17549214. DYNAMICALLY ENHANCING SUPPLY CHAIN STRATEGIES BASED ON CARBON EMISSION TARGETS simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

DYNAMICALLY ENHANCING SUPPLY CHAIN STRATEGIES BASED ON CARBON EMISSION TARGETS

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

Kedar Kulkarni of Bangalore (IN)

Reginald Eugene Bryant of Naiorbi (KE)

Isaac Waweru Wambugu of Nairobi (KE)

Ivan Kayongo of Nairobi (KE)

Smitkumar Narotambhai Marvaniya of Bangalore (IN)

Komminist Weldemariam of Ottawa (CA)

Shantanu R. Godbole of Bangalore (IN)

DYNAMICALLY ENHANCING SUPPLY CHAIN STRATEGIES BASED ON CARBON EMISSION TARGETS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17549214 titled 'DYNAMICALLY ENHANCING SUPPLY CHAIN STRATEGIES BASED ON CARBON EMISSION TARGETS

Simplified Explanation

The patent application describes methods, systems, and computer program products for dynamically enhancing supply chain strategies based on carbon emission targets. Here are the key points:

• The method involves obtaining enterprise-related data and carbon emissions-related data associated with the enterprise.
• A machine learning-based model is trained using the obtained data to enhance carbon emissions reduction and value increase for the enterprise.
• Carbon emissions data for a given period is processed using the trained machine learning-based model.
• Based on the results of the processing, one or more enterprise-related recommendations are generated.
• Automated actions are performed based on the recommendations.

Potential Applications

This technology can be applied in various industries and sectors, including:

• Manufacturing: Optimizing supply chain strategies to reduce carbon emissions and increase value.
• Transportation and logistics: Enhancing efficiency and sustainability in the movement of goods.
• Retail: Improving inventory management and distribution processes to minimize carbon footprint.
• Energy: Optimizing energy usage and reducing emissions in the supply chain.

Problems Solved

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

• Lack of real-time insights: By using machine learning models, the system can provide timely recommendations based on current data.
• Complex decision-making: The system simplifies decision-making by analyzing and processing large amounts of data to generate actionable recommendations.
• Balancing carbon emissions reduction and value increase: The technology helps enterprises find the optimal balance between reducing emissions and increasing profitability.

Benefits

Implementing this technology offers several benefits, including:

• Environmental sustainability: By optimizing supply chain strategies, enterprises can reduce their carbon footprint and contribute to environmental conservation.
• Cost savings: Efficient supply chain management can lead to cost reductions in areas such as transportation, energy usage, and inventory management.
• Enhanced competitiveness: Adopting sustainable practices can improve a company's reputation and attract environmentally conscious customers.
• Data-driven decision-making: The use of machine learning models enables enterprises to make informed decisions based on accurate and up-to-date data.

Original Abstract Submitted

Methods, systems, and computer program products for dynamically enhancing supply chain strategies based on carbon emission targets are provided herein. A computer-implemented method includes obtaining enterprise-related data and carbon emissions-related data associated with the enterprise; training, using at least a portion of the obtained enterprise-related data and carbon emissions-related data, at least one machine learning-based model configured for enhancing at least one of carbon emissions reduction by the enterprise and value increase for the enterprise; processing carbon emissions data attributed to the enterprise for a given temporal period using the at least one trained machine learning-based model; generating one or more enterprise-related recommendations based at least in part on results of the processing of the carbon emissions data using the at least one trained machine learning-based model; and performing one or more automated actions based at least in part on the one or more enterprise-related recommendations.