How a Company in São Paulo Solved a Problem Where a Company Spent More Than 18 Hours Per Week Collecting Machine Performance Logs Manually Using n8n

For industrial operations, timely and accurate machine performance data is critical. However, manual collection of these performance logs can be time-consuming and error-prone, draining valuable resources. In São Paulo, Brazil, a mid-sized manufacturing company faced this exact challenge, spending more than 18 hours per week manually compiling machine data.  Automating this process using the powerful workflow automation tool n8n enabled the client to reclaim time, improve data quality, and streamline operational visibility. This case study details how RestFlow delivered a custom automation solution that integrated multiple systems, simplified complex workflows, and significantly reduced manual effort.

Startup CTOs, automation engineers, and operations specialists will gain hands-on knowledge from this step-by-step exploration, including the architecture, workflow design, tool choices, error handling, and results. If you’re considering automating repetitive data collection tasks, read on to see how automation transformed this company’s process efficiently and robustly.

Case Context & Problem

The client is a São Paulo-based mid-sized manufacturing company specializing in automotive parts production, operating within Brazil’s industrial sector. Their operations team was responsible for maintaining daily records of machine performance logs from over 50 machines scattered across multiple factory floors.

Before automation, the team spent approximately 18 to 20 hours each week manually collecting machine performance logs. This involved logging into disparate machines’ local systems and extracting data reports, then manually compiling them into centralized spreadsheets. The process introduced numerous challenges:

• High manual labor: Over 18 hours per week diverted from higher-value tasks.
• Human errors: Manual copy-pasting led to data entry mistakes, impacting analysis quality.
• Delays and visibility issues: Log reports were often delayed by 1–2 days, reducing operational responsiveness.
• Lack of centralized reporting: No real-time dashboards or alerts to flag machine issues promptly.

The pain was deeply felt across the operations team and production managers, affecting their abilities to monitor equipment health proactively and meet internal Service Level Agreements (SLAs) for uptime and maintenance scheduling. The company sought an automated solution to eliminate repetitive work, reduce errors, and deliver real-time, quality data aggregation.

Our Approach

RestFlow’s team initiated the project with a detailed discovery phase, mapping the existing manual workflow through interviews and process observation. We identified critical bottlenecks and the disparate systems involved in machine log access.

The client’s machine logs were accessible via APIs on select advanced equipment, legacy FTP servers on others, and manual USB exports for the remaining units. Data formats varied widely — ranging from JSON APIs, CSV files on FTP, to local machine text exports.

We recognized the need for an orchestration tool that offered flexibility, custom integrations, and scalability. After evaluating industry-leading automation platforms like Make and Zapier, we selected n8n for its open-source model, extensive node ecosystem, and powerful workflow customization.

The high-level architecture was designed to:

• Automate scheduled data retrieval from different machine data sources (API, FTP, manual uploads).
• Transform and normalize logs into a consistent format.
• Validate data integrity and flag anomalies.
• Update centralized Google Sheets dashboards, send Slack alerts, and notify the operations team via email.

This modular approach simplified future adaptations for new machines or data sources.

Ready to start automating complex workflows? Explore the Automation Template Marketplace to leverage prebuilt n8n workflows tailored for industrial data processes.

The Solution: Architecture & Workflow

Global Architecture Overview

The automation solution consists of the following components:

• Triggers: Scheduled Cron triggers run every 30 minutes during working hours to fetch latest performance logs.
• Orchestrator: n8n manages the entire workflow, controlling data integration, transformation, decision logic, and notifications.
• Integrated Services:
  • Machine APIs (using HTTP Request nodes)
  • FTP servers (FTP node for file retrieval)
  • Google Sheets (for centralized log storage)
  • Slack (for real-time alerts)
  • Gmail (for email notifications)
• Outputs: Daily updated centralized spreadsheets, automated alerts to Slack channels, and summary emails to operations managers.

End-to-End Workflow Walkthrough

1. Trigger: The workflow is triggered by a Cron node set to execute every 30 minutes from 7 AM to 7 PM Monday through Friday.
2. Data Collection: Based on the machine type and data source, HTTP Request nodes call machine APIs; FTP nodes download CSV log files; Webhook nodes receive manual uploads as backups.
3. Data Transformation: Retrieved data is parsed, normalized, and converted into standardized JSON representing machine metrics like uptime, error codes, temperature, and throughput.
4. Validation: Conditional nodes validate data completeness and flag outliers or missing fields.
5. Decision Logic: If anomalies are detected (e.g., error rate > threshold), Slack alert messages are composed and sent; otherwise, data is logged silently.
6. Data Storage: Google Sheets nodes append the cleaned and validated data to the master spreadsheet, maintaining daily logs.
7. Notifications: At end of day, summary emails are automatically generated and sent, providing managers with an overview of machine health and performance.

Step-by-Step Node Breakdown ⚙️

1. Cron Trigger Node

The Cron node triggers every 30 minutes during peak hours. It initiates the workflow without requiring manual input, ensuring consistent log collection cadence.

2. HTTP Request Nodes for API Data

Separate HTTP Request nodes access machine APIs over REST endpoints. They include headers for API keys (stored securely in n8n credentials) and query parameters specifying the time ranges for logs. Responses are parsed with JSONParse nodes to extract relevant metrics.

3. FTP Node for Legacy Machines

FTP node connects to machine FTP servers using stored credentials. It retrieves CSV files with log data from predefined directories and passes them to CSV Parsers. Filters ensure only new files are processed (filename timestamp checks).

4. Manual Upload Webhook

To accommodate legacy machines requiring USB upload, a Webhook node accepts CSV files uploaded manually by technicians through a simple interface. These files enter the same normalization pipeline.

5. Data Normalization Function Node

A JavaScript Function node transforms incoming data into a consistent JSON schema, mapping fields like machine ID, timestamp, runtime, and error codes. It also converts date formats and units as needed.

6. Data Validation & Conditional Checks

Conditional nodes check for missing required fields and metrics outside expected ranges. If invalid data is found, error handling nodes route documents to manual review sheets and send Slack alerts.

7. Google Sheets Append Node

Adds each validated machine log entry as a new row to a centralized Google Sheet, with columns for machine identification, timestamp, metrics, and status.

8. Slack Alert Node 🔔

Sends formatted alert messages to Slack #operations channel when critical errors or data anomalies occur, enabling proactive response from the operations team.

9. End-of-Day Summary Email Node

A composed Gmail SMTP Node emails a summary report extracted from Google Sheets, highlighting performance trends and machine uptime statistics.

Error Handling, Robustness & Security

Error Handling and Retries

Each external call (API/FTP) is wrapped in error-catching nodes, which trigger retries with exponential backoff for transient network issues. Persistent errors get logged to a dedicated Google Sheet and notified via Slack.

Logging and Observability

Workflow runs are logged in n8n’s execution history. Aggregated statistics on success/failure rates are monitored via RestFlow’s managed dashboard to identify bottlenecks.

Alerting Mechanism

Slack notifications are configured for critical failures and anomaly thresholds. Email alerts cover daily summaries and urgent incidents.

Idempotency and Deduplication

Data ingestion nodes include safeguards to check for already processed log IDs or timestamps, preventing duplicate entries in Google Sheets.

Security Practices

• All API keys and credentials are stored securely within n8n’s credential system with restricted access.
• Least-privilege access applies for Google Sheets and Slack integrations.
• All sensitive PII data is excluded from logs; machine logs contain no personal data.
• Access control is enforced via RestFlow’s multi-tenant hosting environment.

Performance, Scaling & Extensibility

The workflow’s modular design allows smooth scaling as machines increase or new data sources appear. Webhooks are preferred over polling where feasible—reducing load and improving data freshness.

Increased volume scenarios use batching steps and concurrency controls within n8n to manage throughput without bottlenecks.

Extending the workflow for new clients or additional departments involves duplicating and customizing modular workflows without disrupting production processes.

RestFlow’s managed hosting guarantees stability under load, with automatic versioning and safe deployment pipelines.

Comparison Tables

Option	Cost	Pros	Cons
n8n	Free (self-host) to moderate (Cloud plans)	Open-source, highly flexible, broad integrations, strong developer community	Requires initial setup, learning curve for complex workflows
Make	Subscription-based tiers, starting $9/mo	Visual interface, great for complex logic, wide app connectors	Monthly task limits, less control over deployment
Zapier	Starting at $19.99/mo	Easy to use, many app integrations, ideal for simple automation	Less suited for complex multi-step workflows, limited conditioning

Integration Method	Cost Impact	Pros	Cons
Webhook	Low	Real-time, efficient, reduces unnecessary calls	Needs endpoints configured, depends on external system push
Polling	Higher with frequent intervals	Simple to implement, pulls data regularly	Higher latency, higher resource usage, possible rate limits

Data Storage Option	Cost	Pros	Cons
Google Sheets	Free (limited quotas)	Easy access, no infrastructure, native integrations	Not ideal for large data volumes or complex queries
SQL Database	Variable, hosting fees apply	Scalable, queryable, ideal for analytics	Requires DB management, connection setup

Results & Business Impact

Following deployment, the client experienced significant gains:

• Time savings: More than 18 hours per week freed from manual log collection — nearly 75% reduction in processing time.
• Error reduction: Data entry mistakes reduced by over 90%, improving reliability of machine performance reports.
• Faster visibility: Near real-time dashboards empowered proactive maintenance, enhancing machine uptime.
• Improved SLA compliance: Quicker incident detection shortened response times by up to 60%.

Operations teams now focus on corrective actions rather than manual data handling, boosting productivity and morale. Managers gain better insights, making data-driven decisions easier.

These KPIs demonstrate how automation fundamentally transforms operational workflows for industrial clients. [Source: to be added]

Pilot Phase & Maintenance Disclaimer

As with any automation implementation, the project began with a pilot phase where the workflow ran with controlled real data to validate robustness and accuracy. During this period, minor bugs and edge cases were identified and resolved to optimize performance.

Post-pilot, RestFlow took over ongoing hosting, monitoring, updates, and periodic audits, ensuring the automation remains reliable, secure, and scalable over the long term.

This approach ensures clients receive a refined automation experience supported by expert maintenance — critical for mission-critical industrial environments.

Frequently Asked Questions

Conclusion

In summary, automating the machine performance log collection process using n8n empowered the São Paulo manufacturing company to save over 18 hours of manual work weekly, drastically reduce errors, and gain faster, more actionable operational insights. RestFlow’s end-to-end Automation-as-a-Service approach — encompassing design, implementation, hosting, monitoring, and maintenance — ensured a smooth transition from manual to automated operations with lasting benefits. Whether you aim to optimize industrial workflows or other business processes, automation can unlock significant productivity and quality gains.

If you’re ready to take your automation journey forward, don’t hesitate to explore our Automation Template Marketplace for ready-made workflows or create your free RestFlow account to start building custom automations today.