Manufacturing · Data Infrastructure

Connected Operations Analytics on AWS

Q: 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.

Q: 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.

Q: 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.

Q: 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.

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.

Data freshness — time from plant-floor event to corporate dashboard

Cross-site OEE visibility and comparability

Quality incident escalation time

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The Problem

What's driving this problem

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.

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 we approach it

The stages through which a solution in this space is typically delivered.

Step 1

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.

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.

Step 2

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.

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.

Step 3

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.

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.

Step 4

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.

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.

Step 5

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.

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.

Step 6

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.

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.

Service Coverage

CodeCones services involved

This solution draws on the following CodeCones service capabilities.

Data Engineering and MLOps

Data engineering and MLOps ensure the pipelines, feature stores, and model lifecycle are production-ready and operationally sound.

Data pipeline design and orchestration
Feature engineering and model deployment workflows
Data quality monitoring and operational runbooks

Cloud DevOps and Platform

Cloud DevOps engineering provides the infrastructure, deployment pipelines, and observability foundation the solution runs on.

Cloud infrastructure design and provisioning
CI/CD pipeline engineering and deployment automation
Observability, alerting, and reliability engineering

Technologies

Technologies involved

Specific tools are selected based on your architecture, existing platforms, and engineering requirements.

IoT and edge data collection

AWS IoT CoreAzure IoT HubMQTT brokers

Streaming data platforms

Apache KafkaAWS KinesisGoogle Pub/Sub

Cloud data platforms

SnowflakeDatabricksGoogle BigQueryAWS Redshift

ETL and pipeline orchestration

dbtApache AirflowFivetran

Data quality and observability

Great ExpectationsMonte CarloSoda

Outcomes

What this delivers

KPIs and operational dimensions this solution is designed to improve.

Data freshness — time from plant-floor event to corporate dashboard

<15 min

Target data freshness from plant-floor event to corporate dashboard in this architecture

Modeled target based on AWS Kinesis + Lambda pipeline benchmarks — actual latency depends on connectivity and processing load

Cross-site OEE visibility and comparability

4–8 sites

Typical multi-plant estate scope in this scenario

Scenario framing only — not a performance metric

Quality incident escalation time

~80%

Reduction in manual shift report preparation time in comparable deployments

Illustrative estimate from Industry 4.0 deployment reports — not a client result

Analyst time spent on manual report assembly

Capital planning data availability

Unplanned downtime detection and response time

Engagement

How we engage

This solution is available through the following engagement models.

Embedded Engineering Specialists

Experienced engineers join your existing team and toolchain.

Managed Product Engineering

CodeCones takes ownership of a defined delivery outcome.

Governance

Controls & governance

Operational controls built into or recommended alongside this solution.

Site-Level Data Isolation