, miss a deadline, or waste time on something avoidable, log it and categorize the cause.

Example: Root Cause Pareto for Missed Deadlines

Imagine you tracked every instance where a task took longer than planned or a deadline slipped. After two weeks, you categorized 42 delay incidents:

Delay Cause	Occurrences	% of Total	Cumulative %
Waiting on input from others	14	33.3%	33.3%
Unclear requirements at start	10	23.8%	57.1%
Unplanned meeting interruptions	7	16.7%	73.8%
Scope changes mid-task	5	11.9%	85.7%
Technical difficulties	3	7.1%	92.9%
Personal energy / motivation	2	4.8%	97.6%
Forgot about the task	1	2.4%	100.0%

Root cause Pareto analysis reveals that the top three delay causes – waiting on others, unclear requirements, and unplanned meetings – account for 73.8% of all deadline misses. Adding scope changes pushes it to 85.7%. Root cause Pareto analysis tells you exactly where to focus improvement efforts. You don’t need better willpower or a fancier to-do app. You need better upfront requirement gathering and a system for getting input from collaborators faster.

Alkiayat’s guide to Pareto charts in quality improvement demonstrates that the method’s greatest strength lies in converting scattered problem observations into a ranked, visual hierarchy that immediately shows practitioners where intervention will have the most impact [2].

This root cause approach pairs naturally with the Eisenhower Matrix – once you know what causes delays, you can categorize those fixes by urgency and importance. For more on how decision science applies to prioritization, that guide covers the research behind why structured methods beat intuition.

Pareto Analysis Applications Beyond Manufacturing

Pareto analysis was born in manufacturing but the underlying logic – that a small number of causes drive a disproportionate share of outcomes – translates to almost any domain where you can collect occurrence data. Here is how the method works across four fields, including the personal task management application covered in this guide.

Manufacturing and Quality Management

Pareto analysis has its deepest roots in manufacturing quality control. In one documented case study, according to a peer-reviewed analysis of coffee sachet production defects, applying the method to identify and address the vital few defect causes reduced the overall defect rate from 2,400 parts per million (PPM) per week to 147 PPM per week – a reduction of approximately 94% [5]. The method didn’t require fixing everything. It required fixing the right things.

In Six Sigma methodology, the Pareto chart appears in the Measure phase of the DMAIC (Define, Measure, Analyze, Improve, Control) process. Teams collect defect data, build Pareto charts, and direct their improvement efforts at the tallest bars [3]. The Pareto chart is one of the seven basic quality tools standardized by the American Society for Quality, used across manufacturing, healthcare, and project management [4].

Healthcare Quality Improvement

Alkiayat’s 2021 guide in the Global Journal on Quality and Safety in Healthcare demonstrated how Pareto charts can identify the most frequent causes of medical errors, patient complaints, or treatment delays [2]. The same logic applies: find the vital few causes and focus resources there first.

Project Management

The Project Management Body of Knowledge (PMBOK) includes Pareto analysis as a quality management tool. Project managers use it to identify which risk categories, defect types, or scope areas deserve the most attention [5]. Research on applying Pareto analysis in project management suggests that focusing on the vital few project variables can reduce timeline overruns and budget deviations [8].

Personal Task Management

This is where we bring the industrial-strength method home. When you run Pareto analysis on your own task data, you’re applying the same rigor that manufacturing teams use to eliminate defects – except you’re eliminating wasted effort.

The process fits well alongside other prioritization methods. Use the Eat That Frog method for tackling your hardest task first each morning, the most important tasks method for daily focus, and Pareto analysis as the periodic audit that tells you whether your daily methods are pointed at the right categories of work. For a full overview of how these methods connect, see our complete guide to prioritization methods.

Pareto Analysis Mistakes: Six Errors and How to Avoid Them

The most common Pareto analysis mistakes involve insufficient data, overly broad categories, and single-dimension measurement. Avoiding these six errors ensures your analysis produces actionable results.

Mistake 1: Not Collecting Enough Data

Running a Pareto analysis on three days of tracking is like drawing conclusions from a coin flipped five times. You need at least 30 data points – and more is better [3]. Two weeks of task tracking for most professionals will get you there. If you’re analyzing something less frequent (like project failures), you may need to look back over several months of records.

Mistake 2: Choosing Categories That Are Too Broad

“Work tasks” isn’t a useful category. Neither is “communication.” Break your categories into specific, distinguishable types. Instead of “communication,” track “client emails,” “internal Slack,” “phone calls,” and “meeting participation” separately. The more specific your categories, the more useful your Pareto chart becomes.

Mistake 3: Only Measuring One Dimension

Classic Pareto analysis tracks frequency or volume – how often something happens or how much time it takes. But for task management, a single dimension often misleads. A task that takes one hour per week might generate more value than a task that takes ten hours. The CFIS Framework addresses this by scoring across three dimensions, but at minimum, run both a time analysis and an outcome analysis to compare.

Mistake 4: Assuming 80/20 Is Always the Split

The 80/20 ratio is a rough guideline, not a law of physics. Your actual split might be 70/30 or 90/10. Juran himself acknowledged this in his 1974 paper – the principle describes a general pattern of concentration, not a precise mathematical ratio [7]. Let your data tell you the actual split. Don’t force it into 80/20.

Mistake 5: Running the Analysis Once and Never Updating

Your vital few will shift over time as projects change, roles evolve, and priorities move. Plan to re-run your task Pareto analysis at least quarterly. What was a vital task category six months ago might have dropped to the trivial many – and something new may have risen to take its place.

Mistake 6: Ignoring the Trivial Many Completely

The “trivial many” aren’t zero-value tasks. They’re lower-value relative to the vital few. Some of them still need to happen – they may just need to happen faster, be delegated, or be batched differently. Pareto analysis identifies where the greatest returns on effort exist, but it does not mean everything outside the vital few is worthless. The method tells you what to prioritize, not what to abandon.

Mistake 7: Using Pareto Analysis When the Conditions Are Wrong

Pareto analysis works best when you have recurring task categories with trackable frequency data. It produces weak or misleading results in specific situations where those conditions do not hold. Skip Pareto analysis when:

• You have no recurring categories – one-off projects with entirely unique tasks have nothing to rank by frequency. Every task is a sample size of one, and a Pareto chart built on that data is not meaningful.
• You already know the root causes with high confidence – if your team has already identified the primary problem and the solution is agreed upon, collecting two weeks of data to confirm what you already know delays action without adding insight.
• Your tracking period is not representative – a week or two does not capture seasonal variation, project cycle variation, or role shifts. If your work changes substantially every few weeks, the data window may not reflect your actual distribution.

Pareto analysis adds the most value when work patterns repeat, outcomes vary across categories, and the cost of misdirected effort is high. When those conditions are not present, simpler methods – a straight priority list, a direct conversation with your team, or a quick judgment call – will get you to a decision faster.

How Does Pareto Analysis Fit Into a Broader Task Management System?

Pareto analysis functions as a strategic diagnostic layer within a multi-tool productivity system. It works best as one layer in a multi-tool system:

• Monthly/Quarterly: Run a full Pareto analysis to identify your vital few task categories and major time drains
• Weekly: Use the Eisenhower Matrix to sort this week’s tasks by urgency and importance, informed by your Pareto findings
• Daily: Apply the 80/20 rule as a quick filter and the 1-3-5 rule to limit your list size
• Per-task: Use the ABC method to grade individual tasks within your daily list

Pareto analysis sits at the strategic layer of task management, answering the big-picture question of which categories of work deserve the most energy. The daily tools then execute on that insight. If you’re looking for ways to track whether your time allocation is actually changing, our goal tracking systems guide covers how to measure progress over time.

Ramon’s Take

I changed my mind about the 80/20 rule about three years ago. I’d been using it as a thinking tool for years – quick gut checks, rough mental filters – and it felt productive. Then I sat down and ran the actual Pareto analysis on my own task data for the first time, and the results surprised me. I was spending nearly a quarter of my week on activities that produced almost nothing measurable, and the work I thought was eating all my time turned out to be a much smaller slice than it felt like. The gap between perception and data was humbling.

I now run this analysis every quarter, and every single time, the vital few shift a little. That drift is the whole point: a system built only on gut feeling would quietly go off course without you noticing.

The CFIS Framework came out of my frustration with standard Pareto analysis being too one-dimensional for knowledge work. Counting hours is fine for a factory floor. But when a single hour of strategy work outweighs ten hours of email, you need a scoring method that captures that difference. If you take one thing from this article, make it this: the 80/20 rule is a good idea, but the Pareto analysis is a good process. And processes beat ideas every time.

Pareto Analysis Conclusion: Your Data Knows the Answer

Pareto analysis takes the 80/20 principle from a vague awareness into a precise, data-backed diagnosis of where your effort actually produces results. The six-step method – define, collect, count, identify, compare, and restructure – turns guesswork into evidence. The CFIS Framework adds the multi-dimensional scoring that knowledge workers need. And the Pareto chart gives you a visual artifact you can reference every time you’re tempted to drift back toward comfortable-but-low-value work.

The question isn’t whether imbalance exists in your task list. It does. The question is whether you’ll measure it or keep guessing.

Next 10 Minutes

• Open a spreadsheet and create your tracking template with columns for Date, Task Category, and Time Spent
• Define your 6 to 10 task categories based on the types of work you did this past week
• Set a recurring phone reminder for noon and 5 PM to log your tasks for the next 14 days

This Week

• Start logging every task – even the small ones – into your tracking sheet with time estimates
• Review our prioritization methods guide to see how Pareto analysis fits with the tools you already use
• Read through the CFIS Framework section again and decide which three dimensions matter most for your specific role
• At the end of the week, do a quick preview tally to see if your categories are specific enough – adjust them before week two if needed

There Is More to Explore

Pareto analysis is one piece of a larger prioritization toolkit. If you’re just getting started with structured prioritization, our complete guide to prioritization methods gives you the full picture of how different methods work together. For the daily application of the Pareto principle as a quick planning lens, see the 80/20 rule for daily tasks – it pairs naturally with the formal analysis covered here. And if you tend to overthink which tasks to tackle first, our guide on overcoming analysis paralysis covers how structured methods like Pareto analysis actually reduce decision fatigue rather than adding to it.

Related articles in this guide

• Prioritization Decision Matrix Guide
• Purpose-Driven Task Selection: The MIT Method
• RICE Prioritization Framework

Frequently Asked Questions

This article is part of our Prioritization Methods complete guide.

References

[1] Juran Institute. “Pareto Principle (80/20 Rule) and Pareto Analysis Guide.” Juran Institute, An Attain Partners Company. https://www.juran.com/blog/a-guide-to-the-pareto-principle-80-20-rule-pareto-analysis/

[2] Alkiayat, M. “A Practical Guide to Creating a Pareto Chart as a Quality Improvement Tool.” Global Journal on Quality and Safety in Healthcare, 4(2):83-84, 2021. DOI: 10.36401/JQSH-21-X1

[3] Montgomery, D.C. Introduction to Statistical Quality Control, 8th Edition. Wiley, 2019. ISBN: 978-1119723097. https://www.wiley.com/en-us/Introduction+to+Statistical+Quality+Control,+8th+Edition-p-9781119399308

[4] American Society for Quality (ASQ). “What is a Pareto Chart? Analysis and Diagram.” ASQ Quality Resources. https://asq.org/quality-resources/pareto

[5] Idris, N.I., Sin, T.C., Ibrahim, S., Ramli, M.F., Ahmad, R. “A Case Study of Coffee Sachets Production Defect Analysis Using Pareto Analysis, P-Control Chart and Ishikawa Diagram.” In: Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, pp. 1295-1305. Springer, Singapore, 2021. DOI: 10.1007/978-981-16-0866-7_115

[6] Juran Institute. “Dr. Juran’s History.” Juran Institute, An Attain Partners Company. https://www.juran.com/about-us/dr-jurans-history/

[7] Juran, J.M. “The Non-Pareto Principle; Mea Culpa.” 1974. https://www.juran.com/wp-content/uploads/2021/03/The-Non-Pareto-Principle-1974.pdf

[8] Mrvica, A., Ceke, D. “Application of the Pareto Analysis in Project Management.” Conference paper, 2016. https://www.researchgate.net/publication/305463099