The cues employed by a problem solver's short-term memory are usually related to both the problem encountered and the creativity technique utilised during.
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Cognitive Foundations of TRIZ Problem-Solving Tools 1
Iouri Belski1, Ianina Belski2 Royal Melbourne Institute of Technology, Australia 2 TRIZ4U, Australia
Abstract This paper considers the cognitive foundations of TRIZ tools. It analyses reasons for the effectiveness of TRIZ tools in directing users to achieve superior solutions. General human inefficiencies in problem solving are considered. Differences in problem solving between discipline experts and novices are discussed. The cognitive bases for application of the systematised Substance-Field analysis, Method of the Ideal Result, Situation Analysis, the 40 Innovative Principles as well as the Contradiction Table are considered. Opinions of both experts and novices on the effectiveness of TRIZ problem solving tools are presented.
Keywords Cognition, TRIZ, Problem solving, Creativity, Idea Generation
1 INTRODUCTION Creative problem solving is normally considered to be a mental process which involves generation of new ideas or concepts to resolve existing problems. Creativity and problem solving has been extensively studied by scientists belonging to various research fields ranging from behavioural and social psychology, to engineering design. Unlike many phenomena in science, creative problem solving does not have an explanation which is accepted by most members of the scientific community. Numerous existing models attribute creativity to various sources, including chance, personality traits, mental illness and even divine intervention. Some of them believe that it is a trait people are born with, and conclude that creativity skills cannot be enhanced by learning a creativity technique [1]. Most researchers, however, believe in purely psychological roots of creative thinking and problem solving and agree that a person can enhance his creative ability by acquiring some suitable methods and techniques. Some of these techniques include Brainstorming, Synectics, the Lateral Thinking techniques of Edward de Bono, and the tools of Theory of Inventive Problem Solving (TRIZ) [2]. The fields of science and engineering heavily depend on the creativity of engineers and scientists. Simonton [3] has shown that some of the major scientific advances of the 20th Century can be attributed to the creativity of individual engineers and scientists. Accordingly, engineering accrediting bodies consider an ability to problem solve as st an exceptionally important skill for engineers of the 21 Century [4]. Many leading engineering companies have invested considerably into enhancing the problem solving abilities of their staff and have trained engineers and scientists in numerous creativity tools. Since their discovery by the western world in the early 1990s, TRIZ problem solving tools have gradually started gaining followers among engineers and are considered by more and more corporations as the tools that their engineers must know well [5, 6]. Usually TRIZ experts and TRIZ teachers explain the tools’ efficiency in terms of a reduction in the psychological inertia [7] of a user. Although such an explanation may be valid, it neither describes how the tools of TRIZ really reduce psychological inertia, nor does it clarify the significant discrepancy in the perceptions of TRIZ
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practitioners of the effectiveness of different TRIZ tools in resolving real problems [8]. On these grounds, it is of interest to investigate the reasons behind the effectiveness of various TRIZ tools and to further clarify how these tools really work. In order to be effective, any creativity tool must somehow influence the operation of the human brain at the time of idea generation. Thus, it is necessary to consider models of human memory and specifically focus on the stage of information retrieval. It is the effectiveness of information retrieval which is likely to be affected by the creativity tools and thus may make human idea generation more efficient. 2
PROBLEM SOLVING AND MEMORY
2.1 Models of memory Memory is normally defined as the human ability to store, retain, and subsequently retrieve information. Numerous models of memory have been presented by researchers. The multi-store model proposed in 1968 by Atkinson and Shiffrin [9] is represented in Figure 1.
Figure 1: The multi-store model of memory. Although the multi-store model of Atkinson and Shiffrin has been criticised for being too simplistic, it is sufficient to offer some insights into the reasons behind the efficiency of TRIZ tools. There are several ways to classify memory. From the information processing perspective, science identifies three main stages in the formation and retrieval of memory [10]: •
Encoding or registration, which includes processing and combining of received information.
•
Storage, which results in the creation of a permanent record of the information encoded in the previous stage.
•
Retrieval or recall, which is related to retrieving the information stored during the previous stage.
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Indeed, all three main stages listed above are of importance in the human ability of creative problem solving. Nevertheless, the stage of retrieval plays the most important role during the application of various creativity tools. A practitioner normally utilises creativity tools to recall some useful information from his or her memory in order to generate suitable ideas to resolve the problem. Inefficient encoding and storage can result in the absence of necessary information in long-term memory. 2.2 Cues and rehearsal in idea generation Appropriate information is normally retrieved in response to some cue or cues, which a practitioner ‘puts’ into his short-term memory storage. These cues are basically used to search the long-term memory database for information which is related to the cues. If such information is found, it brings possible analogies to the mind of a practitioner which can result in novel solution ideas. This process of retrieval and idea generation can be expressed by the model depicted in Figure 2 (sensory memory is not relevant for the discussion on retrieval and will not be considered further).
Figure 2: A model of idea generation. The model in Figure 2 basically suggests that a problem solver tries to search his long-term memory database to find appropriate information which is helpful in resolving the problem. The search is based on cues which a user holds in his short-term memory. These cues are transferred from the short-term memory to the long-term memory to be compared with items in the memory database and to identify useful analogies, which will be retrieved back to the short-term memory as possible solution ideas. This database search is usually not very effective. Therefore, in order to recall more efficiently, a problem solver reminds himself of the cues again and again (rehearses) until some suitable information is found in long-term memory. This model also identifies two essential ingredients for a practitioner to generate ideas efficiently: •
•
Firstly, information from which the idea can be drawn must be present in the long-term memory database of a problem solver. If such information is absent it cannot be retrieved, so a solution idea cannot be generated.
Secondly, the cues used by a practitioner must be effective to ensure not only that appropriate analogies are found in the long-term memory database, but also that these suitable solution ideas are identified within acceptable time. The cues employed by a problem solver’s short-term memory are usually related to both the problem encountered and the creativity technique utilised during the problem solving process. Indeed, efficiency of the cues is affected by the mental and psychological state of the user as well as by his thoughts at the time of problem solving. In order to evaluate whether a creativity technique is efficient, it is necessary to eliminate those human effects. In other words, in order to judge a technique it is
necessary to concentrate specifically on two kinds of cues: •
Cues which a user associates directly with the problem itself; and
•
Cues which are suggested to him by the creativity technique as additional to the cues directly associated with the problem. Under this assumption, we can re-draw the model of idea generation presented in Figure 2 to include the relevant cues, as presented in Figure 3.
Figure 3: Cues and idea generation. 2.3 Cues and short-term memory overload The storage capacity of short-term memory is small, so any problem solving methodology must not overload its existing storage limit with cues. In 1956, George Miller evaluated a forward short-term memory span to be equal to the magical number seven (approximately seven items plus or minus two) [11]. More recent research has shown that although Miller’s evaluation is reasonably accurate, the number of items held in short-term memory can be significantly influenced by various effects [12]. Some authors have also suggested that new items added to short-term memory can cause decay of old items which are already held in short-term memory [13]. This decay is somewhat similar to interference. When a number of items are held in short-term memory at the same time, they compete with each other for retrieval. Newly added items interfere with the old ones and may even fully force them out of short-term memory storage. These findings explain the inefficiencies associated with the method of trial and error. Normally, when no creativity tool is in use, such as when a user deploys the trial and error approach, cues stored and handled by short-term memory are likely to be solely related to the problem itself. Information that the user has about the problem is usually pretty general, unstructured and unfocused. Information about the problem can easily contain significantly more data then what the small storage capacity of short-term memory can handle efficiently for retrieval. This can cause short-term memory overload, resulting in a practitioner’s loss of focus in trying to resolve too many problems at once. Also, random thoughts can easily enter short-term memory and interfere with the problem cues. As a result, a problem solver may lose track of the problem and be carried away by new thoughts that are beyond the problem he wishes to resolve. Based on the existing research into memory storage and recall we can conclude that unclear cues, interference from random thoughts, and excess information about the problem may be considered as the main reasons for why the trial and error method is inefficient. To be successful in helping a practitioner, methodology intended to foster good creativity needs to overcome these problems. It must provide a practitioner with only several strong cues and at the same time ensure that the
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short-term memory of a problem solver is never overloaded during the idea generation (retrieval) process. 2.4 Creativity methodology and rehearsal The low storage capacity of short-term memory is not the only challenge for the problem solving and idea generation process. The information storage time of shortterm memory is also short and is unlikely to exceed 10 seconds. Researchers have limited the duration of shortterm memory storage to between 2 to 30 seconds [14]. This limits the duration of a single retrieval period to only a few seconds. Such a short time may be insufficient to perform a thorough search of the long-term memory database. As a result, some important information which is stored in long-term memory and that can otherwise help resolve a problem, may be entirely lost. The situation is further complicated by the interference effect. New cues degrade the outcomes of recall of old cues by pushing the latter out of short-term memory. Thus, it is of importance to protect the older cues. This can be achieved by rehearsal or by directing attention to the older cues [13]. An effective problem solving methodology needs to take into account the short duration of a single retrieval interval and somehow make it last longer. This can be achieved by appropriately manipulating the natural process of rehearsal and recall. The rehearsal process during retrieval, when deployed without a proper problem solving methodology, is unstructured. Usually this makes the duration of one cycle of information retrieval significantly shorter than would be theoretically possible [15]. This can significantly deteriorate the memory database search time. Moreover, a practitioner using trial and error does not normally continue reinforcing idea retrieval by rehearsing the cues again and again. Cues might re-appear in short-term memory at unexpected times for an unplanned search. The latter can be successful and bring superior solution ideas. Nevertheless, the outcome of trial and error idea generation is unpredictable. Human thoughts are usually unstructured. Although this stochastic tendency can be helpful ‘in jumping outside the box’ on rare occasions, it usually hinders rehearsal. In light of this, it is possible that some creativity techniques may influence the rehearsal process by ensuring that the strong cues stay in short-term memory for as long as required to bring appropriate information out of long-term memory storage. This is likely to impact on the outcomes of the problem solving process and make it more effective in discovering appropriate analogies. 2.5 Experts and short-term memory limitations The model of retrieval of information from long-term memory storage presented in Figure 3 needs some adjustment to explain the problem solving performance of subject experts. Ericsson and Kintsch [17] suggested that long-term memory can significantly improve the operation of short-term memory and minimise its limitations for experts. In their opinion, the extensive knowledge base of an expert helps in creating long-term memory schemas, which significantly increase the potential of short-term memory. Numerous experimental results support this concept, arguing that an expert effectively utilises his extensive experience, which is stored in his long-term memory, to enhance the operations of his short-term memory [18, 19]. As a result, limitations of the short-term memory of a subject expert can be practically eliminated [20]. This means that a subject expert resolving a problem can assess and search his long-term memory storage very Page 95
effectively even without a proper problem solving methodology. Although experimental results support this effect, they also warn that short-term memory restrictions disappear only when information directly related to the area of expertise is accessed. In other words, when an expert is faced with a problem which requires knowledge from outside of his area of experience, long-term memory schemas are likely to be absent. Thus, the advantages created by the schemas of his expert knowledge practically disappear. Every professional is an expert in some areas. Therefore, due to fewer limitations in his short-term memory, he usually achieves significantly better results while solving problems in his own area of expertise. As discussed, when a professional faces a problem from outside his expertise, he is likely to lose the advantages that his knowledge usually provides to short-term memory. Under these circumstances, one would assume that a professional would perform at the same level as a novice, proposing some reasonable solution from outside his area of expertise. This expectation seems theoretically sound, but hardly happens in practice. Usually, experts that must solve problems outside of their expertise perform significantly worse than novices. Instead of considering wide solution options, limited only by their general knowledge, they usually reinforce their own expert experience onto problems from other knowledge areas. This behaviour results in solutions which closely relate to their expertise. Moreover, experts consider their ideas of high quality even when those solution ideas are clearly inefficient and impractical. 2.6 Experts, novices and the human knowledge map We have just found an explanation for a well known fact. Namely, that 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. Experts’ longterm memory schemas force the short-term memory to always operate under the influence of extensive expert knowledge. Thus, short-term memory operates without much limitation, but predominantly searches the database of long-term memory which is occupied by expert knowledge. This situation can be further illustrated with the help of a human knowledge map pictured in Figure 4.
Figure 4: Map of human knowledge: (1 – all human knowledge; 2 – all knowledge acquired by an individual; 3 – expert knowledge possessed by this individual). The knowledge map in Figure 4 consists of three main areas, which can be viewed as three sets representing different categories of human knowledge. The larger ellipse, identified as set “1”, symbolises all the knowledge produced by humans so far. The smaller ellipse of set “2” depicts the knowledge gained by a professional during his
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years of study and experience. The circle, assigned as set “3”, stands for all the expert knowledge of this particular professional. Under such postulation, all the expert knowledge of the professional (set “3”) is a subset of his general knowledge (set “2”), which is, in turn, a subset of all human knowledge (set “1”). A novice who is trying to resolve a problem without any problem solving methodology is likely to propose solution ideas which are based on his general knowledge. If the knowledge areas employed to produce such ideas were identified and mapped graphically using the representation in Figure 4, those knowledge areas would be spread pretty evenly over the smaller ellipse of set “2”. Novices are not biased by their knowledge. They are likely to propose solutions using all the knowledge they possess. Solutions suggested by an expert are usually biased towards his professional field. As we have discussed, expert knowledge can significantly enhance the operation of short-term memory, and so, experts are likely to generate solution ides more efficiently than novices. Those solution ideas, however, tend to occupy the knowledge area inside or in close proximity to the circleset “3” depicting their expert knowledge. Early career professionals are likely to suggest ideas which are spread further from set “3”. Very experienced professionals who have successfully reached the level of experts, on the other hand, are unlikely to propose ideas from outside of the circle-set “3” of their professional knowledge. Such problem solving behaviour can result in the inability of some experts to generate winning ideas beyond their area of expertise. Thus, many experts often apply the same collection of basic solutions to every problem encountered. This is no longer acceptable. More and more engineering solutions today are based on knowledge from more than one area of expertise; more than one discipline. Therefore, it is expected that an expert must be capable of looking beyond his knowledge when solving problems. 3
TRIZ PROBLEM SOLVING TOOLS IN RETRIEVAL
3.1 Effectiveness of problem solving techniques This brings our discussion back to the need for effective problem solving methodologies. It is of interest to discover how these methodologies can help in searching the database of long-term memory for appropriate information on two levels of expertise: both for a novice and for an expert. On the novice level of expertise, creative methodologies need to help a user whose retrieval is affected by severe limitations of short-term memory to overcome those limitations. This demand is threefold: •
Firstly, the methodology needs to ensure that the problem is understood very clearly and is represented in short-term memory by only very few simple cues.
•
Secondly, the methodology is expected to supply a practitioner with some additional cues which make the long-term memory search extremely efficient.
•
Thirdly, it must properly organise rehearsal to ensure that the relevant cues are kept in short-term memory for sufficient time to allow for a thorough search of the long-term memory database. On the level of an expert, appropriate problem solving methodologies are expected to help an expert to ‘lose’ some of his expertise and to exploit his knowledge beyond the area of expertise while generating ideas.
Let us consider how the TRIZ tools of Situation Analysis, 40 Innovative Principles and the Contradiction Table, the Method of the Ideal Result as well as systematised Substance-Field Analysis address the above-mentioned concerns. 3.2 Experts and novices: practical evaluation Once it is recognised that problem solving methodologies need to resolve different issues for experts and for novices, it is necessary to evaluate the usefulness of TRIZ tools by focussing separately on how a methodology influences members of each group, respectively. Experimental evaluation of the influence of any problem solving methodology on a user is not easy to carry out. Measuring changes in the short-term memory limitations of a practitioner while he resolves some real engineering problem is complicated. In this study, we attempted to make judgements about the effectiveness of TRIZ tools based on opinions provided by TRIZ learners. Those opinions have been collected over the past four years through surveying university students, engineers and scientists who were learning the tools. Undergraduate students, most of whom started university directly after completing high school can be considered as novices. Few of them worked as professionals, and so had not been able to build up a sufficient base of expert knowledge and experience. Practicing engineers and scientists fit well into the definition of experts. It is noteworthy that significant differences in experience did exists between professionals, whose years of employment in industry ranged from 3 to over 15 years. Therefore, the current data, which does not discriminate explicitly between professionals’ years of experience, will only identify the tendency. Nevertheless, even with a very broad definition of ‘experts’, we are likely to be obtain some interesting results. The experimental data presented below has been derived from the opinions of nearly one hundred university students who took the TRIZ course at RMIT [21], and from the feedback of over three hundred engineers and scientists, who have participated in TRIZ4U training programs [8]. Students were asked to reflect upon the usefulness of every individual tool separately after they had learnt this tool and applied it to some challenging problem. Engineers were requested to identify the tools they found most useful and to explain why the tools they had chosen were useful to them. 3.3 Situation Analysis Given that short-term memory is susceptible to becoming overloaded, it is important to ensure that the problem is stated clearly and understood. Numerous authors have reported on the importance of a proper understanding of the problem [20, 21]. It has also been found that many TRIZ practitioners see the Situation Analysis (SA) phase as essential [22]. Most TRIZ professionals conduct situation analysis before they start applying a formal problem-solving tool to generate ideas. Usually, the situation analysis procedure requires a user to answer a set of predefined questions. These questions are intended to examine the user’s assumptions on the situation under consideration and to help him in identifying the problem(s) which really need resolution. When well conducted, situation analysis clarifies the problem so a practitioner is able to visualise it clearly. For a novice, this means that the problem is stated concisely and, as a result, it does not occupy all of the available space in his short-term memory. Accordingly,
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some space in short-term memory is left for other cues, which can be brought in by a problem solving technique. An expert conducting a situation analysis procedure is typically able to strip off unnecessary details from the problem. This allows an expert to deploy his expert knowledge schemas and to suggest a number of good solution ideas before any problem solving methodology is used. The fact that experts propose solutions when they understand a problem is well know and supported by human wisdom: understanding the problem helps a user to proceed confidently towards a solution. When university students were asked about the usefulness and efficiency of Situation Analysis, over 90 percent of them expressed their positive opinion of the tool. Many students stated that it helped them to clarify the situation and to understand the problem better, and have accepted the tool as given. The following quotation is a good overall representation of their opinion: “Situation Analysis provides a clear picture of the problem that we need to solve”. As previously stated, most of the students had not been working professionally before they commenced university education. They had not yet solved company problems using accepted rules of the engineering profession. Accordingly, they may have concluded that professionals use this kind of situation analysis, and so, they were willing to use a similar tool. The opinions of practicing engineers were significantly different to those of the students. Just over two thirds of the experts explicitly identified Situation Analysis as one of the most useful tools. Those who mentioned the tool were in most cases more excited about its impact on their problem solving than were the students. The following are some opinions provided by the experts: “… it makes me realise that my "real" problem might not be the "real" problem. Only after applying SA did we realise what the "real" problem was”. “… normally we never go through situation analysis to better understand the problem. With this tool, however, we more clearly understood the main problem”. “What I found most useful was SA – the tool, which actually stopped me from jumping into problem solving. Instead, I analysed the situation first”. As expected, the experts were able to generate numerous solution ideas when they conducted the situation analysis procedure. Moreover, those ideas, as a rule, were closely related to their field of expertise. The one-third of engineers who had not chosen SA as a useful tool were somewhat confused when requested to challenge the original problem formulation. They had spent years working in industry and had learnt not to challenge the problem statements provided to them by their managers. Nevertheless, they indicated that the experience of conducting situation analysis had made an impact on them, and they would try the tool again to see whether it would be helpful in future. Although more university students accepted the situation analysis procedure, they generated significantly fewer ideas to resolve their problems than did the engineers. This fact supports the expected difference in the impact of situation analysis on novices and experts. Overall, the situation analysis procedure seems to suit both novices and experts. Given that the SA procedure does not require too much time, it is highly recommended for use by both experts and novices.
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3.4 40 Innovative Principles and the Contradiction Table The 40 Innovative Principles [7] present a set of hints which are derived from thousands of successful technical solutions. The Principles offer cues related to the principle’s name. Its body helps a practitioner to imagine various ways of utilising the suggested action. This is likely to make the cues stronger. Stronger cues are expected to lead to more solution ideas. Figure 5 depicts the way in which the 40 Principles are used in searching the long-term memory database.
Figure 5: Cues during idea generation with the 40 Principles. It is important to note that the cues related to the problem which short-term memory keeps during the idea generation with the 40 Principles, are not significantly altered by the methodology. Therefore, these cues can be lengthy and can overload short-term memory, minimising the positive role of the 40 Principles. While deploying the 40 Innovative Principles, a practitioner rehearses the search of the long-term memory database by sequentially concentrating on one principle at a time until all the 40 Principles are considered. The 40 Principles can be used separately but yield better solutions when used together with the Contradiction Table (CT). The Contradiction Table offers the practitioner an opportunity to model the problem, and in this way identify the principles which have been most successfully used in the past to resolve problems containing the same technical contradiction as the one under consideration. The short-term memory of a practitioner using the CT with the 40 Principles is likely to be less overloaded than would be the case if the 40 Principles were used alone. The former case requires the user to consider less than 10 principles, not 40, as in the latter case. Consideration of fewer principles reduces the pressure upon short-term memory as well as interference between the principles (cues). This usually results in more efficient ideas being achieved when both the CT and the 40 Principles are used together. Students evaluated the 40 Principles and the Contradiction Table as the easiest tools to learn. Their opinion of the effectiveness of the tools, however, was not that positive. Only around 20% of students thought that the tools were effective. They found the 40 Principles and the CT to elicit fewer solution ideas, compared to the Method of the Ideal Result and Substance-Field Analysis. The following quote represents the opinion of many students: “…using the CT helps to focus on basic principles, that at first glance do not seem relevant to the system at hand. But careful consideration of what these principles mean helps to come up with creative ideas that I would not have thought of otherwise”. Engineers reacted more positively towards the tools and were able to generate many more solution ideas when applying them. Just over 50% of them chose the CT and
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40 Principles as the most efficient tools. Most of the engineers liked the fast pace with which they were able to generate suitable ideas when applying the CT: “… it applied well in my work and it easily provided solution principles to solve problems”. “The Contradiction Table and 40 Principles have been extremely useful as I face many technical problems at work and this tool provided a shortcut solution to the problems”. Further comments and discussion identified the major obstacle that was faced by practically all the students and numerous engineers while applying the 40 Principles and the Contradiction Table. In their studies, students had not come across enough examples of technical solutions which could illustrate the operation of individual principles. Thus, they did not have enough illustrations of real solutions in their long-term memory database to establish vivid analogies of what the principles were really recommending. Hence, many of the principles appeared to the students as empty suggestions which lacked technical meaning. Engineers with less than three to five years of practical experience reported similar problems, only to a lesser degree. Professionals with experience exceeding 10 years were the only group who reported being comfortable with the tools. Overall, the 40 Principles and the Contradiction Table have not been evaluated highly by novices. Experts were significantly more positive about using the tools. 3.5 Method of the Ideal Result Method of the Ideal Result (MIR) has been developed by one of the authors [22]. MIR is based on the TRIZ notion of the Ideal Ultimate Result (IUR). It has been found that when engineers face problematic situations and need to devise some improvements, two main issues are especially challenging. They are [22, 23, 24]: •
to identify the most problematic issue to focus on during the improvement
•
to be able to utilise the resources which are available, almost for free. The first part of MIR is designed to aid users in pinpointing the very element upon which to focus. The second part of the MIR procedure helps a user to identify the resources available, and consider how these resources may be used in improving the situation. Overall, MIR helps a user to accomplish the following: •
to identify the direction towards an effective and simple solution and to formulate the Target Task (TT) – the goal, which is as close as possible to the IUR and is achievable
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to separate different areas of improvement, and to identify the elements one must focus upon to deliver the most efficient improvement
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to recognise all the resources at hand
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to sift through all the available resources, with the aim of seeing whether they may help in improvement The analysis component of the MIR solution process identifies the Target Task, which acts as a cue in shortterm memory. Resources play the role of additional cues, which ensure that the database of long-term memory is searched well (see Figure 6).
Figure 6: Cues during idea generation with MIR. As depicted in Figure 6, the Target Task formally replaces the problem cues in short-term memory. This means that when the Target Task is formulated correctly, it is likely to occupy significantly less space in short-term memory than would the unstructured problem cues. Moreover, as the resources are considered in MIR idea generation individually, the short-term memory is never overloaded. This fact, together with the rehearsal based on sequential consideration of the resources, suggests that the MIR procedure is very effective in generating ideas. The expectation that MIR would be efficient was supported by the opinions of both students and engineers. Both novices and experts had similar responses. Just over half of them identified MIR as an efficient problem solving tool. The following are some of their opinions: “After conducting the MIR I was able to see the problem clearly and identify several resources that I wouldn’t look at before. This has decreased the level of complexity of the problem”. (Student) “MIR helps to identify what is ideal and what tasks are essential and help solve problems”. “MIR is a bold concept helping participants to make bold ideas to solve the root problems”. “Method of Ideal Result - helps to focus thoughts on what the sought-after solution should be”. Overall, both novices and experts evaluated MIR as an efficient way of solving problems, and they planned to utilise this tool in future. 3.6 Substance-Field Analysis Substance-Field (Su-Field) Analysis represents any natural and man-made system as a set of interacting elements – a set of substances interacting with each other by means of fields, which are generated by the substances. 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. Nevertheless, 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 consider different systems in a uniform way and to apply similar rules to resolve dissimilar problems. Su-Field Analysis models a system through a set of interconnecting substances and fields. This converts the real task into its Su-Field model and helps to clearly identify the conflict zones of the system. These conflict zones are broken down into conflict triads. Five model solutions are considered for every conflict triad. The eight fields of MATCEMIB (Mechanical, Acoustic, Thermal, Chemical, Electric, Magnetic, Intermolecular, Biological) are then used to “translate” model solutions into real
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solutions. [25]. Figure 7 depicts the cues which are used for searching long-term memory in Su-Field.
Figure 7: Cues during idea generation with Su-Field. The problem cues during Su-Field idea generation are represented by five model solutions. They are used one at a time, and so do not occupy much space in short-term memory. Additional cues are represented by the “fields” of MATCEMIB, which are considered one at a time. This ensures that short-term memory is not overloaded and results in a very efficient rehearsal procedure, which is able to thoroughly search the database of long-term memory. The opinions of both novices and experts on the effectiveness of Su-Field were overwhelmingly positive. More than 70% of students and almost 80% of engineers identified the tool as very effective. As expected, the reasons for its effectiveness were somewhat different. Some students’ opinions are presented below: “After conducting this method of Substance Field Analysis, I now have a wider perspective”. “…consider all aspects of the problem, and look at it from the winning side and the losing one”. “…actually helped a lot in identifying the more promising solutions”. Engineers expressed similar opinions. They were also very excited about applying Su-Field in failure analysis and Failure Prevention. The following are some of their judgements: “Su-Field Analysis is a perfect birds-eye view of the situation and helps in spotting the problem”. “Su-Field helps me to think and solve problems systematically”. “Substance-Field Analysis applies both in solution development and product failure, which surprises me as we always learn to develop the solution rather than the failure”. The efficiency of Su-Field Analysis was evaluated by both experts and novices as the highest of all the tools considered. Discussions with both groups helped to identify an important difference in its influence on experts and novices. Novices stated that they like the way SuField guides them through the eight fields of MATCEMIB and ensures that they do not forget to consider something. Experts, on the other hand, stated that Substance-Field Analysis forced them ‘outside the square’ of their professional knowledge. Many of them mentioned that this was the very reason that made them unhappy when applying the tool to resolve their first exercise. Nevertheless, once they trialled the tool in solving their work problems, they became very happy with it because of the variety of ideas it helped them to generate. 4 SUMMARY Human thoughts are usually unstructured. Short-term memory has a low storage capacity and short duration of time for which it stores information. These characteristics significantly complicate the efficient retrieval of information Page 99
from long-term memory storage during idea generation. When a professional solves a problem within his area of knowledge, the limitations of short-term can be practically eliminated. Although this is helpful when the problem requires a solution within the field of expertise, it often biases an expert who is asked to solve foreign problems, as he suggests solution ideas that are entirely confined to his area of expertise. An effective problem solving methodology can significantly improve the outcome of idea generation. For the novice, creative methodology can help in retrieval by overcoming the limitations of short-term memory. It can also help an expert to ‘lose’ some of his expertise and to exploit his knowledge beyond that expertise while generating ideas. The TRIZ problem solving tools discussed in this paper help a user to overcome these inefficiencies by •
Representing a problem in short-term memory by only very few simple cues.
•
Supplying a practitioner with some additional cues which make the long-term memory search extremely efficient.
•
Organising rehearsal to ensure that the relevant cues are kept in short-term memory for sufficient time to allow for a thorough search of the long-term memory database.
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Forcing a user to shift his focus from the field of his profession during problem solving. Different TRIZ tools address the inefficiencies of shortterm memory and the expert “biasing” effect differently. Thus, the help these tools offer operates differently on novices and on experts. The current investigation into the cognitive foundations of the TRIZ problem solving tools attempted to comprehend the ways in which different TRIZ tools work in practice. We have identified that the tools of Situation Analysis and Substance-Field Analysis have been rated by both novices and experts as most effective. The Method of the Ideal result has been evaluated by both groups as of medium to high effectiveness in resolving problems. The tools of 40 Principles and the Contradiction Table received a low mark from novices and a medium to high mark from experts. The diversity observed in these evaluations is due to significant differences in the ways that novices and experts solve problems. REFERENCES [1] Taylor, C.W. (1988). "Various approaches to and definitions of creativity", in ed. Sternberg, R.J.: The nature of creativity: Contemporary psychological perspectives. Cambridge University Press. [2] Nickerson, R.S. (1999). "Enhancing Creativity", in ed. Sternberg, R.J.: Handbook of Creativity. Cambridge University Press. [3] Simonton, D.K. (1999). Origins of genius: Darwinian perspectives on creativity. Oxford University Press. [4] National Academy of Engineering (2005). Educating the engineer of 2020: adapting engineering education to the new century. National Academies Press. [5] Adunka, R. (2007), Lessons learned in the introduction of TRIZ at Siemens A&D, Proceedings of the triz-future conference, Frankfurt, Germany. [6] Conley, D. W. (2007), Intel Corporation’s Expert TRIZ Field Guide, Proceedings of the triz-future conference, Frankfurt, Germany.
TRIZFuture2008 - Synthesis in Innovation
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Altshuller G., “Creativity As An Exact Science”, Gordon & Breach Science Publishing House, New York, 1984, ISBN 978-0677212302. Belski,I.,(2005). The Role Of Practice In Mastering TRIZ, Proceedings of MATRIZFest, St. Petersburg, Russia, June. Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory : A proposed system an dits control processes. In W. K. Spence & J. T. Spence (Eds.), The psychology of learning and motivation: Advances in research and theory (Vol. 2, pp. 89195). New York: Academic Press. th Ashcraft, M. H. (2006). Cognition (4 ed.) New Jersey: Pearson Education. Miller, G. (1956): "The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information", Psychological Review, vol. 63 pp. 81–97 Poirier, M., & Saint-Aubin, J. (1995). Memory for related and unrelated words: Further evidence on the influence of semantic factors in immediate serial recall. Quarterly Journal of Experimental Psychology, 48A, pp. 384–404. Oberauer, K., & Kliegl, R. (2006). A formal model of capacity limits in working memory. Journal of Memory and Language, 55, 601-626. Cowan, N. (2001). The magical number 4 in shortterm memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, pp. 1– 185. Tarnow, Eugen (2005). The Short Term Memory Structure In State-Of-The Art Recall/Recognition Experiments of Rubin, Hinton and Wentzel. ([3] (http://cogprints.org/4670/) ) Polya G., (1988), How To Solve It: A New Aspect Of Mathematical Method, Princeton University press, NJ.
[17] Ericsson, K. A., Kintsch, W. (1995). “Long-term working memory”. Psychological Review, 102, 211– 245. [18] Egan, D. E., Schwartz, B. J. (1979). “Chunking in recall of symbolic drawings”. Memory and Cognition, 7, 149–158. [19] Sweller, J., Cooper, G. A. (1985). “The use of worked examples as a substitute for problem solving in learning algebra”. Cognition and Instruction, 2, 59– 89. [20] Kirschner, P. A., Sweller, J., & Clark, R. (2006). “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, 41, 75–86. [21] Belski,I., (2007). TRIZ Course Enhances Thinking and Problem Solving Skills of Engineering Students, Proceedings of the TRIZ-future conference, Frankfurt, Germany, 6-8 Nov., pp 9-14. [22] Belski,I.,(2002). Seven Steps to Systems Thinking, th Proceedings of the 13 Annual Conference and Convention Australian Association of Engineering Educators, Canberra, Australia, September, pp 3339. [23] Belski I., “I Wish The Work To Be Completed By Itself, Without My Involvement: The Method Of The Ideal Result In Engineering Problem Solving”, Proceedings of World of Innovation and Strategy Conference, Sydney, 1998, pp194-199. [24] Belski I., A Course On TRIZ (Su-Field Analysis, Situation Analysis, Method of the Ideal Result and the Contradiction Table), TRIZ4U, Melbourne, 20002005. [25] Belski I., “Improve Your Thinking: Substance-Field Analysis”, TRIZ4U, Melbourne, 2007, ISBN 978-09803293-0-8.
Suggested citation: Belski, Iouri, & Belski, Ianina. (2008). Cognitive foundations of TRIZ problem-solving tools. In T. Vaneker (Ed.), Proceedings of the TRIZ-Future Conference 2008 (pp. 95-102). The Netherlands: University of Twente.
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