The document discusses an open source innovation factory framework that uses innovation metrics and knowbots to help companies reduce the time required to transfer innovative projects to real environments. Specifically, it presents a model that measures innovation activities and goals, uses the open source SpagoBI platform to analyze innovation data, and employs knowbots to acquire external knowledge to inform innovation processes. An example scenario for an aerospace company is provided to illustrate how the framework could optimize engine repair planning.

1. OW2Con 2011, November 23 -24, Orange Labs, Paris
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Open Source Innovation Factory
Paolo Ceravolo, Università degli Studi di Milano
in cooperation with Engineering Group
[Our Framework equipped with Innovation Metrics can
dramatically reduce the time required to transfer an
innovative project to a real environment]

2. OW2Con 2011, November 23 -24, Orange Labs, Paris
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Outlook
 Open Issues in Open Innovation
 Our Proposal
 Our Framework
 Future work

3. OW2Con 2011, November 23 -24, Orange Labs, Paris
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Innovation
 72% of company executives rank innovation in their
top 3 priorities (Boston Consulting Group, 2006)
 80% of new product results will tend to come from only
20% R&D projects (Eduardo, 2003)
 successful companies cancelled as many innovation
projects as non-successful companies. However,
successful companies were able to cancel unattractive
projects much earlier in the process (Ogawa & Ketner,
1997)

4. OW2Con 2011, November 23 -24, Orange Labs, Paris
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Open Innovation
 The central idea behind open innovation is that in a
world of widely distributed knowledge, companies
cannot afford to rely entirely on their own research, but
should instead buy or license processes or inventions
(i.e. patents) from other companies. In addition,
internal inventions not being used in a firm's business
should be taken outside the company (Chesbrough,
2003).

5. OW2Con 2011, November 23 -24, Orange Labs, Paris
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Open Innovation

6. OW2Con 2011, November 23 -24, Orange Labs, Paris
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Open Issues in Open Innovation
 Reduce time to identify unproductive projects
 Understand the synergy that are potentially
relating different projects
 Represent Innovation Activity
 Measure Innovation Activity
 Report on Innovation Activity

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Open Source Innovation Factory
 Mixes three complementary approaches:
1. Innovation Factory Metamodel (IFM), proposed in
the ARISTOTELE project (www.aristotele-ip.eu)
2. Open source platform SpagoBI
(www.spagobi.org), LOAP and Reporting
3. Knowbots, advanced tools for the acquisition of
concepts from internal and external knowledge
sources

8. OW2Con 2011, November 23 -24, Orange Labs, Paris
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Open Source Innovation Factory

9. OW2Con 2011, November 23 -24, Orange Labs, Paris
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Innovation Factory Metamodel

10. OW2Con 2011, November 23 -24, Orange Labs, Paris
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Innovation Metrics
•
Goal 1: Innovation Level Improvement
o Q1.1: Which is the level of innovative knowledge exploited in the Organization?
o Q1.2: How much are innovative the new products?
•
Goal 2: Quality of Innovation Sources
o Q2.1: Which is the quality of sources of innovation process?
•
Goal 3: Open Innovation Permeability
o Q3.1: To what extend the customer contribution is exploited?
o Q3.2: To what extend the concepts coming from competitors' sites are exploited?
o Q3.3: Evaluate the level of technologies that are transferred by the analysis of
competitors
o Q3.4: How much internal proposals influence innovative products?
•
Goal 4: Return of Innovation Investment
o Q4.1: How much innovation process produces profits?
o Q4.2: How much innovation process costs?
o Q4.3: Indirect Advantages

11. OW2Con 2011, November 23 -24, Orange Labs, Paris
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.
www.ow2.org.
Innovation Metrics
Goal 1: Innovation Level Improvement
Q1.1: Which is the level of innovative knowledge exploited in the Organization?
M1.1.1: no. accesses to blog/forum marked as innovation sources
M1.1.2: no. concepts transferred from innovation sources to produced
documents (e.g. cut&paste)
Q1.2: How much are innovative the new products?
M1.2.1: no. tags describing new functionalities / no. concepts in innovative
sources tag cloud
M1.2.2: no. requirements covered by new products

12. OW2Con 2011, November 23 -24, Orange Labs, Paris
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Innovation Metrics
Goal 2: Quality of Innovation Sources
Q2.1: Which is the quality of sources of innovation process?
M2.1.1: % of external requirements, coming from outside the organisation (e.g.
analyzing innovative sources)
M2.1.2: % of internal requirements, coming from inside the organisation (e.g. from
internal meetings)
M2.1.3: % of customer requirements, coming directly from the customer
M2.1.4: % of new products implementing external requirements
M2.1.5: % of new products implementing internal requirements
M2.1.6: % of new products implementing customer requirements
M2.1.7: % of human resources that contribute to the innovation process

13. OW2Con 2011, November 23 -24, Orange Labs, Paris
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Innovation Metrics
Goal 4: Return of Innovation Investment
Q4.1: How much innovation process produces profits?
M4.1.1: no. new products sold per month
M4.1.2: no. new products sold per week
M4.1.3: no. new customers
M4.1.4: % revenue from new products
M4.1.5: % new product within deadline
Q4.2: How much innovation process costs?
M4.2.1: time-to-market of new products
M4.2.2: time-to-market of changes to existing products
M4.2.3: budget spent on human resources training
M4.2.4: no. competences involved in innovation process
M4.2.5: no. human resources involved in innovation process
M4.2.6: no. man-months spent to realize new product
M4.2.7: no. man-months spent to realize changes to existing products
M4.2.8: average number of training hours per human resource
Q4.3: Indirect Advantages
M4.3.1: % customer satisfaction with new products
M4.3.2: % of human resources reaching desired competence level after training
M4.3.3: % difference in productivity before and after training

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SpagoBI

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SpagoBI

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SpagoBI

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KnowBots
• KB consists of two distinct modules which interact
with each other
• One module is running locally in the workspace of
the worker
• A second module is a remote server module that
interrogates various database services across the
network and provides the results to a user agent
running in the local workspace
• This way we can track the information that are
provided by the Knowbots and are used in the
workspace

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Scenario: AVIO
• AVIO traditionally run two separate business:
• Plant in Torino selling engines
• Plant in Brindisi providing overhaul services
• AVIO has no foresight on the mid-term arrivals of
engines and is unable to optimize plans and
procurement processes.
• FLEET MANAGEMENT
• centralizing engine monitoring and scheduling their
overhaul process
• optimizing the flow of engines in arrival and
reducing congestion or underutilization

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Lesson Learned
• Access to data
• process (competences, requirements)
• products (requirements, ROI)
• Define Innovation Profile based on successful story
to support comparability among past and running
processes

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This work is co-funded by the European Commission
as part of the ARISTOTELE project
(FP7-ICT-2009-5 –257886)