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 17546564 titled 'RISK ADAPTIVE ASSET MANAGEMENT

Simplified Explanation

The abstract describes a computer-implemented method for supply chain management. Here are the key points:

The method uses a feature vector to analyze supply chain delivery trends, a given portfolio of items, and the current investment amount.
It determines whether the current supply chain delivery situation is normal or abnormal based on the feature vector.
If the situation is abnormal, a risk-avoidance action is performed to reduce the investment amount and avoid potential delivery losses.
If the situation is normal, a risk adaptive action is performed to increase the investment amount and potentially gain from supply chain deliveries using a distributional reinforcement learning process.

Potential Applications

This technology can be applied in various industries and sectors that rely on supply chain management, including:

Manufacturing: Optimizing inventory levels and investment decisions based on supply chain delivery trends.
Retail: Managing stock levels and investment strategies to minimize losses and maximize gains.
Logistics: Improving decision-making for transportation and distribution based on supply chain data.
E-commerce: Enhancing order fulfillment and inventory management processes for online retailers.

Problems Solved

The technology addresses several challenges in supply chain management, such as:

Uncertainty: By analyzing supply chain delivery trends, it helps identify abnormal situations and take appropriate actions to mitigate potential losses.
Risk management: The risk-avoidance and risk adaptive actions help balance investment decisions and optimize outcomes based on the current supply chain situation.
Decision-making: The use of a feature vector and reinforcement learning process provides a data-driven approach to make informed decisions in supply chain management.

Benefits

Implementing this technology offers several benefits for supply chain management:

Improved efficiency: By analyzing supply chain delivery trends, it enables proactive decision-making and reduces the likelihood of disruptions or losses.
Cost savings: The risk-avoidance action helps minimize potential losses, while the risk adaptive action allows for capitalizing on favorable supply chain situations, leading to overall cost savings.
Enhanced decision-making: The use of data-driven analysis and reinforcement learning improves the accuracy and effectiveness of investment decisions in supply chain management.

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.

17546564. RISK ADAPTIVE ASSET MANAGEMENT simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)