International business machines corporation (20240127347). RISK ADAPTIVE ASSET MANAGEMENT simplified abstract
Contents
- 1 RISK ADAPTIVE ASSET MANAGEMENT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 RISK ADAPTIVE ASSET MANAGEMENT - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
RISK ADAPTIVE ASSET MANAGEMENT
Organization Name
international business machines corporation
Inventor(s)
Tetsuro Morimura of Shinagawa-ku (JP)
RISK ADAPTIVE ASSET MANAGEMENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240127347 titled 'RISK ADAPTIVE ASSET MANAGEMENT
Simplified Explanation
The patent application describes a computer-implemented method for determining actions with respect to a portfolio of items for supply chain management. The method involves analyzing supply chain delivery trends, the portfolio of items, and the current investment amount to make decisions based on the current supply chain delivery situation.
- Acquiring feature vectors for supply chain delivery trends, the portfolio of items, and the current investment amount.
- Determining if the current supply chain delivery situation is normal or abnormal based on the feature vectors.
- Performing a risk-avoidance action to reduce the current investment amount in abnormal situations to avoid potential supply chain delivery losses.
- Performing a risk adaptive action to increase the current investment amount in normal situations to potentially gain from supply chain delivery gains using a distributional reinforcement learning process.
Potential Applications
This technology can be applied in various industries such as retail, manufacturing, logistics, and e-commerce for optimizing supply chain management processes.
Problems Solved
1. Efficient risk management in supply chain operations. 2. Optimizing investment decisions based on supply chain delivery situations.
Benefits
1. Minimizing supply chain delivery losses. 2. Maximizing supply chain delivery gains. 3. Enhanced decision-making in supply chain management.
Potential Commercial Applications
Optimizing inventory management, improving delivery timelines, reducing costs, and enhancing overall supply chain efficiency in various industries.
Possible Prior Art
One possible prior art could be the use of machine learning algorithms in supply chain management to optimize decision-making processes. Another could be the use of data analytics to predict supply chain disruptions and mitigate risks proactively.
Unanswered Questions
How does the method handle real-time supply chain delivery data?
The patent application does not specify how real-time data is incorporated into the decision-making process. It would be interesting to know how the system updates its analysis based on real-time information.
What is the scalability of this method for large-scale supply chain operations?
The scalability of the method for handling a large number of items in a portfolio and complex supply chain networks is not discussed. Understanding how the system can adapt to different scales of operations would be valuable information.
Original Abstract Submitted
a computer-implemented method is provided for determining an action with respect to a given portfolio of items for supply chain management. the method includes acquiring, by a hardware processor, a feature vector for supply chain delivery trends, the given portfolio, and a current investment amount. the method further includes determining, by the hardware processor, whether a current supply chain delivery situation is normal or abnormal based on the feature vector. the method also includes performing a risk-avoidance action to reduce the current investment amount and avoid potential supply chain delivery losses, responsive to a determination that the current supply chain delivery situation is abnormal. the method additionally includes performing a risk adaptive action to increase the current investment amount and incur potential supply chain delivery gains by using a distributional reinforcement learning process, responsive to a determination that the current supply chain delivery situation is normal.
