Production Analytics Guide

Understand, Improve, and Profit from Every Run

MachineMetrics’ Production Analytics module gives manufacturers a powerful lens into production performance by combining real-time machine data with ERP expectations, historical baselines, and ideal machine-calculated values. This guide walks you through how to use it—and why it matters.

🚨 Problems Production Analytics Solves

🔍 Using the Interface: Step-by-Step

👥 Who Benefits and How

✅ Tips for Maximizing Value

🚨 Problems Production Analytics Solves

1. Lack of Visibility into Real Performance

ERP standards often differ from machine reality. Production Analytics:

• Tracks setup, cycle, active, and touch times

• Benchmarks actuals against:

  • ERP standards

  • Machine-calculated ideals

  • Historical baselines

• Shows how you're truly performing versus planning assumptions and machine potential

2. Difficulty Pinpointing Low OEE Causes

• OEE is split into Availability, Performance, and Quality

• Shows both:

  • Parts-Goal OEE (vs ERP cycle standards)

  • Ideal-Cycle OEE (vs theoretical ideal time)

• Drill into specific runs, outliers, or downtime segments to isolate performance loss

3. Inefficient Downtime Management

• The Pareto Chart aggregates all downtime (automated/manual)

• Highlights top loss drivers: setup, changeover, maintenance, idle gaps

• Use this to drive targeted CI (Continuous Improvement) initiatives

4. Inability to Benchmark Machines/Operations

• Filter and compare by:

  • Machine

  • Operation

  • Historic baselines

  • Ideal-cycle times

• Identify top performers (e.g., “CNC Swiss 2”) and model best practices

5. Reactive Instead of Proactive Adjustments

• Charts and distributions show:

  • Cycle time drift

  • Touch-time variability

  • Volatility across runs

• Spot anomalies early—before they become downtime or scrap

6. Inaccurate Costing & Quoting

• Combine burden rate, scrap rate, and actual performance data to generate:

  • Costed Operation Summaries

  • Setup variance impacts

  • Part cost by labor, machine time, and yield

• Base quotes on real shop-floor economics, not averages or assumptions

🔍 Using the Interface: Step-by-Step

1. Open Analytics → Production Analytics

• Filter by run attribute (e.g., “Part Time vs Expected”)

• Use the date picker to isolate time periods

2. Select a Run

Key views include:

🧾 Summary Tab

• OEE + its components

• Good vs Rejected parts

• Baseline comparisons (this machine vs plant-wide)

• Cycle time analysis
  • Run time
  • Touch time

⏱ Cycles Tab

• Active, Cycle, and Touch times

• Mode, Median, Mean, Std Dev

• Pie chart of part distribution

⏳ Downtime Tab

• Categorized downtime

• Pareto and trend charts

• Root causes (e.g., Planned Maintenance, Changeovers)

💸 Cost Tab

• Per-part cost (based on burden rate)

• Cost of losses (from OEE inefficiency)

• Ideal-cycle opportunity value

🛠 Tooling Tab

• Tool usage: number of cycles, time per cycle, frequency

• Flags inefficiencies and wear issues

👥 Who Benefits and How

✅ Tips for Maximizing Value

• Use Baseline Comparisons: See how today’s performance stacks up to your best historical runs

• Prioritize High-Dollar Losses: Use cost summaries to focus CI on highest return opportunities

• Tune Quoting Assumptions: Replace estimates with cycle-validated burden rate calculations

• Track Tool Loads: Optimize tool changes and reduce cycle inefficiencies

• Reinforce with Data: Back up training, investment, and scheduling decisions with real metrics