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Development of a conceptual approach to building a marketing strategy for the oil and gas industry

Abstract This master thesis has aimed to develop conceptual approach to build marketing strategy for oil and gas industry’s company by maximizing market opportunities developing stakeholder engagement, as well as ensuring long-term sustainability.To rich the aim of the research the following research objectives have been solved in this study:● To examine current market trends, based on 4P’s, regulatory frameworks, as well as emerging opportunities based on literature review, studying best practices, and interviewing marketing management professionals.● To address untapped market segments by assessing competitor strategies to inform strategic positioning with differentiation. ● Develop a conceptual approach to build a robust 4Ps marketing strategy utilizing digital technologies to improve brand awareness as well as strengthen the loyalty of all stakeholders such as investors, customers.

Author: Vijay Singh Thakur

Supervisor: Irina Kuzmina-Merlino

Degree: Master

Year: 2024

Work Language: English

Study programme: Business and Management

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Application of time series algorithms for container imbalance forecasting using event data.

The research aim is to evaluate a time series models application for container imbalance forecasting using container event data. Compare the model results and conclude the real-world application options and limitations. The research object is time series models of container imbalance forecasting and subject is performance of models for container imbalance forecasting based on event data.There are three chapters of the research. The first one is State of the Art on Empty Container Repositioning (ECR) forecasting methods and approaches. The second part is investigation of container imbalance forecasting opportunities using event data. The third part is an application of time-series methods for forecasting container imbalance, experiments with real data and attempts to develop a novel data-driven framework for event data trained time series model evaluation. The third part consists of training experiment results analysis and interpretation. The 8 different models of ARIMA, VAR, VECM algorithms were tested and evaluated by different container size and type combinations, as well of 6 different port locations. Finally, the research conclusions are followed by references and attachments.

Author: Vjačeslavs Matvejevs

Supervisor: Dmitry Pavlyuk

Degree: Master

Year: 2024

Work Language: English

Study programme: Computer Sciences

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Apply a Machine Learning Model to Mitigate Bias in the Future AI-based Recruitment

In the contemporary landscape of Human Resources, the integration of artificial intelligence presents both opportunities and challenges, especially in the field of recruitment encompassing all stages of the process, from candidate sourcing to final selection. However, this integration is not without its challenges. Biased data, originating from historical data or societal prejudices, can present a significant obstacle, potentially perpetuating discriminatory practices. The study "Apply a Machine Learning Model to Mitigate Bias in the Future AI-based Recruitment" aims to comprehensively analyze existing biases from both human and artificial intelligence perspectives within the recruitment process. In its framework, answers to the research questions are sought: what are the existing biases in the recruitment process, both explicit and implicit, and how can biases in the recruitment process be effectively mitigated or eliminated through modeling techniques in future AI-based recruitments systems. Through a data-driven approach and the development of machine learning models, will be discover what kind of biases exist in the selection process and how to mitigate them.

Author: Ērika Todjēre

Supervisor: Jeļena Kijonoka

Degree: Master

Year: 2024

Work Language: English

Study programme: Computer Sciences

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