Connected Operations Analytics on AWS
Multi-plant North American manufacturers operating across three to ten production sites generate substantial operational telemetry from SCADA systems, PLCs, MES platforms, and quality inspection equipment — but this data remains siloed in per-site historians and local databases. Plant managers make decisions with two-to-five-day-old data from emailed Excel summaries, while corporate operations leadership has no real-time view of production throughput, quality metrics, or equipment utilisation across the estate.
Region
US & Canada
Industry
Manufacturing
Client
Available on request
The Problem
The operating challenge
Multi-plant North American manufacturers operating across three to ten production sites generate substantial operational telemetry from SCADA systems, PLCs, MES platforms, and quality inspection equipment — but this data remains siloed in per-site historians and local databases. Plant managers make decisions with two-to-five-day-old data from emailed Excel summaries, while corporate operations leadership has no real-time view of production throughput, quality metrics, or equipment utilisation across the estate.
Operating context
This scenario is set in a mid-market discrete manufacturer with four to eight production sites across the US and Canada, operating in automotive components, industrial equipment, or consumer goods. Each plant runs its own SCADA and MES environment, often from different vendors. The corporate engineering and operations team has attempted to establish cross-site reporting using manual data exports and BI tools but cannot achieve the data freshness or granularity needed for meaningful operational decisions. The IT team is managing a mix of on-premises and AWS infrastructure but has not established a unified data platform. Quality, throughput, and OEE data exists in each plant — the gap is a connected layer that makes it available in one place in near-real-time.
Problem signals in this scenario
Plant-level data locked in proprietary historians with no standard export interface
OEE calculated differently at each site, making cross-plant comparison meaningless
Shift reports assembled manually from 3+ local systems by shift supervisors
Quality incidents identified and contained within individual plants rather than escalated to central QA
Corporate dashboards showing last-week data because ETL jobs run overnight
No single authoritative definition of "production throughput" across the estate
Approach
How CodeCones would approach it
Hover any stage to reveal additional technical detail.
Data Source Assessment and Connectivity Design
Each plant's data sources — SCADA, MES, historians, quality systems — are catalogued and assessed for connectivity options, producing a site-by-site integration plan with connection methods and estimated data volumes.
Technical detail
AWS IoT Greengrass or Ignition edge gateways aggregate plant-floor data and forward to AWS IoT Core. MQTT and OPC-UA protocols handle the majority of SCADA and PLC connections. MES systems connected via database replication or REST API where available.
AWS IoT Core and Kinesis Ingestion Layer
Plant-floor telemetry streams into AWS IoT Core, with Kinesis Data Streams providing the ordered, durable event log that downstream processing and storage layers consume.
Technical detail
Kinesis Data Streams partitioned by plant-site and equipment type. IoT Core topic hierarchy standardised across all sites to enable cross-site aggregation. Dead letter queue handling for connectivity interruptions ensures no data loss during network events.
Real-Time Processing and OEE Calculation
Lambda functions and Kinesis Data Analytics compute OEE components — Availability, Performance, Quality — in near-real-time against a standardised metric definition applied consistently across all sites.
Technical detail
AWS Lambda processes raw telemetry events and computes derived metrics against the corporate OEE definition. Kinesis Data Analytics handles windowed aggregations for shift, daily, and weekly rollups. Metric definitions stored in a central configuration service version-controlled alongside the pipeline code.
Redshift Data Warehouse and Historical Analytics
Processed operational data lands in Amazon Redshift, providing the historical depth for trend analysis, plant benchmarking, and capital planning — alongside the near-real-time operational layer.
Technical detail
Kinesis Firehose delivers processed events to S3. AWS Glue crawlers update the Redshift Spectrum external tables. COPY commands load structured operational records into Redshift. Data retention tiered between hot Redshift and cold S3/Glacier.
Operational Dashboards and Alerting
QuickSight dashboards provide corporate operations and plant leadership with a real-time view of OEE, quality yield, throughput, and equipment utilisation — with drill-down from estate level to individual equipment.
Technical detail
QuickSight SPICE datasets refreshed at 15-minute intervals for operational dashboards. CloudWatch alarms trigger SNS notifications when site-level metrics fall below defined thresholds. PagerDuty or similar used for on-call escalation of critical production anomalies.
Data Quality Governance and MDM
A master data management layer establishes canonical definitions for equipment IDs, shift patterns, product codes, and metric standards — ensuring cross-site data is comparable and analytics outputs are trusted.
Technical detail
AWS Glue Data Quality enforces schema validation and completeness checks on ingested records. Master data (equipment registry, product hierarchy, shift patterns) maintained in RDS and pushed to all downstream consumers. Data lineage tracked through the pipeline for audit and troubleshooting.
Architecture
Representative architecture and toolset
Representative technology options. Specific tools are selected based on your architecture, existing platforms, and engineering requirements. Hover any category to see examples.
Representative technology options. Specific tools are selected based on your architecture, existing platforms, and engineering requirements.
IoT and edge data collection
- AWS IoT Core
- Azure IoT Hub
- MQTT brokers
Streaming data platforms
- Apache Kafka
- AWS Kinesis
- Google Pub/Sub
Cloud data platforms
- Snowflake
- Databricks
- Google BigQuery
- AWS Redshift
ETL and pipeline orchestration
- dbt
- Apache Airflow
- Fivetran
Data quality and observability
- Great Expectations
- Monte Carlo
- Soda
Delivery
People. Technology. Outcomes.
People
Engineering disciplines involved in this scenario
Technology
Architecture and toolset categories for this scenario
Outcomes
KPIs this solution can influence
Outcomes
Illustrative outcome profile
These figures are illustrative targets drawn from comparable industry benchmarks. They are not results achieved for a specific client. Actual outcomes depend on your organisation's baseline, technology environment, and implementation approach.
<15 min
Target data freshness from plant-floor event to corporate dashboard in this architecture
4–8 sites
Typical multi-plant estate scope in this scenario
~80%
Reduction in manual shift report preparation time in comparable deployments
Delivery approach
Build. Scale. Ship.
Build
Start with two pilot sites on the same product line. Establish the IoT ingestion path, OEE calculation logic, and a single QuickSight dashboard showing both sites side-by-side. Validate metric definitions with plant engineering teams before expanding.
Scale
Roll out remaining sites progressively, adapting the site-specific integration for each SCADA and MES variant. Add metric coverage — quality, maintenance, energy — as data sources are connected. Expand to Redshift historical analytics once near-real-time layer is stable.
Ship
Full estate deployment with corporate and plant-level dashboards, automated alerting, and shift report elimination. Quarterly data quality review. Data platform team embedded in the corporate engineering function for ongoing operation and expansion.
Recommended engagement model
Embedded Engineering Specialists
Experienced engineers join your existing team and toolchain, extending capacity without a standalone team structure.
Managed Product Engineering
CodeCones takes ownership of a defined delivery outcome with an experienced pod working in your toolchain.
Services
Related service pathways
Hover any card to see the role of each service in this scenario.
Data Engineering and MLOps
CodeCones service
End-to-end data pipeline design, IoT ingestion, streaming processing, OEE calculation, data quality governance, and Redshift warehouse architecture
Cloud DevOps and Platform
CodeCones service
AWS infrastructure design and IaC, IAM policy configuration, cost optimisation, CI/CD pipeline for data platform code, and operational monitoring
Governance
Built-in controls for this scenario
Site-Level Data Isolation
Plant-level data is scoped by site in IAM policies. Corporate team members can view aggregated cross-site analytics; only authorised plant personnel can access plant-specific raw data.
Metric Definition Version Control
OEE and operational metric definitions are version-controlled alongside pipeline code. Changes to metric definitions are deployed through a change control process, with historical data recalculated when definitions change.
Data Quality Alerting
Automated checks on data completeness, schema validity, and expected value ranges alert the data engineering team before quality issues propagate to operational dashboards.
Connectivity Resilience
Edge gateways buffer data locally during network interruptions and replay to the cloud pipeline on reconnection. No data is silently dropped — connectivity events are logged and tracked.
Applicability
Where else this applies
The approach in this scenario transfers to related sectors and use cases.
Related
Related case studies and blueprints
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