🎓 Lesson 12
D5
Calculating Labor-Driven Availability, Performance & Quality Losses
It’s how much of workers’ scheduled time is actually used for productive blasting tasks, how well they perform those tasks when working, and how many blasts meet quality standards—broken down into three clear losses.
🎯 Learning Objectives
- ✓ Calculate labor-driven Availability Loss by analyzing shift logs and downtime records
- ✓ Analyze Performance Loss using observed vs. benchmark drill/blast cycle times
- ✓ Quantify Quality Loss by applying sieve analysis data to fragment size specifications
- ✓ Explain how each loss type maps to root causes (e.g., training gaps, supervision issues, fatigue)
- ✓ Apply L-OEE results to prioritize targeted labor efficiency interventions
📖 Why This Matters
In open-pit mines, up to 35% of total blast-related delays stem from labor factors—not geology or explosives. A single 15-minute delay in stemming crew handover can cascade into 2 hours of shovel idle time. Understanding *where* labor time is lost—before the blast, during execution, or after evaluation—enables precise corrective action: better shift scheduling, standardized work methods, or real-time quality feedback loops. This isn’t about blaming workers—it’s about engineering the system around them.
📘 Core Principles
Labor-Driven Losses decompose total labor inefficiency into three orthogonal dimensions: (1) *Availability Loss* captures time when crews are scheduled but unable to work (e.g., waiting for survey staking, safety briefings delayed, or no-drill orders); (2) *Performance Loss* measures output rate shortfall during active work—such as drilling 8.2 m/min instead of the engineered standard of 10.5 m/min due to inconsistent rod handling or fatigue; (3) *Quality Loss* reflects rework or rejection caused by labor-executed deviations—e.g., misaligned holes leading to oversize fragments requiring secondary breaking. Critically, these losses are *labor-controllable*: they exclude design errors (e.g., flawed burden calculation) or external events (e.g., lightning stoppages), focusing only on actions directly influenced by crew behavior, supervision, and training.
📐 Labor-Centric OEE Components
Each loss is calculated as a percentage deviation from 100% ideal labor utilization. Availability uses scheduled vs. actual productive time; Performance compares actual output rate to the labor-standard rate; Quality uses conforming blast segments vs. total segments assessed. All three are multiplicative to yield Labor-OEE—a true measure of labor’s contribution to blast readiness and quality.
💡 Worked Example
Problem: At El Teniente Copper Mine, a drilling crew was scheduled for 8.0 hrs (480 min). Downtime included: 12 min for delayed blast design sign-off, 8 min for PPE inspection rework, and 15 min for crane unavailability. Actual drilling progressed at 9.3 m/min vs. the labor-standard 11.0 m/min over 420 m drilled. Post-blast fragment analysis showed 18% of the muckpile >76 cm (exceeding spec limit of ≤5%).
1.
Step 1: Calculate Availability = (Scheduled Time – Unplanned Downtime) / Scheduled Time = (480 − (12+8+15)) / 480 = 445 / 480 = 92.7%
2.
Step 2: Calculate Performance = (Actual Output Rate) / (Standard Labor Rate) = 9.3 / 11.0 = 84.5%
3.
Step 3: Calculate Quality = (Conforming Fragments) / (Total Assessed) = (100% − 18%) = 82.0%
4.
Step 4: Compute Labor-OEE = 0.927 × 0.845 × 0.820 = 0.645 → 64.5%
Answer:
The Labor-OEE is 64.5%, indicating significant opportunity: 7.3% Availability Loss points to coordination failures, 15.5% Performance Loss suggests fatigue or skill gaps, and 18.0% Quality Loss signals procedural non-compliance in hole deviation control.
🏗️ Real-World Application
At Newmont’s Boddington Gold Mine (Western Australia), supervisors implemented daily L-OEE tracking across 12 blast crews. Initial baseline revealed 72% Labor-OEE, driven primarily by 22% Performance Loss due to inconsistent stemming procedures. After introducing video-based standardized training and real-time digital checklists with supervisor validation, Performance rose to 94% within 8 weeks—and secondary breakage costs dropped 31%. Crucially, the improvement was sustained only after integrating L-OEE metrics into crew KPIs and weekly cross-shift peer reviews—not just top-down instruction.