Why Traditional Supply Chains Fail: Closing the Gaps with AI in 2026 3 min read ● Silk Team In the world of manufacturing in 2026, the “Butterfly Effect” happens on a daily basis. A small labor dispute with a supplier in a Tier-3 electronic components factory can be felt thousands of miles away and bring [...]
AI for Supply Chain Risk Management: Predict Disruptions Before They Hit 3 min read ● Silk Team Risk management has changed dramatically since the manufacturing world of 2026 became a high-stakes game. As global networks continue to grow and become increasingly unstable, the days of manual audits and quarterly supplier reviews have come to an [...]
Predictive vs. Prescriptive Maintenance: ROI Comparison for 2026 Manufacturers 3 min read ● Silk Team In this era of 2026 manufacturing, the “industry 4.0” gold standard has evolved from simply being able to predict (Predictive Maintenance, PdM) when a part may fail using sensors and Artificial Intelligence, to being able to tell you exactly how [...]
Beyond Predictive Maintenance: Reducing Downtime with Prescriptive AI 3 min read ● Silk Team Predictive Maintenance - A Once-High Point of Industry 4.0 Has Become the Baseline in 2026 In 2026, many manufacturers have made significant progress toward the goal of being able to predict impending failures based upon data collected through sensors. While predicting [...]
How AI Transforms Maintenance Data Into Actionable Decisions 3 min read ● Silk Team Maintenance groups, while having plenty of data, face the problem of taking that data and transforming it into decisions that will help minimize downtime, lower costs, and increase asset reliability. It's in this area that AI has the potential to completely [...]
How RAG-Powered AI Improves Supply Chain Resilience for Distributors and Manufacturers 3 min read ● Silk Team Disruptions never occur due to a lack of data; rather they occur due to fragmented knowledge, slow analysis and inadequate context. RAG-powered AI combines enterprise data retrieval and large language models to provide an AI system which produces [...]
Using LLMs to Surface Supply Chain Risks Hidden in ERP and Operations Data 3 min read ● Silk Team While many supply chain disruptions are visible through the use of dashboards and red flags, other disruptions lie hidden in the data found in ERP systems, operational logs, supplier notes, exception reports, and internal e-mail communication. [...]
Why Traditional Supply Chain Analytics Fall Short — and How RAG Models Fill the Gaps 3 min read ● Silk Team Analytics for supply chains has grown significantly in the past 10 years. We have much richer dashboards, much more accurate KPI's, and so much more data available than ever before. However, we still see [...]
Prescriptive vs. Predictive Maintenance: Bridging the Gap with RAG Models 3 min read ● Silk Team For years, the industrial operation world has been using predictive maintenance (PdM). PdM uses sensor data and machine learning to predict when a component will be down. A prediction is only a heads up. What really matters in terms [...]
How LLMs Connect Maintenance Logs, ERP Data, and Manuals for Smarter Planning 3 min read ● Silk Team Maintenance logs are usually unorganized. They include technician jargon, ambiguous nomenclature, and various degrees of detail. 1. Maintenance History: CMMS Logs The LLM Function: LLMs utilize semantic processing to standardize this data. They comprehend that "hous vibration" [...]
