Statistical process control: an essential ingredient for improving service and manufacturing quality

Statistical process control: an essential ingredient for improving service and manufacturing quality

Statistical process control: an essential ingredient for improving service and manufacturing quality

Introduction

Today's consumer markets experience an ever-increasing demand for better products and services. Customers expect continuously improved quality products and/or services even when they pay less for them than the previous purchase prices. It is therefore logical that if a company wishes to be competitive, one of its main aims should be to focus upon producing products of a consistently high quality.

The word "quality" can be defined in many ways and has different meanings to different people. However, in order to achieve quality, it is desirable to have a definition for quality that will reflect the true needs and expectations of customers. It is widely accepted that the quality of a product is generally thought of as the ability to fulfil specific needs, or conform to and ideally exceed customer expectations.

Quality is therefore an important aspect for any company to maintain competitiveness. During the last half-century, quality and its management has evolved to what is now known as total quality management (TQM). It is probably fair to say that today Dr Deming is recognised as the mentor of what is conceived as TQM. However, there is no standard definition for the term. Existing definitions that are available tend to be very broad. Madu for example defines TQM as:

An organisation-wide quality program to continuously improve products and services delivered to customers by developing supportive organisational culture and implementing statistical and management tools (Madu, 1998).

This definition implies strongly that TQM should be a company-wide policy continuously to improve quality of products and services. Underneath the umbrella of TQM there are several different techniques that can be used to improve product, process and service quality. One of these is statistical process control (SPC).

SPC

SPC is generally accepted to control and manage (management) a process (either manufacturing or service) through the use of statistical methods (Dale, 1994).

SPC is a statistical technique used to control processes and to reduce variation. Variation reduction is a key aspect to improve quality. There are two main causes of variation, assignable (or special) and common (or chance). Special causes of variation are not inherent in the process and can be therefore readily identified. They are relatively larger in magnitude and require some actions on the process/system to eliminate them. Examples of special causes are resetting of machines, tool wear, errors in measurements, errors in calculations and operator error. Common causes affect all products/services of a process, as they are always inherent in a process. Examples of common causes include humidity fluctuations, temperature changes, electrical fluctuations, deterioration of equipment performance and raw material variations. The main objective of control charts used within SPC is to distinguish between the special and common causes of variation.

It is important to note that SPC uses control charts to indicate when there is something wrong with a process and that it is out of statistical control (Caulcutt, 1999). However, it does not tell the user (or operator) exactly what is wrong with the process. Figure 1 shows SPC interactivity.

There seems to be a problem with the general thinking behind TQM. In the western world the consensus is that techniques like SPC should be implemented for customer satisfaction rather than part of a strategic plan by the company (Oakland, 1999). The full benefits of SPC tend to be realised only when the motivation is appropriate:

Organizations that have implemented statistical process control of their own free will experience advantages to a greater extent (Brannstrom-Stenberg and Deleryd, 1999).

There are numerous examples indicating the benefits of SPC and why it should be implemented freely as part of an organisation's quality policy. Watson (1998) provides an excellent discussion on this aspect of SPC.

Why do we need SPC?

At the beginning of the century product quality was checked by inspection. In other words, inspection-based quality control was used for checking the final product/service quality. However, inspection-based quality control was unreliable, inefficient, costly and time-consuming. Moreover, inspection-based quality control is reactive in the sense that defective items or products will be made before they are found and thus will incur scrapping or reworking costs. Employing people and equipment merely to sort the good ones from bad ones is time-consuming and adds no value in terms of continuous improvement. Inspection does not tell the operators why an error has occurred and what corrective action(s) must be taken to eliminate the error. In order to tackle this problem, a preventive measure must be used at the operation stage in order to ensure that desirable product quality can be achieved. Such an approach based on statistical methods (called SPC today) is used to monitor, control, analyse and improve process performance by systematically eliminating special causes of variation in processes.

Figure 2 shows where and how SPC fits into the TQM and competitiveness scenario.

Potential benefits of SPC

One of the main reasons for the failure of SPC implementation within organisations is a lack of understanding the potential benefits of SPC. It is important that SPC awareness and benefits should first be realised by senior management and then be cascaded down through the organisation hierarchy. This should be done before any SPC initiative begins.

The following are the typical benefits that can be gained from the application of SPC:

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  reduction in wasted efforts and costs;

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  process improvement, greater output;

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  better consistency of process output;

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  improved operator information: when to and when not to take action;

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  a predictable process can be achieved;

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  a common language on performance of process for different people across departments;

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  SPC charts help distinguish special from common causes of variation;

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  variation reduction;

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  reputation for high quality products/service and thereby reduce customer complaints;

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  healthy market share or improved efficiency/effectiveness;

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  reduced quality costs;

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  reduced need for checking/inspection/testing efforts;

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  more efficient management, and better understanding of process;

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  reduction in time spent fire-fighting quality problems.

Service quality

Society today is experiencing an ever-increasing growth of the service industry. So much so, that around 80 per cent of the GNP can be attributed to the service sector in the USA and many European countries. However, strangely enough, the use of SPC within the service industry is not common. The application of SPC to the service industry can be just as beneficial as it is to the manufacturing industry, in improving service quality and ultimately customer satisfaction.

Service quality concerns the difference between the customers' expectations and their perceptions of the service they actually receive from the service provider. Measuring service quality requires an understanding of the service quality characteristics. SPC requires the controllable factors to be identified and the measurable responses to be clearly defined (Xie and Goh, 1999). Lack of understanding is perhaps one of the reasons for the lack of SPC application in the service sector.

To improve the quality of a service using SPC, the service performance characteristic associated with the service should be identified and measured. The decision of what and how to measure should be made carefully. The measurement taken should be a fundamental component towards achieving customer satisfaction via service quality.

Table I presents a selection of quality characteristics in service industries and their most appropriate control charts.

Case studies involving the use of SPC within the service industry have reported the following.

Forte plc (Jones and Dent, 1994) carried out a pilot study within the staff restaurant, using SPC to analyse the variety of food, temperature of food and the hygiene in the staff restaurant. In this case Forte used X – R charts to monitor all the three variables. The results showed that all three variables had acceptable performance levels, and none was outside the accepted tolerance level. Encouraged by the study, Forte now proposes a more detailed study within a large business hotel.

Wood (1994) gives an example of SPC usage within a leisure centre. The time taken to respond to external telephone calls is a key measurement for a leisure centre, because failure to respond quickly may lead to lost business. The leisure centre monitored the number of rings before the phone was answered. The manager decided to use X – R charts to try and achieve the "industry target" of 98 per cent of calls answered within three rings. The charts showed that at the time 58 per cent of calls were answered within three rings.

Practical difficulties in the implementation of SPC in industries

Dale et al. (1990) conducted a study involving the use of SPC by part/component suppliers in the automotive-related industry. Two surveys were reported. The aim of the first survey was to identify the practical difficulties experienced when introducing SPC. The second showed what the main barriers were when trying to apply SPC and its further development. Out of 158 respondents, 77 per cent indicated that they had experienced difficulties in introducing SPC, and 82 per cent indicated that they had encountered difficulties with its applications and development.

There are many reasons for failure when it comes to implementation of SPC, some of which are mentioned below:

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  Lack of training and education: this creates problems organisation wide, from the operators to the senior management, because there is a general lack of understanding and awareness of why SPC is being implemented.

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  Management commitment: management need to commit themselves to SPC, and provide necessary human and economic resources for SPC.

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  Not understanding fully the potential benefits: this stems from a lack of appreciation of the potential benefits and can result in personnel taking a flippant attitude towards all aspects of SPC.

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  Failure to interpret control charts and take any necessary actions: if correct training is given to operators then this reason for failure can be eliminated.

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  Lack of knowledge of which product/process characteristics to monitor and measure: the selection of product characteristics or process parameters are absolutely vital for the success of any SPC initiatives.

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  Inadequate measuring system in place: there is a great deal of variation in any measurement method and therefore it is essential to ensure that the gauges are capable of doing their intended function.

For a company to be successful with SPC implementation all of these above points should be addressed. It seems possible that the overarching factor incorporating all these problems stems from a lack of education in quality management techniques, SPC in particular. With the correct training and ongoing education, problems such as not understanding the benefits, incorrect interpretation of control charts and incapable measurement system can be eliminated.

Statistical education for engineers

Perhaps the training and education towards SPC implementation should be taught at an earlier stage and on a wider front in the education system, especially to engineering students in higher education. Currently, within engineering institutions very little time is spent on management and implementation aspects of SPC. The main focus seems to be on control charting of various processes:

Too often organisations look at the "control chart" as the only approach to handle issues and this will not work (Xie and Goh, 1999).

It seems that the word "statistics" invokes fear amongst industrial engineers (Antony et al., 1998). Very few engineers graduating today from UK higher education institutions are exposed to powerful problem-solving techniques such as SPC, design of experiments (DOE) and Taguchi methods (Antony et al., 1999). This probably explains the problem for employers when trying to recruit graduates with the necessary SPC skills.

A user-friendly/practical guide is needed to provide a step-by-step approach of where and how to start implementing SPC in organizations. Rungtusanatham et al. back up this statement:

Very little knowledge has accumulated or has been documented to identify, describe, and define the requisite organisational policies and actions to make the implementation and subsequent practice of SPC an effective and viable part of any organisation's quality management system (Rungtusanatham et al., 1997).

The authors are currently developing a useful and practical framework for the implementation of SPC that will assist industrial engineers with limited skills and knowledge in SPC. The framework will take the form of a systematic methodology for the effective implementation of SPC in any industrial setting.

Essential ingredients for the successful application of SPC

In order to apply SPC effectively in any organization (either service or manufacturing), it is fundamental to understand the essential ingredients that will make the application successful. There are several prerequisites necessary for successful SPC implementation. Four essential areas that should be the focus are:

1. 1.

   Management issues – total company commitment, creating a responsive environment for actions on the processes/systems, necessary resources for training and education.

2. 2.

   Engineering skills – understanding benefits, gauge capability, action taken and understanding of processes/systems.

3. 3.

   Statistical skills – statistical stability, choosing, calculating control limits, and interpreting control charts correctly.

4. 4.

   Teamwork skills – company-wide understanding of SPC, its benefits and rewards, requires co-operation and support from all levels of an organization.

Figure 3 illustrates the four prerequisites for the successful application of SPC in any organization.

First, management commitment is an absolutely essential component for the resolution of common and special causes of variation. Unless top management is prepared to provide the necessary resources and support towards any changes (i.e. in terms of actions on the process/systems), a company is likely to fail in its use of SPC. The next major point for successful SPC introduction is training at all levels, with a bottom-top approach from operators to top management. It is very important to make everyone aware of the key benefits and the role of SPC for continuous improvement of product/process performance. It is important to note that everyone in the SPC team should know why the company is employing SPC for variation reduction in core processes. Furthermore, if sufficient time and resources are spent on training and education, it may be possible to avoid failing in the engineering, statistical and teamwork areas (areas 2-4 above). It is advised to form a process action team (PAT) responsible for taking necessary remedial actions when the process is going out of control. Finally, an SPC implementation team should be selected with an SPC facilitator to lead and steer the team.

Conclusion

This paper attempts to remove the myth that SPC is concerned only with control charting of processes. Furthermore, key factors needed for SPC are defined, namely:

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  management issues;

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  engineering skills;

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  statistical skills;

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  teamwork skills.

Within higher education, there is a need to move away from teaching only control charting of processes, towards a more embracing curriculum regarding SPC. Moreover, engineers should be made fully aware of the necessary ingredients mentioned for successful SPC implementation. A suggestion for the teaching of such quality improvement techniques is to review the successful case studies of SPC implementation. A major issue addressed by this paper is the need for a systematic and practical methodology (i.e. where to start and how to perform an SPC study in an organised manner) for the implementation of SPC in industry. The authors are currently developing such a useful framework which will encourage the wider application of SPC in the UK manufacturing and service organizations with success.

Ben Mason and Jiju AntonyWarwick Manufacturing Group, School of Engineering, University of Warwick, UK

ReferencesAntony, J. and Kaye, M. (1999), Experimental Quality – A Strategic Approach to Achieve and Improve Quality, Kluwer Academic Publishers.Antony, J., Kaye, M. and Frangou, A. (1998), "A strategic methodology to the use of advanced statistical quality improvement techniques", The TQM Magazine, Vol. 10 No. 3, pp. 169-76.Brannstorm-Stenberg and Deleryd (1999), "Implementation of statistical process control and process capability studies: requirements or free will?", Infotrac Electronic Resources.Caulcutt, R. (1999), "Statistical process control", Assembly Automation, Vol. 16 No. 4, p. 13.Dale, B. (1994), Managing Quality, Prentice-Hall, UK.Dale, B., Shaw, P. and Owen, M. (1990), "SPC in the motor industry: an examination of implementation and use", International Journal of Vehicle Design, Vol. 11 No. 2, pp. 213-18.Jones, P. and Dent, M. (1994), "Lessons in consistency: SPC in Forte plc", The TQM Magazine, Vol. 6 No. 1, pp. 18-23.Madu, C. (1998), Handbook of Total Quality Management, Kluwer, London.Mitra, A. (1998), Fundamentals of Quality Control and Improvement, Macmillan Publishers.Oakland, J. (1999), Statistical Process Control, Butterworth-Heinemann, Oxford.Rungtusanatham, M., Anderson, J.C. and Dooley, K.J. (1999), "Conceptualizing organizational implementation and practice of statistical process control", Journal of Quality Management, Vol. 2 No. 1, pp. 113-37.Watson, R. (1998), "Implementing self-managed process improvement teams in a continuous improvement environment", The TQM Magazine, Vol. 10 No. 4, pp. 246-57.Wood, M. (1994), "Statistical methods for monitoring service processes", International Journal of Service Industry Management, Vol. 5 No. 4, pp. 53-68.Xie and Goh (1999), "Statistical techniques for quality", The TQM Magazine, Vol. 11 No. 4, pp. 238-41.

Action points

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  Underneath the umbrella of TQM there are several techniques that can be used to improve quality – one of these is statistical process control.

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  SPC can be used to control processes and reduce variation.

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  The prerequisites for SPC implementation are in four essential areas: management issues, engineering skills, statistical skills, and teamwork skills.

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  There is a need for a framework to enable the encouragement of the wider application of SPC in the UK manufacturing and service organizations.

KEY READINGS

An empirical assessment of internal customer service S. Farner, F. Luthans and S.M. Sommer, Managing Service Quality, (UK), Vol. 11 No. 5, 2001

Conducts empirical research into the role internal customer service plays in delivering top quality service to external customers. Reviews extant literature regarding the conceptualization of process management and continuous improvement, as these principles provide the foundations upon which the concept of internal customer service is built. Considers the arguments for and against the proposition that internal customer service is a valid means of improving quality service to external customers. Seeks to reconcile these opposing viewpoints by conducting an empirical assessment of the relationship between internal and external customer service in a large wholesale distribution firm based in the USA. Describes the study methodology and discusses the findings; establishes that, while sales associates that perceived higher levels of internal service responsiveness were associated with external customers who felt they were receiving better service, these same sales associates perceived that higher levels of reliability were related to lower levels of perceived quality service on the part of internal customers. Concludes that, while internal customer service may be important, the relationship is more complex than first envisaged.

Quality focus says:A highly original article with strong research implications.

Quality – why do organizations still continue to get it wrong?B.G. Dale, A. van der Wiele and A.R.T. Williams, Managing Service Quality, (UK), Vol. 11 No. 4, 2001

Investigates why certain UK organizations and their management fail to operate by the commonly held ethos that organizations need to pursue continuous quality improvements in terms of product and service offerings in order to remain competitive. Profiles five organizations – a transport company, a computer retailer, a software support company, a car dealer and a cable communications company – which have exhibited major quality failings and failed to satisfactorily rectify them. Attempts to pinpoint the reasons for quality failure in these organizations; notes that, due to its unique characteristics, quality failure is particularly evident in the service sector; reveals that the organizations highlighted in the case studies do not appear to be exposed to serious competition for their services and exhibit a lack of quality management heritage, which together manifest themselves in an indifference towards customer satisfaction issues. Considers ways in which these failings could be countered; provides a summary of the key findings of the study.

Quality focus says:A case study – strong on practical implications.

Statistical techniques for qualityM. Xie and T.N. Goh, The TQM Magazine, (UK), Vol. 11 No. 4, 1999

Examines the role of statistical techniques in the history of quality, focusing on recent trends and problems associated with the application of statistical methods in quality studies. Looks at the use of statistical process control techniques such as control charts and process capability studies in product and process quality improvement, suggesting there needs to be a shift from the monitoring of process deterioration to identifying opportunities for improvement. Assesses the feasibility of using statistical design of experiments in a non manufacturing situation. Outlines other statistical-related techniques such as Pareto analysis and the do it right first time philosophy that encourages the use of statistical methods to draw confident conclusions.

Quality focus says: A theoretcial article but with strong implications for both research and practice.