🎓 Lesson 14 D5

Root Cause Cost Mapping Workshop

Root Cause Cost Mapping is a method to trace every cost in mining operations back to its fundamental source—like rock hardness or equipment downtime—so engineers know exactly what to fix to save money.

🎯 Learning Objectives

✓ Analyze a mine’s unit cost report to isolate ≥3 primary cost drivers using causal logic mapping
✓ Calculate cost impact magnitude (in $/ton) attributable to a specific root cause (e.g., suboptimal fragmentation) using attributable cost allocation
✓ Design a corrective action plan linking a root cause (e.g., inconsistent drill pattern) to measurable KPI improvements (e.g., 12% reduction in secondary breakage cost)
✓ Explain how geotechnical uncertainty propagates into cost variance using a validated RCCM causal chain

📖 Why This Matters

In open-pit mines, 60–75% of production cost variance stems from upstream decisions—especially blasting and mucking—not from haul truck fuel or labor rates. Without Root Cause Cost Mapping, teams waste resources optimizing symptoms (e.g., adding more shovels) while ignoring root causes (e.g., poor fragmentation increasing shovel cycle time by 18%). This workshop teaches you to build a forensic cost map—turning cost reports into engineering action plans.

📘 Core Principles

RCCM rests on three pillars: (1) Causal Layering—distinguishing proximate causes (e.g., 'shovel underutilization') from root causes (e.g., 'excessive boulder size due to high burden-to-spacing ratio'); (2) Cost Attribution—allocating shared costs (e.g., maintenance) to specific technical drivers using activity-based tracing; and (3) Sensitivity-Weighted Prioritization—ranking root causes by both cost impact ($/ton) and controllability (engineering feasibility score 1–5). The framework uses Ishikawa-style fishbone diagrams augmented with Monte Carlo-derived cost elasticity coefficients—ensuring statistical rigor behind every causal link.

📐 Attributable Cost Allocation Formula

This formula quantifies how much of a shared cost (e.g., shovel maintenance) is driven by a specific root cause (e.g., fragmentation quality index, FQI). It enables objective prioritization by isolating engineering leverage points.

Attributable Cost per Ton (ACₜ)

ACₜ = C₀ × |E| × (ΔD / Dₜ)

Quantifies the portion of a shared cost attributable to deviation of a technical driver (D) from its target (Dₜ), scaled by its cost elasticity (E) and base cost (C₀).

Variables:

Symbol	Name	Unit	Description
ACₜ	Attributable cost per ton	$/ton	Cost component directly traceable to root cause deviation
C₀	Base unit cost	$/ton	Total observed cost for the cost category before attribution
E	Cost elasticity coefficient	dimensionless	Sensitivity of cost to driver variation; negative sign indicates inverse relationship
ΔD	Driver deviation	units	Absolute difference between actual and target driver value (e.g., m, MPa, index unit)
Dₜ	Target driver value	units	Optimal or design value of the technical driver

Typical Ranges:

Fragmentation quality index (FQI) vs. shovel maintenance: -1.2 to -1.6

Bench height vs. drill utilization cost: +0.7 to +0.9

💡 Worked Example

Problem: A copper mine spends $1.85/ton on shovel maintenance. Fragmentation quality index (FQI) averages 0.72 (target = 0.85). Historical data shows maintenance cost elasticity w.r.t. FQI = −1.42. Current FQI shortfall = 0.13 units.

1. Step 1: Compute relative deviation: ΔFQI = Target FQI − Actual FQI = 0.85 − 0.72 = 0.13

2. Step 2: Apply elasticity: % Cost Increase = |Elasticity| × (ΔFQI / Target FQI) = 1.42 × (0.13 / 0.85) ≈ 0.217 (21.7%)

3. Step 3: Calculate attributable cost: ACₜ = Base Maintenance Cost × % Increase = $1.85/ton × 0.217 ≈ $0.402/ton

Answer: The attributable cost of suboptimal fragmentation is $0.40/ton, representing 21.7% of total shovel maintenance cost—justifying investment in blast optimization.

🏗️ Real-World Application

At BHP’s Escondida Norte pit (Chile), RCA revealed that 38% of shovel maintenance overruns stemmed from high-frequency vibration caused by oversized muck (>1.2 m max dimension), traced to inconsistent burden control in hard diorite zones (UCS = 180 MPa). Using RCCM, engineers redesigned the blast pattern (reducing burden from 4.1 m to 3.6 m), improved FQI from 0.69 to 0.81, and reduced attributable shovel maintenance cost by $0.33/ton—yielding $14.2M annual savings across 43 Mt/y production. Validation used 12-month post-implementation telemetry and maintenance log regression.