A possible real time price predicting model for ships during geopolitical turbulences

 Global Geopolitical Situation:

Oil Price Hike: Integrate real-time data on oil prices into the model to account for fluctuations, adjusting shipping cost predictions accordingly. 

Sentiment analysis can also be used to predict future oil prices ie (news regarding oil prices. Ex.saudi said they will cut oil production by a million barrels) Provide valid suggestions  such as finding appropriate insurance against possible fuel price hike. - Find fuel hedging insurance providers before market witness rise in oil price. 

Historical trends can also be analysed in oil prices rising and give alerts to management timely. 

Safety of Route (e.g., Red Sea War) and also analysis of possible routes: Implement a risk assessment algorithm that considers geopolitical conflicts and provides alternative routes when necessary. This might involve increased security measures and associated costs (higher pay to employees to go to risky routes.).While going on new routes  less major ports may also not support big ships thus increasing further costs when you have to use smaller ships (no economies of scale)

Shipping Insurance Fluctuation:

Dynamic Insurance Premiums: Develop a module that analyzes current insurance rates and adjusts shipping costs based on the insurance premiums associated with specific routes or geopolitical risks. Analyse historical insurance trends and give alerts when there is a possibility for insurance costs to rise

Added Cost of New Trade Route:

Route Length: Consider the length of the new trade route and its impact on fuel consumption, employee salaries, and other operational costs.

Employee Compensation: Adjust labor costs based on the duration and perceived risks associated with the new route.

Security Measures: Factor in the costs of additional security measures required for the new route.(such as security,bulletproof ship,extra safety money to give to pirates)

Port Charges: Include fees associated with using new ports, considering the infrastructure available and potential limitations for larger ships.(most ports dont support very large ships to dock there)

Profitability Margin:

Real-time Margin Adjustments: Implement a profitability margin algorithm that automatically adjusts the quoted shipping costs based on the overall risk and cost factors. This can ensure that the company maintains a desired profit margin in varying conditions.

Scenario-Based Predictions:

Simulation Module: Develop a simulation component that allows users to input different geopolitical scenarios, oil price projections, or route changes to see how these variables impact shipping costs and profitability. (This can help management planning and in advance cut other expenditures to save money for higher costs. Smart planning of routes)

Machine Learning for Continuous Improvement:

Feedback Loop: Establish a feedback loop where the model learns from actual shipping data and historical shipping data  and adjusts its predictions over time, adapting to changing geopolitical situations, insurance market trends, and other dynamic factors. 

By integrating these variables into the model, you create a more robust and adaptive system that considers the multifaceted challenges faced by the shipping industry. This approach enables businesses to make informed decisions, optimize routes, and quote shipping costs that align with the ever-changing global landscape while maintaining profitability.

Market Sentiments (For future predictions,projections and valid input)

Sentiment Analysis: Implement a sentiment analysis algorithm that scours news articles, social media, and other relevant sources for information that might impact insurance markets,oil price,shipping salaries, shipping supplies,and even natural disasters

Insurance Market Trends: Analyze historical data on how market sentiments correlate with insurance cost fluctuations. This can be done using machine learning algorithms to identify patterns and trends.

Predictive Modeling: Develop predictive models that consider the sentiment-driven changes in insurance markets. For example, positive sentiments might suggest stable or decreasing insurance costs, while negative sentiments could indicate an upcoming hike.

Dynamic Insurance Cost Predictions:

Real-time Updates: Integrate real-time updates from news sources and social media to ensure that the model's predictions are informed by the latest market sentiments.

Adjustment Factor: Create an adjustment factor in the model that modifies the predicted insurance costs based on the sentiment-driven insights. For instance, if sentiments indicate a potential increase in insurance costs, the model can proactively adjust shipping cost predictions accordingly.