Recent events such as the war, Brexit, biodiversity, and the Covid-19 pandemic have led to negative consequences for firms, thereby affecting the consumer. The supply chain encompasses the complete value chain from sourcing, manufacturing, and distribution to logistics. Supply chain management is subject to various challenges, such as synchronization, real-time data visibility, irregular reviews of stock levels, and production line imbalance resulting in asset underutilization. Recent advancements in big data analytics have accelerated the new means of arriving at precise predictions that reflect better customer demand while minimizing the cost of supply. Supply chain analytics (SCA) aims to improve the operational efficiency and effectiveness of data-driven decisions to address the above challenges at various levels including strategic, operational, and tactical. To cope with these challenges, it is critical to rethink SCA to cope with extreme conditions using data-driven intelligence to the business, reducing the overall cost to the consumer while improving
service levels.
Existing literature focuses on statistical analysis techniques to forecast demand in SCM using time-series analysis and regression analysis. Modern supply chain management systems are expensive and complex networks with inherent dependencies and dynamicity, making management of these networks challenging. Current efforts to supply chain analytics do not consider the dependencies/relationships that exist in complex supply chain management systems and are not effective for complex SCM systems. In contrast to existing literature to supply chain analytics, this project will model the complex supply chain management using a graph model to show the dependencies/relationship for an accurate overview of the entire supply chain network. The project also aims to propose various graph-based deep learning approaches for important predictions that are critical at the strategic, operational, and tactical levels to improve the entire SCM value chain.
APPLICATIONS
Candidates must submit a brief literature review (500 words, not including the list of references in the word count) of their selected project, accompanied by a short CV (one page) and complete the registration form. An applicant may apply for more than one project, however each application must have a separate literature review.
An Honours Degree (minimum 2.2, but 2.1 or higher is desirable) in the relevant business/computing/engineering disciplines.Candidates from outside the EU are eligible to apply but may be expected to provide evidence of sources of additional funds to cover living expenses for the first month in Ireland.
If either English or Irish is not the applicant’s first language, evidence of English language proficiency is required for registration. Applicants must have attained a minimum of IELTS 6.5 or equivalent, due to the very high academic writing standard required for postgraduate qualifications through research.
Application Form / Terms of Conditions can be obtained on the website: https://www.itsligo.ie/oscar/
Please send your completed application form to Veronica Cawley - [Email Address Removed]
The closing date for receipt of applications is 5pm, (GMT) 6th of June 2022.
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