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Application of TRIZ in Improving the Creativity of Engineering Experts
Available online at www.sciencedirect.com
ScienceDirect Procedia Engineering 131 (2015) 792 – 797
World Conference: TRIZ FUTURE, TF 2011-2014
Application of TRIZ in Improving the Creativity of Engineering Experts Iouri Belskia, Ianina Belskib a
Royal Melbourne Institute of Technology, School of Electrical and Computer Engineering, Melbourne, Australia b TRIZ4U, Melbourne, Australia
Abstract Numerous researchers have extensively studied two causes of problem solvers’ inflexibility which impede creativity: the Einstellung effect [1] and design fixation [2]. The former has been demonstrated experimentally on numerous occasions and is induced by prior experience. The latter is a result of our fixedness on the functions of things which we regularly use. This paper focuses on the third cause of inflexibility, which has not been researched adequately – the detrimental effect of professional expertise on creativity [3]. This detrimental effect is a natural consequence of extensive professional experience and the possession of large amounts of domain knowledge. After approximately 10 years in a profession, due to the construction of effective knowledge schemas, the short-term memory limitations which normally impede effective idea generation can partly or even completely disappear. As a result, experts attain an ability to search for solutions to problems quickly – without significant cognitive and time effort. Although this ‘quickness’ of experts in suggesting solutions is advantageous, it also creates negative consequences. Experts’ solutions are usually confined to their domain-specific knowledge and do not utilize novel ideas. This study reviews existing evidence related to the detrimental influence of expertise on creativity and discusses how the application of TRIZ tools of Substance-Field Analysis and Method of the Ideal Result can minimize this negative influence. © by by Elsevier Ltd.Ltd. This is an open access article under the CC BY-NC-ND license © 2015 2015The TheAuthors. Authors.Published Published Elsevier (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Scientific Committee of TFC 2011, TFC 2012, TFC 2013 and TFC 2014 – GIC. Peer-review under responsibility of the Scientific Committee of TFC 2011, TFC 2012, TFC 2013 and TFC 2014 – GIC Keywords: Engineering creativity; fixation; memory search; Substance-Field analysis; Method of the Ideal Result.
1. Introduction It has been established that expertise requires not only a large amount of domain knowledge, but also many years of professional practice [4, 5]. In order to become an expert in semantically rich domains, such as engineering, science and medicine, extensive professional knowledge must be acquired prior to being permitted to work as a professional. This acquisition of basic professional knowledge usually occurs at university. After
1877-7058 © 2015 The Authors. Published by Elsevier Ltd. 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 Scientific Committee of TFC 2011, TFC 2012, TFC 2013 and TFC 2014 – GIC
doi:10.1016/j.proeng.2015.12.379
Iouri Belski and Ianina Belski / Procedia Engineering 131 (2015) 792 – 797
793
graduating, specialists spend years learning to apply their discipline knowledge in real professional situations. It is usually considered that 10 years of extensive professional practice is the very minimum to accumulate expertise [6]. As a result of many years of professional practice, experts become superior in task recognition and most of the time markedly outperform novices and individuals on intermediate level skills in memory recall in their professional domains [5]. On the other hand, many studies demonstrated that under certain conditions novices and intermediates can outperform experts in their domains of expertise. Chase and Simon [7], for example, showed that novices performed better than experts in recall of randomized chess boards. It has been reported that, although expert radiologists performed better than ones with limited experience in remembering X-ray films with clinical abnormalities, they did worse in remembering films that did not contain abnormalities [8]. Similarly, experts in electronics required longer time than novices to establish atypical faults in electronic circuits [9]. There is also anecdotal evidence that experts are usually less capable than novices in suggesting fresh and novel ideas. This paper focuses on the detrimental influence of expertise on creativity and endeavors to determine the reasons behind this influence and to suggest the ways to minimize it. 2. Expertise and Creativity 2.1. Expert knowledge: limitations of a long-term memory search Years of professional experience considerably change the way practitioners conceptualize domain knowledge. When faced with a problem, novices usually focus on surface structures of the problematic situation. Experts analyze problems on an abstract level and normally disregard surface structures of the problematic situation while making judgments [10]. There are a number of theories that attempt to explain the differences in performance of experts and novices [11]. All these theories essentially agree on the fact that over the years of extensive professional practice, experts develop special memory structures (schemas) that integrate their domain-specific knowledge. These schemas allow experts to rapidly (and often automatically) search their longterm memory (LTM) for information and actions that are the most appropriate in every particular professional situation [5, 12, 13]. In essence, these schemas substantially reduce or fully remove short-term memory (STM) limitation in experts [3, 14], leading to superior expert performance in recognition and recall. Wiley [10] reported that a large amount of domain knowledge can be a disadvantage to experts’ creativity. In a series of three experiments that engaged subjects with different levels of baseball knowledge that used an adapted version of Mednick’s remote association test (RAT) [15], she discovered that expert knowledge can act as a mental set: “It appears that domain knowledge not only biases a first solution attempt but also fixates the highknowledge subject by defining and narrowing the search space, preventing a broad search, and decreasing the chances of finding an appropriate solution” [10]. Although large baseball knowledge does not compare in depth and breadth with domain knowledge of experts in science, engineering and medicine, Belski and Belski [3] advocated that domain-specific schemas can detrimentally impact on the creativity of experts from semantically rich domains [3]. In order to explain why creativity can be diminished by domain knowledge, Belski and Belski [3] proposed to model human knowledge as presented in Figure 1. The model in Figure 1 consists of three main areas that represent three different categories of human knowledge. The larger ellipse, ‘1’, symbolises all the knowledge that has been acquired by humans so far. The smaller ellipse ‘2’ depicts the knowledge gained by a specific person during her/his years of study and experience, her/his knowledge base. The circle, assigned as ‘3’, stands for all the expert knowledge of this particular individual. Although both areas ‘2’ and ‘3’ in Figure 1 designate knowledge possessed by the same individual, the way knowledge from these areas is searched and deployed by this individual differs fundamentally. Expert knowledge is characterised by well-developed knowledge schemas that reduce limitations of STM for the expert knowledge area ‘3’ and make searches of knowledge enclosed in ‘3’ very efficient. Moreover, when the individual faces a problem from her/his professional domain, the expert knowledge area ‘3’ that
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contains knowledge schemas is automatically searched first [16]. When this schema-activated search is successful and a suitable solution is found in ‘3’, it is unlikely that the knowledge base area ‘2’ will be further searched for more solution ideas [16]. Essentially, the individual’s solution ideas become bounded by her/his domain knowledge. When the individual considers a problem from outside of her/his domain of expertise, because schemas for that area of knowledge have not been established, an automatic schema-driven search is not activated by default. Therefore, the individual must utilise some general search strategy to seek for solutions. If a general search strategy is in use, STM limitations for the area of expert knowledge ‘3’ are not reduced and, thus, expert knowledge ‘3’ does not have searching priority over the area of all individual knowledge ‘2’. Consequently, this general search for solutions may fully explore the knowledge contained in area ‘2’. This means that when the individual faces a problem that is outside of her/his domain of expertise, the entire individual’s knowledge is searched for solutions. As a result, solution ideas are not constrained by her/his expertise but restricted only by the individual knowledge base. In sum, when the individual faces a problem from her/his expert domain, solutions are likely to be confined to the area of expert knowledge ‘3’. Solution ideas from the rest of her/his individual knowledge area ‘2’ are unlikely to be considered at all. As a result, for problems that relate to the domain of expertise, a substantial part of the individual’s knowledge is not searched during idea generation. Accordingly, experts’ ideas can be limited to the knowledge area of their expertise. It can be inferred that a practitioner working in a particular field gradually develops a tendency to refer for solutions to her/his collection of domain knowledge that is also continuously growing. Growing domain knowledge is slowly clustered into schemas that, in turn (after many years in the profession), gain automatic priority to search LTM for solutions.
Fig. 1. Map of human knowledge: (1 – all human knowledge; 2 – all knowledge acquired by an individual; 3 – expert knowledge possessed by this individual) [3].
2.2. Impact of expertise on engineering creativity An automatic priority given by STM to a schema-activated search of expert domain-specific knowledge makes engineering experts superior problem solvers. It is specifically the schema-activated search of their expert domain knowledge that allows them to make sound decisions quickly and they are usually not misdirected by inconsequential information. Unfortunately, this automatic priority to search by default for solutions in area ‘3’ can cause a serious drawback – it can impede engineering creativity. It is well known that engineering experts often apply the same collection of basic solutions to every problem encountered: “mechanical engineers offer mechanical solutions for problems which await electrical answers and vice versa – electrical engineers only think of electrical solutions to every problem they encounter” [3].
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Most contemporary engineering problems are open-ended and require knowledge that is beyond one discipline. Therefore, it is expected that an engineering expert must be capable of looking beyond her/his domain knowledge when solving problems. In order to ensure that expert’s solutions are not confined to her/his domain knowledge, it is necessary to stop the activation of an automatic schema-driven search of expert domain knowledge and: x to replace the schema-driven search with some effective search strategy that exploits all the knowledge acquired by the expert in full, and/or x to engage the expert in reframing a problem in order to help her/him to see the problem situation in a novel way. These strategies can be executed by means of the TRIZ tools. 2.3. TRIZ tools for engineering experts: searching long-term memory more efficiently The effectiveness of the systematized Substance-Field analysis (Su-Field), Method of the Ideal Result (MIR), Situation Analysis, the 40 Innovative Principles as well as the Contradiction Table to minimize the inefficiencies of short-term memory have been considered in [3]. Let us specifically focus on the ability of Su-Field analysis and MIR to stop the activation of an automatic schema-driven search of expert domain knowledge. The former can help to replace the schema-driven search with some effective search strategy. The latter is useful in engaging experts in problem reframing. 2.3.1. Substance-Field Analysis as a general search tool Substance-Field Analysis represents technical systems as a set of interacting components – a set of substances interacting with each other by means of fields, which are generated by the substances [17, 18]. Substances and fields in Substance-Field Analysis are not equal in representing systems – substances describe real system elements, and fields show the interactions between these elements. Both substances and fields are represented in a similar manner – by circles. This ensures that vastly different real systems are modelled in a similar way – by means of circle-substances and circle-fields. Such generalisations enable a practitioner to represent complex systems by simple structures. This allows a user to model different systems in a uniform way and to apply similar rules to resolve dissimilar problems. In essence, Su-Field Analysis models technical systems through a set of interconnecting substances and fields. This converts the real task into its Su-Field model and helps a user to disconnect her/himself from the original problem [3]. Moreover, a practitioner is required to reduce the Su-Field model of the original situation to one or more conflict triads – sets of two substances and one field that embody conflicts. Five model solutions are considered for every conflict triad. These five model solutions represent five general solution “recipes”. They suggest adding more substances and fields in order to resolve the conflict embedded in the conflict triad. The eight fields of MATCEMIB (Mechanical, Acoustic, Thermal, Chemical, Electric, Magnetic, Intermolecular, and Biological) are then used to “translate” model solutions into ideas for real solutions [19]. The role of the fields of MATCEMIB is to engage a user in searching her/his knowledge base for the implementation of solution ideas proposed by the five model solutions. Once an expert has modelled a problematic situation with Su-Field and reduced the problem to one or more conflict triads, she/he can no longer directly associate these conflict triads with the original problem. Consequently, expert schemas will not be activated and a schema-induced search (that is limited to the domain-specific knowledge) will not be automatically conducted. Instead, an expert will be searching her/his knowledge base (using the eight fields of MATCEMIB) for ways of implementing solution ideas suggested by the five model solutions. It has been reported that almost 80% of engineers that deployed Su-Field for improvement (see [17] for the application of Su-Field in failure analysis) found it very effective in triggering novel solution ideas systematically [3, 20]. Similar opinions on the effectiveness of Su-Field in systematically searching a knowledge database by engineering students have been reported in [21, 22].
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2.3.2. Method of the Ideal Result as a tool for reframing problems Method of the Ideal Result (MIR) has been developed by one of the authors [23, 24]. MIR is based on the TRIZ notion of the Ideal Ultimate Result (IUR) [25]. It has been reported that the application of MIR helped expert engineers in the development of a mobile crash barrier [26]. MIR helped expert engineers to reframe their problem. As a result of its application engineers replaced their traditional solution of a crash barrier with attached ballasting weight by an elegant novel solution – a crash barrier, which ‘digs’ into the road when necessary. This novel design was successfully trialled and patented [27]. MIR engages a practitioner in reframing the original situation. This is facilitated by formulating the IUR, by reformulating it into Target Tasks (TTs) and by listing the system’s resources. A well formulated IUR is never achievable (e.g. [26]: “the ideal crash barrier is infinitely small and weightless, is able to stop an infinitely heavy vehicle”). Therefore, when users state the IUR, they are usually puzzled for a little while because the IUR sounds very unusual (e.g. [26]: how could a weightless barrier stop an infinitely heavy vehicle?). The TT, on the other hand, is achievable and realistic. In order to state the TT, a practitioner investigates the reasons that prevent the IUR from occurring based on the principles of physics. These activities can trigger the reframing of the original task and significantly change the practitioner’s perception of the problematic situation. Formulating the IUR and restating it as TTs helped the crash barrier team to reframe their problem. It is interesting to note that both designs attained sufficient crash barrier weight in a similar way – by attaching an additional ballasting weigh to the main body of the barrier. The original design deployed the 5-tonnes ballasting weight that was connected to the main body. This ballasting weight had to be transported with the crash barrier. The second, novel, design utilised the ‘ballasting weight’ of the Earth instead. This ‘ballasting weight’ did not require transportation. Clearly, a small change of viewpoint (from “What weight of the barrier is required to stop a vehicle? to How can a light barrier stop a heavy vehicle?” [26]) resulted in the reframing of the problem and in a substantial creative leap. 3. Discussion and Conclusion Human thoughts are usually unstructured. STM has a low storage capacity and short duration of time for which it stores information. These characteristics significantly complicate the efficient retrieval of information from LTM storage during idea generation. When an expert solves a problem within his professional domain, the limitations of STM can be practically eliminated due to the schemas that are developed over 10 or more years of professional experience. These schemas trigger an automatic search that is confined to expert domainspecific knowledge. This makes engineering experts superior problem solvers. This also can impede experts’ creativity. It has been shown that the TRIZ tools of Su-Field analysis and MIR can help in minimizing the detrimental effect of expertise on creativity. Su-Field analysis offers expert the means to effectively search through their knowledge base. MIR can stop the activation of an automatic schema-driven search by engaging experts in problem reframing.
References [1] M . Bilalic´, P. McLeod, and F. Gobet, Inflexibility of experts—Reality or myth? Quantifying the Einstellung effect in chess masters, Cognitive Psychology, vol. 56, pp. 73-102, 2008. [2] D . G. Jansson and S. M. Smith, Design fixation, Design Studies, vol. 12, pp. 3-11, 1991. [3] I. Belski and I. Belski, Cognitive foundations of TRIZ problem-solving tools, in Proceedings of the TRIZ-Future Conference 2008, The Netherlands, 2008, pp. 95-102. [4] K . A. Ericsson, R. T. Krampe, and C. Tesch-Romer, The Role of Deliberate Practice in the Acquisition of Expert Performance, Psychological Review, vol. 100, pp. 363-406, 1993. [5] K . Anders Ericsson and T. J. Towne, Expertise, Wiley Interdisciplinary Reviews: Cognitive Science, vol. 1, pp. 404-16, 2010. [6] M. T. Chi, Two approaches to the study of experts’ characteristics, The Cambridge handbook of expertise and expert performance, pp. 21-30, 2006.
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[7] W . G. Chase and H. A. Simon, Perception in chess, Cognitive Psychology, vol. 4, pp. 55-81, 1973. [8] M . Myles-Worsley, W. A. Johnston, and M. A. Simons, The influence of expertise on X-ray image processing, Journal of Experimental Psychology: Learning, Memory, and Cognition, vol. 14, p. 553, 1988. [9] D . Besnard and M. Bastien-Toniazzo, Expert error in trouble-shooting: an exploratory study in electronics, International Journal of HumanComputer Studies, vol. 50, pp. 391-405, 1999. [10] J. Wiley, Expertise as mental set: The effects of domain knowledge in creative problem solving, Memory & cognition, vol. 26, pp. 716-730, 1998. [11] F. Gobet, Expert memory: A comparison of four theories, Cognition, vol. 66, pp. 115-152, 1998. [12] F. Gobet and H. A. Simon, The Roles of Recognition Processes and Look-Ahead Search in Time-Constrained Expert Problem Solving: Evidence from Grandmaster Level Chess, Psychological Science, pp. 52-55, 1996. [13] H. A. Simon, Information-processing theory of human problem solving, Handbook of learning and cognitive processes, vol. 5, pp. 271-295, 1978. [14] P. A. Kirschner, J. Sweller, and R. E. Clark, Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching, Educational Psychologist, vol. 41, pp. 75-86, 2006. [15] S. Mednick, The associative basis of the creative process, Psychological Review, vol. 69, p. 220, 1962. [16] M. L. Gick, Problem-Solving Strategies, Educational Psychologist vol. 21, pp. 99-120, 1986. [17] A. Belski, I. Belski, T. C. Teng, and R. Kwok, Application of Substance-Field Analysis for Failure Analysis, in Proceedings of the 13th ETRIA world TRIZ future conference 2013, Paris, France, 2013, pp. 483-490. [18] I. Belski, Improve your Thinking: Substance-Field Analysis. TRIZ4U, Melbourne, 2007. [19] I. Belski, Improvement of Thinking and Problem Solving Skills of Engineering Students as a result of a Formal Course on TRIZ Thinking Tools, in Proceedings of the 13th International Conference on Thinking, Norrkoping, Sweden, 2007, pp.11-17. [20] I. Belski, Reinventing Triz Thinking Tools: Substance – Field Analysis, in Proceedings of the 5th International Conference TRIZFutures 06, Kortrijk, Belgium, 2006, pp.35-40. [21] I. Belski, J. Baglin, and J. Harlim, Teaching TRIZ at University: a Longitudinal Study, International Journal of Engineering Education, vol. 29, pp. 346-354, 2013. [22] I. Belski, Teaching Thinking and Problem Solving at University: A Course on TRIZ, Creativity And Innovation Management, vol. 18, pp. 101-108, 2009. [23] I. Belski, I Wish The Work To Be Completed By Itself, Without My Involvement: The Method Of The Ideal Result In Engineering Problem Solving, in Proceedings of World Innovation and Strategy Conference, Sydney, Australia, 1998, pp. 199-206. [24] I. Belski, Solving problems with Method of the Ideal Result (MIR), in Transactions from the 11th Symposium on Quality Function Deployment, Novi, Michigan, USA, 1999, pp. 192-203. [25] G. Altshuller, Creativity as an Exact Science, Gordon & Breach Science Publishing House, New York, 1984. [26] I. Belski, T. C. Teng, A. Belski, and R. Kwok, TRIZ Wins a Tender: Designing a Crash Barrier, in TRIZ Future Conference 2012, Lisbon, Portugal, 2012, pp. 425-436. [27] C. M. Tay, T. C. Teng, E. L. Sioh, A. J. Shi, and C. N. Sim, Vehicle barrier, International Patent, WO 2006/088428, 2006.
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ScienceDirect Procedia Engineering 131 (2015) 792 – 797
World Conference: TRIZ FUTURE, TF 2011-2014
Application of TRIZ in Improving the Creativity of Engineering Experts Iouri Belskia, Ianina Belskib a
Royal Melbourne Institute of Technology, School of Electrical and Computer Engineering, Melbourne, Australia b TRIZ4U, Melbourne, Australia
Abstract Numerous researchers have extensively studied two causes of problem solvers’ inflexibility which impede creativity: the Einstellung effect [1] and design fixation [2]. The former has been demonstrated experimentally on numerous occasions and is induced by prior experience. The latter is a result of our fixedness on the functions of things which we regularly use. This paper focuses on the third cause of inflexibility, which has not been researched adequately – the detrimental effect of professional expertise on creativity [3]. This detrimental effect is a natural consequence of extensive professional experience and the possession of large amounts of domain knowledge. After approximately 10 years in a profession, due to the construction of effective knowledge schemas, the short-term memory limitations which normally impede effective idea generation can partly or even completely disappear. As a result, experts attain an ability to search for solutions to problems quickly – without significant cognitive and time effort. Although this ‘quickness’ of experts in suggesting solutions is advantageous, it also creates negative consequences. Experts’ solutions are usually confined to their domain-specific knowledge and do not utilize novel ideas. This study reviews existing evidence related to the detrimental influence of expertise on creativity and discusses how the application of TRIZ tools of Substance-Field Analysis and Method of the Ideal Result can minimize this negative influence. © by by Elsevier Ltd.Ltd. This is an open access article under the CC BY-NC-ND license © 2015 2015The TheAuthors. Authors.Published Published Elsevier (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Scientific Committee of TFC 2011, TFC 2012, TFC 2013 and TFC 2014 – GIC. Peer-review under responsibility of the Scientific Committee of TFC 2011, TFC 2012, TFC 2013 and TFC 2014 – GIC Keywords: Engineering creativity; fixation; memory search; Substance-Field analysis; Method of the Ideal Result.
1. Introduction It has been established that expertise requires not only a large amount of domain knowledge, but also many years of professional practice [4, 5]. In order to become an expert in semantically rich domains, such as engineering, science and medicine, extensive professional knowledge must be acquired prior to being permitted to work as a professional. This acquisition of basic professional knowledge usually occurs at university. After
1877-7058 © 2015 The Authors. Published by Elsevier Ltd. 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 Scientific Committee of TFC 2011, TFC 2012, TFC 2013 and TFC 2014 – GIC
doi:10.1016/j.proeng.2015.12.379
Iouri Belski and Ianina Belski / Procedia Engineering 131 (2015) 792 – 797
793
graduating, specialists spend years learning to apply their discipline knowledge in real professional situations. It is usually considered that 10 years of extensive professional practice is the very minimum to accumulate expertise [6]. As a result of many years of professional practice, experts become superior in task recognition and most of the time markedly outperform novices and individuals on intermediate level skills in memory recall in their professional domains [5]. On the other hand, many studies demonstrated that under certain conditions novices and intermediates can outperform experts in their domains of expertise. Chase and Simon [7], for example, showed that novices performed better than experts in recall of randomized chess boards. It has been reported that, although expert radiologists performed better than ones with limited experience in remembering X-ray films with clinical abnormalities, they did worse in remembering films that did not contain abnormalities [8]. Similarly, experts in electronics required longer time than novices to establish atypical faults in electronic circuits [9]. There is also anecdotal evidence that experts are usually less capable than novices in suggesting fresh and novel ideas. This paper focuses on the detrimental influence of expertise on creativity and endeavors to determine the reasons behind this influence and to suggest the ways to minimize it. 2. Expertise and Creativity 2.1. Expert knowledge: limitations of a long-term memory search Years of professional experience considerably change the way practitioners conceptualize domain knowledge. When faced with a problem, novices usually focus on surface structures of the problematic situation. Experts analyze problems on an abstract level and normally disregard surface structures of the problematic situation while making judgments [10]. There are a number of theories that attempt to explain the differences in performance of experts and novices [11]. All these theories essentially agree on the fact that over the years of extensive professional practice, experts develop special memory structures (schemas) that integrate their domain-specific knowledge. These schemas allow experts to rapidly (and often automatically) search their longterm memory (LTM) for information and actions that are the most appropriate in every particular professional situation [5, 12, 13]. In essence, these schemas substantially reduce or fully remove short-term memory (STM) limitation in experts [3, 14], leading to superior expert performance in recognition and recall. Wiley [10] reported that a large amount of domain knowledge can be a disadvantage to experts’ creativity. In a series of three experiments that engaged subjects with different levels of baseball knowledge that used an adapted version of Mednick’s remote association test (RAT) [15], she discovered that expert knowledge can act as a mental set: “It appears that domain knowledge not only biases a first solution attempt but also fixates the highknowledge subject by defining and narrowing the search space, preventing a broad search, and decreasing the chances of finding an appropriate solution” [10]. Although large baseball knowledge does not compare in depth and breadth with domain knowledge of experts in science, engineering and medicine, Belski and Belski [3] advocated that domain-specific schemas can detrimentally impact on the creativity of experts from semantically rich domains [3]. In order to explain why creativity can be diminished by domain knowledge, Belski and Belski [3] proposed to model human knowledge as presented in Figure 1. The model in Figure 1 consists of three main areas that represent three different categories of human knowledge. The larger ellipse, ‘1’, symbolises all the knowledge that has been acquired by humans so far. The smaller ellipse ‘2’ depicts the knowledge gained by a specific person during her/his years of study and experience, her/his knowledge base. The circle, assigned as ‘3’, stands for all the expert knowledge of this particular individual. Although both areas ‘2’ and ‘3’ in Figure 1 designate knowledge possessed by the same individual, the way knowledge from these areas is searched and deployed by this individual differs fundamentally. Expert knowledge is characterised by well-developed knowledge schemas that reduce limitations of STM for the expert knowledge area ‘3’ and make searches of knowledge enclosed in ‘3’ very efficient. Moreover, when the individual faces a problem from her/his professional domain, the expert knowledge area ‘3’ that
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contains knowledge schemas is automatically searched first [16]. When this schema-activated search is successful and a suitable solution is found in ‘3’, it is unlikely that the knowledge base area ‘2’ will be further searched for more solution ideas [16]. Essentially, the individual’s solution ideas become bounded by her/his domain knowledge. When the individual considers a problem from outside of her/his domain of expertise, because schemas for that area of knowledge have not been established, an automatic schema-driven search is not activated by default. Therefore, the individual must utilise some general search strategy to seek for solutions. If a general search strategy is in use, STM limitations for the area of expert knowledge ‘3’ are not reduced and, thus, expert knowledge ‘3’ does not have searching priority over the area of all individual knowledge ‘2’. Consequently, this general search for solutions may fully explore the knowledge contained in area ‘2’. This means that when the individual faces a problem that is outside of her/his domain of expertise, the entire individual’s knowledge is searched for solutions. As a result, solution ideas are not constrained by her/his expertise but restricted only by the individual knowledge base. In sum, when the individual faces a problem from her/his expert domain, solutions are likely to be confined to the area of expert knowledge ‘3’. Solution ideas from the rest of her/his individual knowledge area ‘2’ are unlikely to be considered at all. As a result, for problems that relate to the domain of expertise, a substantial part of the individual’s knowledge is not searched during idea generation. Accordingly, experts’ ideas can be limited to the knowledge area of their expertise. It can be inferred that a practitioner working in a particular field gradually develops a tendency to refer for solutions to her/his collection of domain knowledge that is also continuously growing. Growing domain knowledge is slowly clustered into schemas that, in turn (after many years in the profession), gain automatic priority to search LTM for solutions.
Fig. 1. Map of human knowledge: (1 – all human knowledge; 2 – all knowledge acquired by an individual; 3 – expert knowledge possessed by this individual) [3].
2.2. Impact of expertise on engineering creativity An automatic priority given by STM to a schema-activated search of expert domain-specific knowledge makes engineering experts superior problem solvers. It is specifically the schema-activated search of their expert domain knowledge that allows them to make sound decisions quickly and they are usually not misdirected by inconsequential information. Unfortunately, this automatic priority to search by default for solutions in area ‘3’ can cause a serious drawback – it can impede engineering creativity. It is well known that engineering experts often apply the same collection of basic solutions to every problem encountered: “mechanical engineers offer mechanical solutions for problems which await electrical answers and vice versa – electrical engineers only think of electrical solutions to every problem they encounter” [3].
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Most contemporary engineering problems are open-ended and require knowledge that is beyond one discipline. Therefore, it is expected that an engineering expert must be capable of looking beyond her/his domain knowledge when solving problems. In order to ensure that expert’s solutions are not confined to her/his domain knowledge, it is necessary to stop the activation of an automatic schema-driven search of expert domain knowledge and: x to replace the schema-driven search with some effective search strategy that exploits all the knowledge acquired by the expert in full, and/or x to engage the expert in reframing a problem in order to help her/him to see the problem situation in a novel way. These strategies can be executed by means of the TRIZ tools. 2.3. TRIZ tools for engineering experts: searching long-term memory more efficiently The effectiveness of the systematized Substance-Field analysis (Su-Field), Method of the Ideal Result (MIR), Situation Analysis, the 40 Innovative Principles as well as the Contradiction Table to minimize the inefficiencies of short-term memory have been considered in [3]. Let us specifically focus on the ability of Su-Field analysis and MIR to stop the activation of an automatic schema-driven search of expert domain knowledge. The former can help to replace the schema-driven search with some effective search strategy. The latter is useful in engaging experts in problem reframing. 2.3.1. Substance-Field Analysis as a general search tool Substance-Field Analysis represents technical systems as a set of interacting components – a set of substances interacting with each other by means of fields, which are generated by the substances [17, 18]. Substances and fields in Substance-Field Analysis are not equal in representing systems – substances describe real system elements, and fields show the interactions between these elements. Both substances and fields are represented in a similar manner – by circles. This ensures that vastly different real systems are modelled in a similar way – by means of circle-substances and circle-fields. Such generalisations enable a practitioner to represent complex systems by simple structures. This allows a user to model different systems in a uniform way and to apply similar rules to resolve dissimilar problems. In essence, Su-Field Analysis models technical systems through a set of interconnecting substances and fields. This converts the real task into its Su-Field model and helps a user to disconnect her/himself from the original problem [3]. Moreover, a practitioner is required to reduce the Su-Field model of the original situation to one or more conflict triads – sets of two substances and one field that embody conflicts. Five model solutions are considered for every conflict triad. These five model solutions represent five general solution “recipes”. They suggest adding more substances and fields in order to resolve the conflict embedded in the conflict triad. The eight fields of MATCEMIB (Mechanical, Acoustic, Thermal, Chemical, Electric, Magnetic, Intermolecular, and Biological) are then used to “translate” model solutions into ideas for real solutions [19]. The role of the fields of MATCEMIB is to engage a user in searching her/his knowledge base for the implementation of solution ideas proposed by the five model solutions. Once an expert has modelled a problematic situation with Su-Field and reduced the problem to one or more conflict triads, she/he can no longer directly associate these conflict triads with the original problem. Consequently, expert schemas will not be activated and a schema-induced search (that is limited to the domain-specific knowledge) will not be automatically conducted. Instead, an expert will be searching her/his knowledge base (using the eight fields of MATCEMIB) for ways of implementing solution ideas suggested by the five model solutions. It has been reported that almost 80% of engineers that deployed Su-Field for improvement (see [17] for the application of Su-Field in failure analysis) found it very effective in triggering novel solution ideas systematically [3, 20]. Similar opinions on the effectiveness of Su-Field in systematically searching a knowledge database by engineering students have been reported in [21, 22].
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2.3.2. Method of the Ideal Result as a tool for reframing problems Method of the Ideal Result (MIR) has been developed by one of the authors [23, 24]. MIR is based on the TRIZ notion of the Ideal Ultimate Result (IUR) [25]. It has been reported that the application of MIR helped expert engineers in the development of a mobile crash barrier [26]. MIR helped expert engineers to reframe their problem. As a result of its application engineers replaced their traditional solution of a crash barrier with attached ballasting weight by an elegant novel solution – a crash barrier, which ‘digs’ into the road when necessary. This novel design was successfully trialled and patented [27]. MIR engages a practitioner in reframing the original situation. This is facilitated by formulating the IUR, by reformulating it into Target Tasks (TTs) and by listing the system’s resources. A well formulated IUR is never achievable (e.g. [26]: “the ideal crash barrier is infinitely small and weightless, is able to stop an infinitely heavy vehicle”). Therefore, when users state the IUR, they are usually puzzled for a little while because the IUR sounds very unusual (e.g. [26]: how could a weightless barrier stop an infinitely heavy vehicle?). The TT, on the other hand, is achievable and realistic. In order to state the TT, a practitioner investigates the reasons that prevent the IUR from occurring based on the principles of physics. These activities can trigger the reframing of the original task and significantly change the practitioner’s perception of the problematic situation. Formulating the IUR and restating it as TTs helped the crash barrier team to reframe their problem. It is interesting to note that both designs attained sufficient crash barrier weight in a similar way – by attaching an additional ballasting weigh to the main body of the barrier. The original design deployed the 5-tonnes ballasting weight that was connected to the main body. This ballasting weight had to be transported with the crash barrier. The second, novel, design utilised the ‘ballasting weight’ of the Earth instead. This ‘ballasting weight’ did not require transportation. Clearly, a small change of viewpoint (from “What weight of the barrier is required to stop a vehicle? to How can a light barrier stop a heavy vehicle?” [26]) resulted in the reframing of the problem and in a substantial creative leap. 3. Discussion and Conclusion Human thoughts are usually unstructured. STM has a low storage capacity and short duration of time for which it stores information. These characteristics significantly complicate the efficient retrieval of information from LTM storage during idea generation. When an expert solves a problem within his professional domain, the limitations of STM can be practically eliminated due to the schemas that are developed over 10 or more years of professional experience. These schemas trigger an automatic search that is confined to expert domainspecific knowledge. This makes engineering experts superior problem solvers. This also can impede experts’ creativity. It has been shown that the TRIZ tools of Su-Field analysis and MIR can help in minimizing the detrimental effect of expertise on creativity. Su-Field analysis offers expert the means to effectively search through their knowledge base. MIR can stop the activation of an automatic schema-driven search by engaging experts in problem reframing.
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