AI in Supply Chain
-
AI‑Driven Risk & Resilience: Building Antifragile Supply Chains
AI‑DriAI‑Driven Risk & Resilience: Building Antifragile Supply Chains Context Supply‑chain risk is no longer an abstract threat managed by a handful of specialists. After a string of pandemics, port closures, tariffs and wars, resilience now sits on the CEO’s agenda. Yet despite years of investment in digital tools, vulnerability remains high. An article from Everstream Continue reading
-
AI‑Enabled Cybersecurity: Safeguarding Supply‑Chain Networks from Emerging Threats
As global supply chains become increasingly digitized and autonomous, they are facing a new kind of vulnerability: cyber threats. From port terminals and warehouse management systems to vendor portals and logistics bots, every node of the network is now a potential entry point for malicious actors. A recent Accenture study found that only 36 % of Continue reading
-
AI-Powered Sustainability and Circularity: Building the Supply Chains of Tomorrow
Building Sustainable and Circular Supply Chains with AI Context Sustainability has moved from a corporate social responsibility initiative to a strategic imperative for supply‑chain leaders. Governments are rolling out stricter emissions and waste regulations, investors are scrutinising environmental, social and governance (ESG) metrics, and consumers demand transparency on sourcing and lifecycle impacts. However, most supply chains Continue reading
-
AI-Driven Demand Sensing: Lessons from Unilever and Amazon for the Supply Chain
Context Traditional demand planning in supply chains relies heavily on historical sales data and periodic forecasting. While this approach worked reasonably well in stable markets, the pandemic, climate volatility, geopolitical disruptions and rapidly shifting consumer preferences have exposed its limitations. Demand now swings unpredictably, influenced by social media trends, extreme weather events, promotional campaigns and Continue reading
-
Edge AI and IoT: Decentralised Intelligence for Resilient Logistics
Context Edge computing moves data processing and analytics closer to where data is generated. In traditional logistics systems, sensor readings from vehicles, containers or production lines are sent to a central cloud, analysed there and then returned to the edge. This model works when connectivity is reliable and timing is not critical. However, in remote Continue reading
-
AI -Powered Digital Twins: Transforming Scenario Planning and Resilience
Context Digital twin technology creates a virtual replica of physical assets, processes and entire supply chains. These dynamic models mirror operations in real time by ingesting data from sensors, IoT devices, enterprise systems and external sources. The concept originated in engineering and aerospace but has rapidly expanded into manufacturing, logistics and retail. As supply chains Continue reading
-
Generative‑AI Network Design: Optimising Supply Chains for Resilience and Efficiency
Context Supply‑chain network design has always been a balancing act between cost, service and risk. Companies decide where to locate factories, distribution centres and cross‑docking facilities, which transportation modes to use, and how to allocate inventory and production across multiple nodes. These decisions drive billions of dollars of capital spending and determine how quickly and Continue reading
-
Autonomous Trucking and the AI‑Powered Logistics Revolution
Context Autonomous trucks – self‑driving vehicles equipped with LiDAR, radar, cameras and AI‑driven control systems – have graduated from science fiction to reality. Early pilots began hauling freight across the United States a few years ago, and by 2025 the global logistics industry is treating driverless vehicles as a cornerstone of its future. A recent Continue reading
-
Automating Supply‑Chain Communications with AI Bots: 24/7 Service and Real‑Time Coordination
Context Global supply chains operate across time zones, languages and cultures. Businesses rely on timely communication to coordinate orders, schedule pick‑ups and deliveries, share documentation and solve problems. Yet traditional communication methods—email threads, phone calls and manual status updates—struggle to keep pace with the speed and complexity of modern logistics. When an order is delayed Continue reading
-
Why Predictive Analytics Is the Backbone of Future Supply‑Chains
Context: A Volatile Supply‑Chain Landscape Over the past few years the global supply‑chain has moved from a relatively predictable environment to one characterised by volatile demand, geopolitical tensions and rapid technological change. Port closures during the pandemic, shifting consumer preferences and trade policy shocks have revealed how fragile traditional planning practices are. In response, businesses Continue reading
AI in the Chain 