Current positions for postdoctoral research fellows and PhD students.
Research fellows
Opportunities upcoming.
PhD students
Supply chain quality control on the cloud with machine learning
This research is to develop and analyse process instrumentation and data collection for in-situ process conditions of equipment and workstations in a production supply chain, including alternative schemes for individual part coding and tracking. Integration with cloud based time series databases. Use with process simulation tools and uncertainty quantification studies, including variance based sensitivity analysis and Shapley values. Research alternative methods to identify defect and performance deviations patterns in the assimilated data. Exploration of machine learning classifiers suitable for datasets with few defects, including decision trees, oversampling techniques and gradient boosting. Study interactions among factors early in part fabrication of supplier operations and factors later in system assembly and operation.
Supervisors
The primary supervisor is Prof Kevin Otto with the research team of Prof.s Wen Li, Jo Staines, and Guilherme Tortorella.
Position Requirements
An Honours or Masters degree in engineering or computer science. Experience in machine learning, statistical quality control, or the industrial internet will be an advantage.
Contact
Prof Kevin Otto
kevin.otto@unimelb.edu.au
Probabilistic geometric form uncertainty optimisation
This research is to develop and analyse geometric form tolerances such as roundness, flatness, cylindricity, etc. and quantify the uncertainty suitable for forward and inverse probabilistic uncertainty quantification studies with sampling based methods, e.g., Monte-Carlo simulation. The focus is in particular for use in optimization studies to minimize form variation impact on system performance degradation. Exploration of alternative means to parameterise coordinate measuring machine datasets into useful geometric distribution functions including maximum likelihood estimation. Exploration of alternative means to use these results to compute optimised system designs with geometric uncertainties, including sample based methods as well as maximum probable point reliability optimisation methods.
Supervisors
The primary supervisor is Prof Kevin Otto with the research team of Prof.s Wen Li, Jo Staines, and Guilherme Tortorella.
Position Requirements
An Honours or Masters degree in engineering or computer science. Experience in machine learning, statistical quality control, or the industrial internet will be an advantage.
Contact
Prof Kevin Otto
kevin.otto@unimelb.edu.au
Supply chain resilience
Over the past decades, the globalisation of markets has increased the interrelationship between supply chains, tightly coupling their agents and entities (e.g., suppliers, customers, partners, distributors, manufacturers, and retailers). While this trend has generated significant competitive advantages and prosperity, it has also exposed supply chains vulnerability to disruptive events, such as natural disasters, economic crises, geopolitical conflicts and health crises. Those disruptions are unanticipated breakdowns that impact the normal flow of materials, information, and money within a supply chain, entailing operational and financial losses in organisations. Supply chain resilience addresses the supply chain's ability to cope with the consequences of unavoidable risk events to return to its original operations or move to a new, more desirable state after being disturbed. Resilient performance is an emergent property arising from interactions between elements that form a complex system, such as a supply chain. This project aims at investigating the different approaches for increasing supply chain resilience in face of severe disruptive events.
Supervisor
Dr Guilherme Tortorella
Position Requirements
An Honours or Masters degree in engineering or business management. Experience in supply chain management will be an advantage.
Contact
Sustainable assessment of critical metals for clean energy technologies
As a part of the sustainable development, the world is witnessing a rapid development of clean energy technologies which shifts the attention from fossil fuels to metals. Particularly, the renewable energy technologies (eg, solar, wind, etc), energy storage systems and e-mobility are largely dependent on critical metals which are sensitive to supply risks but also contribute to significant impacts through their life cycle. This project aims to develop a modelling platform to quantitatively analyse the material, resource and waste flows for the metals associated with clean energy technologies. This project will engage relevant industries in the analysis and provide the foundations for identifying key impacts and opportunities for improving the sustainable performance.
Supervisor
Dr Wen Li
Position requirements
An Honours or Masters degree in industrial or manufacturing engineering, mechanical engineering, chemical engineering, environmental engineering. Experience in life cycle assessment and material flow analysis will be an advantage.
Contact
Dr Wen Li
wen.li3@unimelb.edu.au
Digitalisation of manufacturing systems and supply chain
Manufacturing industry and its’ supply chain are exposed to a revolution of technological advances in cyber-physical systems, digital twin, industrial internet of things, cloud computing, blockchain, artificial intelligence, and machine learning. The potential of smart integration of these technologies is to vastly improve the operational efficiency and effectiveness of manufacturing activities and underpin a firm’s competitiveness and sustainability. Acquiring and monitoring sensors and collecting relevant data to derive meaningful analytics and optimal decisions poses multiple engineering challenges from sensor technology, analytic algorithms to data security and ownership, system integration, and policymaking. These challenges are further amplified when integrating to a legacy system or a multiparty supply chain. The project under this research theme will require you to engage with local or international industry partners to develop methods and solutions for the digitalisation of a manufacturing system or supply chain.
Supervisors
Dr Wen Li, Assoc Prof Jo Staines
Position requirements
An Honours or Masters degree in industrial or manufacturing engineering, mechanical engineering, mechatronics engineering, electrical engineering, computer science. Experience in descrete event simulation and modelling, industrial internet of things will be an advantage.
Contact
Dr Wen Li
wen.li3@unimelb.edu.au
Operational excellence in the Fourth Industrial Revolution era
Industry 4.0 (I4.0) is the new paradigm for factories of the future, inducing remarkable improvements due to changing operative framework conditions. I4.0 contributes to decentralised and simple structures over large and complex systems, while aiming for small and easily integrated modules with lower levels of complexity. From a business perspective, I4.0 has been claimed as an approach for significantly improving performance through automation and digitalisation. This performance improvement is enabled by higher levels of interconnectivity among people, products, processes, services and equipment, big data analytics, as well as both horizontal and vertical integration of value chains.
Complementarily, researchers have envisioned I4.0 as a strategic framework that provides competitive advantages through the enhancement of operational performance, such as cost reduction, quality improvement, higher customer satisfaction, and shorter lead times. Such performance improvement corroborates to the achievement of Operational Excellence (OE), which is the execution of the business strategy more consistently and reliably than the competition. OE’s scope goes beyond the traditional event-based model of improvement; it encompasses a long-term change in organisational culture. There are two main aspects that characterise companies in pursuit of OE: (i) systematic management of business and operational processes, and (ii) development of an organisational culture that supports the continuous improvement initiatives. OE is also denoted by an integrated performance across revenue, cost, and risk, focusing on meeting customer expectations through the continuous improvement of the operational processes and the culture of the organisation. This project aims at examining how the integration of novel digital technologies from I4.0 may impact the achievement of OE in organisations.
Supervisor
Dr Guilherme Tortorella
Position Requirements
An Honours or Masters degree in engineering or business management. Experience in supply chain management will be an advantage.