TRIZ Paper: Japan TRIZ Symposium 2009 
Bringing Together Forecasting and Scenario Planning to find Innovative Opportunities
(University of Bergamo, Italy)
| TRIZ Paper: Japan TRIZ Symposium 2009 |
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| Interconnectivity and White-Space Opportunity: Bringing Together Forecasting and Scenario Planning to find Innovative Opportunities |
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| Davide Russo, Caterina Rizzi, Tiziano Montecchi (University of Bergamo, Italy) |
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| The Fifth TRIZ Symposium in Japan, Held by Japan TRIZ Society on Sept. 10-12, 2009 at National Women's Education Center, Ranzan-machi, Hiki-gun, Saitama, Japan | |
| Introduction by Toru Nakagawa (Osaka Gakuin Univ.), Nov. 22, 2009 | |
| [Posted on Jul. 11, 2010] |
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Editor's Note (Toru Nakagawa, Jun. 30, 2010)
This is one of the two nice papers presented by the Italian group last year at the 5th TRIZ Symposium in Japan. In my "Personal Report of Japan TRIZ Symposium 2009"
, I wrote at the start of reviewing contributed papers:
The topic of Technology Forecasting is closely related to the two Keynote Lectures by Boris Zlotin
The paper by Davide Russo et al.is posted on this page, while the other written by Gaetano Cascini et al.
Presentation slides in PDF
and presentation slides in Japanese translation by Kozo Nakahata (Hitachi) in PDF
. Nakagawa's introduction to this paper was extracted from his "Personal Report of Japan TRIZ Symposium 2009"
[1] Abstract
Interconnectivity and White-Space Opportunity:
Bringing Together Forecasting and Scenario Planning
to find Innovative OpportunitiesDavide Russo, Caterina Rizzi, Tiziano Montecchi
(University of Bergamo, Italy)Abstract
Nowadays an even more hard market competition pushes companies to continue innovation in the process and product development. Therefore, the role of emerging technologies forecasts can play as an information source in the decision-making of the private and public companies. Dozens of forecasting methods, dealing with social, economic, financial, environmental and technical aspects have been proposed so far in order to support decision makers, but limitations in accuracy on middle and long-term forecast, the poor repeatability and adaptability have limited their applications and diffusion.
In this paper a methodology aimed to provide a visual synthesis of a system in all its evolution steps, design variants and future potential configurations, is presented. Such a method integrates new criteria for patent searching/clustering and knowledge organization. The Knowledge Mapping framework shows in a very concise way what has already been explored by competitors and highlights what can still be done. The outcome permits to identify key variants at the structure level both for a rapid and for a following deeper forecasting activity. A software implementation (called KOM- Knowledge Organizing Module) is under development to make the information extraction process more automatic.
A case study of the method, already widely tested in different engineering domains, is here proposed.
[2] Presentation Slides in PDF
Presentation Slides in English in PDF
Presentation Slides in Japanese in PDF
[3] Introduction to the Presentation (by Nakagawa)
Excerpt from:
Personal Report of The Fifth TRIZ Symposium in Japan, 2009, Part B
by Toru Nakagawa (Osaka Gakuin University), Nov. 22, 2009
Posted on Nov. 23, 2009 in "TRIZ Home Page in Japan"
Davide Russo, Caterina Rizzi, Tiziano Montecchi (Univ. of Bergamo, Italy) [E07 O-18] gave an Oral Presentation on "Interconnectivity and White-Space Opportunity: Bringing Together Forecasting and Scenario Planning to find Innovative Opportunities". Here is the Authors' Abstract:
Nowadays an even more hard market competition pushes companies to continue innovation in the process and product development. Therefore, the role of emerging technologies forecasts can play as an information source in the decision-making of the private and public companies. Dozens of forecasting methods, dealing with social, economic, financial, environmental and technical aspects have been proposed so far in order to support decision makers, but limitations in accuracy on middle and long-term forecast, the poor repeatability and adaptability have limited their applications and diffusion.
In this paper a methodology aimed to provide a visual synthesis of a system in all its evolution steps, design variants and future potential configurations, is presented. Such a method integrates new criteria for patent searching/clustering and knowledge organization. The Knowledge Mapping framework shows in a very concise way what has already been explored by competitors and highlights what can still be done. The outcome permits to identify key variants at the structure level both for a rapid and for a following deeper forecasting activity. A software implementation (called KOM- Knowledge Organizing Module) is under development to make the information extraction process more automatic.
A case study of the method, already widely tested in different engineering domains, is here proposed.At the initial part of presentation the Authors critically review the currently present technologies; they include:
- NET (Network of Evolutionary Trends) Forecast: by Cascini and Russo (Italy)
- DE (Directed Evolution): by Zlotin and Zusman (USA)
- Evolution Trees: by Nikolay Shpakovsky (Russia)
- Generic scheme of technology evolution: by Petrov (Israel)
- Generic scheme of technology evolution: by Yuri Salamatov (Russia)
An example of the NET diagram by Cascini and Russo is show here (right). Various stages of evolution of the product are shown by the bubbles in the upper diagram. In each bubble, the technology at the stage is further described by showing design variants.
For the sake of clarity and saving space, I would like to skip these review parts and jump into their new original part.
The new model by Davide Russo et al. is called the KOM (Knowledge Organization Method). This is a model for organizing information extraction and text mining. By using several components of methods and tools, this method builds up a tree-like diagram of all the information related to the evolution/variation of the product.
This slide (right) shows the overall structure of the method. It has 3 macro levels. (1) In the Function level, a TRIZ approach is used to explore all possible directions of intervention. Then several Knowledge Management tools are used in combination to generate an exhaustive set of verbs and nouns for representing the functions. (2) Further, the Effects Databases are used to describe all possible variants. By following these steps a tree diagram is built with the leaves characterized by a specific query. (3) Such a query can find a very limited number of patents (in the structure level), the Authors say.
This slide explains the tree structure to be built (downward). At the top level, the TRIZ problem solving tools (i.e. Inventive Standards) are used to classify the solution directions schematically. Then the NIST classification of functions, and the exploration of synonymous functions through thesaurus and IPC are carried out to develop the branches of the tree. At the physical level, the Effects Databases are used. Finally at the structure level, patent databases are searched with queries with the support of text mining tools, resulting examples having different structure types.
The first step is shown here. First to identify MUF (Most Useful Function) and determine the user's needs. The needs are classified as (a) problem about revealing the cause, (b) detection or measurement problem, (c) conflict problem, (d) harmful function problem, and (e) absent (insufficient) function problem. According to these classifications, Standard solutions in TRIZ are suggested for the schematic, functional solutions.
This slide (right) shows how to generate synonyms of the given MUF at different abstraction levels. NIST's functional bases are first used, and then dictionary, conceptual thesauri, and advanced searches of strategies on the basis of IPC are used in turn. At the left in this slide an example of abstraction of function is shown (i.e. Xray is abstracted to Scan --> Recognize --> Detect.)
In the Physical level, various Effects Databases are used. As shown here, more than 40 Effects Databases are collected to use.
*** These collections of databases seem to be effective.
Then in the Structure level, for each branch of physical effects design parameters are mapped on the graph. Such parameters are divided into 3 levels (types 1, 2, 3).
The slide (right) shows the leaf structure of the tree diagram. A case study was shown for the 'Water purifier'. One of the branch comes down like: Water purifier --> separate --> extract --> filter, purify --> distillate, clear, clean --> electromagnetic --> UV light. Now at the structure level, the design parameters are chosen, e.g. (type 1) lamp placement, (type 2) wave length range centered, (type 3) transmissibility of material. Example of patents searched are illustrated in the figure.
In the manner described so far, the tree diagram is built step by step to explore the existing/possible solution space. By filling the results of the patent search, the state of the art of the Water Purifier is demonstrated in this slide. As shown in this example, there are cases where some of the leaves do not have known patents. Such cases represent potential technological opportunities.
*** This method utilizes various public modules/databases effectively and seems to be useful to explore existing and possible solution space systematically.
| Top of this page | Abstract | Slides in PDF |
Nakagawa's Introduction | Slides in Japanese, PDF |
Nakagawa's Personal Report of Japan TRIZ Symp. 2009 |
Japan TRIZ Symp. 2009 |
Japanese page |
Last updated on Jul. 11, 2010. Access point: Editor: nakagawa@ogu.ac.jp
