利用人工智能预测分析推动E＆U的供应链弹性

The energy and utilities supply chain faces unprecedented pressure as global energy demand increases rapidly. Howcan companies build supply chains that don’t just recover from disruption, but anticipate and outperform it? The answer lies inAI-driven predictive analytics. With it, energy and utilities can build supply chain resilienceand reduce costs by forecasting risks, predicting material requirements, and proactively planning sourcing and This paper explores the transformative power of predictive analytics in anticipating demand shifts, supply risks, andasset failures that drive outage-related material needs. Beyond risk detection, it offers a roadmap for optimizingsourcing strategies, supplier management, and project delivery through smarter planning and proactive procurement Why Supply Chains Need Data-Driven Resilience Traditional mitigation strategies, such as expedited orders, alternate vendors, and inflated buffer stocks, offer onlytemporary relief. To add to this, they often increase long-term inefficiencies and tie up capital in non-productive inventory. With rising global energy demand, the need for grid modernization materials such as transformers, relays, andconductors has surged. However, supply remains constrained by long lead times, rising costs, labor shortages, and Predictive Analytics Predictive analytics is a subset of AI that uses historical and real-time data to predict future outcomes.Unlike traditional analytics, which focuses on descriptive or past performance, predictive analytics Techniques include data mining, data modeling, machinelearning (ML), and artificial intelligence (AI). Data •External sources:Weather forecasts, tariff changes,supplier financials, port congestion alerts, and •Internal sources:Historical procurement data, stocklevels, asset health metrics (SCADA, ERP), and By combining these datasets, utilities can forecastmaterial requirements, evaluate supplier reliability, andforesee disruptions before they ripple through the grid- Figure 2: Illustrates the key technologies that utilities are deploying or planning to enhance supplychain operations. These predictive capabilities form the analytical foundation for supply chain resilience, enabling it to anticipate,absorb, and adapt to disruptions. Next, let’s explore how predictive intelligence translates into action across planning, sourcing, and asset management. Supply Chain Resilience in Transmission &Distribution (T&D) In the T&D sector, supply chain resilience refers to the system’s ability to anticipate, absorb, adapt to, and recoverfrom disruptions while continuing to support critical infrastructure projects and grid reliability. Given the capital-intensive nature of T&D and the long lead times for procuring key equipment such as transformersand breakers, every delay ripples across grid modernization timelines and customer commitments. The Five Key Dimensions of T&DSupply Chain Resilience 1. Agility:Rapid adaptation to shifts in demand,delivery schedules, or sourcing strategies 5. Asset Failure Management:Leveraging predictiveanalytics to anticipate equipment degradation andfailures, enabling proactive procurement of spares, 2. Robustness:Flexibility through alternatesuppliers and scenario-based planning 3. Visibility:End-to-end transparency acrosssuppliers, inventory, and logistics These capabilities shift utilities from reactive recoverytopredictive resilience. By anticipating disruptions,impacts are minimized, and system reliability improves 4. Collaboration:Seamless coordination betweenprocurement, engineering, and supplier networks Predicting and Preventing Asset Failures with AI Utility infrastructure assets, from transformers and relays to circuit breakers and inverters, operate under demandingelectrical, thermal, and environmental conditions. As these pressures build over time, the risk of failure increases.Predictive analytics applies artificial intelligence and sensor data to detect early warning signs, forecast remaining Why Assets Fail Equipment failures in transmission and distribution networks arise from multiple interacting causes: 1. Electrical Stresses and Faults:Lightning strikes,switching surges, and overvoltage events damage 5. Human or Process Errors:Deferred maintenance,incorrect switching, and incomplete documentation 6. Cyber and Software Risks:Increasing digitalizationexposes remote-managed assets (relays, inverters, 2. Thermal Overload and Cooling Loss:Overheatingfrom blocked or failed cooling systems accelerates 3. Aging and Material Degradation:Moisture ingress,dielectric breakdown, and oil contamination weaken Each of these failures produces measurable data signals,gas levels, temperature rises, acoustic emissions,vibration profiles, or firmware anomalies that AI models 4. Mechanical and Manufacturing Defects:Vibration,fatigue, or poor assembly introduces progressive How Failures Are Predicted Predictive approaches combine sensor data, physics-bas