Data-driven Optimisation
Boosting Interoperability & Supply Chain Resilience
Dynamic Network Configuration and Simulation
Developing a comprehensively integrated supply chain simulation solution with semantic modelling to analyse, design and optimise SCNs. This serves to deepen user understanding of supply networks, to strengthen impromptu decision-making capability in dynamic and disruptive scenarios, and to achieve efficient and accurate network configuration and production facility design and planning.
Flexible Order Management and Re-planning
Designing a Natural Language Processing (NLP)-supported interpretive order capturing application, a framework to enable end-to-end no-touch order processing and a order planning system supported by in-memory database technique for dynamic replanning, which can be applied to critical sites along the supply chain testbed to fortify supply chain planning capabilities.
Predictive Analytics in Supply and Demand Planning
Devising user-friendly engineering tools with Machine Learning/Deep Learning demand/sales forecasting models for existing and new products, paired with proper graphic user interfaces to aid end-users in minimising human intervention. The solution analyses big data captured on all nodes of the supply chain, enabling seamless production planning and scheduling, hence stabilising production.
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