Risk Reduction and Opportunity Exploitation
through TRIZ‐based Technology Forecast
GAETANO CASCINI
Politecnico di Milano
gaetano.cascini@polimi.it - http://www.kaemart.it

2Politecnico di Milano

Established in 1863
Organized in 12 departments (devoted to research) and a network ofSchools of Engineering, Architecture and Industrial Design spreadover 7 campuses over the Lombardy region
Ranked as one of the most outstanding European technicaluniversities
 48th World Engineering & Technology 2012
 14th Europe Engineering & Technology 2012
 1st Italy Engineering & Technology 2012

© 2013 Gaetano Cascini – gaetano.cascini@polimi.it3Politecnico di Milano

17% of Italianarchitects graduatedfrom
Politecnico di Milano
56% of Italiandesigners
graduated from
Politecnico di Milano© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

16% of Italianengineers
graduated from
Politecnico di Milano

Gaetano Cascini – short resume
1999
1999 – 2008
2008 – now

4: PhD in Machine Design – First acquaintance with TRIZ
: Assistant Professor at University of Florence: Associate Professor at Politecnico di Milano

Past: 2003‐2005
 2005‐2009
 2006‐2009: Founder and first President of Apeiron, the Italian TRIZ Association: Founder and Vice‐Chair of the IFIP 5.4 Working Group (Computer‐Aided Innovation): President of the European TRIZ Association

Currently:
 Coordinator of the Marie Curie Project IAPP (PIAP‐GA‐2011‐286305):
FORMAT (FOrecast and Roadmapping for MAnufacturing Technologies)
Member of the Editorial Board of the Journal of Integrated Design & Process Science
Member of the Editorial Advisory Board of the International Journal of Design Creativity and Innovation
Member of the ETRIA Executive Board
Chair of the Computer‐Aided Innovation workgroup and Publications and Events Officer of the TC‐5Committee (Computer Applications in Technology) of IFIP (International Federation for InformationProcessing) Author of 120+ papers presented at International Conferences and published in authoritative Journals
 Author of 13 patents (assignees University of Florence, Whirlpool Europe, Bracco Imaging, Logli, SCAM,
Meccaniche Fiorentine, Otlav, Politecnico di Milano, Saes Getters, Rold)© 2013 Gaetano Cascini – gaetano.cascini@polimi.itOutline

5Introduction
 Innovation, opportunities and threats Innovation, Market and the Society

Anticipating future innovations
 Technology Forecasting, scope and approaches
 TRIZ‐based Technology Forecasting

FORMAT project Aims and partners
 Reference models and partial achievements
 Case Study: Chilean Mining IndustryConclusions© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

Introduction

6

Technology Forecasting:WHY?

© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

7Introduction
Innovation, Opportunities and Threats From basic research, to market exploitation

source: US Dept. of Energy© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

Introduction

8

Innovation, Opportunities and Threats From basic research, to market exploitation

source: ca.gov© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

9Introduction
Innovation, Market and the Society Role in the Society and time perspectivenext
1÷4years

next
1÷4quarters

Businessman
Politician

next
1÷4
decades

Scientist© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

R&D
Innovation
Manager

10Introduction
Innovation, Market and the Society Role in the Society and time perspective
ptec
ac or ard
g
res
idlls
up

Innovation

MarketProblems:-finite amount of
resources- conflicting
expectations ofdifferent segmentsof the society

making existing
problems of the society

evident

Society© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

11Introduction
Innovation, Market and the Society
 Reflections (4 causal steps)

Mapping
the current
situation(technologyandmarket)

Anticipating
which
resources
will lack

Anticipatingwhich
expectations
will conflict

Anticipating
whatproblems
the society
will face© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

Anticipatingwhatmarket will
demandGoal:
Makingbusiness

12Introduction
An example Best Practice from IBM

Since 1982, IBM Research has marshaled the uniquecapabilities of its worldwide community of top scientists tocreate the Global Technology Outlook (GTO).
The GTO is a comprehensive analysis that looks three to ten
years into the future seeking to identify signiﬁcant, disruptive
technologies that will change IBM and the world.
The completed GTO is used within IBM to deﬁne areas offocus and investment and is shared broadly with a range of IT
inﬂuencers, including clients, academics, and partners,through education programs and client brieﬁngs.

GTO is not perfect:
predicting is difficult

GTO is not
speculative: driven bybusiness© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

GTO is not ignored:
100M$+ investmentsbased on GTO

Anticipating future innovations

13

Technology Forecasting, scope and approaches
 Technological forecast:o a comprehensible description of emergence, performance,features, and impacts of a technology in a particular place of aparticular point of time in the futureo (What? When? Where? Why?) Prediction:o a statement made about the future, anticipatory vision orperception. This statement is mostly qualitativeo (What? Why?)source: Dmitry Kucharavy© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

Anticipating future innovations

14

Technology Forecasting, scope and approaches Types of forecast:

Source: Phillips, Heidrick, Potter© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

Anticipating future innovations

15

Technology Forecasting, scope and approaches
 Types of forecast: cumulative appearance of articles© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

Anticipating future innovations
TRIZ‐based Technology Forecasting© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

16

17

FORMAT Project
www.format-project.eu

General Info Project Duration: 48 months (Jan 2012‐Dec 2015)
 Contract Number: FP7‐PEOPLE‐2011‐IAPP‐ 286305
 Total EU contribution: 1,690,454.00€
 Partners:© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

18

FORMAT Project
www.format-project.eu

Objectives
 The FORMAT project aims at developing an innovative
forecasting methodology supporting decision making in
Manufacturing Industries, facing and answering the 3perspectives:o 1. Product Evolution: the design of new products can require newprocess technologies for the product itself to be manufactured;o 2. Technological Evolution: technological pressure leads tochanges and improvements in the manufacturing processes forquality improvement and reduction of resources consumption;o 3. Organizational Evolution: new regulations and industrial
strategies can imply changes in the manufacturing processes, e.g.
in terms of productivity, personnel employment, integration etc.
© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

19

FORMAT Project
www.format-project.eu

Objectives© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

20

FORMAT Project
www.format-project.eu

Reference models and partial achievements
 Researching Future Methodology (D. Kucharavy, 2008)© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

21

FORMAT Project
www.format-project.eu

Reference models and partial achievements
 Researching Future Methodology (D. Kucharavy, 2008)© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

22

FORMAT Project
www.format-project.eu

Reference models and partial achievements
 Stage‐Gate Process Model for Technology Forecasting
LegendIdentify the needsof the TF

Motivation
for the TF:
Go/NoGoPrepare the TF
Project

Detailed
Objectives andPlan of the TF
Project

Build a model of theCurrent
Manufacturing
Process (MP)Boundary,
Driversand Barriers ofthe MP

Forecast theProblems that theMP will face with

Network ofProblems

Envision Evolutionof the MP

Network ofEvolutionary
TrendsQuantitative TrendExtrapolation

LogisticRegressions

Prepare the TF
ReportFORMAT
TF Report

Activities(Methods and Tools aresuggested, but are notmandatory)

Internal
Outputs(StandardFormat,mandatory)

Outputs andDecisions(StandardTemplate,mandatory)

Reconciliate theKnowledge about
the TF and assess
limits of validity© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

Validated
TechnologyForecast

23

FORMAT Project
www.format-project.eu

Stage‐Gate Process Model for Technology Forecasting
Identify the needsof the TF

Motivation
for the TF:
Go/NoGoPrepare the TF
Project

Detailed
Objectives andPlan of the TFProject Identify the needs of the TF
o What are main objectives and expected outputs?
o How it will be applied for decision making process?
o Can we satisfy formulated needs without TF?  Go / No Go

 Prepare the TF Projecto What are available and necessary resources to perform study?
o What is an optimal time span to realize project?
o Who are clients, core team, and necessary external participants?
o What are the specific objectives of the TF project?
o What questions should be answered?  Detailed plan of project© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

24

FORMAT Project
www.format-project.eu

Stage‐Gate Process Model for Technology Forecasting
Build a model of theCurrent
Manufacturing
Process (MP)Boundary,
Driversand Barriers ofthe MP

Source: Becattini N., Cascini G., Nikulin C.: “Modelling the dynamics of products and processes requirements”,
13th TRIZ Future Conference, Paris (France), 29th–31st October 2013.
© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

25

FORMAT Project
www.format-project.eu

Stage‐Gate Process Model for Technology Forecasting
Forecast theProblems that theMP will face withNetwork ofProblems

Nikolai KhomenkoKhomenko N., De Guio R., Lelait L., Kaikov I., 2007, “A framework for OTSM‐TRIZ Based Computer Support to be used inComplex Problem Management”, Int. Journal of Computer Applications in Technology, Vol.30(1‐2) / 2007, pp. 88‐104.
© 2013 Gaetano Cascini – gaetano.cascini@polimi.itSolution/Colloid
Droplets of
Solution/Colloid

Solution /Colloid
Tanks

Solution/Colloid
Droplets ofSolution/Colloid

Compacted
mass
grainsSolution/Colloid
Droplets of
Solution/Colloid

Environment/ treatedgas

Solution/Colloid
Solution/
Colloid Droplets

Powersupply

Compacted
massà BigflakeCompacted massPowerUnit© 2013 Gaetano Cascini – gaetano.cascini@polimi.it
PowerUnit

Dry“spaghetti”grains

Small flake
Fluent grains

Compacted
massgrains

Dry“spaghetti”
grainsPowersupply

Small flake
Fluent grains

Air
SupplyEnvision Evolutionof the MP

Compacted
massgrains

Compacted
massgrains

PowerUnit

FORMAT Project
26

www.format-project.eu

Stage‐Gate Process Model for Technology Forecasting
Network ofEvolutionary
TrendsCascini G., Rotini F., Russo D.: “Networks of trends: systematic development of system evolution scenarios”, 8th ETRIATRIZ Future Conference, The Netherlands, November 5‐7, 2008 ‐ Procedia Engineering, Volume 9, 2011, Pages 355‐367

27

FORMAT Project
www.format-project.eu

Stage‐Gate Process Model for Technology Forecasting
Quantitative Trend
Extrapolation

LogisticRegressions

Meyer (1994)

Growth under competition• Natural growth of autonomous systems incompetition might be described by LOGISTICEQUATION and logistic S‐curve• Natural growth is defined as the ability of a'species' to multiply in finite 'niche capacity'• For socio‐technical systems the 3‐parameter
S‐shaped growth model is applied for describing"trajectories" of growth or decline in time
© 2013 Gaetano Cascini – gaetano.cascini@polimi.itSource: Dmitry Kucharavy

28

FORMAT Project
www.format-project.eu

Stage‐Gate Process Model for Technology Forecasting
Quantitative Trend
Extrapolation

LogisticRegressions

Source: Dmitry Kucharavy© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

29

FORMAT Project
www.format-project.eu

Stage‐Gate Process Model for Technology Forecasting
Quantitative Trend
Extrapolation

LogisticRegressions

Market Cumulative Growth of Product XXX

China

United States

Case Study: European company working in the Food Processing Sector© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

Case study: Mining Process (Chile)© 2013 Gaetano Cascini – gaetano.cascini@polimi.it30

Case study: Mining Process (Chile)
Law of increasing the ideality(segmentation of objects)

monolithic

Law of increasing the ideality(segmentation of space)

Monolithic
System

GB 941766 A(1963)
GB 1552209 A(1979)
Monolithic
Monolithic/
Several
2 piecespiecesSystem witha cavity

until
Granulated

US 2011119964 A1(2011)
US 2360315 A(1994)
System/Porousmultiple
capillarycavities
systemPaste or gel

LiquidVacuum

Bi sistema
multifunctional
InversePartialtrimming
convolution

Trimming
convolution

Poli sistema
multifunctional
InversePartialtrimming
convolution

Activeporessystem

US 1648783 A(1927)
UA 14288 U (2006)
DE 3618199 A1(1987)

Mono system(Cutting onlyone part)Law of increasing the ideality(Mono-bi-poli)

Bi system
mono funtional
homogeneus(2 part cuttingedge, samematerial)

Bi systemmono
functional
partialproperties(2 part cuttingedge, differentmaterial)

Bi system
multifunctional
heterogeneous

JP 9137477 A(1997)
JP 11148149 A(1999)
JP 11247240 A(1999)
JP 11293716 A(1999)
JP 2001323518 A(2001)
JP 2003147811 A(2003)
JP 2003184135 A(2003)
KR 20010067912 A(2001)
KR 20100065837 A(2010)
KR 20100097314 A(2010)
US 4550512 A(1985)
US 6070345 A(2000)

Plainsurface

Law of increasing the ideality(segmentation of surface)

Poli-systemmono funtional
homogeneus

Poli-systemmono funtional
partialproperties

Poli-system
Multifunctional
heterogeneousProtuberance
surfaceRoughsurface

Surfaceactive pores
CN 1397693 A(2003)
RU 2116406 C1(1998)
CN 201372455 Y(2009)
GB 2251641 A(1992)

US 4455771 A(1984)
GB 1091740 A(1967)
US 1803654 A(1931)
Law of increasing the ideality(geometric evolution)

Straight line

Curved line

Complexline

PlainsurfaceComplex
surface

CPU:Control/Signal
StickPiston

BucketCuttingedge

Ore

Boom Piston

Human machineinterface© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

ComplexVolumen31

 Mining shovel

Case study: Mining Process(Chile)© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

32

 SAG MillConclusions

33

The Investments Risk can be reduced by appropriate
forecasting of technological evolution and social changes
TRIZ provides an effective support to Technological Forecasting,but it is not suitable to address questions like When? Where?
(Predictions, not Forecasts)
Many complementary theories and methods exist in the field
The integration of models based on the TRIZ Laws of Evolution
with mathematical models for trends extrapolation seems tohave adequate capabilities to perform qualitative andquantitative Technological Forecast
Follow the future developments of the FORMAT project on:
 Deliverables freely downloadable on www.format‐project.eu
 Handbook of the FORMAT methodology expected on Spring 2014
© 2013 Gaetano Cascini – gaetano.cascini@polimi.it

Gaetano CasciniryofskThan!
emit
our!

gaetano.cascini@polimi.it
www.kaemart.it
www.mecc.polimi.it
www.format-project.eu© 2013 Gaetano Cascini – gaetano.cascini@polimi.it