Human-Driven Digital Transformation in the Supply Chain (Management) VILNIUS TECH

Overview

Research topic description: 

Objectives: Humans as the main element in the rapid digital transformation, particularly by ensuring that technology supports a strong emphasis on human empowerment and enhances the roles of workers rather than replacing them. This PhD thesis will address the growing need for a balanced approach that focuses on how digital technologies can be used to empower workers and improve operational efficiency simultaneously. Specifically, the objectives are: 1) Investigate what human-driven digital transformation brings to SC and to which pillars of sustainability it is focusing. 2) Study the potential of human-centred aspects in SC digitalization. Focus will be placed on how digital tools can improve decision-making, skill development, safety, and job satisfaction, ensuring that human workers are not sidelined by automation but instead are empowered by it. 3) Assess the factors contributing to and hindering the human-centric digital transformation that may be relevant to the development of new technologies. 4) Understand how digitalisation can provide a solid foundation for the introduction of new technologies that will shape the future of data science in a more automated environment.

Expected Results: The results of this study are expected to make a technical and practical contribution to the field of SCM. The main conclusions envisaged are as follows: 1) Develop a conceptual model in which advanced digital technologies are integrated into the SC and that human work is at the heart of the decision-making process. This will be supported by evidence-based insights on how companies can implement technologies that provide benefits and not only increase operational efficiency by improving the accuracy and speed of SC operations but also maintain or enhance human roles through better training and user-friendly interfaces. 2) Quantifiable measures for assessing pre-digital transformation and post-digital transformation will be used with a focus on how technology can help achieve sustainability goals without compromising on the role of human oversight. 3) Contribute to the knowledge and practice of SCM by demonstrating how the integration of digital technologies can achieve operational efficiency without displacing the human workforce. The study will address current gaps in the digital transformation of SC, particularly concerning worker empowerment, the development of new skill sets, and the role of human intuition and decision-making in an increasingly automated environment. 4) Support businesses and policymakers to navigate the challenges of digital transformation while fostering human-centric innovations for future-ready SCs using the combination of academic insight and practical solutions.

The selected candidate will work on the PhD thesis under the supervision of Prof. Dr Aurelija Burinskiene. The successful applicant will have to attend scientific conferences, meetings and internships at the other universities. 

Requirements:

• Required background: Academic background which is relevant to the PhD research field (e.g., supply chain, management, economics, engineering, computer science).

• Expected skills and knowledge: 

o Previous research experience, published publications, statistical data analysis or participation in projects, which demonstrate candidate skills; 

o The ability of the candidate to integrate knowledge across different disciplines, including operations research and management; 

o The ability of the candidate to cooperate with research supervisors and hosts of secondments (using communication, teamwork, and problem-solving skills); 

o Knowledge about digital transformation, human-centricity, supply chain management and research methods.

It is a prerequisite you can be present at and accessible to the institution daily.

Shortlisted candidates will be invited for an interview. The position may not be opened if no qualified candidate is found. Additional information regarding the post may be obtained from Prof. Dr Aurelija Burinskiene, e-mail: aurelija.burinskiene@vilniustech.lt