Yaniv Proselkov

University of Cambridge | Cam ·  Institute for Manufacturing

Recent global disruptions, such as the COVID-19 pandemic and the ongoing geopolitical conflicts, have profoundly exposed vulnerabilities in traditional supply chains, requiring exploration of more resilient alternatives. Among various solution offerings, Autonomous supply chains (ASCs) have emerged as key enablers of increased integration and visib...

Recent global disruptions, such as the COVID-19 pandemic and the ongoing geopolitical conflicts, have profoundly exposed vulnerabilities in traditional supply chains, requiring exploration of more resilient alternatives. Among various solution offerings, Autonomous supply chains (ASCs) have emerged as key enablers of increased integration and visib...

Trade disruptions, the pandemic, and the Ukraine war over the past years have adversely affected global supply chains, revealing their vulnerability. Autonomous supply chains are an emerging topic that has gained attention in industry and academia as a means of increasing their monitoring and robustness. While many theoretical frameworks exist, the...

Tightening lending standards are motivating companies to adopt supply chain financing, with invoice backed lending to remedy financial stress. These financial objects depend on company-to-company relationships. The accumulation of these dyadic relationships creates complex supply network topologies. Companies within these networks are selfish and h...

Tightening lending standards are motivating companies to adopt supply chain financing , with invoice backed lending to remedy financial stress. These financial objects depend on company-to-company relationships. The accumulation of these dyadic relationships creates complex supply network topologies. Companies within these networks are selfish and...

Smart manufacturing uses data-driven solutions to improve performance and operations resilience, requiring large amounts of data delivered quickly, enabled by telecom networks. Disruptions can shut down a network; avoiding them needs responsiveness to network usage, achievable by embedding autonomy into the network with fast and scalable algorithms...

A network is a collection of nodes and edges, used ubiquitously to model real world systems. There exist network models that update node states in time, such as the voter model and the SIS model. When they also update edge configurations, they are called adaptive or co-evolutionary. These are both examples of network dynamics, and moment approximati...

Smart manufacturing uses advanced data-driven solutions to improve performance and operations resilience requiring large amounts of data delivered quickly, enabled by telecom networks and network elements such as routers or switches. Disruptions can render a network inoperable; avoiding them requires advanced responsiveness to network usage, achiev...

Smart manufacturing uses advanced data-driven solutions to improve performance and operations resilience requiring large amounts of data delivered quickly, enabled by telecom networks and network elements such as routers or switches. Disruptions can render a network inoperable; avoiding them requires advanced responsiveness to network usage, achiev...

Telecommunication networks are designed to route data along fixed pathways, and so have minimal reactivity to emergent loads. To service today’s increased data requirements, networks management must be revolutionised so as to proactively respond to anomalies quickly and efficiently. To equip the network with resilience, a distributed design calls f...

This paper proposes a framework to analyse traffic-data processes on a long-haul backbone infrastructure network providing internet services at a national level. This type of network requires low latency and fast speed, which means there is a large demand for research focusing on near real-time decision-making and resilience assessment. To this aim...