The Lost Art of Control Points: What IT Can Learn from Manufacturing Floors

Part 2 of a 3-part series on bringing manufacturing reliability principles to modern IT operations

It's 2 AM at a large-scale dairy facility. A temperature sensor detects a 0.5°C rise in a pasteurization tank. Without human intervention, the system automatically adjusts cooling flow, maintaining perfect conditions. Quality isn't monitored—it's controlled.

Meanwhile, across town in a major e-commerce company's operations center, teams scramble to respond to hundreds of alerts, trying to determine which ones actually matter. They have more monitoring than ever, yet less control.

The Control Point Crisis

In part 1 of this series, we explored how manufacturing's golden rules of safety and quality could transform IT operations.

Today, we'll dive deeper into a critical concept: control points.

The irony of modern IT operations is stark:

• Teams drowning in alerts while critical systems fail silently
• Dashboards showing everything while telling us nothing
• "Advanced observability" tools being purchased while fundamental alerting remains incomplete
• Less than 40% of critical services having comprehensive alert coverage

The issue isn't a lack of tools—it's a lack of mechanisms.

Manufacturing vs. IT: The Dairy Plant Parallel

Let's examine how a modern dairy plant maintains quality through mechanisms, not just tools:

1. Input Quality Control

Dairy Plant Control Mechanisms:

• Temperature sensors at milk collection points
• Automatic diversion of milk that exceeds temperature limits
• Real-time pH monitoring with automated acceptance/rejection
• Comprehensive tracking of supplier quality metrics

IT Equivalent:

• API response time monitoring
• Automatic circuit breakers for degraded services
• Real-time dependency health checks
• Third-party service quality tracking

2. Process Control Points

Dairy Plant:

• Continuous temperature monitoring during pasteurization (71.7°C for 15 seconds)
• Automated flow control based on temperature readings
• Pressure monitoring across heat exchangers
• Automatic product diversion if parameters deviate

IT Equivalent:

• Service latency monitoring at critical paths
• Automated scaling based on load metrics
• Resource utilization tracking
• Automatic traffic shifting on deviation

3. Output Quality Verification

Dairy Plant:

• Automatic sampling after pasteurization
• Continuous monitoring of cooling temperatures
• Real-time microbial testing
• Product hold until verification complete

IT Equivalent:

• Synthetic transaction monitoring
• Error rate tracking
• End-user experience monitoring
• Canary deployment verification

4. Control Mechanism Verification

Dairy Plant:

• Daily verification of temperature sensors
• Regular testing of diversion systems
• Automated recording of deviations and control responses
• Trend analysis of control point violations
• Review of recurring deviations
• Regular audit of control effectiveness

IT Equivalent:

• Alert coverage measurement
• Tracking of threshold violations and system responses
• Analysis of recurring anomalies
• Pattern detection in service deviations

The Key Insight

In dairy processing, these mechanisms ensure:

• Every critical point has a control
• Every control has automation
• Every automation is verified
• Every verification is recorded

This isn't achieved through more sensors or better monitoring tools. It's achieved through mechanisms that ensure comprehensive control at every critical point.

The Fundamental Shift Required

We must move from:

"Monitoring everything" to "Controlling what matters"

• Identify true control points and golden metrics
• Deploy standardized alerts across all critical services
• Measure comprehensive coverage with clear scoring

"Adding observers" to "Building in reliability"

• Automate alert deployment for new services
• Enforce consistent control mechanisms
• Enable auto-mapping of services to control points

"Responding to failures" to "Preventing failures"

• Set static and anomaly-based thresholds
• Monitor third-party API dependencies proactively
• Implement automatic remediation

"Tool-first thinking" to "Principle-first thinking"

• Start with control mechanisms, not tools
• Focus on coverage and effectiveness
• Build on proven reliability patterns

"Reliability as a feature" to "Reliability as a foundation"

• Design systems around control points
• Automate control deployment
• Enable context-aware responses

For Leaders Reading This

Ask yourself:

• Have you mapped all critical control points in your infrastructure?
• Are your control mechanisms automated or manual?
• Do you have verification systems for your controls?
• How quickly can your team identify and respond to control violations?

Because in the end, as we learned in part 1, watching things fail better isn't the same as making them work reliably. Control points aren't just about monitoring—they're about building mechanisms that prevent failures before they occur.

Stay tuned for our final piece in the series: "Signal vs. Noise: Why More Data Often Means Less Understanding."

Further Reading:

1. Alert analytics and fatigue reduction

2. Noise reduction strategies

3. Default metrics and customization guide

4. Alert threshold configuration

5. ALCOM scoring and alert coverage

6. AI-powered contextual runbooks

7. Temperstack-reliability-transformation [3 min feature walkthrough] See these principles in action:

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About the author

Mohan Narayanaswamy Natarajan is a technology executive and entrepreneur with over 20 years of experience in operations and systems management. As co-founder and CEO of Temperstack, he focuses on Site Reliability Engineering (SRE) process automation. His career includes leadership roles at ITC, Inmobi, Pinelabs, Practo & Amazon,  Mohan has also worked as a consultant at The Boston consulting group (BCG),  He has experience in implementing large-scale systems, leading teams, and establishing business resilience mechanisms across various industries.

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