The Effects Of The Spc Intervention Accounting Essay


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Statistical Process Control was initially developed by Dr. Walter Shewart in the early 1920s. He was working at the Bell Telephone Laboratories, conducting research on statistical methods to improve quality and to lower the costs. He recognized that variation in a production process can be understood and controlled through the use of such tools. The wider recognition and use of SPC came during World War II, when the need for higher quality production to support the defense industry was necessary.

The American industry, in comparison to Japan, was not convinced about the importance and the benefits of SPC. After the war, Dr. W. Edwards Deming, one of Shewhart's disciples, was invited to train Japanese engineers with the concepts of SPC. Japanese were eager to rebuilt their economy, and so they applied the statistical methods, they developed programs in human resources and they focused on continuous improvement to better respond to customer needs, thus resulting to the growth and development of Japanese companies into powerful global competitors.

Around 1980 the contributions of Statistical Process Control became finally recognized in America. From that moment on the strategy started growing and expanding. Those who used it added on it, improving the system or even developing new, different strategies to assure quality and improve productivity. So Statistical Process Control is not the only technique available. Nowadays such strategies are widely used in manufacturing companies and service organizations. Statistical Process Control is a very popular strategy, because it is easy to use and it can have accurate results without interrupting the process under examination. However, it is my opinion that SPC, or any strategy for that matter, can no longer stand alone. A combination of different techniques and strategies should be preferred, since every single one has something different to offer. Generally, the best results will be delivered when a statistical data analysis is being used combined with an action regarding managerial or technical corrections. I do not believe that SPC has an expiring date at the near future, but maybe as other techniques and software are developed it might be incorporated to an a more advanced improvement strategy.

Statistical Process Control uses software such as Minitab, with which a set of functions and tools can be used, enabling us to monitor and improve products or processes by evaluating process variability. Such tools are the calculation of measures of location, dispersion and distribution, ways to display data (charts and graphs) and analyze their variation and the determination of the capability, which can be either the actual performance of the process or the potential performance of the process. Some techniques used are the Measurement Systems Analysis, which ensure that measurement error does not overly contribute to the process variation, the attribute agreement analysis (AAA), which is used to assess the ratings given by multiple appraisers, and the Kappa statistics, which tests the agreement of the appraisers. Finally, the most famous SPC tool is the control chart, which is the key to quality improvement. The control charts are run charts which include the average or mean of data points, the upper control limit (UCL) and the lower control limit (LCL).

Statistical process Control has been applied to many manufacturing industries or services. For example it has been applied in healthcare to improve patient help and operations, in aircraft maintenance management by using p-charts and Pareto diagrams, in the analysis of machines for replacement, in plastic injection moulding companies and in software development. In other words SCP can be applied in any field that has facts, figures, measures, counts or numbers about the three problems arising in performance: delay, defect and deviation. In order for SPC to have successful results and for the organization to show continuous improvement it has been shown that top management commitment is vital. The following table shows the effects of SPC on some industries where it was applied. It is obvious that quality improves, since SPC aims to that improvement.

Table : A sample of studies reporting the effects of the SPC intervention (1)


Effects on

Process quality

Product quality

Other effects

Benneyan and Chute (1993)

↑ productivity

↓ costs

Kumar and Gupta (1993)

↓ scrap rate

Dondero (1991)

↓ costs

↑ product quality

Rucinski (1991)

↑ accuracy

↓ scrap rate

↑ cycle time

Cantello et al. (1990)

↑ process stability

↑ process capability

[Sower, 1990] and [Sower, 1993]

↑ product consistency

↑ positive attitudes towards the organization

↓ customer complaints

no effects on control over work

no effects on attitudes towards management

Neidermeier (1990)

↓ process variability

↑ customer satisfaction

Oakland and Followell (1990)

↑ process uniformity

↑ product uniformity

↑ market share

↑ defective rate

↓ failure costs

Chaudhry and Higbie (1989)

↑ process efficiency

↑ product uniformity

↑ customer relations

Manson and Dale (1989)

↑ process yields

↓ scrap rate

↑ job satisfaction

↓ process variability

Depew (1987)

no effects on productivity

no effects on product quality

Keefer (1986)

↓ defective rate

Followell and Oakland (1985)

↑ process uniformity

↑ product quality

↓ external failure costs

Harmon (1984)

↓ scrap rate

The term total quality management (TQM) was first used by the Naval Air Systems Command in order to describe its Japanese-style management approach to quality improvement. TQM derived directly from SPC and it can be defined as a management system for a customer-focused organization that involves all employees in continual improvement. By using principles and knowledge of behavioral sciences, analysis of qualitative and quantitative data, economic theories and process analysis, within effective communication, it succeeds in integrating the quality discipline into the fundamental ideas and the everyday activities of the organization. The preliminary elements of TQM are:

Cross-functional product design

Process management

Supplier quality management

Continual improvement

Customer focused

Information and feedback

Committed leadership

Strategic and systematic planning

Cross-functional training

Total employee involvement

TQM tools can be divided into two categories, tools for quality planning and tools for continuous improvement. As in SPC there are CTQ's (Critical to Quality) in order to translate customer language into qualified requirements for the product and KPI's (Key Performance Indicators) to assess how well the CTQ's are being met, in TQM the quality function deployment (QFD) is measured by applying the actual customer statements, referred to as "Voice of the Customer" to comprehensive matrixes called "The House of Quality". Another tool of TQM is the Concurrent Engineering (CE), which is a systematic approach to the integrated design of products and their process including manufacture and support. That means that through consideration of the costs of production, the aesthetics of the produce-ability, assemble-ability, maintainability and recyclability of the product, CE aims to improve the design phase of the product. Finally, the most common tools of TQM in use today are referred to as "The Seven Management and Planning Tools" and they include pie charts, bar graphs, histograms, run charts, scatter diagrams, control charts, trend charts, Pareto charts and analysis, flowcharts, modeling diagrams, the 5 Whys and process capability. All the above can be found in the Statistical Process Control toolbox as well. However TQM makes common use of two more types of graphs: the relations diagram, which helps to make clear the relationships between various factors, issues, events, etc. so as to understand their importance in the overall organizational view, and the fishbone diagrams (or else known as ishikawa or cause and effect diagram). The latter, is being used in order to illustrate multiple levels of potential causes (inputs) and ultimate effects (outputs), of problems and issues that may arise in the course of the analysis. Other tools and techniques being used are the SIPOC table analysis (Suppliers, Inputs, Process, Outputs, Customers), FMEA (Failure Modes and Effects Analysis), which analyses the failure mode within a system for classification by the severity and likelihood of the failures, PDCA (Plan-Do-Check-Act), which is used in business for the control and continuous improvement of processes and products.

Six Sigma - DMAIC (define, measure, analyze, improve, control)

Six Sigma is a high-performance, data based approach and methodology for eliminating defects in any process, by analyzing the root causes of business problems and solving them. In other words it's a way of scientifically measuring the current success (or failure) rate of a business in relation to what customer expect, regardless of industry sector, and then ensuring that the appropriate improvements are made. Two Six Sigma sub-methodologies are being used in order to achieve that: DMAIC (define, measure, analyze, improve, control) and DMADV (define, measure, analyze, design, verify). The Six Sigma DMAIC process methodology is an improvement system for existing processes falling below specification limits and looking for incremental improvement. On the other hand, Six Sigma DMADV process is an improvement system used to develop new processes or products within Six Sigma quality levels.

Six Sigma DMAIC approaches solution with the use of data and facts and drives defects to less than 3.4 per million opportunities. The primary elements of Six Sigma DMAIC are five. The definition of the project goals and the internal and external customer deliverables, followed by the measurement of the process to determine current performance, the analysis and determination of the root causes of the defects, improving then the process by elimitating defects and finally maintaining the high performance by controlling the future process.

Tools and methods used in Six Sigma DMAIC include charts and graphs used in TQM and SPC or in one of them, such as histograms, run charts, Pareto charts and analysis and fishbone diagram, control charts, process capability, gauge R&R, the CTQ tree, 5 Whys, QFD and root-cause analysis. Apart from the above tools and methods, DMAIC also uses pick charts, analysis of variance (ANOVA), axiomatic design, which uses matrix methods to transform customer needs into functional requirements, correlation, which refers to any broad class of statistical relationships involving dependence, cost-benefit analysis, regression analysis, for estimating the relationship among variables, SIPOC table analysis, stratification, Taguchi methods and Taguchi Loss Function and TRIZ.

The differences in tools and techniques are summarized in Table 2.




Critical to Quality tree

Quality Function Deployment

Voice of the Customer

CTQ tree + QFD

Key Performance Indicators

The House of Quality

Cost of Poor Quality (COPQ)

pie charts

pie charts

bar graphs

bar graphs

scatter diagrams

scatter diagrams

pick charts

Pareto charts and analysis

Pareto charts and analysis

Pareto charts and analysis



frequency histograms



run charts

run charts

run charts

control charts


control charts

control charts


modeling diagrams

modeling diagrams

relations diagram

fishbone diagrams

fishbone diagrams

check sheets


Check sheets

machine and process capability

process capability

process capability

gauge R&R

gauge R&R

5 Whys

5 Whys

5 Whys

analysis of variance (ANOVA)

axiomatic design


cost-benefit analysis

regression analysis

SIPOC table analysis

SIPOC table analysis


Taguchi methods

Taguchi Loss Function







The main difference between TQM and Six Sigma is the approach, since TQM is mainly a management approach to long-term success through customer satisfaction. As for SPC and the other two Continuous Improvement strategies the main differences lie on the fact that SPC deals with attributes as well, which is a different kind of quality measurement.

In this essay I am going to present briefly a case study on the application of Statistical Process Control on some basic chemicals used in pure water production in Nigeria, by some Privately Owned Water Enterprises (POWE). These POWEs produce sachet water, which is affordable but with uncertain purity. So in this case study, statistical process control charts are used to monitor the production process of pure water, combined with the assessment of some chemicals used in the production and process of the water. The data were collected from randomly selected packaged water producers and they are referred to as "pure water producers". Each sample undergoes some laboratory tests (physical and chemical) in order to determine some overall properties, such as pH and conductivity (μS/cm) and the level of some basic chemicals used in the production of pure water. The tests showed that most chemicals used, were out of process control and jeopardizing the health of the customers. The physical visual examination included odour and appearance such as colour, turbidity and presence of floating particles. The samples were also subjected to chemical tests, in accordance to the standard methods of the American Public Health Association and these included the chemical analysis of Lead (Pb), Chloride (Cl), Iron (Fe) and Aluminum (Al). The results derived from the use and interpretation of the respective control charts and taking in accordance the Western Electric Rules, which state that a process is out of control when one of the following occur: eight consecutive points are on one side of the center line, one point is outside the 3-sigma control limits, two out of three consecutive points are outside the 2-sigma warning limits on one side of the center line or four out of five consecutive points are at a distance of 1-sigma or more from the center line on one side of the center line. Overall, pH, conductivity, iron (Fe) and lead (Pb), were out of control. The representative control chart of lead, which exceeds the upper control limit by a lot, resulting to probable deleterious health effects, is presented below.

It is clear now that by using control charts the process variability can be monitored and reduced. It is also possible to determine whether a process needs adjusting and when not, as well as establish process stability and detect and process changes. In the specific case study some of the chemicals used in the production of packaged water are at times out of control and do not conform to standards for quality drinking water, resulting to possible health risk of the consumers. It is therefore recommended that statistical process control charts should be plotted periodically, to monitor the variation of the products and to improve the process and the final product, reducing at the same time scrap and rework.

A similar procedure can be followed in all processes, no matter how big, small or complex are. For example the products of every petroleum industry need to meet some standards (EN ISO). Nowadays the European regulations state that transport diesel must be a blend of up to 5% biodiesel and these blends need to compromise with the European Standard 590 for diesel fuels containing up to 7% of biodiesel. SPC can therefore be applied in the production of automotive diesel fuel, in order to monitor product variability and control the production. The upper control limits are determined mainly by environmental and health risks and the lower control limits by the performance requirements of the engine. The specification requirements stated in EN 590 include many properties of the fuel, but in this essay the application of SPC will be limited in three of them. The most important properties that need to be within limits are the sulfur content of the fuel, Fatty Acid Methyl Ester (FAME), which determines the quality of biodiesel used and the cetane index. Cetane index has only a lower control limit, as the bigger the cetane index of our diesel is, the better the performance of the fuel. The lower control limit is 46 and it can be calculated by the EN ISO 4264 test method, which uses the four-variable equation. Sulfur content and FAME content only have upper control limits, as values beyond these limits can cause either engine failure or increase the health risk of the population, whereas very small values are either the aim of the industry (sulfur content) or have no negative impact on the engine (FAME). These control charts will have a form as shown below.

Sulfur content is measured by EN ISO 20846 and has as upper limit the value of 10mg/kg. FAME content is measured by EN 14078 and the maximum value is 7%(V/V). Within all these methods, other tools of SPC are applicable, such as when measuring the precision of the test method, by using gauge R&R, the bios and the linearity. Apart from control charts, run charts can also be drawn, in order to detect any pattern and to differentiate common from random causes of variability. The capability indices can also be measured by using the graphs and the specification limits, which in this case coincide with the control limits. In conclusion, by monitoring the production the industry can always be within control limits, improve the performance of the production and minimize the scarp and the need of rework.

1. Rungtusanatham, Manus. The Quality and Motivational Effects of Statistical Process Control. Journal Quality Management. March 1999, Vol. Volume 2, Issue 2, p. 243-264.

2. Abubakar Usman, Nasir Mu'Azu Kontagora. Statistical Process Control on Production: A Case Study of Some Basic. Pakistan Journal of Nutrition. 2010, Vol. 9, 4, p. 387-391.

3. European Standard. Automotive fuels - Diesel - Requirements and test methods. Brussels : Technical Committee CEN/TC 19, 2009. EN 590.

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