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" [...]
RAG-Based AI in Manufacturing: Turning Maintenance Data Into Downtime Reduction 3 min read ● Silk Team Unplanned shutdowns continue to be among the biggest cost challenges faced by manufacturing today. Although numerous investments have been made in CMMS platforms, IoT sensors, and predictive maintenance tools, many plants are still unable to effectively utilize the data [...]
Calculating the Real ROI of RAG and LLM Models for Industrial Distribution 3 min read ● Silk Team Many industrial distributors face increasing challenges to meet customers' growing demands for speed and accuracy while managing large product portfolios and supporting increasingly technical buyers' purchasing processes. With this comes increased interest in Large Language Models (LLMs) [...]
Where LLMs Drive Measurable Value Across Manufacturing and Distribution Operations 3 min read ● Silk Team Large Language Models (LLMs) are now everywhere in discussions about manufacturing and distribution; however, actual operational value is quite selective compared to all of the hype that surrounds them. A chatbot alone will not cause KPI improvements. No LLM [...]
Scaling AI in Manufacturing Safely: How RAG Models Reduce Risk and Control Cost 3 min read ● Silk Team Many manufacturers want to take advantage of the power of AI, but most experience the same issue once they move beyond pilot projects. As AI enters production environments, concerns around risk, cost, and control increase rapidly. [...]
