PhD Studentship in a decision support system for additive manufacturing adoption to enhance the resilience of supply chains

To address this gap, we are seeking a highly motivated and enthusiastic candidate for a PhD studentship to work on the development of a Decision Support System (DSS) tool that incorporates multiple inventory management policies and an optimization procedure for adopting AM in SC systems. The proposed study aims to develop a comprehensive and user-friendly DSS tool that incorporates both make-to-stock (MTS) and make-to-order (MTO) policies and considers long-term cost-benefit and customer satisfaction when evaluating the impact of AM on SC performance. By providing a comprehensive and user-friendly DSS, this research will enable businesses to make informed decisions about whether to adopt AM and how best to integrate it into their SC systems.

The focus of this project is to develop a DSS that facilitates the AM adoption decisions which also takes into account the potential effects of AM on SC design. The project will examine diverse input factors, including but not limited to 3D printing time, quantity of 3D printers, unit production, and overall procurement costs of 3D printers. In addition, utilization policies of 3D printers within SC such as make-to-stock or make-to-order production policies, will be analysed. Hence, the main goal is to create a tool that can assist SC managers in making informed decisions about the most suitable SC design for the adoption of AM under a given set of those input data, with the aim of improving SC resilience and costs.

This research project will be conducted at Cranfield University, in the Logistics and Supply Chain Management group, which is a world-renowned institution in the field of SC and manufacturing. The project is sponsored by the Cranfield University, which has a strong interest in advancing the SC resilience. The university will be providing funding, expertise, and access to equipment to support the project. The project will be supervised by experienced researchers in the field of AM, DSS and SC design who will provide guidance and support to the student throughout the project.

The student involved in this project will gain valuable experience in AM, DSS, SC design, inventory optimisation and data analysis. They will learn how to apply their knowledge to real-world problems and work on a project with significant impact in the resilient SC design. The modelling, optimisation and simulation experience gained through the project will enhance the student's skills and ability to work effectively in a team of business environment. Additionally, the student will have the opportunity to work with experts in the field, attend industry conferences and training sessions, and gain exposure to cutting-edge technology and research facilities such as laboratories, libraries, etc. The experience gained from this project will equip the student with transferable skills, such as problem-solving, critical thinking, and project management, which will be highly valuable in their future career in the manufacturing or engineering industry.

The successful candidate will have a background in Industrial Engineering, Operations Management, or a related field with experience in optimization and simulation techniques and programming. Strong analytical and problem-solving skills, as well as excellent written and verbal communication skills, are essential. Experience with DSS, inventory and SC management, and additive manufacturing will be an added advantage.