Strategic Supply Chain Design and Optimisation for Advanced Manufacturing

Location: Southbank, Melbourne VIC

Duration: 5 months

Proposed start date: 1 July 2018

Keywords: Computer Science, Mathematical Modelling, Optimization, Advanced Manufacturing, Engineering, Software Development, Programming, Data manipulation & processing

Please note: Due to funding requirements students must have Australian Citizenship or Permanent Residency to apply. Applicants not meeting this requirement will be ineligible for the project.

Project Background

Opturion develops decision support software for industry. In manufacturing, an end product can potentially be partially made at different sites. Opturion already provides a number of optimisation tools to the manufacturing industry, including software to optimise transport (inbound and outbound), warehouse layout, workforce management, and production scheduling.

The aim of this project is to look at a combination of the above: given a set of customer orders with delivery due dates and locations and a set of manufacturing facilities with varying capacities, what path should each order take through the supply chain so that cost is minimal? The answer to this question depends on the following:

  • Time considerations: given the due date and working backwards, how long does it take to transport (for each transport option, e.g. shipping, rail, road, air freight), how long does it take to produce the finished goods or the next level of Work in Progress (WIP)
  • Capabilities: what types of products can be made at which facility, what machines are available, at what rate do they produce, how much staff is available
  • Cost factors: what is the cost of transport for each mode, what are the labour costs, electricity costs, etc.; in particular, when choosing between local and overseas manufacturing

Opturion aim to create a tool to help organisations answer this question, at different levels of granularity: long term to strategically determine the capabilities to maintain at each site, medium term to develop a master plan outlining what gets produced where, and short term to create an operational day-to-day plan that includes production scheduling and staff allocation.

Research to be Conducted

Working mainly with one a client who has manufacturing sites in Australia (Melbourne), New Zealand and China, as well as customers in all three areas, the intern will receive data on orders to be produced, the different stages of WIP, capabilities, costs, and transport options. This data will be used primarily to create a high-level model of the supply chain network and a master production schedule. The model will make abstraction of aspects such as the setup time between different products on a machine, or the last mile delivery schedule for delivering the end product to the customer. When combined with sales forecasts the model will be used to create a short to medium term master plan.

Due to the time it takes to ship goods from overseas (e.g. China to Australia) and often short lead times, it may be necessary to produce and ship before a product is even ordered. This creates a risk if the order does not eventuate. The second objective of this project is to use this model for ‘what-if’ scenarios, e.g. what if we are more pro-active in producing before the orders are in? What if we move a machine/change our staffing levels?

Finally, the intern will delve deeper into the operational aspects of the supply chain. There are steps in a production process for which a more fine-grained decision support is useful, for example, taking into account changeover times between different products. While Opturion has created a number of production scheduling tools, they are mainly used to plan week-by-week and thus are not always able to react to quickly changing circumstances (delays, breakdowns, order move-ups, etc.). The intern will investigate how to use existing tools to alter a plan in near real-time in an operational setting. In order to achieve these objectives the intern will be provided with access to Opturion’s optimisation platform, will receive day-to-day guidance and software development support, as well as access to data in collaboration with clients.

Skills Required

Opturion are looking for a PhD student with the following skills:


  • Undergraduate in Mathematics, Computer Science, Engineering or STEM disciplines
  • Highly proficient using Excel for data manipulation and interchange, and Java for processing


  • Experience in Mathematical Modelling
  • Experience in Programming and software development

Expected Outcomes

There are three key outcomes of this research project. The first is a prototype optimisation model, which will function as a proof of concept. The second is to create a set of scenarios to demonstrate how the model can be used to answer ‘what-if’ questions. The final outcome is to produce a set of recommended operating parameters that allow the use of short to medium term planning tools for on-the-day operational planning in a highly dynamic setting. The Intern is expected to produce a final presentation and report summarising the research outcomes. As an educational exercise and with the support of their Industry Supervisor, the Intern may extend their presentation to provide high-level recommendations to management.

Additional Details

The intern will receive $3,000 per month of the internship, usually in the form of stipend payments.

It is expected that the intern will primarily undertake this research project during regular business hours, spending at least 80% of their time on-site with the industry partner.  The intern will be expected to maintain contact with their academic mentor throughout the internship either through face-to-face or phone meetings as appropriate. The intern and their academic mentor will have the opportunity to negotiate the project’s scope, milestones and timeline during the project planning stage.

To participate in the APR.Intern program, all applicants must satisfy the following criteria:

  • Be a PhD student currently enrolled at an Australian university
  • PhD candidature must be confirmed
  • Applicants must have the written approval of their Principal Supervisor to undertake the internship. This approval must be submitted at the time of application.
  • Internships are also subject to any requirements stipulated by the student’s and the academic mentor’s university
Applications Close

 3 June 2018


INT – 0433