Developing and implementing a collaborative Data Control Tower (DCT) system for Dynamic MRO Supply Network Optimisation

This project will develop through conception, test and implementation of a digitally-enabled DCT during the project period,
with the following objectives:

- Digitalising MRO inventory data capturing Diageo stock information with that of Entec and its sub-suppliers. This
addresses the challenge of MRO data inconsistency, enabling dynamic forecasting, modelling, and least-cost routing.

- Establishing new datasets of machinery performance and operating environment parameters which will address the
challenge in spares' demand forecasting that currently relies on ad-hoc and intuitive demand. This is the key step towards
reducing overstock and waste.

- Developing a three-tier multi-node network modelling and optimisation solution with consideration of additive
manufacturing - 3D printed spares/parts as possible substitution. This is the key step towards inventory leveraging and
optimal logistics, reducing transportation distance, costs, and carbon emission.

- Developing a data-driven decision support tool: A Control Tower for the MRO network, allowing real-time visibility and
rational/proactive decision-making. This addresses the challenge of intuitive and reactive decision making.

- Implementing a new MRO industrial supply practice by reconfiguring MRO process and systems, this is the key step to
achieve transformation of non-core MRO procurement to integrated services.

This project will develop and implement a DCT to manage the supply of services and spare parts optimally. It will collate real-time data across multiple plants, with service support and supply data from multiple UK and global spares manufacturing,
distribution and stock sources.

Optimisation network model, intelligent multi-criteria decision making