7 basic problem solving tools

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7 Powerful Problem-Solving Root Cause Analysis Tools

The first step to solving a problem is to define the problem precisely. It is the heart of problem-solving.

Root cause analysis is the second important element of problem-solving in quality management. The reason is if you don't know what the problem is, you can never solve the exact problem that is hurting the quality.

Manufacturers have a variety of problem-solving tools at hand. However, they need to know when to use which tool in a manner that is appropriate for the situation. In this article, we discuss 7 tools including:

• The Ishikawa Fishbone Diagram (IFD)
• Pareto Chart
• Failure Mode and Effects Analysis (FMEA)
• Scatter Diagram
• Affinity Diagram
• Fault Tree Analysis (FTA)

1. The Ishikawa Fishbone Diagram IFD

The model introduced by Ishikawa (also known as the fishbone diagram) is considered one of the most robust methods for conducting root cause analysis. This model uses the assessment of the 6Ms as a methodology for identifying the true or most probable root cause to determine corrective and preventive actions. The 6Ms include:

• Measurement,
• Mother Nature- i.e., Environment

Related Training: Fishbone Diagramming

2. Pareto Chart

The Pareto Chart is a series of bars whose heights reflect the frequency or impact of problems. On the Chart, bars are arranged in descending order of height from left to right, which means the categories represented by the tall bars on the left are relatively more frequent than those on the right.

Related Training: EFFECTIVE INVESTIGATIONS AND CORRECTIVE ACTIONS (CAPA) Establishing and resolving the root causes of deviations, problems and failures

This model uses the 5 Why by asking why 5 times to find the root cause of the problem. It generally takes five iterations of the questioning process to arrive at the root cause of the problem and that's why this model got its name as 5 Whys. But it is perfectly fine for a facilitator to ask less or more questions depending on the needs.

Related training: Accident/Incident Investigation and Root Cause Analysis

4. Failure Mode and Effects Analysis (FMEA)

FMEA is a technique used to identify process and product problems before they occur. It focuses on how and when a system will fail, not if it will fail. In this model, each failure mode is assessed for:

• Severity (S)
• Occurrence (O)
• Detection (D)

A combination of the three scores produces a risk priority number (RPN). The RPN is then provided a ranking system to prioritize which problem must gain more attention first.

Related Training: Failure Mode Effects Analysis

5. Scatter Diagram

A scatter diagram also known as a scatter plot is a graph in which the values of two variables are plotted along two axes, the pattern of the resulting points revealing any correlation present.

To use scatter plots in root cause analysis, an independent variable or suspected cause is plotted on the x-axis and the dependent variable (the effect) is plotted on the y-axis. If the pattern reflects a clear curve or line, it means they are correlated. If required, more sophisticated correlation analyses can be continued.

Related Training: Excel Charting Basics - Produce Professional-Looking Excel Charts

6. Affinity Diagram

Also known as KJ Diagram, this model is used to represent the structure of big and complex factors that impact a problem or a situation. It divides these factors into small classifications according to their similarity to assist in identifying the major causes of the problem.

7. Fault Tree Analysis (FTA)

The Fault Tree Analysis uses Boolean logic to arrive at the cause of a problem. It begins with a defined problem and works backward to identify what factors contributed to the problem using a graphical representation called the Fault Tree. It takes a top-down approach starting with the problem and evaluating the factors that caused the problem.

Finding the root cause isn't an easy because there is not always one root cause. You may have to repeat your experiment several times to arrive at it to eliminate the encountered problem. Using a scientific approach to solving problem works. So, its important to learn the several problem-solving tools and techniques at your fingertips so you can use the ones appropriate for different situations.

ComplianceOnline Trainings on Root Cause Analysis

P&PC, SPC/6Sigma, Failure Investigation, Root Cause Analysis, PDCA, DMAIC, A3 This webinar will define what are the US FDA's expectation for Production and Process Control / Product Realization, the use of statistical tehniques, 6 sigma, SPC, for establishing, controlling , and verifying the acceptability of process capability and product characteristics, product acceptance or validation and other studies. Non-conformance, OOS, deviations Failure Investigations, and Root Cause Analysis, PDCA, DMAIC, and similar project drivers to improvement, A# and similar dash boards.

Accident/Incident Investigation and Root Cause Analysis If a major workplace injury or illness occurred, what would you do? How would you properly investigate it? What could be done to prevent it from happening again? A properly executed accident/incident investigation drives to the root causes of the workplace accident to prevent a repeat occurrence. A good accident/incident investigation process includes identifying the investigation team, establishing/reviewing written procedures, identifying root causes and tracking of all safety hazards found to completion.

Root Cause Analysis - The Heart of Corrective Action This presentation will explain the importance of root cause analysis and how it fits into an effective corrective and preventive action system. It will cover where else in your quality management system root cause analysis can be used and will give examples of some of the techniques for doing an effective root cause analysis. Attendees will learn how root cause analysis can be used in process control.

Addressing Non-Conformances using Root Cause Analysis (RCA) RCA assumes that systems and events are interrelated. An action in one area triggers an action in another, and another, and so on. By tracing back these actions, you can discover where the issue started and how it grew into the problem you're now facing.

Introduction to Root Cause Investigation for CAPA If you have reoccurring problems showing up in your quality systems, your CAPA system is not effective and you have not performed an in-depth root cause analysis to be able to detect through proper problem solving tools and quality data sources, the true root cause of your problem. Unless you can get to the true root cause of a failure, nonconformity, defect or other undesirable situation, your CAPA system will not be successful.

Root Cause Analysis and CAPA Controls for a Compliant Quality System In this CAPA webinar, learn various regulations governing Corrective and Preventive Actions (CAPA) and how organization should collect information, analyze information, identify, investigate product and quality problems, and take appropriate and effective corrective and/or preventive action to prevent their recurrence.

Root Cause Analysis for CAPA Management (Shutting Down the Alligator Farm) Emphasis will be placed on realizing system interactions and cultural environment that often lies at the root of the problem and prevents true root cause analysis. This webinar will benefit any organization that wants to improve the effectiveness of their CAPA and failure investigation processes.

Root Cause Analysis for Corrective and Preventive Action (CAPA) The Quality Systems Regulation (21 CFR 820) and the Quality Management Standard for Medical Devices (ISO 13485:2003), require medical device companies to establish and maintain procedures for implementing corrective and preventive action (CAPA) as an integral part of the quality system.

Strategies for an Effective Root Cause Analysis and CAPA Program This webinar will provide valuable assistance to all regulated companies, a CAPA program is a requirement across the Medical Device, Diagnostic, Pharmaceutical, and Biologics fields. This session will discuss the importance, requirements, and elements of a root cause-based CAPA program, as well as detailing the most effective ways to determine root cause and describing the uses of CAPA data.

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7 Basic Tools of Quality for Process Improvement

Japan is known worldwide for its quality products and services. One of the many reasons for this is its excellent quality management. How did it become so? Japan has Dr. Kaoru Ishikawa to thank for that.

Postwar Japan underwent a major quality revolution. Companies were focused on training their employees in statistical quality control. But soon they realized that the complexity of the subject itself could intimidate most of the workers; so they wanted more basic tools.

Dr. Kaoru Ishikawa, a member of the Japanese Union of Scientists and Engineers (JUSE), took it to his hands to make quality control easier for everyone – even those with little knowledge of statistics – to understand. He introduced the 7 basic tools of quality. They were soon adopted by most companies and became the foundation of Japan’s astonishing industrial resurgence after World War 2.

This post will describe the 7 basic quality tools, how to use them and give you access to templates that you can use right away.

Quality Tools: What Are They?

How can teams and organizations use the 7 basic quality tools, cause and effect diagram, scatter diagram, check sheets.

• Control chart
• Pareto chart

The 7 basic tools of quality, sometimes also referred to as 7 QC tools – represent a fixed set of graphical tools used for troubleshooting issues that are related to quality.

They are called basic quality tools because they can be easily learned by anyone even without any formal training in statistics. Dr. Kaoru Ishikawa played the leading role in the development and advocacy of using the 7 quality tools in organizations for problem-solving and process improvement.  

The 7 basic quality tools include;

• Cause-and-effect diagram
• Scatter diagram
• Check sheet

The 7 quality tools were first emphasized by Kaoru Ishikawa a professor of engineering at the University of Tokyo, who is also known as the father of “Quality Circles” for the role he played in launching Japan’s quality movement in the 1960s. During this time, companies were focused on training their employees in statistical quality control realized that the complexity of the subject could intimidate most of the workers; hence they opted for simpler methods that are easy to learn and use. 7 basic tools of quality were thus incorporated company-wide.

Quality tools are used to collect data, analyze data, identify root causes, and measure results in problem-solving and process improvement. The use of these tools helps people involved easily generate new ideas, solve problems, and do proper planning.

• Structured approach: They provide a systematic approach to problem-solving and process improvement, ensuring that efforts are well-organized and focused.
• Data-driven decision making: The tools enable data collection, analysis, and visualization, empowering teams to make informed decisions based on evidence.
• Improved communication and collaboration: Visual representations and structured tools facilitate effective communication and collaboration among team members, leading to shared understanding and alignment.
• Problem identification and prioritization: The tools help identify and prioritize problems or improvement opportunities, enabling teams to allocate resources efficiently and address critical issues first.
• Continuous improvement: By using these tools, teams can establish a culture of continuous improvement, as they provide a framework for ongoing monitoring, analysis, and refinement of processes.

7 Basic Quality Tools Explained with Templates

The 7 quality tools can be applied across any industry.  They help teams and individuals analyze and interpret the data they gather and derive maximum information from it.

Flowcharts are perhaps the most popular out of the 7 quality tools. This tool is used to visualize the sequence of steps in a process, event, workflow, system, etc. In addition to showing the process as a whole, a flowchart also highlights the relationship between steps and the process boundaries (start and end).

Flowcharts use a standard set of symbols, and it’s important to standardize the use of these symbols so anyone can understand and use them easily. Here’s a roundup of all the key flowchart symbols .

• To build a common understanding of a process.
• To analyze processes and discover areas of issues, inefficiencies, blockers, etc.
• To standardize processes by leading everyone to follow the same steps.

Real-world examples of usage

• Documenting and analyzing the steps involved in a customer order fulfillment process.
• Mapping out the workflow of a software development lifecycle.
• Visualizing the process flow of patient admissions in a hospital.

Enhances process understanding, highlights bottlenecks or inefficiencies, and supports process optimization and standardization efforts.

How to use a flowchart

• Gather a team of employees involved in carrying out the process for analyzing it.
• List down the steps involved in the process from its start to end.
• If you are using an online tool like Creately , you can first write down the process steps and rearrange them later on the canvas as you identify the flow.
• Identify the sequence of steps; when representing the flow with your flowchart, show it from left to write or from top to bottom.
• Connect the shapes with arrows to indicate the flow.

Who can use it?

• Process improvement teams mapping and documenting existing processes for analysis.
• Business analysts or consultants analyzing workflow and process optimization opportunities.
• Software developers or system designers documenting the flow of information or interactions in a system.

To learn more about flowcharts, refer to our Ultimate Flowchart Tutorial .

A histogram is a type of bar chart that visualizes the distribution of numerical data. It groups numbers into ranges and the height of the bar indicates how many fall into each range.

It’s a powerful quality planning and control tool that helps you understand preventive and corrective actions.

• To easily interpret a large amount of data and identify patterns.
• To make predictions of process performance.
• To identify the different causes of a quality problem.
• Analyzing the distribution of call wait times in a call center.
• Assessing the distribution of product weights in a manufacturing process.
• Examining the variation in delivery times for an e-commerce business.

Provides insights into process performance and variation, enabling teams to target areas for improvement and make data-driven decisions.

How to make a histogram

• Collect data for analysis. Record occurrences of specific ranges using a tally chart.
• Analyze the data at hand and split the data into intervals or bins.
• Count how many values fall into each bin.
• On the graph, indicate the frequency of occurrences for each bin with the area (height) of the bar.
• Process engineers or data analysts examining process performance metrics.
• Financial analysts analyzing expenditure patterns or budget variances.
• Supply chain managers assessing supplier performance or delivery times.

Here’s a useful article to learn more about using a histogram for quality improvement in more detail.

This tool is devised by Kaoru Ishikawa himself and is also known as the fishbone diagram (for it’s shaped like the skeleton of a fish) and Ishikawa diagram.

They are used for identifying the various factors (causes) leading to an issue (effect). It ultimately helps discover the root cause of the problem allowing you to find the correct solution effectively.

• Problem-solving; finding root causes of a problem.
• Uncovering the relationships between different causes leading to a problem.
• During group brainstorming sessions to gather different perspectives on the matter.
• Investigating the potential causes of low employee morale or high turnover rates.
• Analyzing the factors contributing to product defects in a manufacturing process.
• Identifying the root causes of customer complaints in a service industry.

Enhances problem-solving by systematically identifying and organizing possible causes, allowing teams to address root causes rather than symptoms.

How to use the cause and effect diagram

• Identify the problem area that needs to be analyzed and write it down at the head of the diagram.
• Identify the main causes of the problem. These are the labels for the main branches of the fishbone diagram. These main categories can include methods, material, machinery, people, policies, procedures, etc.
• Identify plausible sub-causes of the main causes and attach them as sub-branches to the main branches.
• Referring to the diagram you have created, do a deeper investigation of the major and minor causes.
• Once you have identified the root cause, create an action plan outlining your strategy to overcome the problem.
• Cross-functional improvement teams working on complex problems or process improvement projects.
• Quality engineers investigating the root causes of quality issues.
• Product designers or engineers seeking to understand the factors affecting product performance.

The scatter diagram (scatter charts, scatter plots, scattergrams, scatter graphs) is a chart that helps you identify how two variables are related.

The scatter diagram shows the values of the two variables plotted along the two axes of the graph. The pattern of the resulting points will reveal the correlation.  

• To validate the relationship between causes and effects.
• To understand the causes of poor performance.
• To understand the influence of the independent variable over the dependent variable.
• Exploring the relationship between advertising expenditure and sales revenue.
• Analyzing the correlation between employee training hours and performance metrics.
• Investigating the connection between temperature and product quality in a production line.

Helps identify correlations or patterns between variables, facilitating the understanding of cause-and-effect relationships and aiding in decision-making.

How to make a scatter diagram

• Start with collecting data needed for validation. Understand the cause and effect relationship between the two variables.
• Identify dependent and independent variables. The dependent variable plotted along the vertical axis is called the measures parameter. The independent variable plotted along the horizontal axis is called the control parameter.
• Draw the graph based on the collected data. Add horizontal axis and vertical axis name and draw the trend line.
• Based on the trend line, analyze the diagram to understand the correlation which can be categorized as Strong, Moderate and No Relation.  
• Data analysts exploring relationships between variables in research or analytics projects.
• Manufacturing engineers investigating the correlation between process parameters and product quality.
• Sales or marketing teams analyzing the relationship between marketing efforts and sales performance.

Check sheets provide a systematic way to collect, record and present quantitative and qualitative data about quality problems. A check sheet used to collect quantitative data is known as a tally sheet.

It is one of the most popular QC tools and it makes data gathering much simpler.

• To check the shape of the probability distribution of a process
• To quantify defects by type, by location or by cause
• To keep track of the completion of steps in a multistep procedure (as a checklist )
• Tracking the number of defects or errors in a manufacturing process.
• Recording customer complaints or inquiries to identify common issues.
• Monitoring the frequency of equipment breakdowns or maintenance needs.

Provides a structured approach for data collection, making it easier to identify trends, patterns, and areas for improvement.

How to make a checksheet

• Identify the needed information.
• Why do you need to collect the data?
• What type of information should you collect?
• Where should you collect the data from?  
• Who should collect the data?
• When should you collect the data?
• How should you measure the data?
• How much data is essential?

Construct your sheet based on the title, source information and content information (refer to the example below).

Test the sheets. Make sure that all the rows and columns in it are required and relevant and that the sheet is easy to refer to and use. Test it with other collectors and make adjustments based on feedback.

• Quality inspectors or auditors who need to collect data on defects or issues.
• Process operators or technicians responsible for tracking process parameters or measurements.
• Customer service representatives who record customer complaints or inquiries.

Control Chart

The control chart is a type of run chart used to observe and study process variation resulting from a common or special cause over a period of time.

The chart helps measure the variations and visualize it to show whether the change is within an acceptable limit or not. It helps track metrics such as defects, cost per unit, production time, inventory on hand , etc.

Control charts are generally used in manufacturing, process improvement methodologies like Six Sigma and stock trading algorithms.

• To determine whether a process is stable.
• To monitor processes and learn how to improve poor performance.
• To recognize abnormal changes in a process.
• Monitoring the variation in product dimensions during a manufacturing process.
• Tracking the number of customer complaints received per day.
• Monitoring the average response time of a customer support team.

Enables real-time monitoring of process stability, early detection of deviations or abnormalities, and prompt corrective actions to maintain consistent quality.

How to create a control chart

• Gather data on the characteristic of interest.
• Calculate mean and upper/lower control limits.
• Create a graph and plot the collected data.
• Add lines representing the mean and control limits to the graph.
• Look for patterns, trends, or points beyond control limits.
• Determine if the process is in control or out of control.
• Investigate and address causes of out-of-control points.
• Regularly update the chart with new data and analyze for ongoing improvement.
• Production supervisors or operators monitoring process performance on the shop floor.
• Quality control or assurance personnel tracking variation in product quality over time.
• Service managers observing customer satisfaction levels and service performance metrics.

Pareto Chart

The Pareto chart is a combination of a bar graph and a line graph. It helps identify the facts needed to set priorities.

The Pareto chart organizes and presents information in such a way that makes it easier to understand the relative importance of various problems or causes of problems. It comes in the shape of a vertical bar chart and displays the defects in order (from the highest to the lowest) while the line graph shows the cumulative percentage of the defect.

• To identify the relative importance of the causes of a problem.
• To help teams identify the causes that will have the highest impact when solved.
• To easily calculate the impact of a defect on the production.
• Analyzing customer feedback to identify the most common product or service issues.
• Prioritizing improvement efforts based on the frequency of quality incidents.
• Identifying the major causes of delays in project management.

Helps focus improvement efforts on the most significant factors or problems, leading to effective allocation of resources and improved outcomes.

How to create a Pareto chart

• Select the problem for investigation. Also, select a method and time for collecting information. If necessary create a check sheet for recording information.
• Once you have collected the data, go through them and sort them out to calculate the cumulative percentage.
• Draw the graph, bars, cumulative percentage line and add labels (refer to the example below).
• Analyze the chart to identify the vital few problems from the trivial many by using the 80/20 rule . Plan further actions to eliminate the identified defects by finding their root causes.
• Quality managers or improvement teams looking to prioritize improvement initiatives.
• Project managers seeking to identify and address the most critical project risks.
• Sales or marketing teams analyzing customer feedback or product issues.

What’s Your Favorite Out of the 7 Basic Quality Tools?  

You can use these 7 basic quality tools individually or together to effectively investigate processes and identify areas for improvement. According to Ishikawa, it’s important that all employees learn how to use these tools to ensure the achievement of excellent performance throughout the organization.

Got anything to add to our guide? Let us know in the comments section below.

Join over thousands of organizations that use Creately to brainstorm, plan, analyze, and execute their projects successfully.

FAQs about 7 Basic Quality Tools

Quality problems in an organization can manifest in various forms and affect different areas of operations.

• Product defects: Products may have defects or non-conformities that deviate from quality specifications, leading to customer dissatisfaction, returns, or warranty claims.
• Service errors: Service errors can occur when services do not meet customer expectations, such as incorrect billing, delays in delivery, or inadequate customer support.
• Process inefficiencies: Inefficient processes can lead to delays, errors, or rework, resulting in increased costs, decreased productivity, and customer dissatisfaction.
• Poor design or innovation: Inadequate product design or lack of innovation can lead to products that do not meet customer needs, lack competitive features, or have usability issues.
• Supplier quality issues: Poor quality materials or components from suppliers can affect the overall quality of the final product or service.
• Ineffective quality management systems: Inadequate quality management systems, such as lack of quality standards, processes, or documentation, can contribute to quality problems throughout the organization.

The basic quality improvement steps typically follow a systematic approach to identify, analyze, implement, and monitor improvements in processes or products.

• Clearly articulate the problem or identify the area for improvement.
• Collect relevant data and information related to the problem.
• Analyze the collected data to identify patterns, root causes, and opportunities for improvement.
• Brainstorm and generate potential improvement ideas or solutions.
• Assess the feasibility, impact, and effectiveness of the generated improvement ideas.
• Develop an action plan to implement the chosen solution.
• Continuously monitor and measure the results of the implemented solution.
• Based on the monitoring results, evaluate the effectiveness of the implemented solution.
• Once the improvement is successful, document the new processes, best practices, or standard operating procedures (SOPs).
• Iterate through the steps to continuously improve processes and products.

More Related Articles

Amanda Athuraliya is the communication specialist/content writer at Creately, online diagramming and collaboration tool. She is an avid reader, a budding writer and a passionate researcher who loves to write about all kinds of topics.

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Nine essential problem solving tools: The ultimate guide to finding a solution

October 26, 2023 by MindManager Blog

Problem solving may unfold differently depending on the industry, or even the department you work in. However, most agree that before you can fix any issue, you need to be clear on what it is, why it’s happening, and what your ideal long-term solution will achieve.

Understanding both the nature and the cause of a problem is the only way to figure out which actions will help you resolve it.

Given that most problem-solving processes are part inspiration and part perspiration, you’ll be more successful if you can reach for a problem solving tool that facilitates collaboration, encourages creative thinking, and makes it easier to implement the fix you devise.

The problem solving tools include three unique categories: problem solving diagrams, problem solving mind maps, and problem solving software solutions.

They include:

• Fishbone diagrams
• Strategy maps
• Mental maps
• Concept maps
• Layered process audit software
• Charting software
• MindManager

In this article, we’ve put together a roundup of versatile problem solving tools and software to help you and your team map out and repair workplace issues as efficiently as possible.

Let’s get started!

Problem solving diagrams

Mapping your way out of a problem is the simplest way to see where you are, and where you need to end up.

Not only do visual problem maps let you plot the most efficient route from Point A (dysfunctional situation) to Point B (flawless process), problem mapping diagrams make it easier to see:

• The root cause of a dilemma.
• The steps, resources, and personnel associated with each possible solution.
• The least time-consuming, most cost-effective options.

A visual problem solving process help to solidify understanding. Furthermore, it’s a great way for you and your team to transform abstract ideas into a practical, reconstructive plan.

Here are three examples of common problem mapping diagrams you can try with your team:

1. Fishbone diagrams

Fishbone diagrams are a common problem solving tool so-named because, once complete, they resemble the skeleton of a fish.

With the possible root causes of an issue (the ribs) branching off from either side of a spine line attached to the head (the problem), dynamic fishbone diagrams let you:

• Lay out a related set of possible reasons for an existing problem
• Investigate each possibility by breaking it out into sub-causes
• See how contributing factors relate to one another

Fishbone diagrams are also known as cause and effect or Ishikawa diagrams.

2. Flowcharts

A flowchart is an easy-to-understand diagram with a variety of applications. But you can use it to outline and examine how the steps of a flawed process connect.

Made up of a few simple symbols linked with arrows indicating workflow direction, flowcharts clearly illustrate what happens at each stage of a process – and how each event impacts other events and decisions.

3. Strategy maps

Frequently used as a strategic planning tool, strategy maps also work well as problem mapping diagrams. Based on a hierarchal system, thoughts and ideas can be arranged on a single page to flesh out a potential resolution.

Once you’ve got a few tactics you feel are worth exploring as possible ways to overcome a challenge, a strategy map will help you establish the best route to your problem-solving goal.

Problem solving mind maps

Problem solving mind maps are especially valuable in visualization. Because they facilitate the brainstorming process that plays a key role in both root cause analysis and the identification of potential solutions, they help make problems more solvable.

Mind maps are diagrams that represent your thinking. Since many people struggle taking or working with hand-written or typed notes, mind maps were designed to let you lay out and structure your thoughts visually so you can play with ideas, concepts, and solutions the same way your brain does.

By starting with a single notion that branches out into greater detail, problem solving mind maps make it easy to:

• Explain unfamiliar problems or processes in less time
• Share and elaborate on novel ideas
• Achieve better group comprehension that can lead to more effective solutions

Mind maps are a valuable problem solving tool because they’re geared toward bringing out the flexible thinking that creative solutions require. Here are three types of problem solving mind maps you can use to facilitate the brainstorming process.

4. Mental maps

A mental map helps you get your thoughts about what might be causing a workplace issue out of your head and onto a shared digital space.

Because mental maps mirror the way our brains take in and analyze new information, using them to describe your theories visually will help you and your team work through and test those thought models.

5. Idea maps

Idea maps let you take advantage of a wide assortment of colors and images to lay down and organize your scattered thought process. Idea maps are ideal brainstorming tools because they allow you to present and explore ideas about the best way to solve a problem collaboratively, and with a shared sense of enthusiasm for outside-the-box thinking.

6. Concept maps

Concept maps are one of the best ways to shape your thoughts around a potential solution because they let you create interlinked, visual representations of intricate concepts.

By laying out your suggested problem-solving process digitally – and using lines to form and define relationship connections – your group will be able to see how each piece of the solution puzzle connects with another.

Problem solving software solutions

Problem solving software is the best way to take advantage of multiple problem solving tools in one platform. While some software programs are geared toward specific industries or processes – like manufacturing or customer relationship management, for example – others, like MindManager , are purpose-built to work across multiple trades, departments, and teams.

Here are three problem-solving software examples.

7. Layered process audit software

Layered process audits (LPAs) help companies oversee production processes and keep an eye on the cost and quality of the goods they create. Dedicated LPA software makes problem solving easier for manufacturers because it helps them see where costly leaks are occurring and allows all levels of management to get involved in repairing those leaks.

8. Charting software

Charting software comes in all shapes and sizes to fit a variety of business sectors. Pareto charts, for example, combine bar charts with line graphs so companies can compare different problems or contributing factors to determine their frequency, cost, and significance. Charting software is often used in marketing, where a variety of bar charts and X-Y axis diagrams make it possible to display and examine competitor profiles, customer segmentation, and sales trends.

9. MindManager

No matter where you work, or what your problem-solving role looks like, MindManager is a problem solving software that will make your team more productive in figuring out why a process, plan, or project isn’t working the way it should.

Once you know why an obstruction, shortfall, or difficulty exists, you can use MindManager’s wide range of brainstorming and problem mapping diagrams to:

• Find the most promising way to correct the situation
• Activate your chosen solution, and
• Conduct regular checks to make sure your repair work is sustainable

MindManager is the ultimate problem solving software.

Not only is it versatile enough to use as your go-to system for puzzling out all types of workplace problems, MindManager’s built-in forecasting tools, timeline charts, and warning indicators let you plan, implement, and monitor your solutions.

By allowing your group to work together more effectively to break down problems, uncover solutions, and rebuild processes and workflows, MindManager’s versatile collection of problem solving tools will help make everyone on your team a more efficient problem solver.

Download a free trial today to get started!

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Why choose MindManager?

MindManager® helps individuals, teams, and enterprises bring greater clarity and structure to plans, projects, and processes. It provides visual productivity tools and mind mapping software to help take you and your organization to where you want to be.

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What are the 7 basic quality tools, and how can they change your business for the better?

Reading time: about 6 min

What are the 7 basic quality tools?

• Stratification
• Check sheet (tally sheet)
• Cause and effect diagram (fishbone or Ishikawa diagram)
• Pareto chart (80-20 rule)
• Scatter diagram
• Control chart (Shewhart chart)

The ability to identify and resolve quality-related issues quickly and efficiently is essential to anyone working in quality assurance or process improvement. But statistical quality control can quickly get complex and unwieldy for the average person, making training and quality assurance more difficult to scale. 

Thankfully, engineers have discovered that most quality control problems can be solved by following a few key fundamentals. These fundamentals are called the seven basic tools of quality. 

With these basic quality tools in your arsenal, you can easily manage the quality of your product or process, no matter what industry you serve.

Learn about these quality management tools and find templates to start using them quickly.

Where did the quality tools originate?

Kaoru Ishikawa, a Japanese professor of engineering, originally developed the seven quality tools (sometimes called the 7 QC tools) in the 1950s to help workers of various technical backgrounds implement effective quality control measures.

At the time, training programs in statistical quality control were complex and intimidating to workers with non-technical backgrounds. This made it difficult to standardize effective quality control across operations. Companies found that simplifying the training to user-friendly fundamentals—or seven quality tools—ensured better performance at scale

7 quality tools

1. stratification.

Stratification analysis is a quality assurance tool used to sort data, objects, and people into separate and distinct groups. Separating your data using stratification can help you determine its meaning, revealing patterns that might not otherwise be visible when it’s been lumped together. 

Whether you’re looking at equipment, products, shifts, materials, or even days of the week, stratification analysis lets you make sense of your data before, during, and after its collection.

To get the most out of the stratification process, consider which information about your data’s sources may affect the end results of your data analysis. Make sure to set up your data collection so that that information is included. 

2. Histogram

Quality professionals are often tasked with analyzing and interpreting the behavior of different groups of data in an effort to manage quality. This is where quality control tools like the histogram come into play. 

The histogram represents frequency distribution of data clearly and concisely amongst different groups of a sample, allowing you to quickly and easily identify areas of improvement within your processes. With a structure similar to a bar graph, each bar within a histogram represents a group, while the height of the bar represents the frequency of data within that group. 

Histograms are particularly helpful when breaking down the frequency of your data into categories such as age, days of the week, physical measurements, or any other category that can be listed in chronological or numerical order. 

3. Check sheet (or tally sheet)

Check sheets can be used to collect quantitative or qualitative data. When used to collect quantitative data, they can be called a tally sheet. A check sheet collects data in the form of check or tally marks that indicate how many times a particular value has occurred, allowing you to quickly zero in on defects or errors within your process or product, defect patterns, and even causes of specific defects.

With its simple setup and easy-to-read graphics, check sheets make it easy to record preliminary frequency distribution data when measuring out processes. This particular graphic can be used as a preliminary data collection tool when creating histograms, bar graphs, and other quality tools.

4. Cause-and-effect diagram (also known as a fishbone or Ishikawa diagram)

Introduced by Kaoru Ishikawa, the fishbone diagram helps users identify the various factors (or causes) leading to an effect, usually depicted as a problem to be solved. Named for its resemblance to a fishbone, this quality management tool works by defining a quality-related problem on the right-hand side of the diagram, with individual root causes and sub-causes branching off to its left.   

A fishbone diagram’s causes and subcauses are usually grouped into six main groups, including measurements, materials, personnel, environment, methods, and machines. These categories can help you identify the probable source of your problem while keeping your diagram structured and orderly.

5. Pareto chart (80-20 rule)

As a quality control tool, the Pareto chart operates according to the 80-20 rule. This rule assumes that in any process, 80% of a process’s or system’s problems are caused by 20% of major factors, often referred to as the “vital few.” The remaining 20% of problems are caused by 80% of minor factors. 

A combination of a bar and line graph, the Pareto chart depicts individual values in descending order using bars, while the cumulative total is represented by the line.

The goal of the Pareto chart is to highlight the relative importance of a variety of parameters, allowing you to identify and focus your efforts on the factors with the biggest impact on a specific part of a process or system. 

6. Scatter diagram

Out of the seven quality tools, the scatter diagram is most useful in depicting the relationship between two variables, which is ideal for quality assurance professionals trying to identify cause and effect relationships. 

With dependent values on the diagram’s Y-axis and independent values on the X-axis, each dot represents a common intersection point. When joined, these dots can highlight the relationship between the two variables. The stronger the correlation in your diagram, the stronger the relationship between variables.

Scatter diagrams can prove useful as a quality control tool when used to define relationships between quality defects and possible causes such as environment, activity, personnel, and other variables. Once the relationship between a particular defect and its cause has been established, you can implement focused solutions with (hopefully) better outcomes.

 7. Control chart (also called a Shewhart chart)

Named after Walter A. Shewhart, this quality improvement tool can help quality assurance professionals determine whether or not a process is stable and predictable, making it easy for you to identify factors that might lead to variations or defects. 

Control charts use a central line to depict an average or mean, as well as an upper and lower line to depict upper and lower control limits based on historical data. By comparing historical data to data collected from your current process, you can determine whether your current process is controlled or affected by specific variations.

Using a control chart can save your organization time and money by predicting process performance, particularly in terms of what your customer or organization expects in your final product.

Bonus: Flowcharts

Some sources will swap out stratification to instead include flowcharts as one of the seven basic QC tools. Flowcharts are most commonly used to document organizational structures and process flows, making them ideal for identifying bottlenecks and unnecessary steps within your process or system. 

Mapping out your current process can help you to more effectively pinpoint which activities are completed when and by whom, how processes flow from one department or task to another, and which steps can be eliminated to streamline your process. 

Learn how to create a process improvement plan in seven steps.

Lucidchart, a cloud-based intelligent diagramming application, is a core component of Lucid Software's Visual Collaboration Suite. This intuitive, cloud-based solution empowers teams to collaborate in real-time to build flowcharts, mockups, UML diagrams, customer journey maps, and more. Lucidchart propels teams forward to build the future faster. Lucid is proud to serve top businesses around the world, including customers such as Google, GE, and NBC Universal, and 99% of the Fortune 500. Lucid partners with industry leaders, including Google, Atlassian, and Microsoft. Since its founding, Lucid has received numerous awards for its products, business, and workplace culture. For more information, visit lucidchart.com.

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Streamlining Six Sigma Projects with The 7 QC Tools

Updated: September 26, 2023 by Ken Feldman

As in any tool chest, you can have dozens, if not hundreds, of different tools for all types of specialized tasks. Such is the case with Six Sigma. There are many tools available for your use depending on what you want to accomplish. However, like your home tool chest, there are a small set of basic tools that are your go-to tools you will use most often and on most projects. 

Let’s review the 7 QC tools that are most commonly used in Six Sigma , the benefits of those tools, and some best practices for using them.

Overview: What are the 7 QC tools? 

It is believed that the 7 QC tools were introduced by Kaoru Ishikawa in postwar Japan, inspired by the seven famous weapons of Benkei. Benkei was a Japanese warrior monk who armed himself with seven weapons and was on a personal quest to take 1,000 swords from samurai warriors who he believed were arrogant and unworthy.

Ishikawa was influenced by a series of lectures on statistical quality control given by Dr. W. Edwards Deming in 1950 to a group of Japanese scientists and engineers. Unfortunately, the complexity of the subject intimidated most workers, so Ishikawa focused primarily on a reduced set of tools that would suffice for most quality-related issues.

The 7 QC tools are:

• Check sheet 
• Fishbone diagram (cause and effect diagram, or Ishikawa diagram)
• Pareto chart
• Control chart
• Scatter diagram
• Stratification

Let’s explore each in a little more detail.

Check sheet: A form to collect and tally data for further analysis.

Image source:  techqualitypedia.com .

Fishbone diagram: Fishbone diagrams are used to drill down to find the root cause of a problem. As the name implies, the diagram looks like the bones of a fish, where each main bone represents a specific category of possible root cause, and the subsequent drilling down is shown as smaller and smaller bones.

Image source:  asq.org .

Histogram: This is a bar graph showing the frequency of a set of data, usually continuous data. The histogram allows you to see the center of the data, the range of the data, and the distribution of the data. It is a very useful snapshot. The downside is that you can’t see the sequence or order of the data.

Image source:  statisticsbyjim.com .

Pareto chart: This chart is based on the 80/20 principle that says 80% of your effect is caused by 20% of your causes. For example, 80% of your sales comes from 20% of your customers. Dr. Joseph Juran, who developed this chart, often referred to this principle as the vital few and trivial many . He later revised that to the vital few and useful many . The Pareto chart lists the causes in descending order of frequency or magnitude. It is used to prioritize what you should look at first to improve your process.

Image source:  www.automateexcel.com .

Control chart: A control chart is a statistical tool that looks at your process data over time for the purpose of distinguishing between special cause and common cause variation.

Image source:  www.spcforexcel.com .

Scatter diagram: These are also known as scatter plots. They’re used to show a graphical correlation between a set of paired data on an X and Y axis. It is the graphical representation of what you would use for regression analysis.

Image source: www.spcforexcel.com .

Stratification: This is a graph that shows data that has been stratified when the data comes from different sources. It is useful to view the data by certain strata such as shift, gender, geographic location, machines, or suppliers.

Image source: www.systems2win.com .

3 benefits of the 7 QC tools 

These seven tools are easy to understand and apply and will help you understand what is going on in your process. 

1. Easy 

These 7 QC tools are easy to understand and implement yet powerful in identifying root causes, in discriminating between types of variation, and as a visual description of your data. A picture is truly worth 10,000 words (or statistical calculations). 

2. Software-driven 

Gone are the days when you had to draw all of your graphs by hand. There are many simple and cost-effective software packages that will take your data and quickly produce graphs. 

3. 80/20 

The Pareto principle applies to the 7 QC tools as well. 80% of your quality issues can be addressed by using 20% of the most common tools.

Why are the 7 QC tools important to understand? 

The key thing to understand is when to use each tool — which one is appropriate for your specific situation?

Tools address different issues

The more familiar you are with these common tools, the quicker you’ll be able to select the right one to help you solve your problem or answer your question. The Fishbone diagram is used to search for root causes of your problem. A control chart is used to distinguish between common and special cause variation. A scatter diagram is used to look for correlation or relationship between an X and Y variable. 

Graphs don’t tell the whole story 

Graphs and diagrams are useful for providing an overview and directional indicator of your process, but statistical analysis will provide greater confidence than a graph alone. 

Flexibility 

These seven tools can be used for different types of data and across any type of function. Their flexibility makes them useful in myriad situations and industries, so becoming familiar with them can be a wise investment.

3 best practices when thinking about the 7 QC tools 

Use these tools for as many applications as is feasible. Keep it simple, and only use the more sophisticated and complex tools if you need the additional information and analysis. 

1. Have a clear idea of what question you’re trying to answer 

Since each of the tools can be used to answer different data and process questions, be sure you’ve clearly defined the question you’re trying to answer. 

2. Use them as your primary presentation  

Use the 7 QC tools and their accompanying graphs and diagrams as your primary presentation format. Reserve the statistical analysis for questions that go beyond what’s answered in the graphs.

3. Make sure they’re self-explanatory 

Be sure your graphs are succinct and self-explanatory so people can understand what you’re trying to tell them without the need for a long-winded explanation.  

Frequently Asked Questions (FAQ) about the 7 QC tools

What is meant by stratification .

If you collected production data throughout the day across all three shifts and five machines, you might want to stratify or separate your data and look at it by shift and by machine. This would allow you to understand whether there were any differences between the strata. This might indicate the source of a root cause or an opportunity to improve the other shifts if one is found to be doing better than the others. 

What are the 7 basic QC tools? 

Scatter diagrams, Pareto charts, control charts, histograms, stratification, fishbone diagrams and check sheets.

Do I have to draw the graphs and diagrams for the 7 QC tools by hand? 

With the use of current software and computer technology, you will rarely be required to create the graphs by hand. Still, it might be interesting to do it by hand once to fully appreciate the tools and software available to us.

Let’s review what’s in your tool belt 

The 7 QC tools are basic graphical representations of your data. They can be used to answer a wide variety of questions about your data and your process. Use them as your primary presentation format when talking about what your data is telling you. While they are not a complete list of tools, they should be robust enough to address many of your improvement issues.

The 7 QC tools, while basic, are foundational to the Six Sigma methodology and have stood the test of time. Their simplicity and versatility make them indispensable for professionals across industries. As businesses evolve and data becomes more integral to decision-making, the importance of these tools only grows. They bridge the gap between raw data and actionable insights, allowing teams to make informed decisions. Moreover, in today’s digital age, with the integration of AI and machine learning, these tools can be further enhanced to provide even deeper insights. However, the essence remains the same: understanding and improving processes through data visualization.

Key Points About The 7 QC Tools:

Origin and Influence: Introduced by Kaoru Ishikawa, inspired by Benkei’s seven weapons and influenced by Dr. W. Edwards Deming’s lectures on statistical quality control.

List of 7 QC Tools: Check sheet, Fishbone diagram, Histogram, Pareto chart, Control chart, Scatter diagram, and Stratification.

Benefits: These tools are easy to understand, software-driven, and adhere to the 80/20 principle, addressing 80% of quality issues with 20% of the most common tools.

Importance: They address different issues, provide an overview of processes, and offer flexibility across data types and functions.

Best Practices: Clearly define the question, use the tools as the primary presentation format, and ensure graphs are self-explanatory.

About the Author

Ken Feldman

Table of Contents

Introduction & Why use the 7 QC Tools?

The 7 QC tools help to analyze the data and are most helpful in problem-solving methods. It is the fundamental tool to improve our product and process quality by identifying and analyzing the problems.

As per the Deming chain to achieve the organizational goal, we must tackle the product & process-related problems, and analyze these problems we get help from 7 QC tools. These 7 QC tools give us the analytical and statistical competence to solve the problems .

What are 7 QC tools?

7 Basic Quality techniques

• Pareto Charts
• Cause and Effect Diagrams
• Check sheet
• Scatter Diagrams
• Control Charts
• Flow Charts

Pareto Chart

• Prioritize problems.
• Pareto Charts are used to apply the 80/20 rule of Joseph Juran which states that 80% of the problems are the result of 20% of the problems. A Pareto Chart can be used to identify 20% of route causes of problems.

How is it done?

• Create a preliminary list of problem classifications.
• Tally the occurrences in each problem classification.
• Arrange each classification in order from highest to lowest
• Construct the bar chart

• Pareto analysis helps graphically display results so the significant few problems emerge from the general background
• It tells you what to work on first

To know the detail of What Pareto Principle is?, How to Make Pareto in Excel?

Cause & Effect Analysis

• Graphical representation of the trial leading to the root cause of a problem
• It’s a diagram that demonstrates the relationship between Effects and the categories of their causes
• The Arrangement of the Diagram lets it look like a fishbone it is therefore also called a fish-bone diagram
• Decide which quality characteristic , outcome, or effect you want to examine (may use a Pareto chart)
• Backbone –draw a straight line
• Ribs – categories
• Medium-size bones –secondary causes
• Small bones – root causes

• Breaks problems down into bite-size pieces to find the root cause
• Fosters teamwork
• A common understanding of the factors causing the problem
• Road map to verify picture of the process
• Follows brainstorming relationship

To learn in detail How to create a cause and effect diagram (Fishbone diagram)?

• A Histogram is a bar graph
• To determine the spread or variation of a set of data points in a graphical form
• usually used to present frequency

• Collect data, 50-100 data point
• Determine the range of the data
• Calculate the size of the class interval
• Divide data points into classes Determine the class boundary
• Count # of data points in each class
• Draw the histogram
• Allows you to understand at a glance the variation that exists in a process
• The shape of the histogram will show process behavior
•  Often, it will tell you to dig deeper for otherwise unseen causes of variation.
• The shape and size of the dispersion will help identify otherwise hidden sources of variation
•  Used to determine the capability of a process
• The starting point for the improvement process

Check Sheet

• Tool for collecting and organizing measured or counted data
• Data collected can be used as input data for other quality tools
• Collect data in a systematic and organized manner
• To determine the source of the problem
• To facilitate the classification of data (stratification).

Scatter Diagram

• To identify the correlations that might exist between a quality characteristic and a factor that might be driving it
• A scatter diagram shows the correlation between two variables in a process.
• These variables could be Critical to Quality (CTQ) characteristic s and a factor affecting it two factors affecting a CTQ or two related quality characteristics.
•  Dots representing data points are scattered on the diagram.
• The extent to which the dots cluster together in a line across the diagram shows the strength.
• Decide which paired factors you want to examine. Both factors must be measurable on some incremental linear scale.
• Collect 30 to 100 paired data points.
• Find the highest and lowest value for both variables.
• Draw the vertical (y) and horizontal (x) axes of a graph.
• Plot the data
• Title the diagram

The shape that the cluster of dots takes will tell you something about the relationship between the two variables that you tested.

You may occasionally get scatter diagrams that look boomerang- or banana-shaped.

• To analyze the strength of the correlation, divide the scatter plot into two sections.
• Treat each half separately in your analysis
• Helps identify and test probable causes. 
• By knowing which elements of your process are related and how they are related, you will know what to control or what to vary to affect a quality characteristic.

Control Chart

• The primary purpose of a control chart is to predict expected product outcomes.
• Statistical tool, showing whether a process is in control or not.
• Taking samples of a process and detecting the possibility of the process being out of control

How does it Work?

• Define Upper limit, lower limit, and medium value
• Draw Chart.
• Gather values and draw them into the chart
• Predict process out of control and out of specification limits
• Distinguish between specific, identifiable causes of variation
• Can be used for statistical process control

Strategy for eliminating assignable-cause variation:

• Get timely data so that you see the effect of the assignable cause soon after it occurs.
• As soon as you see something that indicates that an assignable cause of variation has happened, search for the cause.
• Change tools to compensate for the assignable cause.

Strategy for reducing common-cause variation:

• Do not attempt to explain the difference between any of the values or data points produced by a stable system in control.
• Reducing common-cause variation usually requires making fundamental changes in your process
• Visual illustration of the sequence of operations required to complete a task.
• Schematic drawing of the process to measure or improve.
• The starting point for process improvement
• A potential weakness in the process is made visual.
• Picture the process as it should be.
• Way of representing a Procedure using simple symbols and arrows
• List major steps
• Write the process step inside each symbol
• Connect the Symbols with arrows showing the direction of the flow
• List sub-steps under each in the order they occur

• Identify process improvements
• Understand the process
• Shows duplicated effort and other non-value-added steps
• Clarify working relationships between people and organizations
• Target specific steps in the process for improvement.
• Simplest of all flowcharts
• Used for planning new processes or examining an existing one
• Keep people focused on the whole process
• Show what happens at each step in the process
• Show what happens when non-standard events occur
• Graphically display processes to identify redundancies and other wasted efforts

Benefits of all – Tool-wise

Related posts.

Cost of Quality | Quality engineer essential guide

Total Quality Management (TQM) Fundamentals

SIPOC – The complete guide

Root cause analysis

Control Charts & Types of control chart

7 Management and Planning Tools

7 Basic Quality Tools for Improvement | Templates Attached

The 7 basic quality tools are also known as ‘The Old Seven’ and ‘The First Seven.’ Kaoru Ishikawa, a professor at Tokyo University, is considered the father in quality and emphasized these quality tools.

These 7 basic tools are a set of graphical representations with statistical techniques. These are very helpful in solving queries related to quality. One can apply these tools after simple training. These tools helped Japan during the quality revolution.

Though these tools are old but are effective with the same popularity, quite interestingly, The Deming Chain (Reducing costs through process improvement), Six Sigma ,  Lean Project Management (waste reduction), etc., use these tools for improvement.

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Cause and Effect (Fish Bone) Diagrams

Flow charts, checksheets, pareto charts (80/20 rule), control charts (shewhart chart), scatter diagrams, when to use the 7 qc tools, faq’s about 7 qc tools, featured posts, what are basic quality tools.

Nowadays, several organizations make sure to use these tools. This helps to monitor and ultimately manage quality initiatives.

In today’s market, we see many types of controlling tools. But the old seven tools for quality control that are very common as a different tool is used for different problem-solving techniques. Hence, you have to juggle and find the right one for any particular issue on a project or process.

Being a manager , if you are familiar with and know well how to implement these tools, then life is quite easy – on the job, at least!

These tools are namely;

• Cause and Effect Diagrams
• Control Charts or Shewhart Chart

Let us see one by one in detailed scenarios;

The Father in ‘Quality Circles’ Professor K. Ishikawa, was the first to develop the Cause and Effect Analysis in the 1960s.

Brainstorming is done, and then a diagram is developed after all of the possible causes of a problem. In this way, a thorough analysis is done of any specifics of the situation. This method is best when dealing with complicated issues. It is also known as the ‘fishbone diagram’ as the final shape looks like a fish Skelton.

How to Draw Fish Bone Diagram

The following steps are to follow to get a fishbone diagram;

Step – 1:  Identify the problem you are trying to solve

Step – 2: Write it on the head of the fish

Step – 3:  Write the significant causes of the problem on the spine of the fish

Step – 4: Make categories of people, processes, materials, and equipment.

Step – 5:  Do brainstorming and find a group familiar with the problem

After you are done with all the above steps, analyze, modify if required in categories, and resolve the identified problem.

Make sure that there may many causes as your project like the client, management, environment, etc. The purpose of this fishbone diagram is to identify all the reasons behind an effect.

How to Use Fish Bone Diagram

Analyzing the diagram will help you to find out;

• To Identify the problem.
• To work out the major factors.
• Identify possible causes.

Note: You can download the template late from here. Cause and Effect Diagram Template  Downloaded and modified from  PowerPoint School .

Everyone is familiar with flow charts nowadays, as an organization’s hierarchy is the most common example of any flow chart. It gives the idea of reporting structure inside any organization.

But,  here, we are going to discuss flow charts that are used for process flows. This tool is the best to find out any bottleneck in a process flow. It shows how a process looks like and going through the steps. These charts also help to improve the process as well.

Benefits of Flow Chart

• The Simplest of all flowcharts
• Use for planning new processes or examining an existing one.
• To keep people focused on the overall scenario.
• Displays what happening at each step
• Indicates what happens when non-standard events occur
• Graphically display any wasted effort.

How to Draw a Flow Chart

• First of all, enlist all major steps.
• Write them across the top of the chart.
• Now enlist sub-steps under each in the order they occur.
• Write the process step inside each symbol.
• Connect the Symbols with arrows showing the direction of flow

Information is collected in quantitative form. We listed down all the important checkpoints in a tabular form in checksheets and kept updating the status. This helps to understand the progress pattern and helps to find the causes of defects.

As an example, the Project management institute may track the number of questions by the student, per domain, per minute is answering. This will help them make a test that can be solved in a time-effective manner and is logical. If students take a particular question to crack, they will be eliminated from the database to make it more exam-proficient.

In the same pattern, other processes can be dealt with for improvement.

Benefits of Check Sheets

• The best tool for collecting and organizing measured data
• The data collected helps for input for other quality tools.
• Collect data in a systematic and organized manner
• It helps to determine the source of the problem
• Helps to facilitate classification of data (stratification)

Karl Pearson introduced a bar graph representing the frequency distribution on each bar with time. A histogram helps to see the density of data. Ultimately this distribution leads to find the causes of major incidents.

Difference between Histogram & Pareto Charts

A histogram is a bar chart representing each attribute in a column and its frequency occurring as the column’s height.

A Pareto chart always has an arc for the cumulative percentage of the issues.  A Pareto helps to prioritize corrective actions.

Example of a Histogram

I will explain here all with an example, although it is quite clear from the histogram. You can see which is basically a delay analysis for the delay on a different project. If you see, data was collected from all over the project—segregated, keeping in view these top categories.

Once data was uploaded in Microsoft Excel, histogram peaks clearly show the procurement department’s major issue. It does not mean that no other one is hampering the situation but a clear indication that the top management will work on procurement issues.

Hence, a  histogram helps to prioritize the issues to present to the top management to deal with as soon as possible.

There is a limitation to draw a histogram. You should have enough data to draw and drive the results. It may mislead if you don’t have enough data.

A Pareto chart is a bar graph of data showing the largest number of frequencies to the smallest and a cumulative percentage, as shown in the graph below.

In this example, we look at the number of product defects in each of the listed categories.

When you look at the number of defects from the largest to the smallest occurrences, it is easy to prioritize improvement efforts.

How to Draw a Pareto Chart

To draw a Pareto chart is quite simple. The major part is collecting the right data. Pareto Chart Template

Steps to make a Pareto Chart

In the below chart, I need to find the major root cause of my quality issues related to the steel industry during the casting process.

You can download the TemplateTemplate and follow along the below steps;

• First of all, I collected the data from Quality logs.
• On MS Excel, I entered the data in high to low order.
• It also calculated the cumulative percentage.
• Then draw the graph from the insert section.
• Segregate the data as per their categories.
• Analyzed and found Vital few and Trivial many.

When to Use Pareto

Pareto Charts can be valuable when

• Analyzing data about the frequency of problems in any process
• You have many issues and need to find the most significant.
• Analyzing major causes
• To make a report for top management.

As explained in Histogram, the Pareto charts should not be mixed with those. Pareto charts have a cumulative percentage curve as well, categorized from high to low level.

Pareto charts are like a thumb rule where you want to get an instant result to take action.

A control chart is a statical chart, also called Shewhart Chart, named after Walter A. Shewhart.

This is one of the best tools to understand the fluctuation in a process over time. It is also called a run chart or a time series plot. It helps to find when and how o take action on set limits. Contro charts help to find the consistency of any process.

How to Draw a Control Chart?

First of all, we have to get the calculations for that particular process. You can download the Control Chart Template .

• Find the Mean – Average of those calculations (Target)
• Then, set Upper Control Limit – UCL.
• After that, set Upper Control Limit – UCL.

How to Conclude?

A process is out of control and needs immediate attention when;

• Suppose there is a single point outside the limits of UCL or LCL. Like in the above graph, 3rd point is at the upper control limit.
• A run of eight in a row is on the same side of the mean.
• Persistent patterns that suggest something unusual about your data and your process.

You can see in the below graph; a run can help you find consistency in your process. We set the Control Limits to not bother about all the stuff of our process.

A scatter diagram or scatter plot is also a statistical tool. It uses variables like dependent variables on Y-Axis and Independent Variable on X-axis plotted as dots on their common intersection points. By joining these plotted dots, we can get any relationship between these variables or an equation in format Y = F(X) + C, where is C is an arbitrary constant.

A scatter diagram is used to find the root cause of any problem, but only if there is any relationship.

You can download here the Scatter Chart Template .

The above graph explains a positive relationship with the time a village is getting populated. These relationships can be linear, exponential, quadratic, logarithmic, polynomial, etc. The variables can be positively or negatively related, defined by the equation’s slope derived from the scatter diagram.

It is not an easy question, but if your concepts are clear enough, you will enjoy using all the tools. let see how and where we can use these tools effectively

Flow Chart: Defining a Process 

Fishbone Diagram, Pareto Chart, and Control Charts: Measuring Phase

The Control Chart: Process Improvement Phase

The Scatter Diagram, Histogram, and Checksheets: Analyzing Phase

Question: What are the major causes of the fishbone diagram?

Answer: These are considered to be the major ones;

• Measurements
• Environment

Question: These tools are only for the process industry?

Answer: No, these apply where ever quality is required.

Question: How to know which tools are to use for a job?

Answer: Experience, and if you know about the tools insight well, you can easily understand the process.

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A great piece of content. I really liked the way you covered all the QC tools in an article. Thank you for your effort.

Thank you, Gerry

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The 7 Basic Quality Tools for Process Improvement

Enhancing Efficiency and Excellence in Business

Written By: Rei Takako Proofread By: MSI Staff

In the fast-paced and competitive world of business and manufacturing, the pursuit of excellence is not just an ambition but a necessity. Quality and efficiency are the cornerstones of this pursuit, and mastering the art of process improvement is crucial for any organization aiming to thrive. This is where the “7 Basic Quality Tools for Process Improvement” come into play, serving as essential instruments in the toolkit of quality management professionals.

Originating from the foundational practices of Total Quality Management (TQM) and Six Sigma, these tools are not just tools but beacons that guide businesses through the complexities of process optimization. They are revered for their simplicity, versatility, and profound impact. Whether it’s a multinational corporation or a small startup, these tools are universally applicable, transcending industry boundaries and scaling to fit various operational scopes.

The beauty of these tools lies in their ability to transform complex, abstract problems into tangible, manageable components. They enable teams to dissect issues, analyze data, and craft strategic solutions. By implementing these tools, organizations can identify and rectify inefficiencies and foster a culture of continuous improvement and strategic foresight.

The 7 Basic Quality Tools are more than methodologies; they build a resilient, agile, and quality-focused business environment. As we delve into each of these tools, it becomes evident how they collectively form a powerful arsenal for driving process improvement, enhancing product quality, and ensuring customer satisfaction in today’s dynamic business landscape.

1. Cause-and-Effect Diagram (Ishikawa or Fishbone Diagram)

The Cause-and-Effect Diagram , also known as the Ishikawa or Fishbone Diagram, is a fundamental tool in the quality management arsenal. It is named after its creator, Kaoru Ishikawa. Its primary function is to facilitate the systematic exploration of potential causes for a specific problem or issue. The diagram’s unique fishbone structure visually organizes the causes into various categories, making complex problem-solving more manageable and structured.

How it Works

The diagram typically starts with a problem statement, placed at the “head” of the fish. Branching out from this problem statement are the “bones,” representing different categories of potential causes. Common categories include Methods, Machinery, Materials, Manpower, Measurement, and Environment, though these can vary depending on the problem’s specific context.

Application in Business

In a business context, the Cause-and-Effect Diagram is a powerful brainstorming tool. It encourages teams to consider all possible aspects of a problem, avoiding a narrow focus on the most apparent causes. For example, suppose a manufacturing company is facing a decline in product quality. In that case, the diagram can help the team explore various potential causes such as equipment malfunctions (Machinery), untrained staff (Manpower), inconsistent raw materials (Materials), or even environmental factors like humidity or temperature (Environment).

Comprehensive Analysis : It ensures a thorough exploration of all potential causes of a problem, not just the most apparent ones.

Team Collaboration : It fosters team collaboration and collective problem-solving, as various team members can contribute insights from their expertise.

Visual Clarity : The visual nature of the diagram makes complex problems more understandable and manageable.

Root Cause Identification : It aids in identifying the root causes of problems, which is crucial for developing effective solutions.

Over-Complexity : The diagram can sometimes become overly complex if too many potential causes are considered.

Misidentification of Causes : There is a risk of incorrectly identifying causes, leading to ineffective solutions.

Overall, the Cause-and-Effect Diagram is a versatile and effective tool for identifying, categorizing, and exploring the potential causes of problems in business processes. Its ability to break down complex issues into manageable parts makes it an invaluable quality and process improvement tool.

2. Check Sheet (Tally Sheet)

The Check Sheet, often referred to as a Tally Sheet, is a fundamental data collection tool in quality management. Its simplicity belies its power in capturing, organizing, and analyzing data, which is crucial for any process improvement initiative.

Nature and Functionality

A Check Sheet is a structured, prepared form for collecting and analyzing data. This customizable tool allows users to record and compile data systematically in real-time. It typically consists of several rows and columns, where each row represents a category or specific item to be observed, and each column is often used to tally the occurrences or measure other relevant data.

Diverse Applications

In a business context, Check Sheets serve various purposes, such as tracking defects’ frequency, monitoring events’ occurrence over time, or even conducting simple surveys. For instance, a Check Sheet might be used in a manufacturing setting to record the types and frequencies of machine breakdowns. In customer service, it could track the nature and number of customer complaints.

Ease of Use : Its simple format makes it easy for anyone to use without extensive training.

Real-Time Data Collection : It facilitates on-the-spot recording, reducing the likelihood of errors and omissions.

Versatility : It can be customized for various data collection needs.

Visual Representation : When analyzed, the data from Check Sheets can be easily transformed into other quality tools like histograms or Pareto charts for further analysis.

Subjectivity in Data Recording : The effectiveness of a Check Sheet can be compromised if the data recording is not standardized or if there’s ambiguity in what is being recorded.

Limited to Quantitative Data : It primarily collects quantitative data, and might not be suitable for capturing more nuanced, qualitative information.

Implementation Tips

Clear Definition : Ensure each category or item on the Check Sheet is clearly defined to avoid ambiguity.

Training : Train staff on how to use the Check Sheet effectively.

Review and Adaptation : Regularly review the Check Sheet for its relevance and adapt as necessary to meet changing needs.

The Check Sheet is a versatile and straightforward tool in the quality management toolkit. When used effectively, it can provide invaluable insights into process performance, thereby laying the groundwork for more detailed analysis and improvement strategies.

3. Control Charts

Control Charts, a pivotal tool in statistical process control, are crucial in monitoring and improving process performance over time. Developed by Walter A. Shewhart in the 1920s, these charts are fundamental for ensuring that processes are stable and predictable, a key aspect in maintaining consistent quality.

Understanding Control Charts

A Control Chart is a graphical representation used to monitor the variability and performance of a process. It typically consists of points plotted in time order, a central line for the average, an upper control limit, and a lower control limit. These limits are calculated based on the data and represent the threshold at which the process is considered in or out of control.

Applications in Various Sectors

In manufacturing, Control Charts can track production processes to detect any deviations from the norm, such as variations in product dimensions. In service industries, they might monitor transaction times or service quality. Essentially, any process that can be measured over time can benefit from the use of Control Charts.

Early Detection of Problems : They help identify process variations before they escalate into more significant issues.

Process Optimization : By monitoring process stability, they aid in identifying opportunities for process improvement.

Reduced Variation : They assist in maintaining process consistency, which is crucial for quality assurance.

Data-Driven Decision Making : Decisions based on Control Charts are grounded in concrete data, enhancing the reliability of the decisions.

Potential Challenges

Misinterpretation of Data : Misunderstanding the data or control limits can lead to incorrect conclusions about process stability.

Setting Inappropriate Limits : Inaccurately set control limits can either fail to detect real problems or signal problems where none exist.

Over-Reliance on the Tool : While Control Charts are powerful, they need to be used as part of a broader quality management approach.

Effective Usage

Regular Monitoring : Regularly update and review the Control Charts to keep track of the process performance.

Training : Ensure that staff responsible for monitoring and interpreting the charts are adequately trained.

Integration with Other Tools : Combine Control Charts with other quality tools, like Pareto Charts or Cause-and-Effect Diagrams, for comprehensive process analysis.

Control Charts are indispensable in the quality management toolkit, especially for maintaining and improving the stability of processes. Their ability to provide visual and statistical analysis of process variations makes them essential for organizations striving for excellence in their operations.

4. Histogram

A Histogram is a statistical tool that plays a critical role in quality management and process improvement. It is essentially a bar chart representing the distribution of numerical data. By showing the frequency of data points within successive intervals, histograms provide a clear visual snapshot of data variation and distribution, which is vital for understanding and improving processes.

Fundamentals of Histograms

Histograms display data in columns, where each column represents a range or bin of values, and the height of the column indicates the frequency of data points within that range. This representation makes it easy to see patterns such as skewness, the presence of outliers, and whether data is evenly or unevenly distributed.

Application Across Fields

In manufacturing, histograms can be used to analyze the consistency of product dimensions, like the diameter of a batch of bearings. In service industries, they might be utilized to understand customer wait times or service delivery times. This versatile tool can be applied to any process where quantifiable data is collected.

Visualization of Data Distribution : Histograms clearly visualize how data is distributed across different ranges.

Identification of Patterns and Anomalies : They help in identifying common patterns, outliers, or anomalies in the data.

Facilitation of Comparative Analysis : Histograms allow for the comparison of data distributions over different periods or under different conditions.

Informing Process Improvements : Organizations can make informed decisions to streamline and improve processes by understanding data distribution.

Data Misinterpretation : Without proper statistical knowledge, there’s a risk of misinterpreting what the histogram represents.

Selection of Bins : Choosing inappropriate bin sizes or ranges can lead to misleading data representations.

Over-Simplification : While histograms are great for displaying distribution, they don’t show everything, such as the relationship between two variables.

Best Practices

Appropriate Bin Size : Carefully determine the range and size of bins to accurately reflect the distribution of data.

Contextual Analysis : Always analyze histogram data in the context of other relevant data and information.

Integration with Other Tools : Combine the insights from histograms with other quality tools like Control Charts and Pareto Charts for a more comprehensive analysis.

Histograms are invaluable in the quality manager’s toolkit, offering a simple yet effective means to visualize and analyze data distribution. This insight is essential for identifying potential areas for process improvement and ensuring that decisions are data-driven and focused on enhancing quality and efficiency.

5. Pareto Chart

The Pareto Chart is a vital tool in the quality management field, embodying the principle that a small number of causes are often responsible for a large percentage of the effect – a concept known as the Pareto Principle or the 80/20 rule. This tool is crucial for prioritizing problem-solving efforts and focusing on the changes that will have the greatest impact.

Overview of Pareto Charts

A Pareto Chart is a visual tool that combines both a bar graph and a line graph. The individual values are represented in descending order by bars, and the cumulative total is represented by the line. This format helps in identifying the most significant factors in a dataset.

Applications in Different Sectors

In manufacturing, Pareto Charts can be used to identify the most common sources of defects or production delays. In service industries, they can help pinpoint the most frequent types of customer complaints or service bottlenecks. They are valuable in any scenario where prioritizing resources and efforts can lead to significant improvements.

Focuses Efforts on Key Issues : By identifying the most critical factors contributing to a problem, Pareto Charts help in focusing efforts where they can make the most difference.

Data Visualization : They provide a clear visual representation of data, making it easier to understand and communicate issues.

Decision-making Aid : Pareto Charts are powerful tools for decision-makers, guiding them in allocating resources effectively.

Over-Simplification : While Pareto Charts are useful for highlighting major issues, they may oversimplify complex situations where multiple interrelated factors contribute to a problem.

Data Interpretation : Misinterpretation of data can lead to incorrect conclusions about what the key issues are.

Effective Implementation

Accurate Data Collection : Ensure the data used is accurate and comprehensive.

Regular Updates : Update the Pareto Chart regularly to reflect the current state of the process or problem.

Combine with Other Tools : Use in conjunction with other quality tools, such as the Cause-and-Effect Diagram, to delve deeper into the root causes of the issues identified.

Pareto Charts are essential in the toolkit of quality improvement methodologies. They guide teams to focus on the ‘vital few’ rather than the ‘trivial many’, ensuring that efforts and resources are channeled towards making the most impactful improvements. As a result, they play a pivotal role in enhancing the efficiency and effectiveness of business processes.

6. Scatter Diagram

The Scatter Diagram, also known as the scatter plot, is an indispensable tool in quality management and process improvement, primarily used for analyzing the relationship between two variables. This tool is crucial for identifying patterns, correlations, or potential cause-and-effect relationships, providing invaluable insights for decision-making and process optimization.

The Essence of Scatter Diagrams

A Scatter Diagram plots pairs of numerical data, with one variable on each axis, to look for a relationship or trend between them. Each point on the graph represents an individual data point. The pattern of these points can indicate whether and how strongly two variables are related.

Application Across Various Domains

Scatter Diagrams are widely used in numerous industries. In manufacturing, they might be used to examine the relationship between machine settings and product defects. They can analyze the correlation between advertising spend and sales revenue in marketing. These diagrams are versatile and can be applied to any scenario where understanding the relationship between two variables is beneficial.

Identifying Correlations : Scatter Diagrams are excellent for identifying whether a relationship exists between two variables and how strong that relationship is.

Visual Clarity : They provide a clear visual representation that can often reveal trends and patterns more effectively than numerical statistics.

Hypothesis Testing : They can be used to test hypotheses about cause-and-effect relationships.

Data Exploration : Scatter Diagrams are useful for initial exploration of data, guiding further detailed analysis.

Causation vs. Correlation : A common pitfall is mistaking correlation (how variables are related) for causation (one variable causing the other).

Over-interpretation : There’s a risk of over-interpreting the data without proper statistical knowledge.

Complex Relationships : They may not effectively reveal complex relationships involving more than two variables.

Use with Other Tools : For a comprehensive analysis, combine Scatter Diagrams with other tools like the Cause-and-Effect Diagram to explore underlying causes.

Statistical Expertise : Seek statistical expertise when necessary to interpret the diagrams correctly.

Continual Refinement : Continuously refine and explore data with additional scatter plots as more variables and data are considered.

In summary, Scatter Diagrams are a powerful tool in the quality improvement toolkit, providing clarity and insights into the relationships between variables. By effectively utilizing this tool, organizations can uncover hidden patterns and relationships, leading to more informed decisions and improved processes and products.

7. Flow Chart

The Flow Chart is a fundamental tool in process improvement, offering a clear and systematic visual representation of a process from start to finish. It is instrumental in understanding, analyzing, and optimizing complex processes, thereby playing a critical role in enhancing efficiency and effectiveness in various business operations.

Basics of Flow Charts

A Flow Chart is a diagram that depicts the steps of a process through a series of shapes connected by arrows. Each shape represents a different type of action or decision point, and the arrows show the flow and sequence of these steps. This tool is essential for mapping out processes in a way that is easy to understand and communicate.

Wide-Ranging Applications

In manufacturing, Flow Charts can be used to detail the production process, from raw material handling to finished product. In services, they can map out customer service protocols or administrative procedures. Their versatility makes them applicable in virtually any industry where processes need to be understood and improved.

Clarifies Complex Processes : Flow Charts make it easier to understand even the most complex operations by visually breaking down a process.

Identifies Redundancies and Inefficiencies : They help pinpoint redundant or inefficient steps, paving the way for streamlining and optimization.

Facilitates Communication : They are an excellent tool for communicating processes and changes within a team or organization.

Enhances Problem-Solving : By providing a clear view of the process, Flow Charts aid in identifying areas for improvement and problem-solving.

Over-Simplification : There’s a risk of oversimplifying complex processes, which might lead to missing out on important nuances.

Maintenance : As processes evolve, Flow Charts need to be regularly updated, which can be time-consuming.

Best Practices for Implementation

Detailing Each Step : Ensure that every step of the process is clearly and accurately represented.

Involving Stakeholders : Include input from all stakeholders involved in the process to get a comprehensive view.

Regular Review and Update : Continually review and update the Flow Chart to reflect any changes in the process.

Use in Conjunction with Other Tools : Combine Flow Charts with other quality tools, like Pareto Charts or Control Charts, for a holistic approach to process improvement.

Flow Charts are invaluable in the quality management toolkit, offering a structured and clear methodology for dissecting and understanding processes. Their use facilitates a deeper insight into operational workflows, aiding businesses in refining and optimizing their processes for greater efficiency and effectiveness.

The 7 Basic Quality Tools for Process Improvement are foundational in any quality improvement initiative. They are versatile and can be applied in various industries and processes. Organizations can significantly improve quality, efficiency, and overall performance by effectively utilizing these tools. These tools help in problem-solving and foster a culture of continuous improvement and strategic thinking within the organization.

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How to master the seven-step problem-solving process

In this episode of the McKinsey Podcast , Simon London speaks with Charles Conn, CEO of venture-capital firm Oxford Sciences Innovation, and McKinsey senior partner Hugo Sarrazin about the complexities of different problem-solving strategies.

Podcast transcript

Simon London: Hello, and welcome to this episode of the McKinsey Podcast , with me, Simon London. What’s the number-one skill you need to succeed professionally? Salesmanship, perhaps? Or a facility with statistics? Or maybe the ability to communicate crisply and clearly? Many would argue that at the very top of the list comes problem solving: that is, the ability to think through and come up with an optimal course of action to address any complex challenge—in business, in public policy, or indeed in life.

Looked at this way, it’s no surprise that McKinsey takes problem solving very seriously, testing for it during the recruiting process and then honing it, in McKinsey consultants, through immersion in a structured seven-step method. To discuss the art of problem solving, I sat down in California with McKinsey senior partner Hugo Sarrazin and also with Charles Conn. Charles is a former McKinsey partner, entrepreneur, executive, and coauthor of the book Bulletproof Problem Solving: The One Skill That Changes Everything [John Wiley & Sons, 2018].

Charles and Hugo, welcome to the podcast. Thank you for being here.

Hugo Sarrazin: Our pleasure.

Charles Conn: It’s terrific to be here.

Simon London: Problem solving is a really interesting piece of terminology. It could mean so many different things. I have a son who’s a teenage climber. They talk about solving problems. Climbing is problem solving. Charles, when you talk about problem solving, what are you talking about?

Charles Conn: For me, problem solving is the answer to the question “What should I do?” It’s interesting when there’s uncertainty and complexity, and when it’s meaningful because there are consequences. Your son’s climbing is a perfect example. There are consequences, and it’s complicated, and there’s uncertainty—can he make that grab? I think we can apply that same frame almost at any level. You can think about questions like “What town would I like to live in?” or “Should I put solar panels on my roof?”

You might think that’s a funny thing to apply problem solving to, but in my mind it’s not fundamentally different from business problem solving, which answers the question “What should my strategy be?” Or problem solving at the policy level: “How do we combat climate change?” “Should I support the local school bond?” I think these are all part and parcel of the same type of question, “What should I do?”

I’m a big fan of structured problem solving. By following steps, we can more clearly understand what problem it is we’re solving, what are the components of the problem that we’re solving, which components are the most important ones for us to pay attention to, which analytic techniques we should apply to those, and how we can synthesize what we’ve learned back into a compelling story. That’s all it is, at its heart.

I think sometimes when people think about seven steps, they assume that there’s a rigidity to this. That’s not it at all. It’s actually to give you the scope for creativity, which often doesn’t exist when your problem solving is muddled.

Simon London: You were just talking about the seven-step process. That’s what’s written down in the book, but it’s a very McKinsey process as well. Without getting too deep into the weeds, let’s go through the steps, one by one. You were just talking about problem definition as being a particularly important thing to get right first. That’s the first step. Hugo, tell us about that.

Hugo Sarrazin: It is surprising how often people jump past this step and make a bunch of assumptions. The most powerful thing is to step back and ask the basic questions—“What are we trying to solve? What are the constraints that exist? What are the dependencies?” Let’s make those explicit and really push the thinking and defining. At McKinsey, we spend an enormous amount of time in writing that little statement, and the statement, if you’re a logic purist, is great. You debate. “Is it an ‘or’? Is it an ‘and’? What’s the action verb?” Because all these specific words help you get to the heart of what matters.

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Simon London: So this is a concise problem statement.

Hugo Sarrazin: Yeah. It’s not like “Can we grow in Japan?” That’s interesting, but it is “What, specifically, are we trying to uncover in the growth of a product in Japan? Or a segment in Japan? Or a channel in Japan?” When you spend an enormous amount of time, in the first meeting of the different stakeholders, debating this and having different people put forward what they think the problem definition is, you realize that people have completely different views of why they’re here. That, to me, is the most important step.

Charles Conn: I would agree with that. For me, the problem context is critical. When we understand “What are the forces acting upon your decision maker? How quickly is the answer needed? With what precision is the answer needed? Are there areas that are off limits or areas where we would particularly like to find our solution? Is the decision maker open to exploring other areas?” then you not only become more efficient, and move toward what we call the critical path in problem solving, but you also make it so much more likely that you’re not going to waste your time or your decision maker’s time.

How often do especially bright young people run off with half of the idea about what the problem is and start collecting data and start building models—only to discover that they’ve really gone off half-cocked.

Hugo Sarrazin: Yeah.

Charles Conn: And in the wrong direction.

Simon London: OK. So step one—and there is a real art and a structure to it—is define the problem. Step two, Charles?

Charles Conn: My favorite step is step two, which is to use logic trees to disaggregate the problem. Every problem we’re solving has some complexity and some uncertainty in it. The only way that we can really get our team working on the problem is to take the problem apart into logical pieces.

What we find, of course, is that the way to disaggregate the problem often gives you an insight into the answer to the problem quite quickly. I love to do two or three different cuts at it, each one giving a bit of a different insight into what might be going wrong. By doing sensible disaggregations, using logic trees, we can figure out which parts of the problem we should be looking at, and we can assign those different parts to team members.

Simon London: What’s a good example of a logic tree on a sort of ratable problem?

Charles Conn: Maybe the easiest one is the classic profit tree. Almost in every business that I would take a look at, I would start with a profit or return-on-assets tree. In its simplest form, you have the components of revenue, which are price and quantity, and the components of cost, which are cost and quantity. Each of those can be broken out. Cost can be broken into variable cost and fixed cost. The components of price can be broken into what your pricing scheme is. That simple tree often provides insight into what’s going on in a business or what the difference is between that business and the competitors.

If we add the leg, which is “What’s the asset base or investment element?”—so profit divided by assets—then we can ask the question “Is the business using its investments sensibly?” whether that’s in stores or in manufacturing or in transportation assets. I hope we can see just how simple this is, even though we’re describing it in words.

When I went to work with Gordon Moore at the Moore Foundation, the problem that he asked us to look at was “How can we save Pacific salmon?” Now, that sounds like an impossible question, but it was amenable to precisely the same type of disaggregation and allowed us to organize what became a 15-year effort to improve the likelihood of good outcomes for Pacific salmon.

Simon London: Now, is there a danger that your logic tree can be impossibly large? This, I think, brings us onto the third step in the process, which is that you have to prioritize.

Charles Conn: Absolutely. The third step, which we also emphasize, along with good problem definition, is rigorous prioritization—we ask the questions “How important is this lever or this branch of the tree in the overall outcome that we seek to achieve? How much can I move that lever?” Obviously, we try and focus our efforts on ones that have a big impact on the problem and the ones that we have the ability to change. With salmon, ocean conditions turned out to be a big lever, but not one that we could adjust. We focused our attention on fish habitats and fish-harvesting practices, which were big levers that we could affect.

People spend a lot of time arguing about branches that are either not important or that none of us can change. We see it in the public square. When we deal with questions at the policy level—“Should you support the death penalty?” “How do we affect climate change?” “How can we uncover the causes and address homelessness?”—it’s even more important that we’re focusing on levers that are big and movable.

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Simon London: Let’s move swiftly on to step four. You’ve defined your problem, you disaggregate it, you prioritize where you want to analyze—what you want to really look at hard. Then you got to the work plan. Now, what does that mean in practice?

Hugo Sarrazin: Depending on what you’ve prioritized, there are many things you could do. It could be breaking the work among the team members so that people have a clear piece of the work to do. It could be defining the specific analyses that need to get done and executed, and being clear on time lines. There’s always a level-one answer, there’s a level-two answer, there’s a level-three answer. Without being too flippant, I can solve any problem during a good dinner with wine. It won’t have a whole lot of backing.

Simon London: Not going to have a lot of depth to it.

Hugo Sarrazin: No, but it may be useful as a starting point. If the stakes are not that high, that could be OK. If it’s really high stakes, you may need level three and have the whole model validated in three different ways. You need to find a work plan that reflects the level of precision, the time frame you have, and the stakeholders you need to bring along in the exercise.

Charles Conn: I love the way you’ve described that, because, again, some people think of problem solving as a linear thing, but of course what’s critical is that it’s iterative. As you say, you can solve the problem in one day or even one hour.

Charles Conn: We encourage our teams everywhere to do that. We call it the one-day answer or the one-hour answer. In work planning, we’re always iterating. Every time you see a 50-page work plan that stretches out to three months, you know it’s wrong. It will be outmoded very quickly by that learning process that you described. Iterative problem solving is a critical part of this. Sometimes, people think work planning sounds dull, but it isn’t. It’s how we know what’s expected of us and when we need to deliver it and how we’re progressing toward the answer. It’s also the place where we can deal with biases. Bias is a feature of every human decision-making process. If we design our team interactions intelligently, we can avoid the worst sort of biases.

Simon London: Here we’re talking about cognitive biases primarily, right? It’s not that I’m biased against you because of your accent or something. These are the cognitive biases that behavioral sciences have shown we all carry around, things like anchoring, overoptimism—these kinds of things.

Both: Yeah.

Charles Conn: Availability bias is the one that I’m always alert to. You think you’ve seen the problem before, and therefore what’s available is your previous conception of it—and we have to be most careful about that. In any human setting, we also have to be careful about biases that are based on hierarchies, sometimes called sunflower bias. I’m sure, Hugo, with your teams, you make sure that the youngest team members speak first. Not the oldest team members, because it’s easy for people to look at who’s senior and alter their own creative approaches.

Hugo Sarrazin: It’s helpful, at that moment—if someone is asserting a point of view—to ask the question “This was true in what context?” You’re trying to apply something that worked in one context to a different one. That can be deadly if the context has changed, and that’s why organizations struggle to change. You promote all these people because they did something that worked well in the past, and then there’s a disruption in the industry, and they keep doing what got them promoted even though the context has changed.

Simon London: Right. Right.

Hugo Sarrazin: So it’s the same thing in problem solving.

Charles Conn: And it’s why diversity in our teams is so important. It’s one of the best things about the world that we’re in now. We’re likely to have people from different socioeconomic, ethnic, and national backgrounds, each of whom sees problems from a slightly different perspective. It is therefore much more likely that the team will uncover a truly creative and clever approach to problem solving.

Simon London: Let’s move on to step five. You’ve done your work plan. Now you’ve actually got to do the analysis. The thing that strikes me here is that the range of tools that we have at our disposal now, of course, is just huge, particularly with advances in computation, advanced analytics. There’s so many things that you can apply here. Just talk about the analysis stage. How do you pick the right tools?

Charles Conn: For me, the most important thing is that we start with simple heuristics and explanatory statistics before we go off and use the big-gun tools. We need to understand the shape and scope of our problem before we start applying these massive and complex analytical approaches.

Simon London: Would you agree with that?

Hugo Sarrazin: I agree. I think there are so many wonderful heuristics. You need to start there before you go deep into the modeling exercise. There’s an interesting dynamic that’s happening, though. In some cases, for some types of problems, it is even better to set yourself up to maximize your learning. Your problem-solving methodology is test and learn, test and learn, test and learn, and iterate. That is a heuristic in itself, the A/B testing that is used in many parts of the world. So that’s a problem-solving methodology. It’s nothing different. It just uses technology and feedback loops in a fast way. The other one is exploratory data analysis. When you’re dealing with a large-scale problem, and there’s so much data, I can get to the heuristics that Charles was talking about through very clever visualization of data.

You test with your data. You need to set up an environment to do so, but don’t get caught up in neural-network modeling immediately. You’re testing, you’re checking—“Is the data right? Is it sound? Does it make sense?”—before you launch too far.

Simon London: You do hear these ideas—that if you have a big enough data set and enough algorithms, they’re going to find things that you just wouldn’t have spotted, find solutions that maybe you wouldn’t have thought of. Does machine learning sort of revolutionize the problem-solving process? Or are these actually just other tools in the toolbox for structured problem solving?

Charles Conn: It can be revolutionary. There are some areas in which the pattern recognition of large data sets and good algorithms can help us see things that we otherwise couldn’t see. But I do think it’s terribly important we don’t think that this particular technique is a substitute for superb problem solving, starting with good problem definition. Many people use machine learning without understanding algorithms that themselves can have biases built into them. Just as 20 years ago, when we were doing statistical analysis, we knew that we needed good model definition, we still need a good understanding of our algorithms and really good problem definition before we launch off into big data sets and unknown algorithms.

Simon London: Step six. You’ve done your analysis.

Charles Conn: I take six and seven together, and this is the place where young problem solvers often make a mistake. They’ve got their analysis, and they assume that’s the answer, and of course it isn’t the answer. The ability to synthesize the pieces that came out of the analysis and begin to weave those into a story that helps people answer the question “What should I do?” This is back to where we started. If we can’t synthesize, and we can’t tell a story, then our decision maker can’t find the answer to “What should I do?”

Simon London: But, again, these final steps are about motivating people to action, right?

Charles Conn: Yeah.

Simon London: I am slightly torn about the nomenclature of problem solving because it’s on paper, right? Until you motivate people to action, you actually haven’t solved anything.

Charles Conn: I love this question because I think decision-making theory, without a bias to action, is a waste of time. Everything in how I approach this is to help people take action that makes the world better.

Simon London: Hence, these are absolutely critical steps. If you don’t do this well, you’ve just got a bunch of analysis.

Charles Conn: We end up in exactly the same place where we started, which is people speaking across each other, past each other in the public square, rather than actually working together, shoulder to shoulder, to crack these important problems.

Simon London: In the real world, we have a lot of uncertainty—arguably, increasing uncertainty. How do good problem solvers deal with that?

Hugo Sarrazin: At every step of the process. In the problem definition, when you’re defining the context, you need to understand those sources of uncertainty and whether they’re important or not important. It becomes important in the definition of the tree.

You need to think carefully about the branches of the tree that are more certain and less certain as you define them. They don’t have equal weight just because they’ve got equal space on the page. Then, when you’re prioritizing, your prioritization approach may put more emphasis on things that have low probability but huge impact—or, vice versa, may put a lot of priority on things that are very likely and, hopefully, have a reasonable impact. You can introduce that along the way. When you come back to the synthesis, you just need to be nuanced about what you’re understanding, the likelihood.

Often, people lack humility in the way they make their recommendations: “This is the answer.” They’re very precise, and I think we would all be well-served to say, “This is a likely answer under the following sets of conditions” and then make the level of uncertainty clearer, if that is appropriate. It doesn’t mean you’re always in the gray zone; it doesn’t mean you don’t have a point of view. It just means that you can be explicit about the certainty of your answer when you make that recommendation.

Simon London: So it sounds like there is an underlying principle: “Acknowledge and embrace the uncertainty. Don’t pretend that it isn’t there. Be very clear about what the uncertainties are up front, and then build that into every step of the process.”

Hugo Sarrazin: Every step of the process.

Simon London: Yeah. We have just walked through a particular structured methodology for problem solving. But, of course, this is not the only structured methodology for problem solving. One that is also very well-known is design thinking, which comes at things very differently. So, Hugo, I know you have worked with a lot of designers. Just give us a very quick summary. Design thinking—what is it, and how does it relate?

Hugo Sarrazin: It starts with an incredible amount of empathy for the user and uses that to define the problem. It does pause and go out in the wild and spend an enormous amount of time seeing how people interact with objects, seeing the experience they’re getting, seeing the pain points or joy—and uses that to infer and define the problem.

Simon London: Problem definition, but out in the world.

Hugo Sarrazin: With an enormous amount of empathy. There’s a huge emphasis on empathy. Traditional, more classic problem solving is you define the problem based on an understanding of the situation. This one almost presupposes that we don’t know the problem until we go see it. The second thing is you need to come up with multiple scenarios or answers or ideas or concepts, and there’s a lot of divergent thinking initially. That’s slightly different, versus the prioritization, but not for long. Eventually, you need to kind of say, “OK, I’m going to converge again.” Then you go and you bring things back to the customer and get feedback and iterate. Then you rinse and repeat, rinse and repeat. There’s a lot of tactile building, along the way, of prototypes and things like that. It’s very iterative.

Simon London: So, Charles, are these complements or are these alternatives?

Charles Conn: I think they’re entirely complementary, and I think Hugo’s description is perfect. When we do problem definition well in classic problem solving, we are demonstrating the kind of empathy, at the very beginning of our problem, that design thinking asks us to approach. When we ideate—and that’s very similar to the disaggregation, prioritization, and work-planning steps—we do precisely the same thing, and often we use contrasting teams, so that we do have divergent thinking. The best teams allow divergent thinking to bump them off whatever their initial biases in problem solving are. For me, design thinking gives us a constant reminder of creativity, empathy, and the tactile nature of problem solving, but it’s absolutely complementary, not alternative.

Simon London: I think, in a world of cross-functional teams, an interesting question is do people with design-thinking backgrounds really work well together with classical problem solvers? How do you make that chemistry happen?

Hugo Sarrazin: Yeah, it is not easy when people have spent an enormous amount of time seeped in design thinking or user-centric design, whichever word you want to use. If the person who’s applying classic problem-solving methodology is very rigid and mechanical in the way they’re doing it, there could be an enormous amount of tension. If there’s not clarity in the role and not clarity in the process, I think having the two together can be, sometimes, problematic.

The second thing that happens often is that the artifacts the two methodologies try to gravitate toward can be different. Classic problem solving often gravitates toward a model; design thinking migrates toward a prototype. Rather than writing a big deck with all my supporting evidence, they’ll bring an example, a thing, and that feels different. Then you spend your time differently to achieve those two end products, so that’s another source of friction.

Now, I still think it can be an incredibly powerful thing to have the two—if there are the right people with the right mind-set, if there is a team that is explicit about the roles, if we’re clear about the kind of outcomes we are attempting to bring forward. There’s an enormous amount of collaborativeness and respect.

Simon London: But they have to respect each other’s methodology and be prepared to flex, maybe, a little bit, in how this process is going to work.

Hugo Sarrazin: Absolutely.

Simon London: The other area where, it strikes me, there could be a little bit of a different sort of friction is this whole concept of the day-one answer, which is what we were just talking about in classical problem solving. Now, you know that this is probably not going to be your final answer, but that’s how you begin to structure the problem. Whereas I would imagine your design thinkers—no, they’re going off to do their ethnographic research and get out into the field, potentially for a long time, before they come back with at least an initial hypothesis.

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Hugo Sarrazin: That is a great callout, and that’s another difference. Designers typically will like to soak into the situation and avoid converging too quickly. There’s optionality and exploring different options. There’s a strong belief that keeps the solution space wide enough that you can come up with more radical ideas. If there’s a large design team or many designers on the team, and you come on Friday and say, “What’s our week-one answer?” they’re going to struggle. They’re not going to be comfortable, naturally, to give that answer. It doesn’t mean they don’t have an answer; it’s just not where they are in their thinking process.

Simon London: I think we are, sadly, out of time for today. But Charles and Hugo, thank you so much.

Charles Conn: It was a pleasure to be here, Simon.

Hugo Sarrazin: It was a pleasure. Thank you.

Simon London: And thanks, as always, to you, our listeners, for tuning into this episode of the McKinsey Podcast . If you want to learn more about problem solving, you can find the book, Bulletproof Problem Solving: The One Skill That Changes Everything , online or order it through your local bookstore. To learn more about McKinsey, you can of course find us at McKinsey.com.

Charles Conn is CEO of Oxford Sciences Innovation and an alumnus of McKinsey’s Sydney office. Hugo Sarrazin is a senior partner in the Silicon Valley office, where Simon London, a member of McKinsey Publishing, is also based.

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TECHIEQUALITY

7qc tools for problem solving | what are 7 qc tools.

7QC Tools for Problem Solving techniques are generally used in manufacturing, Non-manufacturing industries, and service sectors to resolve problems.

Download 7-QC Tools Template/ Format

Definition and History:-

The 7QC Tools (Also Known as “Seven Basic Tools of Quality”) originated in Japan. First emphasized by Kaoru Ishikawa, a professor of engineering at Tokyo University and the father of “quality circles”. These tools are used to solve critical quality-related issues. You can use the 7 basic tools of quality to help understand and solve problems or defects in any industry. With the help of Excel, you can plot the graphs / Diagrams to resolve the daily quality problems. I will help you to understand the basic ideas and knowledge of 7QC Tools and their usage.

For solving problems seven QC tools are used Pareto Chart, Cause & Effect Diagram, Histogram, Control Charts, Scatter Diagrams, Graphs/Process Flow Diagram, and Check Sheets. All these tools are important tools used widely in the manufacturing field to monitor the overall operation and continuous process improvement. seven QC tools are used to find out the Root cause of the problem and implement the action plan to improve the process efficiency.

7QC tools are:-

• Pareto Chart
• Cause and effects diagram
• Scatter Diagram
• Control Chart
• Check Sheet
• PFD(Process Flow diagram)/Graphs

  Benefits of 7QC Tools:-

• Improve management decisions.
• Simple and easy for implementation
• Continuous quality improvement
• Quick results
• Enhances customer satisfaction through improved quality product
• Reduce cycle time and improve efficiency
• Control cost of poor quality / Cost of quality
• Reduce defects and optimize the production
• Reduce variations and improve the quality of Products
• Encouragement of teamwork and confidence
• Enhancement of customer focus.

Pareto Chart:-

A Pareto Chart is named after the Italian Economist Vilfredo Pareto. It is a type of chart that contains both bars and a line graph, where the individual values are represented in the bar graph in descending order (largest to smallest value) and the cumulative percentage is represented in the line graph.

Click here to learn “How to Plot Pareto Chart In Excel”.

Understanding the Pareto Chart principle (The 80/20 rule):  

The Pareto principle is also known as the 80/20 rule derived from the Italian Economist Vilfredo,

The principle is understood as –

20% of the input creates 80% of the results

80 % of the effects come from 20% of the causes.

In the above Pareto Chart[Figure-1], we can see the cumulative% in the line graph, According to the Pareto Chart principle 80/20 rule, the 80% cumulative in the line graph is filling under the low hardness, which means BH, Damage, SH and Low hardness defers are coving the 80% of contribution over total types of defects. And those 80 % of contributions were due to the 20% caused.

  Histogram:-

The histogram is one of the 7QC tools, which is the most commonly used graph to show frequency distribution.

Helps summarize data from a process that has been collected over a period of time.

Click here to know the “How to Plot Histogram in Excel:

Fish-bone  Diagram/Cause and Effects /Ishikawa Diagram:-

The cause and Effects Diagram looks like a fish that’s why it’s called Fish-bone Diagram, also called the Ishikawa diagram.

It’s a visualization tool for categorizing the potential causes of a problem in order to identify its root causes.

CFT members are identifying the potential cause through the Brainstorming process of individuals and together.

  The Potential cause is related w.r.t below as

• Environment
• Measurement

Scatter diagram:-

The scatter diagram graphs pairs of variable data, with one variable on each axis, to look for a relationship between them. If the variables correlate, the points will fall along a line or curve. The better the correlation, the more points will strongly cluster to the line. It generally gives the idea of the correlation between the variables.

In the above figure-4, the positive and Negative correlation is only due to the direction, and in both the correlation, points are clustered to the line but in the last figure in figure-4, Points are not clustered to the line but spread over the X and Y-axis.  

Control Chart:-

A line on a control chart is used as a basis for judging the stability of a process. If the observed points are beyond a control limit then it is evidence that special causes are affecting the process.

Control Charts can be used to monitor or evaluate a process.

There are basically two types of control charts, those for variable data and those for attributes data.

Click here to learn more about the Control Chart and Statistical Process Control.  

Benefits: -Higher Quality, Lower Unit Cost, Higher effective Capability, etc.

Selection of Control Charts based on Attribute / Variable Type Data:-

Calculation of Average and Range Charts-

Click here to know the details.

The formula of the Attributes Control Chart:-

Click here to learn the formula and calculation.

Nomenclature of Control Chart:-

Check Sheet:-

Check Sheet is a simple document used for collecting data in real time. Variable or Attribute type data is collected through a Check sheet. A check sheet generally helps to make the decision on the basis of a fact and to collect the data for analysis and evaluation.

Sample check Sheet:-

Process Flow diagram/Graphs:-

A process flow diagram is a diagram used to indicate the general flow of plant processes and equipment.

The 7QC tools are the most commonly used tool in the industry for improvement, With the help of the 7QC tools you can understand the process/activities, analyze the data, and interpret the result/graph/output.

Which are the 7 QC tools?

The seven QC tools are

• Fishbone diagram
• PFD(Process Flow diagram)/Graphs /Stratification

Useful Article:

why why analysis methodology | 5-why analysis step by step guide

Rework vs Repair |IATF Requirement for Control of Reworked/ Repaired Product

How to plot the Run Chart in Minitab

Run Chart Example | Concept & Interpretation of Result with Case Study | Industrial Example:

Thank you for reading..keep visiting Techiequality.Com

I hope the above article “7QC Tools for Problem Solving” is useful to you…

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The Author is an Expert in Quality Management System, Operation Management, Business Excellence, Process Excellence, IATF 16949, ISO 9001, ISO 14001, ISO 45001, ISO 17025, TQM, TPM & QA. He is Certified as an IA for ISO 9001, IATF 16949, ISO 14001, ISO 17025 & ISO 45001 Standard.

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Productivity

7 qc tools for quality improvement.

*Optional training delivery method for this course:  Remote Online Training (ROT) or Face-to-Face (F2F) ! 

COURSE DESCRIPTION

7 QC Tools For Quality Improvement course provides an in-depth study of the 7 Basic QC (Quality Control) Tools. They are scientific management tools, used worldwide by almost every organizations to collect and analyze data/facts for the purpose of quality improvements.  The 7 QC Tools are simple tools, low-cost and easy-to-use; but they are powerful tools that forms the critical foundation for all problem solving and quality improvement activities.   Once mastered, these tools will serve as the solid foundation for individuals’ and team’s root cause analyses, problem solving sessions, and continuous improvement projects.

Manufacturing and service industry professionals, quality technicians and auditors, and industrial engineers will all benefit from understanding these tools.  In addition to the foundational principles and concepts, this training examines practical real-world applications of the 7 QC Tools in the workplace. Participants will be provided with value-added MS Excel templates. These templates will help construct those QC Tools fast, easy and accurately.

This course is a MUST for all Quality Assurance staff such as Quality Engineers, Technicians, QC Leaders and also serve as a foundational course for Data Analysis & Lean Six Sigma practitioners.

CERTIFICATION

No certification.

Certificate of Achievement (for those who scored 80% and above for post-test) or Certificate of Attendance.

LEARNING OUTCOMES

At the end of the course, participants will be able to:

• Describe the benefits and power of each of the 7 QC Tools and its applications.
• Construct the QC tools accurately and fast using Microsoft Excel templates.
• Interpret , analyze and make accurate quality improvement decisions using the correct tools
• Perform data analysis and process monitoring using the QC tools
• Solve problems systematically and effectively.
• Experience the Quality Control Circle (QCC) team dynamics in solving problems.
• Make convincing presentations with the data collected from their workplace.

COURSE OUTLINE

Please see eBrochure above for more information.

To register, please Whatsapp : +60-19-502 2718  or email us at [email protected]

Course Features

• Skill level All level
• Language English
• Students 1632
• Assessments Yes

You May Like

Root cause analysis (rca).

Quality Control Circle (QCC) / Innovative & Creative Circle (ICC)

KAIZEN Strategies for Quality and Productivity Improvement

Measurement System Analysis (MSA)

Statistical Process Control -Level 1 (SPC1)

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N7 (New 7 Tools)

The N7 tools are problem-solving techniques developed by Japanese quality management experts in the 1970s. These tools were created to complement the 7 Basic Quality Tools (7QC Tools), focusing on addressing more complex and data-intensive problems.

• Affinity Diagram: A tool that organizes many ideas, issues, or data points into related groups or themes for better analysis and understanding.
• Relations Diagram: Also known as Interrelationship Digraph, it is a tool used to identify the cause-and-effect relationships among different factors or issues.
• Tree Diagram: A tool that breaks down broad objectives or problems into smaller, more manageable components or tasks, creating a hierarchical structure.
• Matrix Diagram: A tool to analyze the relationships between different factors, elements, or issues by organizing them into a matrix format.
• Matrix Data Analysis: A technique used to analyze and prioritize the relationships identified in a matrix diagram, determining their relative importance or impact.
• Arrow Diagram: Also known as the Activity Network Diagram, it is a tool used to visualize and manage the sequence of tasks or activities in a project, identifying the critical path and dependencies.
• Process Decision Program Chart (PDPC): A tool used to identify potential risks or problems in a process and develop appropriate contingency plans or countermeasures.

Purpose: The primary purpose of the N7 (New 7 Tools) is to provide a set of problem-solving techniques to address complex and data-intensive issues that may not be adequately resolved using the traditional 7 Basic Quality Tools. The N7 tools help organizations systematically analyze and understand problems, leading to better decision-making and continuous improvement.

Role: The N7 tools play a crucial role in enhancing the problem-solving capabilities of organizations by providing a structured approach to identify root causes, evaluate relationships between factors, plan and manage tasks, and develop contingency plans.

Components: The N7 tools consist of the following techniques:

• Affinity Diagram
• Relations Diagram (Interrelationship Digraph)
• Tree Diagram
• Matrix Diagram
• Matrix Data Analysis
• Arrow Diagram (Activity Network Diagram)
• Process Decision Program Chart (PDPC)

Importance: The N7 tools are essential in the modern business environment as they enable organizations to tackle complex problems that require a deeper understanding of relationships, dependencies, and cause-and-effect patterns. These tools help organizations make data-driven decisions, optimize processes, and achieve strategic objectives.

History: The N7 tools were developed by Japanese quality management experts in the 1970s to complement the existing 7 Basic Quality Tools (7QC Tools). The need for a more sophisticated set of problem-solving tools arose as organizations faced increasingly complex challenges that required a more systematic and data-intensive approach.

• Enhanced problem-solving: The N7 tools provide a structured framework for understanding and solving complex issues, leading to better decision-making and continuous improvement.
• Data-driven decisions: By using the N7 tools, organizations can make more informed decisions based on a thorough data analysis and relationships between factors.
• Continuous improvement: The N7 tools support identifying areas for improvement, facilitating a culture of continuous improvement within organizations.
• Versatility: The N7 tools can be applied to various industries and functions, making them a valuable asset for organizations seeking to address complex problems.

Pros and Cons:

• Improved problem-solving capabilities for complex issues.
• Facilitates data-driven decision-making.
• Encourages a culture of continuous improvement.
• Applicable across various industries and functions.
• May require significant time and effort to implement effectively.
• Some tools may not apply to all problems or situations.
• Requires training and expertise to use the tools effectively.

Examples to illustrate key concepts:

• A manufacturing company uses the Affinity Diagram to analyze customer feedback data, organizing the information into related categories that highlight areas for improvement. This helps the company prioritize issues and develop targeted solutions.
• An organization uses the Relations Diagram to identify the root causes of decreased product quality. By mapping the cause-and-effect relationships between factors, the organization can pinpoint the primary factors contributing to the issue and develop a targeted action plan.
• A project team utilizes the Arrow Diagram to visualize and manage the sequence of tasks in a complex project. This helps the team identify the critical path and dependencies, ensuring the project stays on schedule and allocating resources effectively.

In summary, the N7 tools provide a comprehensive set of problem-solving techniques designed to address complex and data-intensive issues. By implementing these tools, organizations can enhance their problem-solving capabilities, make more informed decisions, and promote a culture of continuous improvement.

• Project Management
• Problem Solving