Data driven evaluation of supply chain performance

Supply chain (SC) is one of the most important concepts proposed and applied in the production/manufacturing in the last few decades [1]. SC is characterised by many parameters related to its structure, such as suppliers, production/manufacturing and warehouses, locations and capacities of these facilities, various types of cost, such as

inventory, production and transportation cost, SC demand, and many others. Different SC performance measurements and metrics have been defined to evaluate SC effectiveness and efficiency. Typically, these measures consider cost incurred to deliver order to customers, time required to deliver orders from supply of raw materials to delivery of products to customers, quality of the delivered products and others.

Nowadays, organisations are obtaining growing amount of information of various types that can be used to enhance their decision making. There are different data analytics methods that have been applied to a variety of business problems including Supply Chain Management. However, possible applications of data analytics methods to Supply Chain Management problems have not been fully explored and utilised yet. [2, 3].

This project concerns development of a framework of Data Analytics methods that can be used to identify and analyse relationships between different SC parameters and SC performance measurement. Furthermore, Data Analytics methods will be developed to evaluate the impact of different SC strategies, such as Lean, Agile, Sustainable, or Resilient SC strategies on SC performance measures.

The successful candidate will develop novel Data Analytics methods to identify and explore relationships between SC parameters, SC strategies and SC performance measures.

The candidate will gain valuable multidisciplinary skills in the area of Data Analytics methods and their application to SC management, strategies and performance measures.

The successful candidate will be based in Nottingham Business School, Nottingham Trent University, which has achieved an established track record in research and has earned a reputation for solving real-world problems in a wide range of industrial sectors.

A data mining-based framework for supply chain risk management manufacturing company that is involved in supply chains can be contacted if needed, to inform the development of data analytics methods.

[1] S. Chopra, P. Meindl P, (2001), Supply chain management: strategy, planning, and operation, Prentice Hall, London, 2001.

[2] M. E. Karaa, S. U. O. Fırata, A. Ghadgeb, (2020), A data mining-based framework for supply chain risk management, Computers & Industrial Engineering, 139, 105570.

[3] D. Bechtsis, N. Tsolakis, E. Iakovoud and D. Vlachos, (2021) International Journal of Production Research, https://doi.org/10.1080/00207543.2021.1957506

Entrants must have a Master’s degree  in computer science, mathematics, operations management, operational research, or a related discipline; interest in Data Analytics, SC management as well as good programming skills are advantages.

This is a self-funded PhD project for UK and International applicants.