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Review

Tools of Theory of Inventive Problem Solving Used for Process Improvement—A Systematic Literature Review

by
Vladimir Sojka
and
Petr Lepsik
*
Department of Machine Parts and Mechanism, Faculty of Mechanical Engineering, Technical University of Liberec, 461 17 Liberec, Czech Republic
*
Author to whom correspondence should be addressed.
Processes 2025, 13(1), 226; https://doi.org/10.3390/pr13010226
Submission received: 18 December 2024 / Revised: 6 January 2025 / Accepted: 10 January 2025 / Published: 14 January 2025
(This article belongs to the Section Chemical Processes and Systems)
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Abstract

:
One of today’s great challenges is radical process improvement. TRIZ (theory of inventive problem solving) could help to resolve this. This study aims to answer the following research question: Which tool of TRIZ is best for process improvement? To answer this, a systematic literature review was conducted, and the gathered data were analyzed. The main focus was on the tools, the rate of improvement, the improved parameters, and the combination of tools. The results show that the technical contradiction with inventive principles should be easy to use and provide sufficient results. If the technical contradiction is not enough or the process is too complex, an algorithm or framework to use more advanced tools of TRIZ should be used. That should help to resolve the most challenging problems related to radical process improvement.

1. Introduction

There has been much written about the need for the continuous improvement of all business activities [1,2,3,4,5,6,7,8]. One of the largest impacts on the reduction in time or cost is the improvement of production processes. There are currently many approaches to dealing with improvements in production [9,10,11,12,13,14,15,16]. As we pursue better processes, we are also trying to develop new and better tools, methods, and techniques to achieve small improvements and radical innovations more easily or faster [17,18,19,20,21,22]. This can be achieved by various approaches: combining several methods together [23,24]; using a method originally intended for a different purpose, such as product development, for instance [25,26,27,28,29,30,31,32,33]; modifying one of the current tools; or developing a new method from scratch.
This leads to the primary purpose of this article. Several authors have described the highly beneficial use of TRIZ (theory of inventive problem solving) and its tools for process improvement [34,35,36]. The TRIZ and its tools and principles comprise one of the most powerful approaches to resolving the most complex or challenging technical problems. This is due to its systematic approach to searching for innovative ideas based on the trends of the evolution of technical systems. Several reviews on TRIZ, its use, and its combination with other tools have already been written [37,38,39,40,41,42,43].
After inspecting these reviews and the current state of the literature, the authors see a lack of a description of which of the TRIZ tools should be used, which are most often used, and which should provide the best approach to using TRIZ for process improvement.
This paper aims to answer the abovementioned questions using a systematic literature review.

2. Methods

A systematic literature review was conducted to answer the question of which TRIZ tools and methods are most often used and which ones are the best for application to process improvement.
The literature review steps are visualized in Figure 1 below.
Firstly, resources from existing reviews were inspected, and only studies that followed the defined requirements were selected.
The requirements were as follows:
  • The study must include a practical case study;
  • There must be application of TRIZ to process improvement;
  • The used TRIZ tools must be clearly described (just TRIZ is not enough);
  • Optionally, the rate of achieved improvement is mentioned.
From the selected papers, duplicates were removed.
Secondly, new literature from the year 2020 to the year 2023 was reviewed. In citation databases such as the Web of Science and Scopus, articles focusing on the use of TRIZ for process improvement were searched. Searching queries such as (((ALL = (TRIZ)) AND ALL = (process improvement)) OR ALL = (inventive principles)) AND ALL = (case study) ((ALL = (TRIZ)) AND ALL = (Process improvement)) for the Web of Science and triz AND process AND improvement AND case AND study AND PUBYEAR > 2019 AND PUBYEAR < 2024 AND (LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “cp”) OR LIMIT-TO (DOCTYPE, “re”) OR LIMIT-TO (DOCTYPE, “ch”)) AND (LIMIT-TO (EXACTKEYWORD, “TRIZ”) OR LIMIT-TO (EXACTKEYWORD, “Lean Six Sigma”) OR LIMIT-TO (EXACTKEYWORD, “Lean”) OR LIMIT-TO (EXACTKEYWORD, “Lean Production”) OR LIMIT-TO (EXACTKEYWORD, “Manufacturing”) OR LIMIT-TO (EXACTKEYWORD, “Manufacture”) OR LIMIT-TO (EXACTKEYWORD, “Theory Of Inventive Problem Solving”) OR LIMIT-TO (EXACTKEYWORD, “Process Improvement”) OR LIMIT-TO (EXACTKEYWORD, “Process Monitoring”) OR LIMIT-TO (EXACTKEYWORD, “Lean Manufacturing”) OR LIMIT-TO (EXACTKEYWORD, “Production System”) OR LIMIT-TO (EXACTKEYWORD, “Production Process”) OR LIMIT-TO (EXACTKEYWORD, “Process Innovation”) OR LIMIT-TO (EXACTKEYWORD, “Process Control”) OR LIMIT-TO (EXACTKEYWORD, “Productivity”) OR LIMIT-TO (EXACTKEYWORD, “Maintenance”) OR LIMIT-TO (EXACTKEYWORD, “ARIZ”)) AND (LIMIT-TO (LANGUAGE, “English”)) for the Scopus database were used. On the Web of Science database, 29 and 48 papers, respectively, were found and sorted. In the Scopus database, 193 papers were found and sorted. The results were examined and reduced to only those studies following the abovementioned requirements. Duplicates were removed.
The TRIZ tools were categorized based on the abovementioned studies. The chosen categories are
  • Technical contradiction, contradiction matrix, and inventive principles;
  • Physical contradiction and separation principles;
  • Function modeling (FM) and function analysis (FA);
  • Trimming;
  • Trends (TESE—trends of engineering system evolution, laws, patterns, …);
  • Substance-field analysis (S-F);
  • RCA+ (root cause contradiction), and CECA (cause and effect chains analysis);
  • Ideality and ideal final result (IFR);
  • Standards;
  • Scientific effects;
  • Resources;
  • ARIZ (algorithm for inventive problem solving);
  • 9 windows;
  • Patent search.
This categorization helps connect data from what is practically the same tool but with different names (for example, a technical contradiction uses a contradiction matrix to find inventive principles).
The list of the selected studies is shown in Table 1 below.
As can be seen in the table above, individual tools used in particular studies were analyzed.

3. Results

The number of uses of individual tools (by category) is visualized in Figure 2 below.
Technical contradictions with the matrix and inventive principles comprises the most known tool of the TRIZ, so it makes sense that it is used frequently also for process improvement. The process of overcoming the technical contradiction is also not very difficult, which may be the reason for its common use. Function modeling and substance-field analysis are very similar according to their structure and usability. The user must have a deeper understanding of the TRIZ principles and the interactions between system parts. On the other hand, both these tools can have powerful results and deep understanding of the system (in our case, the process). The low number of uses of a particular tool could mean two things. The tool provides relatively weak solutions, or it is difficult to use. These are correct when we see the ARIZ and 9 windows. Based on the authors’ experiences, the 9 windows are very easy to use, but they often do not provide radical solutions. In addition, applying them to a process where there are significant changes over time can be complicated. The ARIZ is probably the most complex approach to problem solving from TRIZ. It uses almost all the TRIZ tools in the sequence and gives us very strong solutions to the most complex technical problems. For common process problems, it may be too complex and time-consuming to use.
Data about the rate of improvement were also collected from the analyzed papers and examined using basic descriptive statistics. The results are shown in the form of a histogram in Figure 3 and Table 2 below.
The descriptive values of the data sample are summarized in Table 2.
The data sample visualized in Figure 3 was tested for normality (see Figure 4).
From the test of normality, we can conclude that under the assumption of normality, the approximate probability of seeing a normal probability plot correlation as small or smaller than r = 0.98 is approximately 0.177. This is to say that at the α = 0.05 level of significance, this test does not rule out the possibility that our data come from a normal distribution.
The data show the overall possibility for improvements that can be achieved by using TRIZ tools for process improvement. Further data analysis was conducted to better understand the connection between particular TRIZ tools, the parameters to improve, and the rate of improvement. The visualized data can be seen in Figure 5 below.
There is no visible trend based on the improvement rate and the number of TRIZ tools used together. This could be caused by differences between processes and their problems. A similar rate of improvement could be achieved by one TRIZ tool on a relatively simple problem and with the combination of many tools on a relatively complex system.
The trend between a used tool and the rate of improvement was not visible from the raw visualization. The same was true for the relationship between the rates of improvement and the improved parameters. That is why the data were visualized in box plot form. See Figure 6.
The results are also summarized in Table 3 and Table 4 below.
From the data shown above, it can be concluded that the best improvement could be achieved by the use of substance-field analysis. Unfortunately, there was a small data sample for most of the individual tools. So, the results presented can be further refined in the future by analysis of more studies, as can the rate of improvement and area of improvement. Most of the cases focused on time, but the best improvements were achieved in cases focused on quality improvement. From cases focused on the improvement of productivity, the results varied in most cases from 20 to 80% in terms of the improvement rate, which can still be seen as very good. There was also a small data sample, so the results can be refined in the future.

4. Discussion

An assumption can be made that the tools used more often should be easier to use and, at the same time, provide sufficient results. Tools used less often will then probably be difficult to use or do not provide adequate solutions. The tools in the middle range could be seen as opportunities for further research.
In terms of improvement rates, there is no clear connection between the rate of improvement and particular tools. The visualized results show the achieved rates of individual tools. Their rates could be affected by many factors, such as the type of process, the original state of a process, the complexity of the problem, the openness of the company to radical change, and the change in the effect delivered by combining more TRIZ tools together (more tools could help to resolve more complex problems and deliver higher improvement rates; on the other hand, a particular tool can be mostly responsible for the delivered result affecting the scores of the rest of the tools used), along with others.
It should be mentioned that the number of analyzed papers was not the same as the number of datasets collected from these papers. Particular papers could include several studies or deal with some sub-problems. In addition, in some of the studies, only one TRIZ tool was used. In others, several TRIZ tools were used together. Similarly, from several studies, the improvement efforts were focused on time, costs, quality, productivity, or others. That is why the sums of the numbers of data samples in Table 3 and Table 4 are not the same.
Since there are only limited data available, the presented results can be refined in the future to be more statistically robust. Although the results can be used as a guide for the decision of which TRIZ tools should be used, more studies should be analyzed in the future to improve the robustness and precision of the results.
If we group the studies by the year of publication, a clear trend of increase in the number of publications dealing with the application of TRIZ tools to process improvement can be seen in Figure 7.
This publication trend suggests a continual increase in the number of publications. Expanding this analysis can refine the results presented in this study in the future.

5. Conclusions

The presented paper concludes that the TRIZ tools for process improvement have roughly the same rate of improvement capability, with an average improvement of around 50%. That means whatever tool of TRIZ is used in process improvement efforts, the achieved improvement can still be radical.
The most used tools are probably more straightforward to use and provide sufficient solutions to many problems. This can be seen by technical contradiction, which was used in 31 cases, but the improvement rate varied from units of percentage points to improvements of 100%. Combining multiple tools should provide a broader range of ideas for solutions, so it may be beneficial for more complex problems or processes. The degree of improvement depends on many aspects, so it is difficult to use it to choose the most efficient tool.
The results showed that from the individual tools, the substance-field with a mean of 67% and standard deviation of 17.7%, might be the best tool for process improvement efforts. More interestingly, without focusing on which TRIZ tool is used, the best results could be achieved on projects focused on process quality, where the mean of improvement is 79.9%, with a standard deviation of 7.5%.
Based on the abovementioned results, the authors recommend first using the easy way—trying to overcome the problems with technical contradiction and inventive principles. When the solution is not sufficient, or the process seems too complex for that, then one can use a combination of multiple more advanced TRIZ tools. That could be achieved by using multiple individual tools, a particular algorithm or framework such as ARIZ, or other specialized methods based on TRIZ tools and principles.

Author Contributions

Conceptualization, V.S. and P.L.; methodology, V.S.; validation, V.S. and P.L.; formal analysis, V.S.; resources, V.S.; writing—original draft preparation, V.S.; writing—review and editing, V.S. and P.L.; visualization, V.S.; supervision, P.L.; funding acquisition, P.L. All authors have read and agreed to the published version of the manuscript.

Funding

This publication was written at the Technical University of Liberec, Faculty of Mechanical Engineering, with the support of the Institutional Endowment for the Long-Term Conceptual Development of Research Institutes, as provided by the Ministry of Education, Youth, and Sports of the Czech Republic in the year 2024. The research reported in this paper was supported by institutional support for nonspecific university research.

Data Availability Statement

Datasets are available on request from the authors.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Processes 13 00226 g001
Figure 1. Schematic of the systematic literature review.
Figure 1. Schematic of the systematic literature review.
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Processes 13 00226 g002
Figure 2. Number of TRIZ tools used in reviewed studies.
Figure 2. Number of TRIZ tools used in reviewed studies.
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Processes 13 00226 g003
Figure 3. Histogram of rates of improvement achieved by TRIZ tools.
Figure 3. Histogram of rates of improvement achieved by TRIZ tools.
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Processes 13 00226 g004
Figure 4. Test for normality of collected data sample.
Figure 4. Test for normality of collected data sample.
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Processes 13 00226 g005aProcesses 13 00226 g005b
Figure 5. Improvement rate analysis: (a) improvement rate based on the number of tools; (b) the number of tools based on the improved parameter; (c) improvement rate based on a specific tool; (d) improvement rate based on improved parameter.
Figure 5. Improvement rate analysis: (a) improvement rate based on the number of tools; (b) the number of tools based on the improved parameter; (c) improvement rate based on a specific tool; (d) improvement rate based on improved parameter.
Processes 13 00226 g005aProcesses 13 00226 g005b
Processes 13 00226 g006
Figure 6. Boxplots of improvement rates: (a) improvements achieved by individual TRIZ tools; (b) improvements achieved in projects aiming at particular process parameters.
Figure 6. Boxplots of improvement rates: (a) improvements achieved by individual TRIZ tools; (b) improvements achieved in projects aiming at particular process parameters.
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Processes 13 00226 g007
Figure 7. The number of published papers dealing with process improvement (meeting the review criteria).
Figure 7. The number of published papers dealing with process improvement (meeting the review criteria).
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Table 1. List of studies with the use of specific tools of TRIZ for process improvement.
Table 1. List of studies with the use of specific tools of TRIZ for process improvement.
StudyTechnical Contradiction,
Contradiction Matrix, Inventive Principles		Physical Contradiction,
Separation Principles		Function Modeling (FM),
Function Analysis (FA)		TrimmingTrends (TESE, Laws, Patterns, …)Substance-Field Analysis (S-F)RCA+; CECAIdeality, Ideal Final Result (IFR)StandardsScientific EffectsResourcesARIZ9 WindowsPatent Search
Mann and Stratton, (2000) [44] ×
Winkless and Mann, (2002) [45]× × ×
Kumar, (2005) [46]×
Robles et al., (2006) [47]×
Ru and Ru, (2006) [48] ×
Domb and Kling, (2006) [49]× × × × ×
Duflou and D’hondt, (2011) [50] (orig. 2007) × ×
Jin et al., (2008) [51]× ×××
Brad et al., (2009) [52]× ×
Lee and Leu, (2010) [53]× ×
StudyTechnical Contr.Physical Contr.Function AnalysisTrimmingTESESu-FieldRCA+IdealityStandardsSci. EffectsResourcesARIZ9 WindowsPatent Search
F.-K. Wang and Chen, (2010) [54]×
T.-C. Hsia and Huang, (2011) [55]× ×
T. C. Hsia et al., (2011) [56]×
Kumaresan and Saman, (2011) [57]×
C.-N. Wang and Hsiung, (2011) [58]×
Soti et al., (2012) [59]×× ×× × × ××
Ferrer et al., (2012) [60]× × ××
Kim and Yoon, (2012) [61]×
Moreira, (2012) [62]× ×
Regazzoni et al., (2013) [63]×× ×
Petrović et al., (2013) [64]×
Lanke and Ghodrati, (2013) [65]× ×
Muruganantham et al., (2013) [66]×
Sheu and Hou, (2013) [67] ××
Annamalai et al., (2014) [68]× × ×
Cabrera and Li, (2014) [69]×
Guo et al., (2014) [70]×
Muruganantham et al., (2014) [71]×
Jiang and Nguyen, (2015) [72]×
Li et al., (2017) [73] (orig. 2015) × ×
Pokhrel et al., (2015) [74]×
Arcidiacono and Bucciarelli, (2016) [75]× ×× ×
Alves et al., (2016) [76]× ×
de J. Pacheco et al., (2016) [77]× ×××
Ghani et al., (2016) [78] ×
C.-H. Lin et al., (2016) [79]×
Karaulova and Bashkite, (2016) [80]×
Su and Su, (2018) [81] ×
Chan and Chin, (2020) [82]× × ×××
Indrawati et al., (2020) [83]×
Lepšík, (2020) [84]××
Purnomo and Lukman, (2020) [85]×
S. Yu and Wang, (2020) [86]×
Boangmanalu et al., (2020) [87]×
Gamboa and Singgih, (2021) [88]×
Araújo et al., (2021) [89]×
StudyTechnical Contr.Physical Contr.Function AnalysisTrimmingTESESu-FieldRCA+IdealityStandardsSci. EffectsResourcesARIZ9 WindowsPatent Search
Sucipto et al., (2021) [90]×
Sojka and Lepsik, (2021) [91]×× × ××× ×
C.-N. Wang et al., (2021) [92]×
C.-N. Wang, Tibo, et al., (2021) [93]×× ×
C.-N. Wang et al., (2021) [94]×
Kandukuri et al., (2021) [95]×
Sojka and Lepsik, (2022) [96]×× × ××× ×
Ng et al., (2022) [97]×
Purushothaman and Ahmad, (2022) [98]×
Sojka and Lepsik, (2023) [99]×× × ××× ×
Števko et al., (2023) [100]×
Soares and Navas, (2023) [101]×
Syu et al., (2023) [102]×
Reza et al., (2023) [103] ×
Vanko et al., (2023) [104] × ×
Koay et al., (2023) [105] ×
Phuah et al., (2023) [106]×
Table 2. Capability of TRIZ for process improvement by descriptive statistics.
Table 2. Capability of TRIZ for process improvement by descriptive statistics.
Number of SamplesMeanMedianSample Standard Deviation (s)Standard Deviation (σ)
3755.845151.430028.155427.7723
Table 3. Individual TRIZ tools and their capability for process improvement by descriptive statistics.
Table 3. Individual TRIZ tools and their capability for process improvement by descriptive statistics.
TRIZ ToolNumber of SamplesMeanMedianSample Standard Deviation (s)Standard Deviation (σ)
Technical Contradictions3153.543951.430028.801528.3332
Physical Contradictions846.935049.500018.133516.9623
Function Modeling645.581650.000032.202029.3962
Substance-Field 567.020064.000019.761817.6755
Trends1039.648040.605021.050919.9707
Ideality and IFR740.068631.210017.651616.3422
Scientific Effects642.033340.605018.478916.8688
Standard Solutions345.440050.000018.433516.4875
Table 4. Capability of TRIZ for process improvement on problems focusing on particular parameters by descriptive statistics.
Table 4. Capability of TRIZ for process improvement on problems focusing on particular parameters by descriptive statistics.
Process ParameterNumber of SamplesMeanMedianSample Standard Deviation (s)Standard Deviation (σ)
Ergonomics250.330050.330028.751020.3300
Finance329.666725.000014.267317.4738
Productivity1146.367350.000030.737829.3073
Quality579.904082.00008.35017.4686
Time1557.870757.520026.455925.5588
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Sojka, V.; Lepsik, P. Tools of Theory of Inventive Problem Solving Used for Process Improvement—A Systematic Literature Review. Processes 2025, 13, 226. https://doi.org/10.3390/pr13010226

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Sojka, V., & Lepsik, P. (2025). Tools of Theory of Inventive Problem Solving Used for Process Improvement—A Systematic Literature Review. Processes, 13(1), 226. https://doi.org/10.3390/pr13010226

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