Make a Final report using the following structure

a. Introduction to the topic. 1-page maximum Summarize in your own words

b. Literature Review (What does current research says on the chosen topic, Refer to articles available through open sources)

c. Methodology: Qualitative analysis/thematic analysis. Why (Pros & Cons) & How methodology was used) (1200 words)

d. Results & Discussion (Contribution and implications)

e. Conclusion

f. References

Question: write methodology, result and discussion, and conclusion for this report

Impacts of Artificial Intelligence on Sustainability in Energy Supply Chain Management

1. Introduction:

Due to its intimate ties to environmental, social, and economic aspects, the energy sector is essential to achieve global sustainability. The supply chain, which includes the movement of commodities, services, and information from the extraction of raw materials to the delivery of finished items, is essential to the sustainability of the energy industry. The energy sector, which includes the extraction, production, and distribution of energy resources, is crucial to the growth of the world economy but is also a major cause of environmental problems such as pollution, resource depletion, and carbon emissions. Innovative strategies are needed to address these issues and create sustainable supply chain operations in the energy sector.

The way that energy supply chains are managed has the potential to undergo a radical change thanks to AI's capacity for processing and analyzing enormous volumes of data, making data-driven decisions, and optimizing complicated operations. Robotics, machine learning, natural language processing, and the internet of things (IoT) are examples of AI technologies that can help energy firms optimize their operations, cut waste, increase efficiency, and make wise decisions in order to meet sustainability goals. AI has the potential to have a significant impact on sustainability in the energy sector, from improving resource management and decreasing emissions to optimizing logistics and increasing the integration of renewable energy.

1. LITERATURE REVIEW:

"Artificial Intelligence in Supply Chain Management in Oil and Gas."(2020)

This article offers a thorough analysis of AI applications in the energy industry's sustainable supply chain management. It talks about the advantages and drawbacks of using AI, such as better decision-making, less negative environmental effects, and more effective operations.

1. "Artificial intelligence could provide a solution to Canada's Supply Chain troubles."(2023). Several businesses have been troubled by Canada's supply chain issues during the epidemic. But these issues might be resolved with the help of artificial intelligence (AI).By analyzing vast information and spotting patterns and trends that humans might not be able to see, AI can assist in supply chain operation optimization.

1. "Artificial Intelligence in Energy Management(Infographic)" (2023). The article describes how AI is already having a significant impact on the energy industry by assisting in the development of safer and more effective methods for energy generation. It highlights the fact that AI is not just a theoretical idea but is now being used in business, and it offers an infographic outlining the algorithms underlying these energy-saving devices. The essay also makes the case that AI will keep helping the energy industry in the future.

1. "Artificial Intelligence in Oil and Gas: Applications, Impact and Benefits"(2019). The oil and gas sector is changing because to AI, which offers several advantages in terms of cost reductions, efficiency, safety, and sustainability. We may anticipate seeing even more AI applications in this sector in the future as technology develops.From exploration and production through refining and distribution, artificial intelligence is widely used in the oil and gas sector.

1. "AI in Supply Chain: six barriers to seeing results" (2019). The article lists the most typical obstacles to supply chain AI application.Good data are required for any computer operations, and AI is no exception.The data required to run supply chains is dispersed among internal and external partners, and they are fundamentally cross-functional and cross-enterprise.

METHODOLOGY

IV. RESULTS AND DISCUSSIONS

V. CONCLUSION