Total Quality Management

Total Quality Management


What is quality?

Dictionary has many definitions: “Essential characteristic,” “Superior,” etc.Some definitions that have gained wide acceptance in various organizations: “Quality is customer satisfaction.”

The American National Standards Institute (ANSI) and the American Society for Quality (ASQ) define quality as:

“The totality of features and characteristics of a product or service that bears on its ability to satisfy given needs.”


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Total quality management or TQM is an integrative philosophy of managment for continuously improving the quality of products and processes.It is used around the world.TQM functions on the premise that the quality of products and processes is the responsibility of everyone who is involved with the creation or consumption of the products or services offered by an organization. In other words, TQM capitalizes on the involvement of management, workforce, suppliers, and even customers, in order to meet or exceed customer expectations. Considering the practices of TQM as discussed in six empirical studies, Cua, McKone, and Schroeder (2001) identified the nine common TQM practices as cross-functional product design, process management, supplier quality management, customer involvement, information and feedback, committed leadership, strategic planning, cross-functional training, and employee involvement.

Total Quality Management (TQM) is an approach that seeks to improve quality and performance which will meet or exceed customer expectations. This can be achieved by integrating all quality-related functions and processes throughout the company. TQM looks at the overall quality measures used by a company including managing quality design and development, quality control and maintenance, quality improvement, and quality assurance. TQM takes into account all quality measures taken at all levels and involving all company employees.

Origins Of TQM

Total quality management has evolved from the quality assurance methods that were first developed around the time of the First World War. The war effort led to large scale manufacturing efforts that often produced poor quality. To help correct this, quality inspectors were introduced on the production line to ensure that the level of failures due to quality was minimized.

After the First World War, quality inspection became more commonplace in manufacturing environments and this led to the introduction of Statistical Quality Control (SQC), a theory developed by Dr. W. Edwards Deming. This quality method provided a statistical method of quality based on sampling. Where it was not possible to inspect every item, a sample was tested for quality. The theory of SQC was based on the notion that a variation in the production process leads to variation in the end product. If the variation in the process could be removed this would lead to a higher level of quality in the end product.

After World War Two, the industrial manufacturers in Japan produced poor quality items. In a response to this, the Japanese Union of Scientists and Engineers invited Dr. Deming to train engineers in quality processes. By the 1950’s quality control was an integral part of Japanese manufacturing and was adopted by all levels of workers within an organization.

By the 1970’s the notion of total quality was being discussed. This was seen as company-wide quality control that involves all employees from top management to the workers, in quality control. In the next decade more non-Japanese companies were introducing quality management procedures that based on the results seen in Japan. The new wave of quality control became known as Total Quality Management, which was used to describe the many quality-focused strategies and techniques that became the center of focus for the quality movement.

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TQM AND SIX SIGMA

The Six Sigma management strategy originated in 1986 from Motorola’s drive towards reducing defects by minimizing variation in processes.The main difference between TQM and Six Sigma (a newer concept) is the approach.At its core, Total Quality Management (TQM) is a management approach to long-term success through customer satisfaction.In a TQM effort, all members of an organization participate in improving processes, products, services and the culture in which they work.The methods for implementing this approach come from the teachings of such quality leaders as Philip B. Crosby, W. Edwards Deming, Armand V. Feigenbaum, Kaoru Ishikawa and Joseph M. Juran.

A core concept in implementing TQM is Deming’s 14 points, a set of management practices to help companies increase their quality and productivity:

1.Create constancy of purpose for improving products and services.

2. Adopt the new philosophy.

3. Cease dependence on inspection to achieve quality.

4. End the practice of awarding business on price alone; instead, minimize total cost by working with a single supplier.

5. Improve constantly and forever every process for planning, production and service.

6. Institute training on the job.

7. Adopt and institute leadership.

8. Drive out fear.

9. Break down barriers between staff areas.

10. Eliminate slogans, exhortations and targets for the workforce.

11. Eliminate numerical quotas for the workforce and numerical goals for management.

12. Remove barriers that rob people of pride of workmanship, and eliminate the annual rating or merit system.

13.Institute a vigorous program of education and self-improvement for everyone.

14.Put everybody in the company to work accomplishing the transformation.

The term "Total Quality Management" has lost favor in the United States in recent years; "Quality management" is commonly substituted. "Total Quality Management", however, is still used extensively in Europe.

Principles Of TQM

Be Customer focused: Whatever you do for quality improvement, remember that ONLY customers determine the level of quality. Whatever you do to foster QUALITY IMPROVEMENT, training employees, integrating quality into processes management, ONLY customers determine whether your efforts were worthwhile.

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2-Insure Total Employee Involvement: You must remove fear from work place, then empower employee... you provide the proper environment.

Process Centered: Fundamental part of TQM is to focus on process thinking. 4- Integrated system: All employee must know the business mission and vision. An integrated business system may be modeled by MBNQA or ISO 9000 5- Strategic and systematic approach: Strategic plan must integrate quality as core component.

6- Continual Improvement: Using analytical, quality tools, and creative thinking to become more efficient and effective.

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7- Fact Based Decision Making: Decision making must be ONLY on data, not personal or situational thinking. 8- Communication: Communication strategy, method and timeliness must be well defined.

TQM Implementation Approaches

You can't implement just one effective solution for planning and implementing TQM concepts in all situations. Below we list generic models for implementing total quality management theory: 1-Train top management on TQM principles. 2- Assess the current: Culture, customer satisfaction, and quality management system.

3- Top management determines the core values and principles and communicates them. 4-Develop a TQM master plan based on steps 1,2,3. 5- Identify and prioritize customer needs and determine products or service to meet those needs.

6- Determine the critical processes that produce those products or services.

7- Create process improvement teams.

8- Managers support the efforts by planning, training, and providing resources to the team. 9- Management integrates changes for improvement in daily process management. After improvements standardization takes place.

10- Evaluate progress against plan and adjust as needed.

11- Provide constant employee awareness and feedback. Establish an employee reward/ recognition process.

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Strategies to develop TQM

1-TQM elements approach: Take key business process and use TQM TOOLS to foster improvement. Use quality circles, statistical process control, taguchi method, and quality function deployment.2 - The guru approach: Use the guides of one of the LEADING QUALITY THINKER. 3- Organization model approach: The organization use benchmarking or MBNQA as model for excellence. 4- Japanese total quality approach: Companies pursue the deming prize use DEMING PRINCIPLES

SIX SIGMA:

Six Sigma is a smarter way to manage business or department. It is a vision of quality that equates with only 3.4 defects for million opportunities for each product or service transactions. Strives for perfection.

Six Sigma is a business management strategy originally developed by Motorola, USA in 1986. As of 2010 , it is widely used in many sectors of industry, although its use is not without controversy.

We believe that defects free product can be in any organization implementing six sigma. In this paper, we presented an overview of the process which explains how six sigma increase the overall quality improvement task into a series of project management stages: Define, Measure, Analyses, Innovation, Improve and Control. We will describe dependence of six sigma on Normal Distribution theory and also process capability. It gives a small note on the assumptions made in six sigma methodology of problem solving and the key elements involved .A brief view on Defects Per Million Opportunities (DPMO) Analysis is given.

Ultimate objectives of the methodology to solve problems, improve the quality, profitability and customers satisfaction.

The main objective of any business is to make profit. For increasing the profit, the selling price should increase and/or the manufacturing cost should come down. Since the price is decided by the competition in the market, hence the only the way to increase the profit is to cut down the manufacturing cost which can be achieved only through continuous improvement in the company’s operation. Six sigma quality programs provide an overall framework for continuous improvement in the process of an organization. Six sigma uses facts, data and root cause to solve problems.

EVOLUTION OF SIXSIGMA:

Six sigma background stretches back eighty plus years, from management science concepts developed in the United States to Japanese management breakthroughs to “TOTAL QUALITY “ efforts in 1970s and 1980s. But the real impacts can be seen in the waves of change and positive results sweeping such companies as GE, MOTOROLA, JOHNSON &JOHNSON and AMERICAN EXPRESS.

CONCEPTS:

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Six sigma is defined a customer oriented, structured, systematic, proactive and quantitative company wide approach for continuous improvement of manufacturing, services, engineering, suppliers and other business process. It is a statistical measure of the performance of a process or a product. It measures the degree to which the process deviates from the goals and then takes efforts to improve the process to achieve total customer satisfaction.

Six sigma efforts target three main areas:

Improving customer satisfaction.

Reducing cycle time.

Reducing defects.

Three key characteristics separates six sigma from quality programs of the past:

Six Sigma is a customer focused.

Six sigma projects produce major returns on investments.

Six sigma changes how management operates.

6 SIGMA= 3.4 defects per million

Six Sigma equates 3.4 defects for every million parts made or process transactions carried out. This quality equates to 99.99966% defect free products or transactions. High quality standards do make sense but the cost required to pursue such high standards have to be balanced with benefits gained. The six sigma processes exposes the root causes and then focuses on the improvements to achieve the highest level of quality at acceptable cost. This is essential to achieve and maintain a competitive advantage and high levels of customer satisfaction and loyalty.

When we say that a process is at six sigma level, such a process is normally yield two instances of non-conformances out of every million opportunities for non-conformances, provided there is no shift in the process average. The same will yield 3.4 instances of non-conformances out of every million opportunities with an expected of 1.5 sigma in the process average. This is considered to be best-in-class quality.

THEORY:

Six Sigma relies on the normal distribution theory to predict defect rates. As we all know, variation is inevitable in any process. The variation can be due to chance causes that are inherent in the process [chance variation] or due to assignable causes that are external to the process [Assignable variation]. If we detect and remove all the assignable causes and bring the process under the influence of chance causes, then the process is said to be under statistical control. The process capability (PC) is defined as six times the standard deviation (). PC represents the measured inherent reproducibility of the product turned out by the process.

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The upper specification limit (USL) and lower specification limit (LSL) of +/- 6 of the mean with a defect rate of 0.002 ppm (refer fig.1).

The process capability index Cp. is defined as ratio of specification width to PC.

Cp= (USL-LSL)/(6)

Cp. is 2 for a six sigma process, which means that the inherent process variation is half of the specification width.

DEFECTS PER MILLION OPPORTUNITIES (DPMO) ANALYSIS:

In practice, most of the delivered products or services will have multiple parts and /or process steps, which represent opportunities for nonconformities or defects. For example, which a watch has numerous parts and assembly steps. In such cases it is important to ask questions such as what is the distribution of defects, how many units can be expected to have zero defect, one defect, two defect, and so on for a given ppm, what will be the defect rates and sigma levels for individual parts and process steps that contributes to the total unit with a given defect rate.

If the number of observed nonconformities as “d” out of the total number of units produced “u”.

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Defects Per Unit (DPU) = d/u

If each unit manufactured has got “m” number of opportunities for nonconformance, we can compute the Defects Per Opportunity (DPO) as

Defect Per Opportunity (DPO)= DPU/m

In the calculation of DPO, we are taking into consideration only the active opportunities (those which are getting measured) and not the passive opportunities (which are not getting measured) with in each unit.

From this, the DPMO can be computed as

Defects Per Million Opportunities (DPMO) = DPO x 10^6

The sigma level can be found out from the DPMO value using statistical tables. If the DPMO and the number of defect opportunities are known for each contributing step, the total DPMO for the completed unit can be computed as follows.

Expected Defects (ppm for each step) = DPMO x Number of opportunities (for each step)

Expected defects (ppm for completed unit) = Sum of expected defects of Individual steps

DPMO for completed unit = (Expected defects)/ (Total number of Opportunities)

PROCESS YIELD:

The process yield represents the proportion of defect- free units before testing or repair. The Poisson Distribution can be used to calculate the

Yield for a unit if the DPU value is known.

YIELD= e^ (-DPU)

If the yield is known for each part or process step, the overall yield for the process (ROLLED THROUGHPUT YIELD [YRT]) can be computed as the product of yields of individual process steps. This value will be less than smallest individual yield since these are all in fractions. This clearly shows that for improving the YRT, the individual yields shall be improved. In other words, for minimizing the overall defect rate, the overall defect rate, the individual defect rates of each part or process step shall be minimized. Hence, only with six sigma parts and process steps will an organization experience high YRT for complex products with numerous parts and process steps.

SIX SIGMA - METHODS

Six Sigma projects follow two project methodologies inspired by Deming's Plan-Do-Check-Act Cycle. These methodologies, composed of five phases each, bear the acronyms DMAIC and DMADV.

DMAIC is used for projects aimed at improving an existing business process. DMAIC is pronounced as "duh-may-ick".

DMADV is used for projects aimed at creating new product or process designs. DMADV is pronounced as "duh-mad-vee".

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DMAIC:

The DMAIC project methodology has five phases:

1. Define the problem, the voice of the customer, and the project goals, specifically.

2. Measure key aspects of the current process and collect relevant data.

3. Analyze the data to investigate and verify cause-and-effect relationships. Determine what the relationships are, and attempt to ensure that all factors have been considered. Seek out root cause of the defect under investigation.

4. Improve or optimize the current process based upon data analysis using techniques such as design of experiments, poka yoke or mistake proofing, and standard work to create a new, future state process. Set up pilot runs to establish process capability.

5. Control the future state process to ensure that any deviations from target are corrected before they result in defects. Implement control systems such as statistical process control, production boards , visual workplaces, and continuously monitor the process.

DMADV OR DFSS:

The DMADV project methodology, also known as DFSS ("Design For Six Sigma"),features five phases:

1. Define design goals that are consistent with customer demands and the enterprise strategy.

2. Measure and identify CTQs (characteristics that are Critical To Quality), product capabilities, production process capability, and risks.

3. Analyze to develop and design alternatives, create a high-level design and evaluate design capability to select the best design.

4. Design details, optimize the design, and plan for design verification. This phase may require simulations.

5. Verify the design, set up pilot runs, implement the production process and hand it over to the process owner(s).

SIX SIGMA PRODUCE MAJOR RETURNS ON INVESTMENT

For example:

At GENERAL ELECTRICALS (GE) six sigma program resulted in the following,

In 1996, costs of $200 million and returns of $150 million

In 1997, costs of $400 million and returns of $600 million

In 1998, costs of $400 million and returns of $1 billion

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CONCLUSION:

The term “sigma” is used to designate the distribution or the spread about the mean of any process. Sigma measures the capability of the process to perform defect-free work. A defect is anything that results in customer dissatisfaction. For a business process, the sigma value is a metric that indicates how well that process is performing. Higher sigma level indicates less likelihood of producing defects and hence better performance.

Six sigma is a performance standard to achieve operational excellence. With six sigma, the common measurement index is “defects-per-unit” where a unit can be virtually anything – a component, piece of material, administrative form etc. Conceptually, six sigma is defined as achieving a defect level of 3.4 ppm or better. Operationally, six sigma is defined a staying within half the expected range around the target. The approach aims at continuous improvement in all the process within the organisation. This works on the belief that quality is free, in that the more we work towards zero-defect production, the more return on investment we will have. The advantages of six sigma approaches are reduction in defects/rejections, cycle time, work in progress etc. and increase in product Quality &Reliability, customer satisfaction, productivity etc. leading ultimately to excellent business results.

TAGUCHI METHOD FOR CONTINUOUS IMPROVEMENT

Taguchi methods are statistical methods developed by Genichi Taguchi to improve the quality of manufactured goods, and more recently also applied to, engineering, biotechnology, marketing and advertising. Professional statisticians have welcomed the goals and improvements brought about by Taguchi methods, particularly by Taguchi's development of designs for studying variation, but have criticized the inefficiency of some of Taguchi's proposals.

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Taguchi's work includes three principal contributions to statistics:

A specific loss function — see Taguchi loss function;

The philosophy of off-line quality control; and

Innovations in the design of experiments.

LOSS FUNCTIONS

LOSS FUNCTIONS IN STATISTICAL THEORY

Traditionally, statistical methods have relied on mean-unbiased estimators of treatment effects: Under the conditions of the Gauss-Markov theorem, least squares estimators have minimum variance among all mean-unbiased estimators. The emphasis on comparisons of means also draws (limiting) comfort from the law of large numbers, according to which the sample means converge to the true mean. Fisher's textbook on the design of experiments emphasized comparisons of treatment means.

Gauss proved that the sample-mean minimizes the expected squared-error loss-function (while Laplace proved that a median-unbiased estimator minimizes the absolute-error loss function). In statistical theory, the central role of the loss function was renewed by the statistical decision theory of Abraham Wald.

TAGUCHI'S USE OF LOSS FUNCTIONS

Taguchi knew statistical theory mainly from the followers of Ronald A. Fisher, who also avoided loss functions. Reacting to Fisher's methods in the design of experiments, Taguchi interpreted Fisher's methods as being adapted for seeking to improve the mean outcome of a process. Indeed, Fisher's work had been largely motivated by programmes to compare agricultural yields under different treatments and blocks, and such experiments were done as part of a long-term programme to improve harvests.

However, Taguchi realised that in much industrial production, there is a need to produce an outcome on target, for example, to machine a hole to a specified diameter, or to manufacture a cell to produce a given voltage. He also realised, as had Walter A. Shewhart and others before him, that excessive variation lay at the root of poor manufactured quality and that reacting to individual items inside and outside specification was counterproductive.

He therefore argued that quality engineering should start with an understanding of quality costs in various situations. In much conventional industrial engineering, the quality costs are simply represented by the number of items outside specification multiplied by the cost of rework or scrap. However, Taguchi insisted that manufacturers broaden their horizons to consider cost to society. Though the short-term costs may simply be those of non-conformance, any item manufactured away from nominal would result in some loss to the customer or the wider community through early wear-out; difficulties in interfacing with other parts, themselves probably wide of nominal; or the need to build in safety margins. These losses are externalities and are usually ignored by manufacturers, which are more interested in their private costs than social costs. Such externalities prevent markets from operating efficiently, according to analyses of public economics. Taguchi argued that such losses would inevitably find their way back to the originating corporation (in an effect similar to the tragedy of the commons), and that by working to minimise them, manufacturers would enhance brand reputation, win markets and generate profits.

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Such losses are, of course, very small when an item is near to negligible. Donald J. Wheeler characterised the region within specification limits as where we deny that losses exist. As we diverge from nominal, losses grow until the point where losses are too great to deny and the specification limit is drawn. All these losses are, as W. Edwards Deming would describe them, unknown and unknowable, but Taguchi wanted to find a useful way of representing them statistically. Taguchi specified three situations:

Larger the better (for example, agricultural yield);

Smaller the better (for example, carbon dioxide emissions); and

On-target, minimum-variation (for example, a mating part in an assembly).

TAGUCHI'S RULE FOR MANUFACTURING

Taguchi realized that the best opportunity to eliminate variation is during the design of a product and its manufacturing process. Consequently, he developed a strategy for quality engineering that can be used in both contexts. The process has three stages:

System design

Parameter design

Tolerance design

SYSTEM DESIGN

This is design at the conceptual level, involving creativity and innovation.

PARAMETER DESIGN

Once the concept is established, the nominal values of the various dimensions and design parameters need to be set, the detail design phase of conventional engineering. Taguchi's radical insight was that the exact choice of values required is under-specified by the performance requirements of the system. In many circumstances, this allows the parameters to be chosen so as to minimize the effects on performance arising from variation in manufacture, environment and cumulative damage. This is sometimes called robustification.

TOLERANCE DESIGN

With a successfully completed parameter design, and an understanding of the effect that the various parameters have on performance, resources can be focused on reducing and controlling variation in the critical few dimensions (see Pareto principle).

DESIGN OF EXPERIMENTS

Taguchi developed his experimental theories independently. Taguchi read works following R. A. Fisher only in 1954. Taguchi's framework for design of experiments is idiosyncratic and often flawed, but contains much that is of enormous value. He made a number of innovations.

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