Developing a risk-adaptive technology roadmap using a Bayesian network and topic modeling under deep uncertainty.

Area of Science:

• Business Management
• Technology Planning
• Risk Management

Background:

• Modern businesses operate in complex, rapidly changing environments requiring adaptive strategies.
• Dynamic adaptive planning, while successful in large industries, needs adaptation for shorter-term technology management.
• Existing technology roadmapping lacks sufficient adaptability to emergent risks and environmental shifts.

Purpose of the Study:

• To propose a novel risk-adaptive technology roadmap (TRM) framework.
• To integrate dynamic adaptive planning principles into technology roadmapping for complex environments.
• To enhance the sustainability and responsiveness of technology roadmapping processes.

Main Methods:

• Risk identification using topic modeling and sentiment analysis on futuristic data.
• Deriving alternative plans through keyword co-occurrence analysis of identified risks.
• Constructing a Bayesian network with a conditional probability table from an existing TRM.
• Estimating posterior probabilities to dynamically adjust plans and remap TRM paths.

Main Results:

• The proposed TRM effectively adapts to changing environments by adjusting plans based on risk probabilities.
• The methodology was validated in the field of artificial intelligence, demonstrating feasibility.
• The approach enhances the dynamic adaptive planning capabilities within technology roadmapping.

Conclusions:

• The risk-adaptive technology roadmap (TRM) offers a viable approach for managing technological uncertainties.
• This framework supports more sustainable and responsive technology roadmapping in dynamic business landscapes.
• The study highlights the potential of integrating advanced risk assessment and adaptive planning into strategic technology management.