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Edge Computing: Preprocessing Data for Efficient Integration with ERP & MES Systems – Part 2

Digital Transformation
April 16, 2024

With the advent of Industry 4.0, modern manufacturing facilities generate an enormous amount of data, predominantly through sensors, automation and control system, HMI/SCADA systems, and IIoT devices. This data, rich in insights, is invaluable to ERP and MES systems that drive business and production decisions. However, transmitting this voluminous raw data to central systems can lead to latency and strain on network resources. Enter edge computing—a paradigm shift that tackles these challenges by preprocessing data locally before its transmission.

Understanding Edge Computing

Edge computing refers to the practice of processing data closer to the data source or "edge" of the network rather than sending it to a centralized cloud-based system. In the context of manufacturing, these edge devices can be embedded in machinery, automation and control systems, and/or localized data centers.

Benefits of Edge Computing in Smart Manufacturing

  • Reduced Latency: Real-time operations, such as machine controls and immediate quality checks, require split-second decision-making. By processing data locally, edge devices eliminate the lag that comes from sending data to and from centralized systems, ensuring immediate responses.
  • Network Efficiency: Transmitting vast amounts of raw data consumes significant bandwidth. By preprocessing and filtering data at the source, only pertinent information is sent to ERP and MES systems, thus reducing network strain.
  • Operational Resilience: In the event of network downtimes or connectivity issues, edge devices can continue to operate, ensuring that production isn’t halted.
  • Enhanced Data Security: Transmitting less data reduces exposure to potential interception. Moreover, sensitive data can be processed and stored locally, limiting its exposure to external threats.

How Edge Computing Devices Preprocess Data for ERP & MES Systems

  • Data Filtering: Not all data generated is relevant. Edge devices can filter out noise and redundant data, ensuring that only valuable information is sent to central systems.
  • Real-time Analysis: Many production processes require immediate analysis for control purposes. Edge devices can perform these analyses in real-time, acting upon them locally and then transmitting only the summarized or resultant data to central systems.
  • Data Aggregation: Edge devices can aggregate data from multiple sensors and sources, consolidating it into a more manageable and coherent format for ERP and MES systems.
  • Data Compression: These devices can also compress data, further minimizing the amount of data that needs to be transmitted.
  • Caching: In scenarios where historical data might be needed for future comparisons or analytics, edge devices can cache data locally. This reduces the need to continuously retrieve older data from central systems.
  • Predictive Analytics: Modern edge devices equipped with AI capabilities can predict machine failures or production anomalies. Instead of constantly sending data for analysis, these devices can send alerts or insights when anomalies are detected.
  • Protocol Translation: Given the heterogeneous nature of manufacturing environments, edge devices can translate various data protocols into a standardized format compatible with central ERP and MES systems.

1. Edge Layer Data Collection

Data Characteristics at the Edge

  • High Velocity: Data is produced in real time and often at high frequency.
  • Variety: Data types include machine status, sensor readings, operator inputs, environmental conditions, etc.
  • Volume: Large quantities of data, often with a significant proportion being noise or redundant.
  • Veracity: Data must be accurate, but given the volume and variety, filtering is required to ensure quality.

Collection Mechanisms

  • Direct from Machine Sensors: Collecting raw data such as temperature, pressure, and vibration.
  • Edge Devices: Employing edge computing for preliminary data processing and analysis.
  • HMI/SCADA Systems: Gathering and controlling process data in real time.

2. Data Communication from Edge to MES/MOM

Data Transformation

  • Filtering and Aggregation: Reducing noise and summarizing data to focus on actionable insights.
  • Contextualization: Adding metadata that provides context, such as machine ID, batch number, or operator details.

Data Communication Protocols

  • Standard Industrial Protocols: Using protocols like OPC UA for platform-independent, secure data communication.
  • Middleware: Utilizing data brokers or IIoT platforms to route and manage data streams.

3. MES/MOM Layer Data Utilization

Data Characteristics at MES/MOM

  • Contextualized: Data arrives with the necessary context for tracking and controlling production processes.
  • Actionable: Information is refined enough to drive decisions such as process adjustments and quality control.
  • Integrated: MES/MOM data is integrated with workflow management, resource scheduling, and plant floor control.

    Processing and Analysis

  • Data Orchestration: MES/MOM coordinates data flows, ensuring that the right information reaches the right systems at the right time.
  • Performance Analysis: Analyzing data for efficiency, productivity, and detecting deviations from the norm.

4. Data Communication from MES/MOM to ERP

Data Transformation for ERP

  • Summarization: Further condensing data into key metrics like Overall Equipment Effectiveness (OEE), production throughput, and quality rates.
  • Strategic Context: Framing data in terms of business impact, such as cost implications, customer order fulfillment, and inventory levels.

Data Communication Protocols to ERP

  • Enterprise Integration Patterns: Employing patterns such as Publish/Subscribe, Request/Reply, or Service-Oriented Architecture (SOA) for reliable messaging.
  • APIs and Web Services: Using RESTful APIs or SOAP for interfacing MES/MOM systems with ERP.

5. ERP Layer Data Integration

Data Characteristics at ERP

  • Business-Centric: Data is relevant for financials, supply chain management, and higher-level decision-making.
  • Strategic: Data is used for forecasting, trend analysis, and strategic planning.
  • Periodic: Data may be updated in real-time, but often periodic updates are sufficient for the strategic layer.

Utilization in Decision-Making

  • Resource Planning: Aligning production data with resource requirements and financial constraints.
  • Supply Chain Management: Integrating production data with supply chain operations to optimize inventory and logistics.
  • Business Intelligence: Leveraging aggregated data for business reporting, trend analysis, and predictive analytics.

Best Practices for Data Communication Across Layers

  • Ensure Data Integrity: Apply checksums, acknowledgments, and transaction logs to guarantee data integrity across transfers.
  • Maintain Data Security: Use encryption and access controls to protect sensitive data in transit.
  • Monitor Data Flows: Implement monitoring and alerting systems to quickly identify and respond to disruptions in data communication.
  • Adapt to Feedback: Use feedback loops from MES/MOM and ERP systems to refine edge data collection, ensuring relevancy and value.

Leveraging Edge Computing

There’s no doubt that modern manufacturing facilities generate an enormous amount of data. It comes from a wide variety of sources on the plant floor and most of it is extremely valuable and rich in insights. But the data needs to be collected and managed properly if it’s really going to provide the insights and drive business and production decisions. 

There are many challenges associated with collecting and managing this volume of data. Edge computing is one technology that can reduce these challenges and help manufacturing operations turn data into knowledge and knowledge into insights that can be used to make better decisions and improve the manufacturing operations. Edge computing. It’s definitely worth taking a closer look.

For further reading, download Laying the Foundation for Data-Driven Operations and Digital Maturity, an insightful analyst report from IDC.

This article was previously published by Harneet on MESA (Manufacturing Enterprise Solutions Association) International’s website. Missed Part 1 of Harneet’s blog series? Read it here to understand the data communications landscape.

About the Author

Harneet Gill Enterprise Software Sales Executive, Plex by Rockwell Automation

Neet Gill is a senior sales leader within Rockwell Automation's Enterprise Software team, where he is responsible for championing ERP, MES, QMS, and IIoT solutions tailored to the needs of manufacturing organizations. With a deep passion for driving advancement and change, Neet is dedicated to enhancing the services provided to Plex customers, aiming to better meet their evolving needs. Prior to Rockwell Automation, Neet served as an implementation partner garnering experience in technologies such as PLM and CAD/CAM, in addition to delivering a broad spectrum of engineering services.

Harneet Gill