Quality Management Tools and Techniques for Continuous Improvement

Quality Management Tools and Techniques for Continuous Improvement

Quality Management Tools and Techniques for Continuous Improvement

Synthesis of Literature

Literature Review

Introduction

This literature illustrates basic quality management tools and techniques which are used in many organizations for continuous improvement. There are some effective contributors to quality who determined on process improving and producing continuous quality results at highly productive levels. The leaders are:

  • Walter Shewhart;
  • W. Edwards Deming;
  • Joseph M. Juran;
  • Taiichi Ohno;
  • Kaoru Ishikawa;
  • Armand V. Feigenbaum and
  • Philip B. Crosby.

Walter Shewhart (1891 – 1967)

In order to reduce the frequency and improve reliability the concept of common cause, special cause variation and statistical control were brought by Walter Shewhart in 1924. Statistical process control (SPC) was introduced by Shewhart in the book “Economic Control of Quality of Manufactured Product”, which has become the vital element for process control in industry.

W. Edwards Deming (1900 – 1993)

Deming developed “14 Principles for Western Management” and combined this with Shewhart’s concept and instructed that by accepting these points organizations can improve the quality, customer loyalty and reduce costs by avoiding rework, waste and employee attrition. From his book “System of profound knowledge” (2000) he promoted that “85% of poor quality was due to bad management, poor process and improper systems and remaining 15% was because of workers”. He explained PDSA Cycle (Plan-Do-Study-Act) for improvement and learning process.

Joseph M. Juran (1904 – 2008)

Juran has specialized in quality management, he created the Pareto principle (80/20 rule) while formulating the ideas of Deming. The book “Quality Control Handbook” in 1951 were Juran is a coauthor explained quality in 2 different concepts 1) Higher quality costs more and

2) Higher quality usually costs less.

“Quality planning, Quality control and Quality improvement” are the interrelated process known as “Juran Trilogy” which is one of the important contributions towards quality improvement by Juran.

Taiichi Ohno (1912 – 1990)

The concept of continuous flow (one-piece) process was developed by Ohno in 1948 to avoid “Batch and Queue” process where he analyzed the waste called “Muda” in Japanese. He classified the waste into 7 types (activities which do not add value to the process).

  • Overproduction
  • Transporting
  • Rejects
  • Motion
  • Waiting
  • Inventory
  • Over-processing

This one-piece process allows completing a product at a time, which results in higher output, greater efficiency and helps to avoid the above 7 wastes.

Kaoru Ishikawa (1915 – 1989)

The Ishikawa diagram (Fishbone diagram) was created by Kaoru Ishikawa, which concentrated on the effect of participation at all levels in an organization for quality improvement actions and in decision making by the use of statistical measurements. The book Guide to Quality Control was published in the year 1968 authored by Ishikawa, which explained the concept of participation and understanding the quality controls at all levels in an organization.

Armand V. Feigenbaum (1922 – 2014)

The book Total Quality Control was authored by Feigenbaum in the year 1951, which made clear that “TQC is excellence is driven rather than defect driven” – which mixes quality development, quality improvement and quality maintenance. He defines quality costs as the costs of prevention, appraisal and internal and external failures.

Philip B. Crosby (1926 – 2001)

The concept of Zero defects was introduced by Philip B. Crosby in 1961, he defined quality as “Conformance to requirements” and explained quality as “Price of nonconformance”. He educated quality improvement as a process rather than a temporary project, Crosby’s principle is DIRFT (Doing it right the first time). He also introduced 4 principles:

  • Quality is defined as conformance to requirements.
  • The system of quality is prevention.
  • The performance standard is close enough i.e., Zero defect.
  • Measurement of quality is not an indication, it is the price of nonconformance.

He sensed that companies should adopt perseverance, education and implementation to avoid nonconformance.

Tools and Techniques

The tool is a device commonly used on its own.

The technique is a set of tools and have broad application.

An organization can be made better by applying proper quality management and quality management tools and techniques. Thus, Dale and McQuater (1998) have determined basic quality tools and techniques which were most commonly used by organizations.

Tools Techniques
Checksheet Departmental purpose analysis
Pareto diagram Poka-yoke
Histogram Fault tree analysis
Control chart Design of experiments
Scatter diagram Quality function deployment

Statistical process control

Flowchart Failure mode and effects analysis
Cause and effect diagram Benchmarking

Quality management tools

The above basic tools are classified under 2 categories “Data Acquisitions” and “Data Analysis”. Check sheet, Histogram and Control chart are Data acquisition and Cause and effect diagram (Fishbone diagram), Pareto diagram, Flowchart and Scatter diagram comes under Data analysis.

Data Acquisitions

Checksheet

Check sheets are the simplest form used for recording data in an organization orderly, the data can be either qualitative or quantitative. It is constructed for quick, easy and effective documentation, data’s collected in the check sheet gives clear information about the frequency of a particular process. The benefit of the check sheet is easily understood and gives a clear description of the condition of the firm, this does have the capacity to analyse the problem but allows to identify.

Histogram

The histogram is similar to bar chart which pictures both quality and variable data of a process, it illustrates the frequency distribution. This chart is much more useful if the data collected is in the shape of numbers, it should be made in such a manner that it is well understandable for those who are engaged in an operational process. It helps to examine and classify the unrevealed issue of a variable being explored.

Control chart

This statistical tool is also called a run chart, which helps to differentiate whether the variation is a common cause or special cause in a process over a period of time. This chart helps to analyse the process is within the “statistical control” or not (i.e., Within UCL and LCL), If the process moves away from it, then the process is out of control and there is an issue with quality. The advantage of this chart is to reduce the variation and judge the parameters in a process. This chart is also known as the Shewhart control chart.

Data analysis

Cause and effect diagram

This problem-solving tool helps to identify and sort the real cause of a particular problem, it graphically shows the relation between a given outcome and the factors influencing it. The problems are classified under these main categories such as man, machine, material, method, measurement and environment, potential causes are indicated under the main causes. This diagram is also called as the Ishikawa diagram or Fishbone diagram.

Pareto diagram

Pareto chart is otherwise known as (80/20 rule), which means 80% of problems are due to 20% of the issues. It is blended with a bar and line graph, where individual values are presented in descending order from the left through the bar and cumulative values are represented by a line. The primary purpose of the Pareto chart is to ascertain the different forms of “nonconformity” from data figures and produce mean for inspecting concerning quality improvement.

Flowchart

The flowchart is a diagrammatic representation, which includes symbols to explain the series of steps involved in an operation to complete a process. This problem-solving tool used to identify and analyse the process methodically and to improve the quality of a process.

Scatter diagram

This powerful tool is used to determine and analyse the correlation between 2 variables (i.e., The 2 variables are related to each other or not). The scatter diagram helps to understand the relationship between the variables is weak or strong and positive or negative, and the shape of the diagram illustrates the correlation between two variables whether it has positive, negative or no correlation. It is useful in regression modelling.

Figure 1 7QC Tools

7QC Tools through PDCA Cycle

To achieve continuous improvement and customer satisfaction, quality management principle is a base to start. Every organization executes a quality management system for analyzing the process. Without quality tools, continuous improvement cannot be fulfilled, which is grouped into Deming’s cycle (PDCA). PDCA cycle is a dynamic model because one cycle performs one entire step of improvement, and it is the necessary part of process management. It is a never-ending process because, improvement program starts with careful planning and results in effective action, and again to careful planning (i.e., Completion of 1 cycle continues with the start of another cycle). It has four steps

Plan – Determination of what should be changed.

Do – Execution of the changes which is determined in plan step.

Check – Measurement of the process according to the changes built in the previous step. Report on results

Act – Keeping improvement ongoing.

The main function of the PDCA cycle is process improvement which is achieved by proper planning, it results in corrective and preventive actions backed with applicable quality assurance tools.

Figure 2 Deming’s Cycle

7QC Tools in Six Sigma

Six Sigma technique requires a creative use of data, the importance of statistical analysis and designed experiments. This methodology goes beyond process improvement and tools, it defines process improvement as DMAIC methodology.

Define – Generate project ideas, Select project and finalise project charter.

Measure – Finalise performance standards for the project, validate the measurement system for project and measure current performance and gap.

Analyse – List all probable root causes, Identify critical root causes and Verify sufficiency of critical root causes for the project.

Improve – Generate and evaluate the alternate solution, Select and optimize best solution and pilot, implement and validate the solution.

Control – Implement control system for the critical root cause, Document solution and benefits, and transfer to process owner, project closure.

Each of the above steps can be fulfilled with different tools and techniques. An altered version of six sigma known as DFSS (Design for Six Sigma) is used for the development of a new process which targets on “problem prevention”. The method used in DFSS is DMADV (Define, Measure, Analyse, Design and Verify) or IDOV (Identify, Measure, Optimize and Validate). Based on the process either DMAIC or DMADV is used.

Figure 3 6 Sigma

Quality management techniques

Techniques – a collection of tools, and has broader usage. Some of the basic techniques used in an organization are.

 

 

 

Departmental purpose analysis

DPA is a practical way of applying concepts and principles, it is constructed in a way that the team members achieves the goal to add value to the company’s strategy. The primary target of DPA is measuring and meeting customer requirements.

Poka-yoke

Poka-yoke is foolproof, which arrests defect. An operator is alerted by producing either a warning signal or to pause in order to avoid producing defective goods.

Fault tree analysis

FTA is widely used in safety and reliability engineering field to analyse the probability of an undesirable event using Boolean algebra. It is a top-down logical failure analysis.

Design of experiments

DOE is a systematic analysis of a process. A process is tested in a sequential manner where changes are made to the input variables, these changes are determined on a pre-defined output.

Quality function deployment

QFD techniques are used for converting customer needs into design features for every stage of product development. It is a systematic way to design customer’s requirement with the combination of corporate functional groups.

Statistical process control

SPC is an essential technique for continuous improvement, special cause variations are removed by using this scientific graphical approach for refining the process.

Failure mode and effects analysis

FMEA is a proactive approach to determine the potential causes of failure and to measure the depth of different failures.

Benchmarking

Benchmarking is a technique used for adopting best practices, it is a self-improvement tool which allows organizations to enhance their comparative skills.

5S

A Japanese tool which helps the workers to control their working area, which helps them to work comfortably and easily. There is a different meaning for each S in “5S”

Seiri (Sort) – Keep things which are necessary.

Seiton (Set) – Arrange and identify the things.

Seiso (Shine) – To keep working area and things clean.

Seiketsu (Standardize) – Use best practices frequently.

Shitsuke (Sustain) – To ensure above “4S” is followed.

Conclusion

Tools and techniques can only be enhanced by providing proper training to the concerned person so that it will be easy for them to understand the effectiveness in it. These are the essential components for improving the process and quality, it can be used in all process development where data collection, analyzing and visualization has a vital place. All these tools and techniques cannot be applied to a particular issue, the implement varies according to the problems. These basic quality tools can be practised in day-to-day life for a better understanding of where and for which problems it should be implemented. Managerial encouragement and commitment are required for complete usage of these basic tools and practices in teams, as these cannot be performed individually.

References

  • Darrell, K. R. (2007). Management Tools 2007. BAIN & COMPANY.
  • David, R. B., & Richard, W. G. (2005). The use of Quality Management Tools & Techniques: A Study of Application in everyday situations. International Journal of Quality & Reliability Management, 376 – 391.
  • Dusko, P., Mirko, S., & Glorija, P. (2008). Practical Application of Quality Tools. 2nd International Quality Conference. Kragujevac.
  • H.S. Bunney, B. D. (1997). The Implementation of Quality Management Tools and Techniques: A Study. The TQM Magazine, 183 – 189.
  • Kevin, W. (2008). Quality Improvement: The Foundation, Process, Tools, and Knowledge Transfer Techniques. In The Healthcare Quality Book (pp. 63 – 69). Washington DC: AUPHA Press.
  • M, S., R, M., K, S., B, D., & J, B. (1998). The use of quality tools and techniques in product introduction: an assessment methodology. The TQM Magazine, 45 – 50.
  • Mirko, S. J., Zdravko, K., & Aleksandar, V. (2009). Basic Quality Tools in Continuous Improvement. Journal of Mechanical Engineering.
  • Mirko, S., Jelena, J., Zdravko, K., & Aleksandar, V. (2009). Basic Quality Tools in Continuous Improvement Process. Journal of Mechanical Engineering.
  • Mohit, S., LA, K., & Sandeep, G. (2012). Tools and Techniques for quality management in manufacturing industries. Trends and Advances in Mechanical Engineering, (pp. 853 – 858). Faridabad.
  • Muhammad, H. K. (2013). Quality Improvement for Manufacturing Process by Using 7 QC Tools in SME.
  • Neyestani, B. (2017). The Appropriate Techniques for Solving Quality Problem in the Organizations. Seven Basic Tools of Quality Control.
  • Oakland, J. S. (2003). Total Quality Management.
  • Rami, H. F., & Adnan, M. (2010). Statistical Process Control Tools: A Practical guide for Jordanian Industrial Organizations. Jordan Journal of mechanical and industrial engineering, 694 – 699.
  • Rhys, R. J., Paul, T. T., & Kelly, P. T. (n.d.). Quality Management Tools & Techniques: ProfilingSME use & Customer Expectations. 2 – 13.
  • Varsha, M. M., & Vilas, B. S. (2014). Application of 7 Quality Control (7 QC) Tools for Continuous Improvement of Manufacturing Process. International Journal of Engineering Research and General Science Volume 2, 364 – 370.

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