Leveraging AI for Workforce Efficiency

About the research project

The shipping industry is a cornerstone of global trade, with shipping companies responsible for transporting the majority of goods worldwide. While much attention is often given to optimising maritime operations, the management of onshore workforce activities plays a crucial role in ensuring the smooth and efficient functioning of shipping companies. Onshore employees are tasked with managing customer service, processing documentation, coordinating logistics, ensuring compliance, and overseeing various administrative functions. However, inefficiencies in these processes can result in significant costs for companies, especially in terms of labour and time. In this context, Artificial Intelligence (AI) offers a transformative opportunity to improve productivity while reducing the reliance on costly manual labour.

In today's competitive shipping environment, companies are under increasing pressure to lower operational costs while maintaining high standards of service. The onshore workforce, responsible for handling vast amounts of data and administrative tasks, often becomes a source of inefficiency due to human error, time-consuming processes, and resource mismanagement. AI technologies can automate routine tasks such as documentation, invoice processing, and customer inquiries, significantly reducing the number of staff needed for these functions. By automating these processes, companies can minimise staffing costs, reduce the risk of errors, and free up employees for more complex, value-added activities. Additionally, AI-driven optimisation tools can improve workforce scheduling, ensuring that the right number of staff with the appropriate skills are allocated to tasks based on demand, further minimising inefficiencies and overstaffing.

Furthermore, AI's ability to analyse large datasets in real time can provide insights that allow shipping companies to optimise both staff productivity and overall operations. For example, machine learning algorithms can predict operational bottlenecks, identify opportunities for cost-saving measures, and streamline workflows across departments. With sufficient data, predictive analytics can help manage staffing levels more effectively, ensuring that resources are aligned with business needs without overburdening the workforce. By implementing AI solutions, shipping companies can achieve higher levels of productivity, lower labour costs, and improve decision-making.

To achieve the above aim the following research questions shall be answered.

PRQ: How can shipping companies leverage AI to enhance decision-making and operational efficiency when managing the onshore workforce?

SRQ1: What are the potential benefits to shipping companies from adopting AI to manage the onshore workforce?

SRQ2: What cost savings and productivity gains can AI-driven workforce optimisation provide to shipping companies.

Primary supervisor

Meet Doctor Peter Fanam

Funding

Applicants will be considered for a Research Training Program (RTP) scholarship or Tasmania Graduate Research Scholarship (TGRS) which, if successful, provides:

• a living allowance stipend funded by University of Tasmania of $34,315 per annum for 3.5 years
• a relocation allowance of up to $2,000
• a tuition fees offset covering the cost of tuition fees for up to four years (domestic applicants only)

A tuition fee offset may be offered to eligible international applicants following competitive assessment 

As part of the application process you may indicate if you do not wish to be considered for scholarship funding.

Other funding opportunities and fees

For further information regarding other scholarships on offer, and the various fees for undertaking a research degree, please visit our Scholarships and fees on research degrees page.

Eligibility

Applicants should review the Higher Degree by Research minimum entry requirements.

Ensure your eligibility for the scholarship round by referring to our Key Dates.

Additional eligibility criteria specific to this project/scholarship:

• Applicants must be able to undertake the project on-campus
• Applicants must be able to demonstrate strong research and data analysis skills
• Applicants must already have been awarded a First Class Honours degree or hold  equivalent qualifications (e.g. master's degree) or relevant and substantial research  experience in an appropriate sector

Selection criteria

The project is competitively assessed and awarded. Selection is based on academic merit and suitability to the project as determined by the College.

Additional essential selection criteria specific to this project:

• Applicants must have knowledge and skills in the following areas:
• Shipping operations
• Artificial intelligence (AI) and machine learning (ML)
• International trade
• Freight operations
• Quantitative research and modelling

Additional desirable selection criteria specific to this project:

• A previous research qualification (Honours or Masters by Research) in a management discipline, preferably shipping, commerce or business.
• Experience in quantitative and qualitative research.
• Good knowledge of international shipping.
• Highly developed written and communication skills.
• Being familiar with AI/ML algorithms, data modelling, and optimisation techniques.

Application process

1. Select your project, and check that you meet the eligibility and selection criteria, including citizenship;
2. Contact Doctor Peter Fanam to discuss your suitability and the project's requirements; and
3. In your application:
   • Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
   • Submit a signed supervisory support form, a CV including contact details of 2 referees and your project research proposal.
4. Apply prior to 1 October 2026.

Full details of the application process can be found under the ' How to apply ' section of the Research Degrees website.

Following the closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email by the advertised outcome date.

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