Scholarly article on topic 'Solving Complex Problems and TRIZ'

Solving Complex Problems and TRIZ Academic research paper on "Economics and business"

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Abstract of research paper on Economics and business, author of scientific article — Alexander Czinki, Claudia Hentschel

Abstract Today, innovators have to deal with greater complexity than ever before. This complexity arises from the requirements companies must meet in order to create an ever increasing value for their specific customers and for society. In general, the requirements for innovators have become more numerous, more dynamic in change, and – even worse – are often in conflict with each other. On the other hand, complexity offers tremendous opportunities for those companies that are able to properly address it and which are able to offer robust processes and solutions to deal with it. In this paper, the authors highlight the contribution of TRIZ to problem solving with a specific focus on solving complex problems. The paper introduces different problem types according to a relatively new, but widely accepted problem model. It points out the definition of a “complex problem” and explains the difference between complex problems and other problem types. Main TRIZ-tools are discussed in context with different problem types and suggestions are made which TRIZ-tools to prefer in context with a given problem situation.

Academic research paper on topic "Solving Complex Problems and TRIZ"

ELSEVIER

Alexander Czinkia *, Claudia Hentschelb

a University of Applied Sciences Aschaffenburg, Würzburger Straße. 45, 63743 Aschaffenburg b University of Applied Sciences HTW Berlin, Treskowallee 8, 10318 Berlin

* Corresponding author. Tel.: +49-6021-4206-909; fax: +49-6021-4206-801. E-mail address: Alexander.Czinki@h-ab.de

Abstract

Today, innovators have to deal with greater complexity than ever before. This complexity arises from the requirements companies must meet in order to create an ever increasing value for their specific customers and for society. In general, the requirements for innovators have become more numerous, more dynamic in change, and - even worse - are often in conflict with each other. On the other hand, complexity offers tremendous opportunities for those companies that are able to properly address it and which are able to offer robust processes and solutions to deal with it. In this paper, the authors highlight the contribution of TRIZ to problem solving with a specific focus on solving complex problems. The paper introduces different problem types according to a relatively new, but widely accepted problem model. It points out the definition of a "complex problem" and explains the difference between complex problems and other problem types. Main TRIZ-tools are discussed in context with different problem types and suggestions are made which TRIZ-tools to prefer in context with a given problem situation.

© 2016 Published by Elsevier B.V. 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 Scientific committee of Triz Future Conference

Keywords: Complexity; Cynefin-Framework, TRIZ, Problem Solving

Available online at www.sciencedirect.com

ScienceDirect

Procedía CIRP 39 (2016) 27 - 32

www. el sevier. com /locate/proced i a

TFC 2015- TRIZ FUTURE 2015

Solving complex problems and TRIZ

1. Introduction

Innovators need to steadily provide new products and features to meet market needs. However, growing markets and diversifying customer needs almost inevitably make innovation increasingly complex. Two major causes for growing complexity in developing innovative products, processes and services may be seen [1]: First, shifting trade barriers and technological progress provide customers with an ever increasing number of offerings and choices: The more options there are, the harder it becomes to gain customer's attention and the more he might reject compromises. Second, the number and interconnectedness of stakeholders that are impacted by an innovation steadily increases: Companies must observe customers, shareholders, suppliers, resellers and stakeholders which leads to ever rising and faster changing requirements that cover wide areas of economics, nature, society and politics. The more requirements there are, the more they tend to be in conflict with each other and the more complex innovation becomes.

A major driver of this development can be seen in the fact that there is no final state for fulfillment of evolution. People in general - and customers in specific - pursue complete customer satisfaction which requires all needs to be satisfied and all problems to be solved. When a customer need is fulfilled, it will be instantly replaced by another one.

In this context innovation plays a key role, since it tries to satisfy customer needs and to this end seeks to identify current problems of customers. Thus, companies more and more become problem-solvers for their customers. With this background, it seems only natural to regard problem solving from a more general perspective and to look for more systematic ways to classify and solve problems. Since TRIZ has got a high reputation as a systematic problem solving tool-set, it seems worth to investigate what type of problems TRIZ can address and to look into what TRIZ-tools are especially suited to address specific problem types. It is the intention of this paper and its corresponding research, to clarify some of the aforementioned questions and aspects. The corresponding research is ongoing.

2212-8271 © 2016 Published by Elsevier B.V. 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 Scientific committee of Triz Future Conference doi:10.1016/j.procir.2016.01.161

2. Problem models and basic categories

It is the intention of this paragraph to give a brief overview on different definitions of problems and their categorization, regularly found in literature. This is done with a special focus on complex problems. An especially promising problem categorization model is introduced at the end of the chapter.

2.1. Problem definition and its inconsistent classification in literature

Sell and Schimweg as well as Funke speak of a problem whenever there is a gap between an initial and a desired situation [2, 3]. Problem solving in this context is regarded as the process of closing the gap between "what is" and "what should be". Literature also shows that there are different types of problems: authors use to differentiate - among other categories - between simple and complex problems. While simple problems are referred to in case of a clear initial situation and a known way to achieve the desired solution, complex problems are generally regarded as not easily solvable [3].

However, there are other classifications for problems as well. Another frequently mentioned term mentioned in context with problems is the class of "complicated problems". Many publications hardly distinguish between complex and complicated problems, the latter being another category often mingled with complexity, if not used as synonyms - but this is misleading. Complicatedness essentially has a quantitative aspect [4, p. 13]: here, one could measure or count or weigh. If quantity becomes extreme, things will get complicated. Complicatedness is a subtle parameter: it depends on the available tools, expertise and mightiness of knowledge to what extend one considers a problem as complicated. Also, the transition from complicatedness to complexity is not a sharp but a blurry one: when is the level of complicatedness high enough to stop speaking of being "complicate" but rather of being "complex"?

Complexity is considered to be no property of well-defined, differentiated systems [4, p. 15]. The world itself and anything we call "reality" is complex - this is even more true, in case human beings are involved. Whenever a system's behavior as a whole is greater than the sum of its parts and behaves in an unforeseeable manner, one has all reason to regard it as a complex one [5, p. 55 ff.]. If two systems of the same kind call forth different behaviors - which especially is the case once people are involved - one single best solution will not exist. Complexity reveals itself in terms of paradox, dilemma, ambiguity, such that there is no true right or wrong and confronts us with ill-knowledge and ignorance.

While some authors distinguish between complexity in the narrower sense (detailed view) and complexity in the broader sense (holistic view) [6], the authors of this paper suggest to look at complexity as something being there - or not. They also suggest not to simply reduce complexity. Complexity exists - or doesn't. One could tame it [7], but if one decides to cut down complexity and "reduce" it by ignorance, one has to live with an all too simple model of the real situation, that -by its nature - can only reveal parts of the whole truth and

may later lead to solutions which perform far worse than it was expected. So, complexity in an uncertain world has obviously to be tackled in different ways than just the classical sequence of "target definition - analysis - ideation - selection -implementation" or by simply following a "plan - do - check -act" sequence.

A more detailed classification of problem types is provided by the so called "Cynefin-Framework", a model developed and spread mainly by Dave J. Snowden who first published it in 1999 and elaborated it further by 2003 [8]. A closer, but brief view on the framework is given in the following.

2.2. Problem definition and classification according to the Cynefin-Framework

A fairly young and - from the authors point of view -especially promising problem categorization model is provided in form of the "Cynefin-Framework". The term Cynefin (pronounced: 'kAnivin) is - following the origin of his main author D. Snowden - a welsh word, expressing our complex roots in terms of culture, geography, religion, tribe and whatever other influence that make for our actual habitat or place. The author has chosen the word "Cynefin" as a name for his problem model, due to its literal incarnation of the multiple affiliations that come along with our individual and collective experiences and stories [8]. The Cynefin-Framework originated in the field of knowledge management and was intended to distinguish formal from informal processes. It is now being widely used in management, IT-design, project management, policy-making and sociology, to name only a few application fields - with presumably more to come.

The Cynefin-Framework offers a decision framework for recognizing causal differences in different types of systems, situations and problems, without seeing one domain more preferable than the others (as is the case in many categorization models, where axes are mostly value dimensions). It helps people flip their thinking between the different problem types, helping to choose the appropriate method and decision-model for the appropriate domain under uncertain constraints.

The framework shows five different classes of problem situations and suggests a (very general) strategy for each of these five different problem types. Since the Cynefin Framework was developed as a decision making tool, the strategies are referred to as "decision processes". The major ideas and categories of the Cynefin Framework shall be briefly introduced here (Figure 1).

Fig. 1: Problem Domains according to Snowden/Boone [9]

The first domain is an ordered one and addresses simple problems. They are defined as problems where there is an obvious relation between cause and effect and where it is easy to predict cause and effect relations in advance.

Therefore, the suggested decision process follows the sequence "sense - categorize - respond" and can be taken by any reasonable person. The "simple problem"-category is the field where best practice is applied and - in the best case - the one single best solution is achieved.

The second domain - that also belongs to the ordered space - is the domain of complicated problems. Here, there still is a relation between cause and effect, but it is not so self-evident. Therefore, the analysis requires some sort of investigation and the solution some expertise. The decision model follows "sense - analyze - respond" and will often require calling in experts who have the expertise to analyze and understand the specific problem. Here, good practice evolves. The difference between good and best practice is that there are a number of good ways to solve the problem, so the answers rather depend on which expert is chosen. Different experts will analyze the system differently and will weigh the specific advantages and disadvantages of a certain solution differently.

The danger in complicated problems lies in focusing on one single solution as there are always many other ways that can provide a solution that might be as good as - or even better - than the chosen one. The fact that a solution is found should not be misinterpreted as this single solution being the only possible or necessarily being the optimal one.

The third domain addresses an unordered problem type, the complex problem domain. Here, one has to deal with a system without fully comprehensible causality. To find out what to do, one can only probe by experiment, and the relationship between cause and effect can only be perceived in retrospect (if at all). So, the decision model here is "probe - sense -respond". One can do experiments, but hardly fail-safe design. The direction of a successful experiment should be amplified, the direction of a failing experiment should be dampened/reduced. This strategy brings up emergent practices, new ways of doing things and novel evolving patterns. In complex situations, experts run the risk of falling victim to their own thinking patterns, and thus fail to see innovative solutions to given or novel problems. Complex problems always provide the chance for finding new and innovative solutions.

The fourth problem category is characterized by situations where no cause and effect relation can be determined at system level. It is named the chaotic problem domain. Chaos can happen accidentally when a situation arises where cause and effect cannot be brought into a reasonable relation to each other. The suggested decision model in this case is "act -sense - respond", and usually one should respond very quickly in order to avoid harm - or even better: to make use of it.

The center domain, the disorder field, addresses all cases, where one does not (yet) know what problem type is at hand or what space one is in. Here, one has to assess the situation first and decide what problem type prevails.

The Cynefin-Framework suggests that, one should act differently and apply different methods to solve the problem depending on what domain one is in. However, the danger is interpreting the situation according to one's personal prefer-

ences or capabilities and that one therefore has a "natural" tendency toward assigning any given problem to a favored category. By this approach, one will miss the chance of assigning a problem to the correct problem domain where it can be handled best.

In general it is supposed that there be a special boundary separating simple and chaotic problems. This boundary is different from all the others which all more or less represent continuous transitions.

Working within the simple problem domain carries the risk to trust solely in one's own capabilities or rely on earlier success strategies, patterns and best practices. This approach may hold for stable situations. However, if circumstances change with time, the problem situation might slightly move toward the chaotic problem domain. Even with only slightly changing constraints, the simple situation can quickly turn into a crisis - very much like falling over the edge of a cliff into the chaos domain - and recovery might be very difficult and/or expensive. It is thus suggested to manage any process involving rapid or accelerated change rather in the complex or complicated domain, leaving only a small number of situations in the simple space, as this being the most critical to imperceptibly transforming into chaos.

For the sake of completeness, it should be stated that the simple and complicated domains of the model belong to the so-called ordered space. The assumption of order, i.e. the existence of underlying relationships between cause and effect in interactions, allows to understand causal links within past system behavior and therefore allows to define best/good practice for treating a specific problem in the future. This is an important aspect to be kept in mind in context with learning organizations and the like [see also: 8, p. 463].

3. Problem solving in an innovation context

In innovation, we often have to deal with irrational results and unpredictable behavior (e.g. of customers, competitors, markets,...). These effects are immanent since highly interconnected systems involving human beings are comprised. Therefore, the authors were especially attracted by the - so-called - unordered space of the Cynefin-Framework, covering the chaotic and the complex problem domains. Merely analytic techniques might not be sufficient for those kinds of problems, as patterns can rather be perceived than predicted in these domains. Order is not necessarily missing, but it is covered and - if at all - only partially emerging.

Some of the questions arising from this situation in context with innovation in general and with TRIZ in particular are: How could we better deal with the complexity within innovation management which is characterized by a high degree of uncertainty (by the way: this being the reason for naming it "fuzzy front-end")? In ever rising time and other constraints the complexity of problems seems to increase continuously. The authors therefore wondered if there is a way to empower problem solvers by providing them a clear problem categorization for problems along with general and robust strategies for dealing with complex problems - and in a later step: How can TRIZ help to solve complex problems? And if

TRIZ turns out to be helpful for solving complex problems: Can the scope of this approach be extended from complex problems also to other problem domains?

The Cynefin model focuses on the question of how people perceive and make sense of a situation in the context of decision-making. In context with TRIZ, the type of decision to be taken is finding a solution for a - usually very - "fuzzy" innovation problem and developing a concept for a later product or process solution.

3.1. TRIZ and problem solving

TRIZ has gained a positive reputation of being a very powerful problem-solving, analysis and forecasting tool. Its approach is unique, being based not only on theoretical conclusions but on the analysis of a vast number of patents in the past. TRIZ is - by many professionals - regarded as the only systematic innovation tool available. TRIZ cares most about inventively solving a mostly technical and often contradictory problem by developing innovative problem solutions based on successful solving patterns of the past. However, it is often stated that TRIZ automatically suffers, when system information is incomplete or uncertain. These aspects would suggest that TRIZ was not suited for solving complex problems, but was rather limited to other problem domains (especially to the "Complicated Problem Domain").

3.2. TRIZ for solving complex problems

One of the major goals of the authors' work was to clarify in how far TRIZ can be used to solve complex problems and which TRIZ tools are especially suited to do so. As a result of in-depth research some major (classical) TRIZ-tools could be assigned to the complex problem domain. The ability of TRIZ in this context could both be derived from applications and also by observing analogies between some TRIZ tools and the tools suggested by the Cynefin Framework: So, let us have a closer look on what Cynefin suggests for complex problem situations in which cause-effect-relations are rather phenomenological, not fully understood, and feature a high number of unknown interdependencies.

Cynefin e.g. uses story-telling ("narrative") as a suitable tool for solving complex problems. Applying the Cynefin-Framework includes asking "people to consider a situation in the past and what movements took place in it from different perspectives, or we may ask people to envision fictional narratives about the past, present or future in which selected movements form the backbone of the story." [8, p. 479]. This definitely shows strong similarities to one of the major TRIZ tools. The backbone of each system operator ("9-windows") application is finding out about the parameters changing. It is also generating a hypothesis on how the parameters will change in the future. In Cynefin, this is referred to as backbones of the story.

Using similar tools in certain problem contexts at least suggest a similar approach and capability to solve them, which encouraged the authors to use the system operator ("9-windows") approach for complex problems. At the end this turned out to be a very suitable approach.

The TRIZ tool "Smart Httle People" is also applicable in highly "fuzzy" situations. It allows to place oneself in different positions - even into the operating zone of the problem or system - and to detect additional and yet unknown resources and functionalities. And last, but not least, Engineering System Evolution takes a closer look at the "voice of the product", as the tool highlights general patterns and universal trends and laws. These technical trends can be used without understanding the complex mechanisms leading to them, thus allowing the users to envision a mid- or long-term solution to a complex problem in the complex problem domain.

Furthermore, ideality also seems to be a good tool for solving complex problems, since it is based on a universal trend. Developing an ideal product usually heavily bases on assumptions concerning the existence and importance of customer needs, and therefore often is a very intuitive judgement. When higher ideality is such a universal trend, the problem solver can again focus on evolution hypotheses and the "voice of the product" prior to any TRIZ solving technique, and find new ways for problem understanding and problem formulation (as shown e.g. in [10]).

Table 1: Main characteristics of complex problems (based on [9]) and adequate TRIZ Tools (Czinki, Hentschel [7]).

Problem Type Complex

Cause-Effect- Phenomenological, Not fully understandable

Relation

Main High number of unknown interdependencies within the

Challenges systems and between the system and its environment

Strategy Work based rather on hypotheses than facts, Deriving and

using patterns, Testing, Learning

Crucial Planning and designing tests, Knowledge of patterns,

Capabilities Ability to transfer patterns to new applications

Preferable Engineering System Evolution (ESE), System Operator

selected TRIZ- ("9 -Windows"), Ideality, Smart Little People

Table 1 lists typical qualities of complex problems along with classical TRIZ tools, having been assigned to this problem domain by the authors. During the author's research, TRIZ turned out to be very well suited to address complex problems.

3.3. TRIZ for solving non-complex problems

As a consequence of the positive experiences the authors gained while investigating the suitability of TRIZ for solving complex problems, the authors started to research to what extent TRIZ can address problems belonging to the other problem domains. Some of the most important thoughts and insights are listed below.

Starting with the simple problems domain, these kinds of situations are characterized by logical and predictable cause-effect-relations where principles and solutions can be easily transferred to the individual problem. Basically, solving simple problems equals searching for the best (possible) solution. TRIZ relies here e.g. on databases of physical and other effects to highlight what effect can be applied to get a certain result.

The complicated problems domain refers to all situations in which patterns exist, but are not immediately apparent. So "zooming in" into the most crucial interactions of the system is useful and thus the focus for problem solving lies on crucial (sub-) systems. It turned out that the complicated problem domain is probably the domain which TRIZ provides especially strong tools for. Here, a wide set of TRIZ tools can be applied, including: Flow analysis, Technical Contradictions, Physical Contradictions, Substance-Field-Analysis (SuF), Trimming, Feature Transfer and many others.

The unordered domain of Cynefin covers - in addition to the complex problem domain - also the chaotic problem domain. Even though chaotic problems are not the focus of this paper, the authors are convinced that TRIZ can also be helpful for problems belonging to this domain.

It needs to be pointed out, that (re-)acting quickly is - apart from letting them just happen and just ignoring them - the preferable action to tackle chaotic situations in order to stabilize them (for more on that, see also [11]). Although TRIZ does not offer concrete tools to tackle chaos, TRIZ can be helpful also in chaotic contexts: when fast sensing in combination with agility is required, TRIZ can be used to derive adaptation strategies to uncertain situations simply by enhancing and guiding creative skills. The mere capability of

thinking more creatively helps reacting faster in a given chaotic situation, being exactly the quality that is most needed while acting within chaotic environments.

For the chaotic problem type and the other problem types, especially the complex one in focus here, the authors have created some ideas on how TRIZ tools can be helpful depending on the type of problem situation at hand (Table 2).

The disorder problem type domain covers situations, in which the user does not know, to which domain the problem belongs to. So, all those tools volunteer to be applied in this domain, that ask questions and assess a given problem situation.

Any transition process between the disorder domain and other problem domains bears the risk that users interpret a given problem situation according to their personal preferences, capabilities or profession which will involuntarily lead them toward a problem type that might not fit to the actual situation. TRIZ offers a particularly strong tool for the initial evaluation of an (innovation-related) situation belonging to the disorder domain: The Innovation Situation Questionnaire. This tool is well-qualified to be used in a situation characterized by disorder.

Table 2: Main Problem types and their main characteristics (based on [9]) with preferable TRIZ Tools (Czinki, Hentschel [7]) Problem Type

Complex

Complicated

Simple

Cause-Effect- Main Challenges Strategy Crucial Preferable selected

Relation Capabilities TRIZ-Tools

Noise dominates High dynamics, Fast adaption Fast sensing, Apart from

signal -> no useful Turbulence, Noise Agility, Flexibility, generally enhanced

relation Creativity, ... creative thinking

capabilities: none*

Phenomenological, High number of Work based rather Planning and Engineering System

Not fully under- unknown interde- on hypotheses than designing tests, Evolution (ESE),

standable pendencies within facts, Deriving and Knowledge of System Operator

the systems and using patterns, patterns, Ability to ("9 -Windows"),

between the system Testing, Learning transfer patterns to Ideality, Smart

and its environment new applications Little People

Discoverable but Identification and Focus on crucial Analytical minds, Flow analysis,

not immediately modelling of the (sub-) systems, Expert knowledge Technical

apparent most crucial Modeling, Analysis Contradictions,

interactions, Physical Contra-

Distinction of dictions, Substance-

systems Field-Analysis

(SuF), Trimming,

Feature Transfer

Logical, Predict- Precise and efficient Adapt & apply Knowledge, Catalogue of

able, Consistent transfer of princi- Accuracy, Effects

ples and solutions to Efficiency

the individual

problem

* Tools for solving chaotic problems are beyond the scope of this investigation

4. General recommendations

The main idea of Cynefin is that, depending on what problem space one is in, one should treat problems differently and apply different tool sets. The selection of the problem domain(s) - in which a given problem is supposed to be solved in - is of high importance since the pure selection already influences the (type of) solution to be found. In general, the selection of higher-level domains will rather lead to more general understanding and more general solutions whereas the selection of lower-level problem domains will rather lead to less generalized solutions (at least in the first place). Advantage can be taken of purposefully changing the problem domain and/or searching for solutions in different domains.

While problem solving, one can also enter a "higher" problem-level deliberately in order to gain a more general perspective, which will often increase the problem understanding and will therefor frequently allow to address more radical innovations. Following this strategy, chances are, that any practice evolving will be completely novel.

5. Conclusion and Outlook

As presented in this paper, the authors have investigated TRIZ from a more fundamental viewpoint of being an - especially powerful - problem solving tool. After an in-depth literature research on problem solving theory (for more on that, see also [7]), the Cynefin-Framework was selected as a basis for future work. Subsequently the general suitability of TRIZ to solve complex problems was investigated. Furthermore the work was extended to other problem domains. Finally, it was possible to assign TRIZ tools to each of the four main problem domains described earlier in this text.

This paper emphasizes the fact that problem solving highly depends on the problem type at hand. Any given problem situation should initially be carefully analyzed concerning which problem domain prevails and in which domain the solutions should be assigned. Depending on the selected domain(s), different TRIZ tools are suggested.

Assessing the situation before applying the tool becomes a crucial prerequisite for solving any type of problem in the future and for creating more value by applying exactly the tools that are best qualified for effective problem solving. There is an immanent danger that problem solvers having been successful with a certain TRIZ tool earlier, tend to use these tools more regularly without considering their suitability for a given problem type. This may also be the reason why some TRIZ tools - according to the authors' experience - are so highly rated and commonly used (e.g. the Contradiction Matrix, Trimming, ...), while other tools are often neglected (e.g. Smart Little People;.). This is true in spite of the high problem solving capabilities of the neglected tools.

The work presented will be further continued. The authors are increasingly deepening their understanding of problem solving models in general and the role of TRIZ in problem solving in particular. They are currently finalizing an extensive list that assigns TRIZ tools to specific problem domains. This list covers a wide set of different TRIZ tools. The authors are also in the process of developing further strategies on when and how to apply certain TRIZ tools in specific problem domains with specific respect to given constraints and targets. This will also include the classification of advanced TRIZ tools.

References

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