Six Sigma

from Wikipedia, the free encyclopedia

Six Sigma (6σ) is a management system for process improvement, statistical quality target and at the same time a method of quality management . Its core element is the description, measurement, analysis, improvement and monitoring of business processes with statistical means. It is a method with a comprehensive set of tools for the systematic improvement or redesign of processes. The work breakdown structure for process improvement projects follows the procedure Define - Measure - Analyze - Improve - Control (DMAIC) . Six Sigma projects ultimately aim to improve company results.

Six Sigma symbol

historical development

The forerunners of Six Sigma were first introduced in Japanese shipbuilding in the 1970s, and later in the Japanese electronics and consumer goods industries. Six Sigma was developed in 1987 by Motorola in the USA.

The Six Sigma approach achieved great popularity through successes at General Electric (GE). Associated with this is the name of the manager Jack Welch , who introduced Six Sigma at GE in 1996. In 2002 the International Society of Six Sigma Professionals (ISSSP) presented him with the ISSSP Premier Leader Award at the second ISSSP Leadership Conference .

Today, numerous large companies work with Six Sigma - not only in the manufacturing industry, but also in the service sector. Many of these companies expect their suppliers to provide evidence of Six Sigma quality in their production processes. More than two thirds (69%) of the companies use Six Sigma to improve processes, while only one third (31%) use the method to develop new processes.

Modified DMAIC or process management processes are used in the product and process development area , which are summarized under the term Design for Six Sigma (DFSS, DMADV). There is also a variant of Six Sigma for software development .

Since around the year 2000, Six Sigma has been combined in many implementations with the methods of Lean Management and referred to as Lean Sigma or Lean Six Sigma or Six Sigma + Lean .

In the course of the sustainability discussion of process changes, process management (in the sense of the management of business processes in day-to-day business , but not primarily in the sense of the GPM IT tool topic) has been an increasing topic since 2005 as a supplement to the project methodologies DMAIC and DFSS .

Roles and tasks

Six Sigma improvement projects are led by specially trained employees. The management psychological concept of Six Sigma is based on role definitions that appeal to the rank indicator (belt color) of Japanese martial arts styles are based (see. Dan and Kyu ):

  • The champion is at the highest hierarchical level in the Six Sigma project. Several champions are classified at Magnusson, Kroslid, Bergman:
    • The head of strategic management is a long-standing successful entrepreneur who runs teaching events at universities. The marking is done via the initials belt (English initial belt ).
    • The delivery champion is a member of the company's management; he is the engine and advocate for Six Sigma in the company.
    • The project champion (also project sponsor ) is usually a member of middle management and client for individual Six Sigma projects in the company. These managers are also often the process owners for the process to be improved.
  • The black master belt (English master black belt ) is a full-time improvement expert ; he works as a coach , trainer and instructor.
  • The Black Belt (English black belt ) also works on a full-time basis as an improvement expert; he takes on project management tasks and has in-depth knowledge of the application of the various Six Sigma methods. The role description of Black Belts provides for the implementation of four improvement projects per year with a resulting reduction in expenditure by 200,000 euros each (depending on the size of the company), as well as the overarching support of around four other projects.
  • The Green Belt (English green belt ) is located in management - these are mostly department heads, group leaders, planners and masters who work in project teams, or even conduct, under reporting to a black belt projects in their areas of responsibility and interdisciplinary teams.

In addition, depending on the company - in the rank below the green belt - there are also "unofficial" belt colors such as white, yellow and blue. These do not take on any project management tasks.

According to a general guideline - quoted in many books - companies should have one black belt for every 100 employees (“1 percent rule”). A black master belt should look after around 20 (experienced) black belts. For every black belt there are about 20 green belts.

The Six Sigma Toolbox

During the DMAIC phases, a variety of quality techniques are used that Six Sigma has adopted from the existing quality management practice. The following table provides an overview:

No. Customer tools Project tools Slimming tools Management tools
1 Kano model Network plan technology standardization Decision tree
2 Structuring of customer requirements , called requirement structuring Project and team description Value stream , bottleneck or material flow analysis Affinity diagram
3 House of Quality CTQ analysis ( Critical to Quality ) Value creation or waste analysis Relationship diagram
4th Loss function by Genichi Taguchi Tree diagram flow chart Tree diagram
5 Customer interviews Process capability analysis Supply chain matrix Matrix diagram
6th Customer questionnaires Cost-benefit analysis Setup time analysis Matrix data analysis
7th Conjoint analysis Control charts Red Tag Analysis Network plan technology
No. Design tools Graphics tools Statistics tools
1 Robust design , parameter design Test forms (including measurement plan) Statistical Design of Experiments (DoE)
2 Quality Function Deployment (QFD) histogram Process capability study
3 TRIZ Pareto chart Regression analysis
4th Pugh concept selection analysis Cause and effect diagram , also called Ishikawa or Fishbone diagram Multivariate analysis
5 FMEA / VMEA Graphic comparison Statistical test procedures ( F-test , ANOVA )
6th Fault tree analysis Relation diagram Probability net
7th Tolerance analysis and tolerance design Control charts Measurement system analysis (Gage R&R)

The Core Six Sigma Process: DMAIC

The most frequently used Six Sigma method is the so-called " DMAIC " cycle (Define - Measure - Analyze - Improve - Control = Define - Measure - Analyze - Improve - Control). This is a project and control loop approach. The DMAIC core process is used to make existing processes measurable and to improve them sustainably.

Define (D)

In this phase, the process to be improved is identified, documented and the problem with this process is described. This usually takes the form of a project charter. This also includes:

  • the desired target state,
  • the suspected reasons for the current deviation from the target state,
  • the project definition (members, use of resources, time planning)
DMAIC cycle for existing processes

In addition to the project charter, other tools are usually used, such as B .:

  • Problem definition using Kepner-Tregoe analysis.
  • SIPOC (Supplier, Input, Process, Output, Customer) - here, as with the flowchart , the process is shown in order to get a better understanding of what is happening within the process. In some cases, customer requirements for the output of the process and its requirements for the inputs (process requirements) are also formulated.
  • CTQ tree (Critical to Quality) - Description of which measurable, critical parameters determine the quality.
  • VoC (Voice of the Customer) - method to go from a verbal customer problem (eg: "The device is difficult to use") to specific target values ​​for eliminating the problem (eg: "The device is running out Each button has a meaningful label in font size 12. The buttons must be arranged in a logical order. "). In the define phase, VoC is one of the most important tools, as it can be used to avoid the customer being dissatisfied with the results in the end because they had different expectations.
  • Scope In / Scope Out - The demarcation of which aspects or areas should be part of the investigation and which should not.

Measure (M)

This phase is about determining how well the process really meets the existing customer requirements. This includes a process capability study for each relevant quality feature .

Tools used in this phase:

To secure the measuring capability is used in the so-called Six Sigma measurement system analysis (Analysis Measurement System), short-MSA.

Analyze (A)

The aim of the analysis phase is to find out the reasons why the process has not yet met customer requirements to the desired extent. Process analyzes such as B. Value creation , material flow or value stream analyzes as well as data analyzes (scatter) are created. During the data analysis, the process or test data collected in the previous phase are evaluated using statistical methods in order to identify the main sources of dispersion and to identify the underlying causes of the problem.

Tools used in this phase:

Improve (I) (or Engineer (E) for new processes)

After understanding how the process works, the improvement is now planned, tested and finally implemented. Tools are used that are also widely used outside of Six Sigma, for example:

  • Digit procedure
  • Knockout analysis
  • Criteria-based matrix
  • Cost-benefit analysis
  • Target process display
  • Poka Yoke
  • Brainstorming and other creative techniques to generate solution ideas
  • FMEA (Failure Mode and Effects Analysis) - method for determining the implementation risks of the improvement ideas

Control (C)

The new process is monitored using statistical methods. This is done mainly with SPC - control charts . In addition, other selected methods are listed from the specialist literature that are important for sustaining improvements, such as:

  • Process documentation
  • Process management and response plan
  • Precontrol
  • Project success calculation.

The Six Sigma Roadmap shows a guide for the chronological use of the most important tools.

The effort for a DMAIC is high, so that the implementation is only worthwhile if the expected added value increases from the improved process are higher than 50,000 EUR. The aim is a project duration of four to five months.

Six Sigma as a statistical quality goal

As a rule, there is an undesirable scatter in the process results for every quality feature. The average or expected value is also often not exactly on the target value.

As part of a so-called process capability study , such deviations from the ideal state are related to the tolerance range of the relevant feature. The standard deviation of the characteristic (letter: σ; spoken: sigma) plays an essential role. It measures the spread of the feature, i.e. how much the feature values ​​differ from one another.

The larger the standard deviation compared to the width of the tolerance range, the more likely it is that the tolerance limits will be exceeded. Likewise, the further the mean value moves away from the center of the tolerance range (i.e. the closer it comes to one of the tolerance limits), the greater the percentage of excess. Therefore, it makes sense to measure the distance between the mean value and the closest tolerance limit in standard deviations. This distance divided by 3 σ is the process capability index C pk ; C pk = 1 applies if the mean value 3σ is away from the closest tolerance limit .

Graph of the normal distribution on which the basic statistical assumptions of the Six Sigma model are based. The Greek small letter σ (Sigma) stands for the horizontal distance between the arithmetic mean µ (peak of the normal distribution curve ) and the turning point of the curve. The larger this distance, the wider the spread of the values ​​of the measured feature. In the figure shown here, the specification limits (USL, LSL) are 6 σ away from the mean. Values ​​beyond the specification limits are extremely unlikely, even if the distribution curve should later shift 1.5 σ to the left or right.

The name "Six Sigma" results from the requirement that the closest tolerance limit should be at least six standard deviations (6σ, pronounced "Six Sigma") away from the mean ("Six Sigma Level", C pk = 2). Only if this requirement is met can one assume that “zero-defect production” will practically be achieved, i.e. that the tolerance limits will almost never be exceeded. Since most products consist of various individual components and are also manufactured in several processes or process steps, a reliable spread of ± 3 σ is not sufficient to ensure almost error-free production. Production processes must therefore be developed that are so robust against external influences that they allow a significantly greater spread.

Expected proportion of errors at the Six Sigma level

When calculating the expected proportion of errors, it is also taken into account that processes in practice are exposed to unavoidable mean value fluctuations over longer observation periods. It would therefore be too optimistic to assume that the distance between the mean value and the critical tolerance limit would always be a constant 6 standard deviations. Based on practical observations, it has become common practice within the framework of Six Sigma to take into account a long-term shift in the mean value by 1.5 standard deviations. If such a shift in the mean should actually occur, the mean instead of 6 would only be 4.5 σ away from the closest tolerance limit.

That is why the exceeding portion for the limit 6σ is given as 3.4 DPMO ( defects per million opportunities , English defects per million opportunities ). In the case of the most common distribution type, the normal distribution , this corresponds to the probability that a value occurs that deviates from the mean value by at least 4.5 standard deviations on the side with the closest tolerance limit and thus exceeds the tolerance limit. The following table gives DPMO values ​​for different sigma levels; all of these values ​​take into account the mentioned mean value shift by 1.5 σ. The DPMO value specified for 3 σ thus corresponds, for example, to the one-sided excess portion for 1.5 σ, that for 4 σ corresponds to the one-sided excess portion for 2.5 σ, etc.

Sigma level DPMO faulty% error free% Short term C pk Long term C pk
1 691,462 68% 32% 0.33 -0.17
2 308,538 31% 69% 0.67 0.17
3 66,807 6.7% 93.3% 1.00 0.5
4th 6.210 0.62% 99.38% 1.33 0.83
5 233 0.023% 99.977% 1.67 1.17
6th 3.4 0.00034% 99.99966% 2.00 1.5
7th 0.019 0.0000019% 99.9999981% 2.33 1.83

Success factors for using the Six Sigma method

The specialist literature names many critical success factors, which are listed below:

  • Management involvement - Since the introduction of Six Sigma is a strategic decision, management support is one of the most important success factors. Even after the introduction, ensuring long-term success depends heavily on the commitment of the management.
  • Six Sigma method knowledge - The Six Sigma method combines the well-known quality assurance methods and applies them in a systematic procedure. Appropriate training of employees is required to be able to use this procedure.
  • Link to business strategy - The Six Sigma method has the primary goal of improving company results while increasing customer benefit. In the business strategy, the interests of customers and companies are combined.
  • Connection to the customer - The Six Sigma method aims not only to improve company results but also to increase customer satisfaction. To do this, the customer requirements must be known. Every Six Sigma project therefore begins with an analysis of external and internal customer requirements.
  • Project selection - The selection of promising projects with a view to the sustainable fulfillment of customer requirements at reduced costs is of particular importance. The measurability of the qualitative improvements is also important, as is the verifiability of the financial success. In addition, when selecting a project, attention must be paid to the feasibility of the project within a specified project duration.
  • organizational infrastructure - a supporting organization that may a. consists of a sufficient number of belts is essential for a successful Six Sigma company.
  • Change in culture - Multi-year Six Sigma application means that the focus shifts from pure cost reduction to increasing customer benefit.
  • Project management skills of the belts - As the Six Sigma method u. a. Based on successful project management, sufficient project management skills are required to achieve a wide variety of milestones and time goals.
  • Liaison with Suppliers - The reason for working with key suppliers is because improvements in suppliers' products and processes carry over to the Six Sigma company.
  • Training the belts in the Six Sigma method - For a successful Six Sigma implementation, it is important that a “critical mass” of adequately trained employees is achieved.
  • Connection to personnel planning - Requirements for analytical-statistical thinking as well as soft skills such as communication skills, teamwork and leadership skills are to be assumed for the belts.

Six Sigma projects

Six Sigma is implemented exclusively in the form of projects. The results of a Six Sigma program depend on the outcome of the individual projects. Therefore, the project selection and the specific project work must be given special attention. The direct responsibility for the project results lies with the process owner.

The following basic data are considered success factors:

  • Project duration: 3 to 6 months
  • Project volume: for large companies an average of € 250,000, for medium-sized companies an average of € 100,000
  • Project framework: thematically and organizationally definable
  • Process focus: there is a repetitive process with a repetitive, measurable process output

The following ranking list for the selection criteria of Six Sigma projects emerged from a survey:

  • Annual cost savings: 68%
  • Process error rate: 66%
  • Customer satisfaction: 44%
  • Repetitive sequence: 34%
  • Limited scope: 28%

One of the most common reasons Six Sigma projects fail is picking the wrong projects.

Combination of Six Sigma with other methods

Six Sigma can easily be combined with other methods that complement it in an ideal way. In most companies, Six Sigma is supplemented with Lean Management methods to create Lean Sigma. For the selection of the Six Sigma projects, the application of the theory of constraints (TOC) makes sense, since projects that remove a bottleneck have the greatest successes - so-called breakthrough successes. More than a third (36%) combine Six Sigma with customer relationship management (CRM). Around a quarter of the application combines Six Sigma with benchmarking, supply chain management and digital transformation. Before digitizing a process, the process should be optimized first.

Six Sigma in the financial industry

In recent years, Six Sigma projects have also been implemented in the financial industry with increasing frequency. In the financial industry there are a large number of processes (e.g. setting the price of financial instruments) for which it is essential that they run quickly and without errors. If this freedom from errors is not guaranteed, unpleasant consequences quickly arise with high follow-up costs (e.g. high tax reclaims). Errors in the master data and market data supply (e.g. incorrect exchange rate supply) can quickly lead to undesirable direct and indirect follow-up costs. Possible effects would be, for example, hanging orders in the system, incorrect price calculation or errors in reporting. As part of a Six Sigma project, the causes of such problems can be identified and made measurable. Individual solutions can be developed that lead to process optimization.

literature

  • Markus Köhler, Daniel Frank, Robert Schmitt: Six Sigma. Chapter 12 In: Tilo Pfeifer, Robert Schmitt (Ed.): Masing Handbook Quality Management . Carl Hanser Fachbuchverlag Munich Vienna, 6th revised edition 2014, ISBN 978-3-446-43431-8 .
  • Schmieder, Matthias; von Regius, Bernd, Leyendecker, Bert: Quality management in purchasing, avoidance of product defects , Wiesbaden 2018.
  • Schmieder, Matthias: Why is Six Sigma successful? Analysis of current studies, in: Practical Guide Six Sigma - Avoiding Errors, Improving Processes, Lowering Costs, ed. C. Gundlach and Roland Jochem, 2nd edition, Düsseldorf 2014, pp. 45–6.
  • Schmieder, Matthias: Introduction of Six Sigma in medium-sized companies, in: Practical Guide Six Sigma - Avoiding Errors, Improving Processes, Lowering Costs, ed. C. Gundlach and Roland Jochem, 2nd edition, Düsseldorf 2014, pp. 321–347.
  • R. Jochem, D. Geers, M. Giebel (Eds.): Six Sigma made easy. A textbook with a sample project for practical success. Symposion Publishing, Düsseldorf. ISBN 978-3-939707-83-7 .
  • Frank Bornhöft, Norbert Faulhaber: Successfully implementing Lean Six Sigma. Frankfurt School Verlag 2010, edition: 2nd expanded and updated, ISBN 978-3-937519-60-9 .
  • J. Moormann, D. Heckl., H. Lamberti (Ed.): Six Sigma in the financial sector . Edition: 3rd, completely new edition. Frankfurt School Verlag 2009. ISBN 978-3-937519-13-5 .
  • Stephan Lunau (eds.), Olin Roenpage, Christian Staudter, Renata Meran, and others: Six Sigma + Lean Toolset: Successful implementation of improvement projects 2nd revised edition, 2007, Springer, ISBN 978-3-540-46054-1 .
  • Stephan Lunau (eds.), Olin Roenpage, Christian Staudter, Renata Meran, and others: Design for Six Sigma + Lean Toolset: Successfully Realizing Innovations 1st edition 2007, Springer, ISBN 978-3-540-69714-5 .
  • Suzanne Birkmayer, Robert Dannenmaier, Sabine Matlasek, Wolfgang Weibert: six sigma toolkit. The DMAIC cycle in 15 steps. The most important tools in a compact form. ifss institute for six sigma, 2009, ISBN 978-3-200-00924-0 .
  • Markus H. Dahm, Christoph Haindl: Lean Management Six Sigma - Quality and Profitability in the Competitive Strategy. 1st edition, Erich Schmidt Verlag Berlin 2009, ISBN 978-3-503-11249-4 .
  • George Eckes: The Six Sigma Revolution - How General Electric and Others Turned Process Into Profits. John Wiley & Sons © 2000.
  • Gundlach, Carsten & Jochem, Roland (Hrsg.): Practical Guide Six Sigma: Avoid Errors, Improve Processes, Reduce Costs. Symposion Publishing, 2008, ISBN 978-3-939707-03-5 .
  • Craig Gygi, Neil DeCarlo, Bruce Williams: Six Sigma for Dummies. ISBN 3-527-70207-5 .
  • Mikel Harry, Richard Schroeder: Six Sigma . Campus 2000. Summary of the book available online .
  • Herbert Hofer, Sven Horsak, Christian Miller, Andreas Wassermann: Six Sigma - A model for small and medium-sized credit institutions? Bankakademie Verlag, 2005 ISBN 3-937519-49-1 .
  • Hutwelker, Reiner: Six Sigma in the Wilhelm Tell AG: a fictional project. , Pp. 181–221, In: Practical Guide Six Sigma: Avoid Errors, Improve Processes, Reduce Costs. Eds. Gundlach, Carsten and Jochem, Roland, Symposion Publishing, 2008.
  • Wilhelm Kleppmann: test planning . Optimize products and processes. 7th updated and expanded edition. Hanser, Munich a. a. 2011, ISBN 978-3-446-42774-7 ( practical series quality knowledge ).
  • Dag Kroslid, Konrad Faber, Kjell Magnusson: Six Sigma. Hanser reference book 2003, ISBN 3-446-22294-4 .
  • Kjell Magnusson, Dag Kroslid, Bo Bergman: Implementing Six Sigma. Hanser Wirtschaft 2003 ISBN 3-446-22295-2 .
  • Thomas Münster: Critical success factors for the use of the Six Sigma method, Südwestdeutscher Verlag für Hochschulschriften 2009, ISBN 978-3-8381-0086-9 .
  • Matthias : Why Six Sigma is successful - Analysis of current studies, pp. 39–64, In: Practice Six Sigma, Avoiding Errors, Improving Processes, Lowering Costs, Ed. Von Gundlach Carsten and Jochem Roland, Düsseldorf 2008.
  • Schmieder, Matthias: Introduction of Six Sigma in medium-sized companies, pp. 311–338, In: Practice Six Sigma, avoid errors, improve processes, reduce costs. Edited by Von Gundlach, Carsten and Jochem, Roland, Düsseldorf 2008.
  • Schmieder, Matthias; Comparison of Six Sigma in financial services companies and manufacturing companies, in Six Sigma in the financial sector, in Ed. Moormann, J. u. a. Frankfurt 2004, pp. 41-60.
  • Rath & Strong: Six Sigma Pocket Guide. TÜV Verlag, ISBN 0-9705079-0-9 .
  • Rolf Rehbehn, Zafer Bülent Yurdakul: With Six Sigma to Business Excellence. Strategies, methods, practical examples. 1st edition Publicis MCD Verlag 2003, ISBN 3-89578-185-1 or 2nd, revised and expanded edition, 2005, ISBN 3-89578-261-0 .
  • Armin Töpfer (Ed.): Six Sigma - conception and examples of success for practiced zero-defect quality 4th edition, Springer, Berlin 2007, ISBN 978-3-540-48591-9 .
  • Helge Toutenburg, Philipp Knöfel: Six Sigma - Methods and Statistics for Practice. Springer, 2007, ISBN 978-3-540-74210-4 .
  • Johann Wappis, Berndt Jung: Zero Defect Management. Implementation of Six Sigma. Carl Hanser Verlag Munich Vienna 2016, ISBN 978-3-446-44630-4 .
  • Andrea Chiarini: From Total Quality Control to Lean Six Sigma . Springer, Milan, New York 2012, ISBN 978-88-470-2658-2 , pp. 59 .
  • Gerd F. Kamiske , Jörg-Peter Brauer: Quality management from AZ: Explanations of modern terms of quality management. Carl Hanser Verlag, Munich 2006, ISBN 3-446-40284-5 .

software

  • Minitab
  • Visual-XSel
  • destra
  • Coding factory - business game / simulation for Six Sigma
  • Witness Simulation by Lanner

Web links

Commons : Six Sigma  - collection of images, videos and audio files

Individual evidence

  1. ↑ Andrea Chiarini: From Total Quality Control to Lean Six Sigma. Springer, 2012, ISBN 978-88-470-2658-2 .
  2. ↑ Matthias Schmieder: Empirical survey on the use of the operational excellence concept in Germany , on six-sigma-deutschland.de
  3. ↑ a b Magnusson, Kroslid, Bergman: Implementing Six Sigma. (2003), p. 23, ISBN 3-446-22295-2 .
  4. ↑ a b Ken Erdrich: Six Sigma - Contents. Retrieved January 1, 2018 .
  5. ↑ a b c d Magnusson, Kroslid, Bergman (2003), p. 24.
  6. ↑ a b c d e Magnusson, Kroslid, Bergman (2003), p. 25.
  7. ↑ Magnusson, Kroslid, Bergman (2003), p. 44.
  8. ↑ a b c d e f R. Jochem, D. Geers, M. Giebel: Six Sigma made easy. A textbook with a sample project for practical success. Symposion Publishing (2011), p. 214 ff.
  9. ↑ a b c d R. Jochem, D. Geers, M. Giebel: Six Sigma made easy. A textbook with a sample project for practical success , Symposion Publishing (2011), p. 269 ff.
  10. ↑ a b c Thomas Pyzdek: Motorola's Six Sigma Program (English)
  11. ↑ Craig Gygi, Neil DeCarlo, Bruce Williams: Six sigma for dummies . Wiley Publishing, Inc., Hoboken, NJ 2005, ISBN 0-7645-6798-5 , pp. 23, front cover .
  12. ^ Basem El-Haik, Nam P. Suh: Axiomatic quality. Integrating axiomatic design with six-sigma, reliability, and quality engineering . John Wiley, Hoboken, NJ 2005, ISBN 0-471-68273-X , pp. 10 .
  13. ↑ Münster: Critical success factors for the use of the Six Sigma method. 2009, ISBN 978-3-8381-0086-9 , p. 108 f.
  14. ↑ Matthias Schmieder, Mehmet Aksel: Questionnaire for the self-check - Does Six suit us? In: QZ. 5/2006, pp. 34-37.
  15. ↑ a b Matthias Schmieder: Empirical survey on the use of benchmarking and Six Sigma in Germany , on .six-sigma-deutschland.de
  16. ^ Matthias Schmieder: Why Six Sigma is successful - analysis of current studies. In: Gundlach Carsten and Jochem Roland (eds.): Practice Six Sigma, avoid mistakes, improve processes, reduce costs. Düsseldorf 2008, pp. 39-64.
  17. ↑ anadeo.com (PDF; 81 kB) Process Optimization in Data Quality Management, Case Study, Anadeo Consulting (viewed June 24, 2011)