Overview
A global leader in chemical processing and industrial manufacturing, with a strong international footprint and multiple production sites worldwide, set out to transform its production ecosystem by adopting Industrial IoT (IIoT). The objective was to unify fragmented factory data, enable real-time analytics, and drive operational efficiency through AI-powered insights. Based on detailed use case documentation and architectural workshop findings, this reference architecture outlines a robust, scalable solution designed to integrate factory systems, deliver AI-supported insights in real time, and empower teams through self-service applications.
The solution leverages PTC’s ThingWorx suite—along with Microsoft Azure services and complementary technologies—to address key challenges in production, quality, and efficiency across engineering, manufacturing, and operations.
About Beyond the Pilot series
Use Case
A. Engineering – Process Optimization & Quality Control
Problem: Resolving Data Integration & Visibility Challenges
Customer’s engineering teams struggled with fragmented data across various factory systems, limiting their ability to analyze process performance and optimize production parameters. Without a unified data platform, engineers could not effectively compare historical and real-time machine center lining values, making it difficult to maintain consistent production quality.
Solution: Unified Data Integration & Advanced Process Analytics
The reference architecture establishes a central, cloud-based data platform that aggregates and correlates machine data from various sources in real time. By integrating OPC Aggregators and Kepware with Azure IoT Hub, factory data is ingested, processed, and made accessible via ThingWorx applications. Engineers can now visualize mechanical and digital process values, set dynamic thresholds, and receive alerts when deviations occur—ensuring precise process control and quality optimization.
Role of PTC Products:
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PTC Kepware: Standardizes and integrates machine data from disparate factory systems, ensuring a seamless flow of real-time process variables.
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ThingWorx Platform: Provides a robust dashboard for analyzing centerlining data, visualizing production trends, and enabling data-driven decision-making.
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ThingWorx Digital Performance Management (DPM): Automates the identification of process inefficiencies, allowing engineers to fine-tune machine settings dynamically.
B. Manufacturing – Scrap Reduction & Production Efficiency
Problem: Enhancing Scalability and Reducing Operational Inefficiencies
Customer faced challenges in scaling its IIoT solution as new sensors and data sources were introduced. Traditional systems struggled with the increased volume of factory data, leading to slow system response times and ineffective real-time analytics. Additionally, manual process adjustments resulted in inconsistencies, contributing to increased scrap rates and wasted materials.
Solution: Cloud-Scalable Infrastructure with Real-Time Process Optimization
To address these issues, the architecture leverages Azure IoT Hub, Azure Data Explorer (ADX), and Influx DB to handle massive data streams and provide low-latency analytics. This ensures that production trends, environmental conditions, and machine parameters are continuously monitored and optimized in real time. Advanced machine learning models predict process inefficiencies, enabling operators to make automatic adjustments to reduce scrap and optimize yield.
Role of PTC Products:
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ThingWorx Platform: Acts as the central command hub, enabling real-time decision-making based on factory data trends.
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ThingWorx Digital Performance Management (DPM): Uses historical data to provide AI-supported recommendations for reducing material waste and improving overall equipment effectiveness (OEE).
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PTC Kepware: Ensures reliable, high-speed data acquisition from sensors, production lines, and environmental monitoring systems, feeding critical information into ThingWorx for optimization.
C. Driving Digital Transformation & Quality Optimization
Problem: Lack of Digital Process Automation & AI-Powered Decision Making
Customer’s previous factory systems relied on manual reporting and fixed thresholds for process control, limiting the ability to detect and respond to process inefficiencies in real time. Operators needed a system that could provide intelligent, self-service applications with AI-driven recommendations for optimal production performance.
Solution: AI-Driven Automation & Dynamic Quality Control
The IIoT architecture integrates AI-powered predictive analytics to analyze deviations in real-time and suggest automatic machine adjustments. Real-time applications, customizable process recipes, and dynamic alerting systems empower production teams with actionable insights. By embedding self-service applications in ThingWorx, engineers and operators can fine-tune process settings and receive automated recommendations for improving quality and efficiency.
Role of PTC Products:
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ThingWorx Platform: Serves as the central analytics hub, delivering AI-powered insights for continuous process improvement.
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ThingWorx DPM: Uses machine learning to correlate scrap rates with process variables, recommending changes that minimize waste and enhance quality.
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PTC Kepware: Captures real-time process data, ensuring that AI models receive accurate inputs for predictive analysis.
Customer’s digital transformation journey is now backed by a robust, PTC-powered IIoT ecosystem that delivers continuous improvement, higher production efficiency, and proactive maintenance capabilities—ultimately driving the future of smart manufacturing.
Technical Architecture and Implementation Details
This section combines detailed technical descriptions with the overall reference architecture. It describes the core components, integration points, and implementation strategies that deliver a robust IIoT solution for the customer.
A. Architecture Overview Diagram
B. Detailed Technical Components
|
Component |
Role |
Key Features |
|---|---|---|
|
OPC Aggregators & Kepware |
Stream and bridge machine data from production, DEV, and QA environments to Azure IoT Hub for real-time processing in ThingWorx. |
Scalable ingestion; latency monitoring; secure device connectivity; segregated closed environments for DEV/QA. |
|
Azure IoT Hub |
Ingests and secures machine telemetry data for analytics. |
Centralized data ingestion; integration with Azure services. |
|
ThingWorx on VMs |
Hosts the core IIoT application that processes data, provides end-user applications, and manages workflows. |
High performance; disaster recovery via VM snapshots; enhanced security through Azure AD integration and SSL support. |
|
Managed PostgreSQL |
Provides high availability for persistent application data through replication and failover. |
Data redundancy; managed service benefits; automated backup and recovery. |
|
Azure Data Explorer / Influx DB |
Handles advanced analytics, timeseries visualization, and predictive insights for telemetry data. |
Real-time analytics; anomaly detection; cost-effective long-term storage. |
|
Monitoring & Logging Tools |
Ensure comprehensive observability and prompt incident response across all components. |
Real-time applications monitoring; alerting; centralized log aggregation. |
|
RESTful APIs |
Enable seamless integration with ERP systems, legacy data sources, and other IoT devices. |
Secure data exchange; standardized connectivity protocols. |
C. User Personas
The success of this solution relies on a well-defined team of technical experts responsible for deployment and ongoing management:
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Persona |
Key Responsibilities |
|---|---|
|
Plant Manager |
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Digital Transformation Lead |
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Operations Manager |
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Quality Assurance Engineer |
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Maintenance Engineer |
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Software Engineer |
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Cloud Architect |
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Security Analyst |
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DevOps Engineer |
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NOTE : Although these personas were required, the needs were fulfilled by a team of only 4–5 developers effectively playing multiple roles.
Outcome
Optimized Production Efficiency
By unifying machine telemetry, process parameters, and historical trends, customer empowers engineers with real-time insights. AI-driven recommendations and automated adjustments replace trial-and-error, enabling precise, dynamic optimizations. Bottlenecks and inefficiencies are identified instantly, allowing rapid corrective actions for peak performance.
Reduced Waste & Enhanced Quality
Real-time process optimization and automated quality control significantly reduce material waste and variability. The system detects deviations at the source, enabling instant adjustments and ensuring consistent product quality, minimizing scrap, rework, and compliance risks.
Seamless Data Visibility & Collaboration
A centralized dashboard provides real-time access to critical metrics, eliminating fragmented reports and delays. Engineers and operators can compare production data across sites, standardize best practices, and drive continuous improvements across the network.
Future-Ready Innovation
Beyond immediate gains, this IIoT transformation lays the foundation for scalable sensor integration, AI-driven automation, and advanced predictive analytics. It’s not just a solution for today—it’s a long-term framework for sustained digital innovation in smart manufacturing.
This reference architecture is not just about solving today’s challenges—it establishes a long-term, adaptive framework that will continue to evolve, enabling our customer to remain at the forefront of smart manufacturing and industrial digitalization.
Additional Information
This section provides further insights into the project implementation and future strategic direction.
|
Parameter |
Description |
Example/Notes |
|---|---|---|
|
Time to First Go-Live |
Estimated duration from project initiation to initial production deployment. |
Approximately 16 weeks |
|
Partner Involvement |
Key strategic and technical partners collaborating on the deployment. |
Microsoft, Ansys, and Deloitte were supporting the digital transformation initiative centered around ThingWorx. |
|
Customer Roadmap |
Future enhancements planned by customer, such as AI-based predictive analytics and further automation. |
An expansion to incorporate AI and advanced machine learning–driven insights is planned |
Vineet Khokhar
Principal Product Manager, IoT Security
Disclaimer: These reference architectures will be based on real-world implementation; however, specific customer details and proprietary information will be omitted or generalized to maintain confidentiality.
Stay tuned for more updates, and as always, in case of issues, feel free to reach out to <support.ptc.com>
