Overview:
Sight Machine, deployed on Microsoft Azure, provides a unified manufacturing data platform that aggregates, contextualizes, and harmonizes data from across a factory’s machines, lines, and processes. For Intertape Polymer Group (IPG), this enables them to transform previously siloed or under-utilized industrial data into actionable, real-time insights — shifting from historical reporting to real-time operational intelligence.
Building on that data foundation, Sight Machine introduced a generative-AI interface, Factory CoPilot, which uses natural-language queries to let non-technical users (shop-floor operators, plant managers, engineers) interrogate data and get human-readable insights. This democratizes access to analytics across all levels of the organization and helps resolve manufacturing issues such as downtime, quality, and throughput, faster and more broadly than before.
Key Features:
Ingests and unifies data from all factory machines, lines, and processes into a common data layer on Azure.
Contextualizes and stores manufacturing-data so that it can be analyzed end-to-end across the full production pipeline (raw material → finished product).
Provides a natural-language interface (Factory CoPilot) that allows users to ask questions in plain English — e.g., “Why did machine X stop?” — and immediately receive actionable answers.
Runs ML and statistical models behind the scenes to produce validated, audit-ready analytics and insights — without manual spreadsheet work.
Automates reporting and trend summarization, for example generating morning-meeting reports or daily/weekly summary dashboards.
Results & Impact:
Reduced onboarding time for the manufacturing data platform (MDP) by up to 50%.
Increased weekly average usage of MDP by 25%, indicating higher adoption and broader utilization across roles.
Lowered friction for non-expert users (operators, floor staff) to access and use manufacturing data — enabling broader data-driven decision-making across the plant.
Enabled faster, data-backed decisions on real-time operations — including identifying downtime causes, quality anomalies, and production inefficiencies — instead of relying on manual reports or spreadsheets.
Strengthened overall operational resilience and agility by making data accessible end-to-end, supporting continuous improvement and sustainable manufacturing operations.
AI Technology:
AI Model Types: Generative AI (via Azure OpenAI Service / GPT-4), statistical / ML models for manufacturing analytics
AI Purpose: Summarize data; Answer natural-language queries; Provide decision-support insights; Automate report generation
Application Type: Operations / Manufacturing (shop-floor analytics, operations management, quality control)
Target Users:
Plant Managers
Production / Operations Engineers
Shop-Floor Operators
Maintenance Teams
Quality Control / Assurance Staff
Senior Management / Executives (for dashboards and operational oversight)
Sources:
https://www.microsoft.com/en/customers/story/1698365434338807911-ipg-azure-ai-customer-story