Advanced Analytics for Resource Efficient Supply Chain

Motivation: Scarcity of resources including water and energy, is an increasingly important concern for our economy, industries and government. The reports by numerous industries and the government (The EU, the United Nations and the UK) have raised their growing concern over the short term use of natural resources and its implications for economy, society and firms. Scientists and researchers from material sciences, environmental sciences and manufacturing engineering have conducted research on resource efficiency for decades, but these research are conducted in isolation. There is a greater need than ever before to address resource efficiency challenges considering the supply chain management aspects. As the majority of resource usage and outputs are originated from end-to-end supply chain operations, research in this area offers significant opportunities for increasing resource efficiency. The increased attention creates an urgent need to measure, analyse and improve the resource efficiency of a supply chain taking into account the environmental sciences approaches including life cycle analysis. A significant improvement could be achieved in the medium term by implementing resource efficient supply chain strategies throughout the end-to-end supply chain.

Challenges: Given the potential impact of supply chain decision-making on resource efficiency, there is a need for research that develops and implements advanced operations research based modelling tools and techniques to enhance resource efficiency of real-world supply chains. Current approaches for this purpose are limited by (i) lack of understanding or a unified framework for the integrated modelling of energy efficiency; (ii) lack of analytical ability to deal with diverse sources of structured and unstructured data: (iii) lack of tools and techniques for data-driven resource efficient decision making. Given the gaps identified in scientific research and known industrial needs, the PhD project will develop novel analytical models, methods and techniques and a management toolkit that will help companies to monitor, manage, optimise and significantly reduce the energy efficiency of their supply chains and increase their resource efficiency.

Objectives: The interdisciplinary PhD project will focus on integrating some of the engineering and management areas including supply chain management, Energy and water management, life-cycle assessment. More specifically, the project has the following research objectives:

1 Develop and apply methods and OR based tools to assess resources (Water and Energy) usage in the context of overall supply chain configuration and logistics implications.
2 Develop a Resource Efficiency Optimiser (CEO) toolset for multi-objective optimisation incorporating self-healing evolutionary algorithms and life cycle assessment integrated with closed loop supply chain modelling that defined KPIs in areas such as networks design, logistics planning and vehicle routeing.
3 Develop operations research based quantitative models to inform supply chain managers and policymakers on how government environmental policies affect resource-efficient supply chain decision making.

This PhD project will promote the advancement of the state of the art in the related areas Mathematical modelling and optimisation, energy modelling and management, integration of life cycle assessment in a novel area, big data analytics and supply chain management. The special emphasis will be placed on the application in industries and impact.

Industrial support: Carbon Trust in the UK has been very supportive of the proposed research, and they will provide access to data and in kind support (the time of their professional at the development and implementation stage). If needed, a letter of support can be provided upon request.