System Dynamics Modeling and TRIZ: A Practical Approach for Inventive Problem Solving

The application of the theory of inventive problem solving (TRIZ) to face complex problems in the current scientific and industrial environment is an active research field. The TRIZ capacity to produce valuable technological solutions is an attractive resource to impel the innovation process and technical performance. The intensification of the research effort has unveiled new paths for proposing more efficient problem-solving tools and techniques. Among these opportunities, two are crucial in this chapter: the TRIZ limitation to observe the progression of an inventive problem in time and the difficulty that any solver faces when the system under analysis contains several interrelated problems. Nonetheless, there is an approach that analyzes a system through time and that offers some tools for modeling and simulating the different system states: system dynamics modeling. The system dynamics (SD) approach analyzes the nonlinear behavior of complex systems over time. SD is a computer-aided approach with a large extent of application domains, practically in any complex system—social, managerial, economic, or natural—defined by a set of interdependence relationships, a flow of information, and effects of causality. Hence, SD can produce useful information within a problem network and create, in combination with TRIZ, a synergy to solve inventive problems.

[1]  V. Kreng,et al.  An innovation diffusion of successive generations by system dynamics — An empirical study of Nike Golf Company , 2013 .

[2]  Guillermo Cortes Robles,et al.  Case-based reasoning and TRIZ: A coupling for innovative conception in Chemical Engineering , 2009 .

[3]  Semyon Savransky,et al.  Engineering of Creativity: Introduction to TRIZ Methodology of Inventive Problem Solving , 2000 .

[4]  Robert Phaal,et al.  A review of TRIZ, and its benefits and challenges in practice , 2013 .

[5]  Gi-Tae Yeo,et al.  Analysis of dynamic effects on seaports adopting port security policy , 2013 .

[6]  Wessel Willems Wits,et al.  Invention software support by integrating function and mathematical modeling , 2015 .

[7]  Federico Rotini,et al.  Model and algorithm for computer-aided inventive problem analysis , 2012, Comput. Aided Des..

[8]  Andrzej Kraslawski,et al.  Adaptation of TRIZ contradiction matrix for solving problems in process engineering , 2015 .

[9]  André C. R. Martins,et al.  An opinion dynamics model for the diffusion of innovations , 2008, 0809.5114.

[10]  Guillermo Cortes Robles,et al.  Eco-innovative design method for process engineering , 2012, Comput. Chem. Eng..

[11]  P. Georgiadis,et al.  The impact of innovation policies on the performance of national innovation systems: A system dynamics analysis , 2012 .

[12]  D. Wu,et al.  Modeling technological innovation risks of an entrepreneurial team using system dynamics: An agent-based perspective , 2010 .

[13]  Chih-Hsuan Wang,et al.  Using the theory of inventive problem solving to brainstorm innovative ideas for assessing varieties of phone-cameras , 2015, Comput. Ind. Eng..

[14]  Kwangsoo Kim,et al.  An automated method for identifying TRIZ evolution trends from patents , 2011, Expert Syst. Appl..

[15]  Xiangdong Li,et al.  Research on TRIZ and CAIs Application Problems for Technology Innovation , 2009, IFIP CAI.

[16]  Victor Fey,et al.  Innovation on Demand: Frontmatter , 2005 .

[17]  Jahau Lewis Chen,et al.  Forecasting the design of eco-products by integrating TRIZ evolution patterns with CBR and Simple LCA methods , 2012, Expert Syst. Appl..

[18]  Qian Zhou,et al.  Exploring the potential of introducing technology innovation and regulations in the energy sector in China: a regional dynamic evaluation model , 2016 .

[19]  Federico Rotini,et al.  Business Process Reengineering driven by customer value: a support for undertaking decisions under uncertainty conditions , 2015, Comput. Ind..

[20]  A. Ash,et al.  Boosting the productivity and profitability of northern Australian beef enterprises: Exploring innovation options using simulation modelling and systems analysis , 2015 .

[21]  Yong Zeng,et al.  Understanding design activities through computer simulation , 2009, Adv. Eng. Informatics.

[22]  Andra Blumberga,et al.  Outlining Innovation Diffusion Processes in Households Using System Dynamics. Case Study: Energy Efficiency Lighting , 2015 .

[23]  Michel Y. Keoula,et al.  Product innovation incentives by an incumbent firm: A dynamic analysis , 2015 .

[24]  John D. Sterman,et al.  System Dynamics: Systems Thinking and Modeling for a Complex World , 2002 .

[25]  G. S. Alʹtshuller,et al.  The Innovation Algorithm:TRIZ, systematic innovation and technical creativity , 1999 .

[26]  Victor Fey,et al.  Innovation on Demand by Victor Fey , 2005 .

[27]  Victor Fey,et al.  Innovation on Demand: New Product Development Using TRIZ , 2005 .

[28]  Stéphane Negny,et al.  Using the Collective Intelligence for inventive problem solving: A contribution for Open Computer Aided Innovation , 2015, Expert Syst. Appl..

[29]  Jui-Chin Jiang,et al.  Six cognitive gaps by using TRIZ and tools for service system design , 2011, Expert Syst. Appl..

[30]  Lowell W. Busenitz,et al.  Turning water into wine? Exploring the role of dynamic capabilities in early-stage capitalization processes , 2015 .

[31]  Jean-Pierre Belaud,et al.  Open computer aided innovation to promote innovation in process engineering , 2015 .

[32]  Altshuller.  Creativity As an Exact Science , 1984 .

[33]  J. Forrester.  Principles of systems : text and workbook, chapters 1 through 10 , 1968 .