1) The document discusses decision making in dynamic and uncertain environments, noting the expansion of risks from just industry to many other fields like banking, insurance, and more. 2) It outlines a process for decision making that involves collecting raw data from various sources, analyzing and validating that data at different levels to create elaborated data and knowledge, and using that knowledge to inform goals, strategies, and actions. 3) Examples provided include cockpit design and nuclear waste management to illustrate applying this knowledge-based approach to decision making.

1. Decision Making in Dynamic World
Facing new crisis and risks
Eunika MERCIER-LAURENT
eunika.mercier-laurent@univ-reims.fr
http://innovation-ecosystems.eu

2. From industrial to all risks
• Industry - automatisation - confidence
• Other fields: banking, economic,
insurance, pharmaceutical,
environment…
• IT
• Growth and jobs
CODIT 2017/EML 2

3. From industrial to all riskshttps://www.gov.uk/guidance/managing-information-risk
CODIT 2017/EML 3

4. Overview of risks
CODIT 2017/EML 4

5. Decision making?
CODIT 2017/EML 5

6. 6
Register first (Data thinking)
Validation
2nd level analysis
Validated data Processing
Elaborated data
1st level analysis
Data collection (one point
of view - (diagnostic,
reliability, design…)
Raw data
Excel, DB, CAM,
CAD..
+ data mining, text mining, deep learning (IA)
REX

7. Knowledge Approach
(Knowledge Management)
• Think first:
– Analysis – who is involved, who needs
what, what knowledge, how it should be
organized…
• Models and tools (AI)
CODIT 2017/EML 7

8. Method
CODIT 2017/EML 8
Knowledge
Goals
Context Actions
Strategy

9. Example
• Cockpit design
• Nuclear waste
CODIT 2017/EML 9

10. Conclusion & Future work
• Holistic and system thinking
• Knowledge management
• Integration of innovation systems,
design, minimizing impact
CODIT 2017/EML 10