A Comparison of the Global-8D-Process and TRIZ

Ina Bauer-Kurz
College of Textiles, North Carolina State University, Raleigh, USA
Fax: +1 (919) 515-3733; E-mail: ikurz@unity.ncsu.edu


KEYWORDS: TRIZ, Global-8D, problem solving, innovation


Global-8D is a problem-solving methodology applied worldwide in industrial practice to improve the product development process. This study compares Global-8D to the Theory of Inventive Problem Solving (TRIZ) and evaluates how both tools may be combined. With an industrial example, it is demon-strated how the implementation of TRIZ into Global-8D can enhance problem solving, process and product quality.


Global-8D is a problem-solving methodology for product and process improvement. It is structured into eight disciplines, emphasizing team synergy. The team as whole is better and smarter than the quality sum of the individuals. Each discipline of G8D is supported by a checklist of assessment questions, such as “what is wrong with what”, “what, when, where, how much” for D2, problem description. Throughout the problem solving process, achievements of each discipline are summarized and recorded in a G8D spreadsheet.

D0 -  Prepare for Global-8D Process

D1 -  Establish a Team

D2 -  Describe the Problem

D3 -  Develop Interim Containment Action (ICA)

D4 -  Define & Verify Root Cause and Escape Point

D5 -  Choose & Verify Permanent Corrective Actions (PCAs)
for Root Cause & Escape Point

D6 -  Implement & Validate Permanent Corrective Actions (PCAs)

D7 -  Prevent Recurrence

D8 -  Recognize Team & Individual Contributions

Figure 1: Overview of Global-8D Disciplines [4]


TRIZ is the Russian acronym for “Theory of Inventive Problem Solving”. The inventor Genrich Altshuller analyzed more than 200,000 patents and discovered that problems, solutions and patterns of evolution were repeated across industries and sciences. Based on the empirical evidence of this patent analysis, Altshuller built the theoretical superstructure TRIZ.

TRIZ is a system of several powerful tools for problem analysis, understanding and solution in any scientific, technological or administrative field. ARIZ, the algorithm of inventive problem solving, may be used as a guide through a problem solving process showing how and when to apply the TRIZ tools. However, each of the tools can be applied separately according to the problem situation.

  • Overcome psychological inertia

  • Use of resources

  • Ideality

  • Physical contradictions

  •  Technical contradictions

  • Su-Field analysis

  • System approach

  • Smart little people modeling

  • Directed evolution & maturity mapping

Figure 2: Some TRIZ Tools for Problem Analysis and Solution [1], [5], [6]

One essential feature of TRIZ is the overcome of psychological inertia, improving and accelerating problem solving by forcing the engineer to think “out of the box”. TRIZ triggers the awareness of resources, and thus the use of what is already available. Ideality helps to think at a functional level and overcome psychological inertia, by identifying the real problem and its ideal solution. The solution mechanism for physical contradictions in a problem helps finding potential solutions by separating the needed functions in time, space, scale or upon condition.  Redefining a problem in terms of standardized technical contradictions generates general solution principles that may then be specified for the initial problem. Substance-Field analysis creates a functional awareness of the problem by taking it apart into essential substances and necessary force fields. For the solution of ineffective or harmful Su-field models, a set of 76 standards solutions is provided. With the system approach, different aspects of a problem are pointed out on a micro and macro level and in past, present and future. Smart little people modeling focuses on the very bottom of the problem, by analyzing the problem situation at a micro level. Finally, maturity mapping is a powerful tool for technological forecasting by fitting a product or process to empirical trends of evolution.


A company is manufacturing commercial heaters according to the production flow depicted below. The problem is, that the quality inspection detects 6% defects in varnish coating, while the “normal, acceptable” rate of defects is 1.5%. When the company was previously facing similar problems, it turned out that some workers were using silicone hand crème, which obviously stuck to the heater parts and prevented adhesion of the electro-statically sprayed varnish. A temporarily effective solution to the problem at that time was the restriction and strict control of hand crème use.

Figure 3: Production Flow for Commercial Heaters [4]


A G8D analysis was applied to solve the problem of VARNISH DEFECTS in industrial heaters. Below, each step and the results of the analysis are explained in detail and compared to a TRIZ analysis of the problem.

D0 – Preparation for the Global-8D Process

The purpose of this preliminary step in the G8D analysis is the quantitative identification of the problem and its consequences for customers, the determination of an immediate action to contain the consequences, if necessary, and the agreement of the problem situation with the G8D criteria.

  • Symptoms defined

  • Customers / consequences identified

  • Symptom quantified with measurements

  • Cause unknown

  • Resources for problem solution & recurrence prevention management-approved

  • Single-person capacity for problem-solving exceeded

Figure 4: Global-8D Criteria for the Preparation Step D0

There is no comparable explicit step or tool to D0 in the TRIZ methodology. Due to its variety of tools, TRIZ can be applied to any problem and thus does not require a crosscheck with application criteria. However, its effectiveness and the practicability of the TRIZ generated solutions depend essentially on the background of the problem, which include data on the symptom, resources, personal capacities and knowledge available. Thus, the step D0 is a useful and important step to assure the effectiveness and success of both, a G8D and a TRIZ analysis.

Figure 5: Step D0 in Global-8D report for the Problem VARNISH DEFECTS [4]

D1 – Team Building

Based on the G8D criteria that a single person is not capable of solving the problem alone, a suitable team with defined team roles needs to be established. G8D relies on team synergy. The whole is more than the sum of its parts. There is no equivalent to D1 in TRIZ. By providing suitable human and intellectual resources, D1 team building, complementary to step D0, sets the frame for a successful problem analysis and solution with both, G8D and TRIZ.

In the case of the VARNISH DEFECTS, a team of 10 people was established, with the production manager as champion and a production engineer as team leader. The team members come from various divisions like maintenance, finance, material lab, quality insurance and production, insuring a wide knowledge and experience base.

D2 – Problem Description

In the G8D process, the problem description is supported by an extensive list of assessment questions such as

The resulting problem description recorded in the G8D report for the case of the VARNISH DEFECTS is: At final quality control, spots without varnish are detected in heaters, easily visible on the metal surface. They appear regularly & lot-wise.

In the TRIZ methodology as well, a comprehensive problem description is considered as the preliminary requirement for any problem analysis and solution. Thus, several TRIZ tools may be used to understand and describe the problem. For the case study of VARNISH DEFECTS, the useful- and effectiveness of some TRIZ tools for problem description complementary to the G8D step D2 can easily be demonstrated:


The formulation of the ideal final result for the case study is:

Every heater is evenly coated with varnish all by itself.

This statement makes clear that there are actually three aspects of the problem. The first problem, as also stated with the G8D analysis, is that the heaters are not coated evenly with varnish, but there are defects in the varnish.

However, ideality also points out that not every heater is perfectly coated. The company obviously accepted a rate of defects of 1.5% as normal before, but does this really have to be normal? For process and product improvement, any defect needs to be considered as a problem and thus as a challenge for optimization. Wouldn’t it be better to build some control mechanism in the process to not deliver any defective heaters to the customers at all?

The third problem the ideal final result statement shows, is that the coating does not happen all by itself. Obviously, the varnish coating is a process requiring fairly complex preparation: The parts need to be cleaned and assembled. They need to stay clean during assembly. They need to be transported and mounted in an appropriate way for spraying. If it is acknowledged as a problem, that the coating process is not simple enough and depends on too many parameters potentially causing defects, process and product improvement to simplify the coating action can be a challenge.


A systematic analysis of resources helps to understand the environment of the problem and identify complementary problems in the process. As guideline, the resources in TRIZ are divided into 6 groups:

  1. Substance

  2. Field

  3. Space

  4. Time

  5. Information

  6. Functional Resources

In addition to raw materials and system elements as substances, the production process for industrial heaters also contains waste such as metal pieces and chemical waste from the cleaning bath. The analysis of the field resources provokes the question, why this waste is not being reused as system energy. Considering space resources, it appears that there is a bathroom available. Why is this bathroom not used for regular hand cleaning, through which varnish defects could obviously be reduced previously? Time analysis shows that 4 cleaning baths may be used at the same time. Is it possible to shift parts from one production line to the baths of the other production line if necessary? The visibility of varnish and its defects can be used as informational resource. Why does the process not include a detection device for varnish faults coupled as feedback to a device that does something in the process to prevent further faults? Functional resources include e.g. the moving of the parts and the assembled heaters for transportation. Can this motion be used to make cleaning or varnish spraying more even?

In summary, the above considerations of resources suggest that there are plenty of problems and insufficiencies in the process contributing to the one apparent problem VARNISH DEFECTS. The solution of these hidden problems may solve the apparent problem automatically and improve the production process: If the chemical waste of the cleaning bath can be reused and the bath can be recycled continuously, the parts could be kept clean constantly and less varnish defects would occur. If the process would contain a feedback loop activating a cleaning action whenever varnish faults occur, these defects would also be reduced.

Smart Little People Modeling

SLP is helpful to understand the problem on a micro-level and to identify the zone of conflict. In the case study of the VARNISH DEFECTS, a close look, as provoked by the SLP modeling in Figure 6, explains the question ‘Why does the varnish not cover heater parts at certain spots?’.  The knowledgeable engineer may answer: ‘The varnish does not stick to the metal surface if the surface is dirty. This is a sign that the cleaning bath is not effective.’ This aspect leads to a redefinition of the problem: ‘The bath for cleaning heaters before coating becomes dirty and ineffective’, instead of ‘the quality control shows defects in varnish of heaters’. Figure 6 may also suggest that an imperfect surface structure is partially responsible for the varnish defects.

Figure 6: Smart Little People Modeling for Heater Surface [1]

D3 – Development of Interim Containment Action

This G8D step instructs the definition, verification and implementation of the temporary preventive measure ICA to isolate the harmful effects of the problem from the internal / external customer until the problem is solved and Permanent Corrective Actions are taken. For the case study, the temporary measure implemented with the G8D study is a 100% visual control of the heaters instead of the quality control by representative sampling. Once it was observed that the high rate of defects only occurred with the model EC25, the 100% visual control was reduced to only this model, while the other heaters were checked like usual with representative sampling.

This G8D step is an effective short-term measure to restrict damage resulting from a process problem and thus very useful for industrial practice. TRIZ cannot contribute to this step. Since TRIZ focuses on problem solving from an analytical standpoint, it does not provide any help for ‘quick fixes’.

D4 - Definition and Verification of Root Cause and Escape Point

The task of the G8D discipline D4 is defined as ‘the isolation and verification of the root cause by testing each possible cause against the problem description and the data, and the

isolation and verification of the place in process where the effect of the root cause should have been detected and contained’. G8D suggestions, how to find the root cause and the escape point, are a time-wise analysis of the discrepancy between “should be” and “is”, the use of a cause-effect diagram, and a list of assessment questions. However, the G8D process is designed very flexible and open and allows the incorporation of additional  problem solving tools. Especially for problems, that have never been solved satisfactory and that seem to require a major process change, G8D suggests tools like robust design and benchmarking.

Analyzing a “should be/is” comparison and a cause-effect diagram, the root cause in the case study VARNISH DEFECTS is identified as the cleaning bath loosing its effectiveness. It is detected that the last five heaters being cleaned before the bath is changed show varnish defects. The escape point is the cleaning bath itself.

TRIZ could help identifying the root cause of the problem in a quick and simple way. Alternatively to the “should be/is” analysis, tracking all changes of the process during the last few months, the root cause may be identified with TRIZ tools incorporated into the G8D structure at steps D2 and D4. The use of TRIZ tools for the problem description under D2 already provoked the identification of the “real” problem (heaters are not clean), which is equivalent to the root cause, instead of the superficial apparent problem, the varnish defects. The effectiveness of TRIZ for identifying root cause and escape point can be demonstrated as follows:


The informational resource ‘quality inspection’ directly shows which model, from which production line, at which times and time intervals the defects occur. The detected periodicity can immediately be related to another periodicity of the same frequency in the process – the change of the cleaning bath. Here, the systematic use of resources in the present system makes it possible to identify the root cause and the escape point without analysis of the formerly satisfactory process in the past and the appearance of the problem.


The ideal final result to achieve perfect varnish coating in terms of cleanness of the heater can be stated as: The heaters are clean by themselves. The question arises why the heaters are or become dirty at all. If it could be prevented that the heater parts become dirty before varnish spraying, no cleaning action would be necessary. Thus, the problem can be restated as ‘how can the parts stay cleaner throughout the process’ instead of making the repair action ‘cleaning bath’ more effective.

Smart Little People Modeling

The identification of the zone of conflict at a micro level provokes the question “Where and why does the varnish not cover heater parts?”, see Figure 6. The varnish molecules can not attach to the heater surface if the surface is not appropriate – or if something is in the way, e.g. a dirt particle. Thus, the root cause for the varnish defects can be identified as ineffective cleaning. Obviously, the cleaning bath is not effective AT ALL TIMES. Thus, the root cause identified with SLP is not the loss of effectiveness of the cleaning bath, but the lack of continuous effectiveness of the bath.

The substance-field analysis of the problem VARNISH DEFECTS is illustrated in Figure 7.  Initially, the substance S2, varnish, is acting insufficiently (dashed arrow) on the substance S1, heater surface, via the mechanical force field FMe, spraying. In the 76 standard solutions for a TRIZ Su-field analysis [7], the introduction of a double Su-field model is one suggestion to improve the system. The substance S3, cleaning bath, is also acting on the heater surface, via the chemical force field FCh, solving dirt, and thus improving the poorly controlled system of varnish spraying.

Su-Field analysis

Figure 7: Su-Field Analysis of the problem VARNISH DEFECTS [1], [5], [6], [7]

This Su-field analysis shows that the varnish coating would be satisfactory, if the heater surface was cleaned sufficiently by a chemical action in the cleaning bath. It can thus be concluded that the actual problem causing the varnish defects is the solution capacity of the cleaning bath.

D5 - Choice and Verification of Permanent Corrective Actions (PCAs)

This G8D step includes the generation of problem solutions and the selection of the best solution as PCA to remove the root cause without causing undesirable effects. Based on the engineering experience and the available knowledge of the team members, the following 8 alternative solution concepts were generated for the case study of VARNISH DEFECTS:

1.      Installation of bigger cleaning tanks
(slows down deterioration of cleaning solution)

2.      Automatic sensor to detect deterioration of cleaning solution, production is then automatically led to other tank while dirty tank is cleaned

3.      Slowing down production rate and weekend work
(for having less throughput in the cleaning baths)

4.      Changing cleaning solution more often on a regular basis

5.      Redirecting part of the production of line 2 to line 1,
to reduce the use and thus deterioration of cleaning solution in line 2

6.      Pre-cleaning of the heater parts with water

7.      Slowing down reduction rate

8.      Continuous filtering and cleaning of the cleaning solution

After quantitative evaluation of “must” and “should” criteria like

alternative 4 was chosen as permanent corrective action, mainly because of its cost effectiveness and its simplicity of implementation. Instead of every 30 minutes, the cleaning solution is now being changed every 27 minutes. The disadvantage of increased use of cleaning solution is mentioned.

For the determination of the permanent corrective action as a solution to the problem, generally all TRIZ tools are applicable. The use of various TRIZ tools is demonstrated for the case study:


For the case of VARNISH DEFECTS, the ideal final result can be stated in two different ways: 1. The heater parts are clean all by themselves.

                              2. The cleaning solution stays clean all by itself.

If the heater parts just stayed clean throughout the mounting process, the varnish problem would be solved even without a cleaning step. There has even been previous experience with what makes the heater parts dirty and how to reduce it: The restriction and control of hand crème use showed to be an effective temporary measure to reduce varnish defects. The solution did not work permanently, because the temporary change in hand crème use could not be turned into a permanent habit. Could the workers be motivated to permanently change their hand crème habits if the company provided a suitable alternative hand crème for free? The company could also offer part of the money being saved on cleaning solution as a bonus to the workers, if the reduction of varnish defects is successful. Could the use of hand crème be officially regulated in the company’s rules like the use of earplugs in loud machinery environment for safety reasons?

Within the huge variety of chemical solvents, can a solution be found that detaches the dirt from metal surfaces without actually absorbing the dirt? In this case, the cleaning solution would act like a transport medium that removes the dirt from the metal, transports it and deposits it somewhere else (e.g. container wall), from where it can be discarded later.


The availability of four cleaning baths suggests the even distribution of the heaters to be cleaned over all the baths in the two lines, in order to exhaust the cleaning capacity of all baths. This suggestion leads to the alternative solution five generated with the G8D analysis.

Is the contamination of the cleaning solution causing the ineffectiveness visible and could it thus be monitored? This thought produces G8D solution alternative 2.

Technical contradictions

The TRIZ contradiction theory can be used to formulate the problem in terms of a contradiction between 39 standardized parameters [2]. The contradiction is that if one parameter is improved, the other parameter deteriorates. For the solution of these contradictions, TRIZ offers 40 generalized solution principles [3]. From empirical evidence and experience of Altshuller’s patent analysis, the contradiction matrix suggests a set of the most suitable solution principles for each set of contradicting parameters. The contradiction situation in the case of VARNISH DEFECTS is illustrated in Figure 8.


       Parameter     ®




13 – Stability of the Object’s Composition

(integrity of varnish)

27 – Reliability

(system’s ability to produce perfect varnish)

 29 – Manufacturing Precision

 (actual varnish coating does not match requirements)

9 – Speed

(rate of process)

Solution Principles:

28, 33, 1, 18

11, 35, 27, 28

10, 28, 32, 25

25 – Loss of Time
(cycle time reduction)

35, 3, 22, 5

10, 20, 4

24, 26, 28, 18

39 – Productivity
(# of heaters / time unit)

35, 3, 22, 39

1, 35, 10, 38

18, 10, 32, 1

Figure 8: TRIZ Contradicting Features and Suggested Solution Principles [2], [3]

The solution principles from the 40 standard solutions, as suggested in the contradiction matrix, can be used to generate specific solutions to the problem VARNISH DEFECTS. Below, they are listed in the order of their magnitude of recurrence, since the principles recurring the most often are considered most likely to solve the problem.

4 x #10: Preliminary Action

The preliminary action can be a preliminary cleaning step, or measures taken not to make the heater parts dirty in the first place. If the parts are sandblasted or rinsed with pressurized water before the chemical cleaning bath, the bath does not deteriorate as fast. To prevent the parts from getting dirty, the workers should use only suitable hand crème or wear clean gloves when touching the parts.

4 x #28: Mechanics Substitution

Since the varnishing is done electrostatically, can the cleaning be done in a similar manner? Can the cleaning solution be an electrolyte solution using charged particles to separate dirt particles from metal surfaces, and transport and deposit the dirt to a waste deposit surface?

4 x #35: Parameter Changes:

The cleaning solution would be easily recyclable if it evaporated after cleaning, leaving the dirt at the vessel ground as solid residue. Is dirt, especially grease, more easily solvable at higher temperatures? If so, it is well worth heating the metal parts or the cleaning bath.

3  x  #1: Segmentation

The degree of fragmentation of the production process is increased by introducing a stage of pre-cleaning of the heater parts. This solution leads to a similar action as suggested already with the solution principle “Preliminary Action”, and also similar to the G8D solution alternative 6.

3 x #18: Mechanical Vibration

Can cleaning be done with ultrasonic devices? Can vibrational motion of the part or in the cleaning bath enhance the efficiency of the bath?

2 x #22: Blessing in Disguise

Could the chemical waste of the cleaning process be used to produce something? Could the metal pieces left over from the production of heater parts be recycled?

1 x #5: Merging

The cleaning action is performed immediately before the varnish spraying, and also by means of spraying a solvent, if necessary with consecutive rinsing and blow drying before varnishing. This solution has the advantage of reducing the risk that the parts get dirty during the transport between cleaning and coating, and that spraying defined amounts of cleaning solvent is more economic than a cleaning bath.

1 x #11: Beforehand Cushioning

A sensor detects when the solution is not effective anymore. If this happens, the parts are immediately sent to another bath for cleaning (G8D solution #2).

1 x #20: Continuity of Useful Action

Instead of using one cleaning tank for 30 minutes, than emptying and cleaning it while switching the parts’ cleaning to the other tank of the line, the second tank could be connected to the first tank and be used as a continuous filtering device for the cleaning solution. If it is not possible to actually filter and recycle the old solution, a defined amount of the used solution could be drained continuously while it is being replaced with fresh solution.

Su-Field analysis

Figure 9: Su-Field Model for the Preparation of Heater Parts for Varnishing

The simplest solution for the insufficient action of the cleaning molecules on the heater surface via a chemical field is the replacement of the field with a different field and the cleaning molecules with a different substance. Since the varnish spraying is done electrostatically, the question arises if this electrostatic field (as resource) could also be used for cleaning the parts. The new cleaning substance S3 would then be some electrically charged cleaning molecules.

D6 - Implementation and Validation of Permanent Corrective Actions (PCAs)

After determination of the PCA, it needs to be implemented and the ICA needs to be removed. The long-term results need to be monitored. TRIZ does not give any guidelines how to monitor the success of a problem solution. However, a new, separate TRIZ analysis could be conducted after implementation of the permanent corrective action to insure process effectiveness and to solve the problem “how to monitor the process”.

D7 - Prevention of Recurrence

Once the permanent corrective action is implemented, recurrence of the problem has to be prevented by modifying all parts of the system including policies, practices and procedures. The G8D analysis should also conclude with recommendations for systemic improvements in similar problem cases.

TRIZ generated solutions for permanent corrective actions under D4 may already include features to prevent recurrence of the problem, e.g. automated feedback control at the quality inspection of the process. Since TRIZ relies essentially on the generalization of a problem, and the generation of a general solution, it is very easy to extract improvement suggestions for similar processes from a TRIZ analysis of a particular problem.

D8 – Recognition of Team and Individual Contributions

The final step in a G8D analysis is the completion of the team experience by recognizing  team and individual contributions. This action is certainly very useful and necessary to encourage and motivate people and to keep the working efficiency at a high level. There is no equivalent tool to D8 in TRIZ, but a separate TRIZ analysis about how to motivate workers could be conducted.


G8D and TRIZ are problem-solving tools at different levels. G8D can be considered as a superstructure of guidelines how to face a problem in industrial practice, how to deal with people, how to implement the solution and how to insure the improved process. TRIZ in contrast deals with the problem in detail and targets the systematic generation of a specific solution from a wide set of general solution principles. TRIZ tools can be implemented in the G8D steps ‘Problem description’ and ‘Definition of root cause and escape point’, and especially in the step D5 ‘Generation of a permanent corrective action’, which is basically the problem solving step.

Figure 10: Implementation of TRIZ Elements in the Global-8D Process

The effectiveness of the use of TRIZ tools in the G8D process is demonstrated with the case study of VARNISH DEFECTS. With TRIZ, additional ‘smart’ potential solutions could be generated in short time, for example cleaning the parts by means of spraying just before the varnish coating, as generated by the contradiction solution principle ‘Merging’ and the Su-Field analysis. Spraying is economical in terms of chemical waste, and the risk of parts getting dirty between cleaning and varnishing is reduced. Furthermore, available resources like the varnish-spraying set-up could be used, possibly in an electrostatic field.

Organizing the team structure and setting up intellectual, financial and substantial resources, G8D is an effective and flexible superstructure for problem solving in a company. In contrast, the strength of TRIZ is the generation of solution ideas beyond psychological inertia. In this case study, without TRIZ, the idea to drop the traditional cleaning bath completely and replace it with a spraying unit, was never considered. TRIZ may be perfectly implemented into the G8D problem solving process. G8D provides the resources, the positive environment, and a systematic step-by-step superstructure, without which TRIZ would be difficult to apply. TRIZ provides systematic engineering tools in addition to the engineering knowledge and experience already available in the team, which may speed up the solution generation and produce additional innovative solution alternatives. Thus, the combination of the G8D process and TRIZ is an effective synergy for problem solving.


I’m greatly indebted to Ford Motor Company in Germany and the USA for their generous contribution of G8-D information material. Without their help, this study would not have been possible. I hope, in turn, my analysis may be of practical use for problem solving at Ford. Furthermore, I especially want to thank Dr. Tim Clapp, professor at NCSU, for his inspiring and motivating guidance throughout this project.


Ina Bauer-Kurz is currently a Ph. D. candidate in Fiber and Polymer Science, with a minor in Mechanical Engineering, at the College of Textiles of North Carolina State University, USA. She will be graduating in December 2000. In November 1997, Ms. Bauer-Kurz obtained the degree “Diplom-Ingenieur” of Mechanical Engineering from the Rheinisch-Westfälische Technische Hochschule Aachen, Germany. Her wide research interests include the analysis and modeling of mechanical behavior of polymeric structures. After receiving her doctorate degree, Ms. Bauer-Kurz is intending to work in the mechanical engineering or textiles related industry in Germany or the USA.


[1] Tim Clapp, Michael Slocum:TE589A, Spring 2000, Theory of Inventive Problem Solving, class lectured at North Carolina State University

[2] Ellen Domb: The 39 Features of Altshuller’s Contradiction Matrix, email editor@triz-joournal.com, web page http://www.triz-journal.com/archives/1998/11/d/index.htm

[3] Ellen Domb: 40 Inventive Principles With Examples, web page http://triz-journal.com/archives/1997/07/b/index.html

[4] Ford Motor Company, Germany, Training-manual for the G-8D Process, 1999

[5] Yuri Salamatov: TRIZ: The Right Solution at the Right Time (A Guide To Innovative Problem Solving), 1999 Insytec B.V., The Netherlands, ISBN 90-804680-1-0, e-mail info@insytec.com, web page www.insytec.com

[6] John Terninko, Alla Zusman, Boris Zlotin: STEP-by-STEP TRIZ: Creating Innovative Solution Concepts, 1996 Responsible Management Inc., New Hampshire, USA, ISBN 1-882382-12-9, e-mail john@terninko.com, web page http://www.mv.com/ipusers/rm

[7] John Terninko, Ellen Domb, Joe Miller: The Seventy-six Standard Solutions, with Examples, Section One and Two, web pages http://www.triz-journal.com/archives/2000/02 and http://www.triz-journal.com/archives/2000/03