sign in sign up
<<
Home , DMAIC, Kaizen, Project charter, Six Sigma

View metadata, citation and similar papers at core.ac.uk brought to you by CORE

provided by Elsevier - Publisher Connector

Available online at www.sciencedirect.com ScienceDirect

Procedia Economics and Finance 34 ( 2015 ) 565 – 572

Business Economics and 2015 Conference, BEM2015 Continuous Improvement by Statistical Process Control

Pavol Gejdoša*

aTechnical University in Zvolen, Masarykova 24, 960 53, Zvolen, Slovakia

Abstract

Article deals with the application of selected tools of statistical process control, through which we can achieve continuous quality improvement. The advantage of these tools is that they can identify the effects of the processes that cause unnatural variability in processes that result of errors and poor quality. Tools like capability index, , model DMAIC, , etc. can reliably determine the anomalous variability in the process and thereby contribute to quality improvement. In the paper through and Shewhart control charts action exposing the systemic implications of the processes and therefore unnatural variability in processes, which result in non-compliance. The results clearly show that through the DMAIC model can systematically improve quality. Histograms show the contribution of the of frequencies monitored quality characteristics while Shewhart control charts show that the investigated processes are under statistical control. Use of DMAIC model as well as other statistical quality tools is a way to achieve continuous quality improvement. ©© 20152016 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. This is an open access article under the CC BY-NC-ND license (Peerhttp://creativecommons.org/licenses/by-nc-nd/4.0/-review under responsibility of the Organizing). Committee of BEM2015. Peer-review under responsibility of the Organizing Committee of BEM2015 Keywords: Quality, Improvement, Statistical Process Control, DMAIC, Variability

1. Introduction

The basic issue in a quality orientated organisation is to what level we are able to satisfy ' expectations. If customers' expectations are defined, it is necessary to quantify how we are able to satisfy these expectations. A product which should be suitable for use should be produced in a stable process, which that the process should be able to produce the product with an acceptable variability of stated quality indexes in terms of their stated

* Corresponding author. Tel.: +421-45-5206-491; fax: +0-000-000-0000 . E-mail address: [email protected]

2212-5671 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Organizing Committee of BEM2015 doi: 10.1016/S2212-5671(15)01669-X 566 Pavol Gejdoš / Procedia Economics and Finance 34 ( 2015 ) 565 – 572

target or nominal values. The basic principle of improving processes is based upon the assumption that the variability of quality index values have two types of causes: x random causes (change, common causes) are a permanent part of the process and influence all process components. They create a wide variety of individually unidentifiable causes, from which each slightly contributes towards overall variability. x definable causes (assignable, special causes) are causes which are not a permanent part of the process, they do not influence all process components but occur as a consequence of specific circumstances. They induce real changes in a process, reflected in the unnatural fluctuation in data used for evaluating process variability.

It is possible to ensure and improve quality in the production stage via so called operative which includes all operational methods and activities focusing upon monitoring processes and removing causes of non-conformity and defects in all stages of a product's life cycle. A decisive part of operative management focuses upon the process itself, i.e. in the production company upon production. It is no longer possible to improve quality during production as a process of transforming input elements into required outputs - products - but when the requirements and conditions stated in the pre-production stages are not met, this can cause a decrease in quality against the required level. The aim of the article is to show the possibilities of using DMAIC model and SPC tools to improve process quality. The advantage of this model is that it allows its individual stages use other tools of quality improvement (capability index, histogram, control chart etc.) The article describes the possibilities of improving quality and high performance of the company which used these tools and this idea is presented on practical example of the enterprise. On the practical examples we want to confirm or refute many of the authors of the efficacy of these instruments and their positive impact on business performance.

2. Material and Methods

A continual improvement process, also often called a continuous improvement process (abbreviated as CIP or CI), is an ongoing effort to improve products, services, or processes. These efforts can seek "incremental" improvement over time or "breakthrough" improvement all at once. (Sujová, Marcineková, 2015) Delivery ( valued) processes are constantly evaluated and improved in the light of their efficiency, effectiveness and flexibility. Some see CIPs as a meta-process for most management systems (such as management, quality management, , and program management). W. Edwards Deming, a pioneer of the field, saw it as part of the 'system' whereby feedback from the process and customer were evaluated against organisational goals. The fact that it can be called a management process does not that it needs to be executed by 'management'; but rather merely that it makes decisions about the implementation of the delivery process and the design of the delivery process itself.( ASQ, 2015) A broader definition is that of the Institute of who defined "continuous improvement as a gradual never-ending change which is: '... focussed on increasing the effectiveness and/or efficiency of an organisation to fulfil its policy and objectives. It is not limited to quality initiatives. Improvement in business strategy, business results, customer, employee and supplier relationships can be subject to continual improvement. Put simply, it means ‘getting better all the time’

2.1. Model DMAIC

DMAIC (an abbreviation for Define, Measure, Analyze, Improve and Control) refers to a data-driven improvement cycle used for improving, optimizing and stabilizing business processes and designs. The DMAIC improvement cycle is the core tool used to drive Six projects. However, DMAIC is not exclusive to and can be used as the framework for other improvement applications (Pande, Neuman, Cavanagh, 2002). Define - The purpose of this step is to clearly articulate the business problem, goal, potential resources, project scope and high-level project timeline. This information is typically captured within document. Write down what you currently know. Seek to clarify facts, set objectives and form the project team. Measure - The purpose of this step is to objectively establish current baselines as the basis for improvement. This is a data collection step, the purpose of which is to establish process performance baselines. The performance Pavol Gejdoš / Procedia Economics and Finance 34 ( 2015 ) 565 – 572 567 metric baseline(s) from the Measure phase will be compared to the performance metric at the conclusion of the project to determine objectively whether significant improvement has been made. The team decides on what should be measured and how to measure it. It is usual for teams to invest a lot of effort into assessing the suitability of the proposed measurement systems. Analyze - The purpose of this step is to identify, validate and select root cause for elimination. A large number of potential root causes (process inputs, X) of the project problem are identified via (for example a fishbone diagram). The top 3-4 potential root causes are selected using multi-voting or other consensus tool for further validation. A data collection plan is created and data are collected to establish the relative contribution of each root causes to the project metric, Y. This process is repeated until "valid" root causes can be identified. Within Six Sigma, often complex analysis tools are used. However, it is acceptable to use basic tools if these are appropriate. Improve - The purpose of this step is to identify, test and implement a solution to the problem; in part or in whole. This depends on the situation. Identify creative solutions to eliminate the key root causes in order to fix and prevent process problems. Use brainstorming or techniques like Six Thinking Hats and Random Word. Some projects can utilize complex analysis tools like DOE (), but try to focus on obvious solutions if these are apparent. Control - The purpose of this step is to sustain the gains. Monitor the improvements to ensure continued and sustainable success. Create a control plan. Update documents, business process and training records as required. A Control chart can be useful during the Control stage to assess the stability of the improvements over time by serving as 1. a guide to continue monitoring the process and 2. provide a response plan for each of the measures being monitored in case the process becomes unstable.

2.2. Statistical process control

Control charts (RD) are the most frequently used tool in the statistical regulation of processes. They allow more accurate distinguishing of random from systematic causes of fluctuations in the value of a mark of quality, i.e. they facilitate regulation and improvement in the quality of the process. When applying Control charts, it is assumed that the behaviour of the process is characterised by the level or one or several qualitative values. We call these values regulated values. Control charts are used in monitoring processes and when ascertaining the need for corrections or changes in the process, in order to achieve a better mean value of the process or in order to reduce variability in the process. In control charts, the horizontal axis contains the times when statistical sampling of regulated values took place, and the vertical axis contains calculated values of the appropriate sample characteristics. Control charts also include criteria for comparing sample characteristics. (Terek, Hrnčiarová, 2004)These criteria are control limits:

UCL 2 u RAx (1)

LCL 2 u RAx (2)

The centre of the zone defined by control limits is a central line (CL).

 1    1 n x = ( x + x + x ) = Xi 1 2 k ¦ (3) k k i 1´

568 Pavol Gejdoš / Procedia Economics and Finance 34 ( 2015 ) 565 – 572

The distance of control limits from the central line equals the triple of the appropriate sample characteristics. For normal distribution of quality characteristics and under the assumption that the production process is statistically maintained, the stated limits illustrate that approximately 99.73% of normally distributed sample characteristics will lie inside the zone bordered by control limits.

Fig. 1. Control chart.

Capability index ppk which describes the preliminary and is based on the selection standard deviation This can be calculated using formula:

§ P  LSL USL  P · p pk min¨ ; ¸ (4) © 3s 3s ¹

where LSL is the lower tolerance limit USL is the upper tolerance limit μ is the average value of the monitored quality feature.

A histogram of frequency distribution represents a graphic form of processing the results of mass discovery or a set of measurements. It is a block diagram which displays the division of absolute or relative frequency of a monitored variable at individual intervals. The base of individual blocks (on the x axis) corresponds to the width of interval, and the height of the blocks (on the y axis) expresses the frequency of variables of the monitored variable at appropriate intervals. In quality management, it mainly refers to the frequency distribution of quality values or values related to production factors influencing the quality of the products (Tošenovský, Noskievičová, 2000).

3. Results and Discussion

DMAIC model and its phases are used for essential improvement tool.

Define – M- A – I – C Throughout the production process is the possibility of occurrence of 88 errors. The product contains 12 critical parameters according to customer specifications. Through Pareto analysis of these 12 parameters we have been divided into two groups. The parameters in the first group should be controlled during the production process due to a possible greater risk of quality defects. The parameters of the second group do not require special procedural Pavol Gejdoš / Procedia Economics and Finance 34 ( 2015 ) 565 – 572 569 attention because they are crucially on the instrument that upon release and dimensional measuring, creating distortions to the minimum possible level. Table 1 describes 7 critical parameters from first group.

Table 1. Seven critical quality parameters.

Parameter Customer requirements

1 Location cone 0,56 ± 0,1mm 2 Height of toothing 2,1 ± 0,05mm

3 The radial run out of the cone to the teeth < 0,15mm 4 Roundness cone < 0,04mm 5 Cone angle 6,5° 5´± 15´

6 Replacing the bridge - gearing 4,60 ± 0,1mm 7 Dimensions of the gearing through the rollers 80,711 – 80, 924mm

D – Measure – A – I - C In this part we focused on the measurement of critical parameters of Group 1. The scope of the investigated samples was set at n = 100. Sampling was carried out at regular 10 minute intervals (every 10 minutes taking one sample). On each individual sample measurements were made all seven critical parameters. D – M – Analyze – I - C According to the standard QS-9000 were measured values arranged in 25 subgroups of size 4. In order to obtain capability index Ppk we evaluate them and see which of these parameters meet the requirements for process capability. For parameters with a capability index below 1.67 we present Shewhart control charts. The following table 2 shows the basic statistical characteristics of the product analyzed data on all parameters.

Table 2. The basic statistical characteristics of the product analyzed data on all parameters.

Parameter

1 2 3 4 5 6 7 Maximum Value 0,615 2,109 0,089 0,034 6,500 4,630 80,819 Minimum Value 0,515 2,078 0,021 0,013 6,444 4,596 80,750 R 0,100 0,03 0,07 0,021 0,06 0,03 0,07 Standard deviation s 0,0213 0,0062 0,0153 0,0048 0,0108 0,0071 0,0146 average x 0,5684 2,0955 0,0490 0,0203 6,4745 4,6134 80,7858 USL 0,660 2,150 0,150 0,040 6,583 4,700 80,924 LSL 0,460 2,050 - - 6,250 4,500 80,711 Ppk 1,431 2,446 2,198 1,370 3,368 4,084 1,713

As shown by the indicator Ppk, five parameters satisfies the condition of process capability Ppk> 1.67. Our aim was to analyze the effects that could be disqualification process with respect to the two parameters studied. The analyzed data for both parameters are shown in the histograms visualizing distribution of the shape measured values of the average. (fig.2 and fig.3)

570 Pavol Gejdoš / Procedia Economics and Finance 34 ( 2015 ) 565 – 572

Fig. 2. Histogram for Location of cone.

Fig. 3. Histogram for Roudness of cone.

Both parameters were subjected to statistical study of stability, based on Shewhart control charts. (Fig.4, Fig.5,)

Fig. 4. Shewhart control charts for average by Location of cone. Pavol Gejdoš / Procedia Economics and Finance 34 ( 2015 ) 565 – 572 571

Fig. 5. Shewhart control charts for average by Roudness of cone.

D – M – A – Improve - Control From the statistical analysis, we determined the stability of the performance of the process can not be reduced by eliminating non-random cause variability. It was therefore important for us to review the current state of in the company. After recapitulating all the results of the analysis and review possible solutions, we concluded that to improve results achieved by the company could use other methods such as DOE, ANOVA, Poka Yoke, Six Sigma and other means, which is able to improve the performance of their processes, which is already currently high. Another element to improve, along with the aforementioned methods is the change in organizational structure, which is an integral part of implementation Six Sigma methods. Pictures 3-7 presents histograms and Shewhart control charts on selected parameters which capability index PPK was lower than 1,67 which is the eligibility requirements for this indicator. The shapes of histograms for both parameters are signs of normal frequency distribution which is a sign of the natural variability of behavior which is confirmed by control charts. Despite this fact, it is possible to improve the performance of these processes by applying other tools because the occurrence of the monitored quality characteristic between the regulatory limits indicates process performance about 3 sigma. As we can see from the results that the company uses in managing quality improvement tool achieves very high performance of their processes and products. The results confirm the claims of various authors about the positive effects of SPC and other quality tools for achieving continuous quality improvement. Our assumption was confirmed by the following example. From the presented results it is evident that through the application of SPC is to achieve high performance processes that are essential for achieving high performance of the entire company. The results confirmed what various authors say. For example John S. Oakland, (2003) says that Statistical process control (SPC) methods, backed by management commitment and good organization, provide objective means of controlling quality in any transformation process, whether used in the manufacture of artefacts, the provision of services, or the transfer of information. SPC is not only a tool kit. It is a strategy for reducing variability, the cause of most quality problems; variation in products, in times of deliveries, in ways of doing things, in materials, in people’s attitudes, in equipment and its use, in maintenance practices, in everything. Masaaki (1997) said that Statistical Process Control is an analytical decision making tool which allows you to see when a process is working correctly and when it is not. Variation is present in any process, deciding when the variation is natural and when it needs correction is the key to quality control. Statistical process control (SPC) is a set of statistical methods based on the theory of variation that can be used to make sense of any process or outcome measured over time, usually with the intention of detecting improvement or maintaining a high level of performance. SPC combines rigorous time series analysis with graphical presentation of data, and can provide early insights into the data in a manner understandable to a wide range of audiences (Benneyan, Lloyd, and Plsek, 2003). Using frequently measured data, SPC can be used to detect, early on, whether any change has taken place since the start of the intervention, long before results from a larger, summative 572 Pavol Gejdoš / Procedia Economics and Finance 34 ( 2015 ) 565 – 572

are available. Such information can be used for purposes ranging from forming hypotheses about changes in outcomes to adapting elements of the intervention to increase the likelihood of success.

4. Conclusion

Article confirmed in its experimental part theoretical assumptions authors who define the SPC and the DMAIC model as tools for continuous quality improvement. Here again, it was confirmed that with Shewhart control charts, capability indexes, histograms can be for achieving the control of variation in the procedure in order to be filled with the requirements of the customer. SPC can be regarded as a very effective tool in ensuring process stability. Benefits of Article are the use of SPC with DMAIC improvement model. This combination of tools is very suitable for achieving the desired objectives of quality improvement and efficient manner can help solve all tasks and problems of the process of quality improvement. The methods developed in the first half of this century by Shewhart and others are still very useful in many current applications. Their familiarity and simplicity relative to other methods can often compensate for loss in efficiency. In our changing manufacturing environment, however, it is important to consider some of the methods developed more recently such as those for several related quality characteristics, multiple processes, and more sophisticated sampling plans. Infusion of new ideas into the body of commonly accepted SPC knowledge has been much too slow and has led to much of the criticism regarding the relevance of SPC in the current manufacturing environment.

References

Benneyan JC, Lloyd RC, Plsek PE. Statistical process control as a tool for research and healthcare improvement. Qual Saf Health Care 2003 Č ierna, H., Sujová, E., Ťavodová, M., 2015.Vybrané aspekty manažmentu pre technikov. Vysokoškolská učebnica, Technická univerzita vo Zvolene, 2015. - 387 s. ISBN 978-80-228-2733-1 Deming, E. W. 2011. Statistical Adjustment of data. Dover Publications, ISBN: 0486646858 Gejdoš, P., 2014 Budovanie a certifikácia systémov manažérstva kvality v teórií a praxi. Vedecká monografia, TU Zvolen, Zvolen 2014, 69s., ISBN 978-80-228-2725-6. Hrubec, J., et al., 2009. Integrovaný manažérsky systém. Nitra: Slovenská poľnohospodárska univerzita, 2009. 544 s. ISBN 978-80-552-0231-0. Ishikawa, K. (1985), What Is Total Quality Control? The Japanese Way (1 ed.), Englewood Cliffs, New Jersey: Prentice-Hall, p. 198, ISBN 978- 01-395-2433-2. Ishikawa, K.: Guide to Quality Control, Tokyo, Asian Productivity Organization, 1982. Masaaki, I., 1997. Gemba : A Commonsense, Low-Cost Approach to Management (1st ed.). McGraw-Hill. ISBN 0-07-031446-2. Montgomery, D.: Introduction to Statistical Quality Control. Hoboken, New Jersey: John Wiley & Sons, Inc..2005. pp. 148. ISBN 97-804-716- 5631-9. Nenadal, J., Plura, J., 2008. Moderní management jakosti, management press, 2008, ISBN 978-80-7261-186-7, s.348-354 Oakland, J., 2003. Statistical process control. MPG Books Limited, Bodmin, Cornwall, 2003, ISBN 0 7506 5766 9 Pande, P., Neuman,R., Cavanagh, R., 2002. Zavádíme Metodu Six Sigma, TwinsCom s.r.o., ISBN 80-238-9289-4 Potkány, M., Giertl, G., 2014. Comparison of selected performance indicators of woodworking industry in the Czech Republic and Slovakia. In. Position and role of the forest based sector in the green economy : proceedings of scientific papers, International Association for Economics and Management in Wood Processing and Furniture Manufacturing - WoodEMA, 2014. p. 131-138. ISBN 978-953-57822-1-6. Simanová, Ľ., 2013. Vybrané koncepcie a metódy zabezpečovania a zlepšovania kvality procesov a ich využívanie pri riadení procesov. In. zborník z medzinárodnej vedeckej konferencie Ekonomika a manažment podnikov 2013. TU Zvolen, Zvolen 2013, s.309-315, ISBN 978- 80-228-2565-8 Sujová, A., Hlaváčková, P., Marcineková, K., 2015. Measuring the Impact of Foreign Trade on Performance Growth of the Woodprocessing Industry. In: Wood Research, Vol. 60, No. 3, 2015, p. 491 - 502. ISSN 1336-4561 Prístupné online: http://www.centrumdp.sk/wr/201503/15.pdf Sujová., , A Marcineková, K. 2015. Modern Methods of Process Management Used in Slovak Enterprises. In: Procedia-Economics and Finance Journal, vol. 23/2015, Elsevier Ltd., p. 889 - 893. ISSN 2212-5671, doi: 10.1016/S2212-5671(15)00381-0 Prístupné online: http://ac.els- cdn.com/S2212567115003810/1-s2.0-S2212567115003810-main.pdf?_tid=f5abbcea-295b-11e5-9d7c- 00000aacb362&acdnat=1436791244_e20038b90e4840c6597448c04b90d27d Terek, M., Hrn čiarová, Ľ., 2014. Štatistické riadenie kvality. Bratislava: IURA EDITION s.r.o. Bratislava 2004, 234s, ISBN 80-89047-97-1. Tošenovský, J., Noskievi čová, D., 2000. Statistické metódy pro zlepšování jakosti. Ostrava: Montanex 2000, 362s. ISBN 80-7225-040-X.