Asset-intensive organisations must align their maintenance strategies with business goals in a fast‑evolving industrial environment. Central to this alignment is criticality analysis—a structured, data‑driven process to rank and prioritise equipment based on its impact on operations, safety, environment, and cost.
For interactive visuals and an explanatory heatmap, see the Risk-Based Maintenance article: Risk Matrix Heatmap →
Drawing from empirical research and industry best practices, this guide presents a practical, technical approach for maintenance and reliability engineers to embed criticality into maintenance prioritisation.
What Is Equipment Criticality?
Equipment criticality measures the impact that equipment failure would have on an organisation. It informs how maintenance resources are allocated and which assets warrant proactive strategies such as predictive maintenance.
A defined methodology enables the classification and prioritisation of assets based first on failure impact, and then on quantitative criteria that determine relative importance (Teixeira et al., 2024).
Key Criteria (Weighted)
- Safety (S) – Potential for harm to personnel (up to 40 points)
- Environment (E) – Risk of spills, emissions, or regulatory breach (up to 40 points)
- Quality (Q) – Defect creation or rework
- Throughput (T) – Production loss or line stoppage
- Customer Service (CS) – Order delays or fulfilment impact
- Operating Cost (OC) – Repair or replacement cost
Total Consequence (TC)
TC = S + E + Q + T + CS + OCRelative Risk (RR)
RR = TC × Probability of Failure (PoF)Example Probability of Failure (PoF) scale:
- 1 = < 1 failure per 10 years
- 5 = 1 failure every 5 years
- 10 = Failure every month
Why Criticality Analysis Matters
Research shows that while 67% of companies prioritise maintenance work orders, only 35% have a formal criticality assessment process in place (Gopalakrishnan & Skoogh, 2018).
Without a structured approach:
- Priorities default to technician judgement
- Maintenance becomes reactive and inconsistent
- Resources are wasted on non‑critical assets
A robust criticality assessment:
- Focuses resources where they prevent the most loss
- Improves system‑wide productivity by targeting bottlenecks
- Reduces downtime across the production chain
Steps to Conduct a Criticality Review
Step 1: Establish an Evaluation Framework
Form a cross‑functional team (Operations, Engineering, EH&S). Define scoring scales, weighting, and thresholds. Combine objective metrics (MTBF, downtime cost) with qualitative criteria (safety, environmental risk).
Step 2: Score Equipment
Each asset receives a 1–10 score for each consequence category and PoF. Safety and environmental criteria often receive higher maximum weights. Use CMMS data and structured interviews to close data gaps.
Step 3: Classify Equipment
Group assets into classes using defined thresholds:
- A‑Class (Critical): TC ≥ 38 or any category = 10
- B‑Class (Moderate): TC ≥ 20 or any category ≥ 6
- C‑Class (Low): TC < 10
Step 4: Integrate with CMMS
Embed criticality scores or classes into asset master data. Use them to drive:
- Preventive maintenance frequencies
- Spare parts stocking strategies
- Predictive maintenance deployment (vibration, IR, oil analysis)
Industry Findings: Reality vs Practice
Empirical studies show that most organisations still rely on operator experience to determine maintenance priority, even when ABC classification systems exist. Technicians frequently override CMMS priorities based on situational urgency, revealing a persistent gap between planning and execution (Gopalakrishnan & Skoogh, 2018).
Simulation studies demonstrate that prioritising bottleneck assets alone can increase throughput by up to 11.2%, purely by executing maintenance in a smarter sequence—without adding resources.
Visualising Risk: Probability–Impact Matrix
| Probability \ Impact | Low (1–3) | Medium (4–6) | High (7–10) |
|---|---|---|---|
| Low (1–3) | Low | Medium | High |
| Medium (4–6) | Medium | High | Very High |
| High (7–10) | High | Very High | Critical |
Common Pitfalls to Avoid
- Static assessments: Update annually or after major operational changes
- Lack of system view: Prioritise assets based on end‑to‑end production impact
- Operator bias: Use data to validate intuition, not replace it blindly
For an interactive risk matrix and heatmap, see the Risk-Based Maintenance article: Risk Matrix Heatmap →
This visual helps planners quickly assess high-risk assets and prioritise maintenance actions.
Conclusion: Moving Toward Data‑Driven Prioritisation
As maintenance strategies evolve toward digital and predictive models, criticality analysis must move beyond static lists to dynamic decision‑support tools.
By adopting a systemic, data‑informed, and collaborative approach, organisations can unlock significant gains in uptime, productivity, and reliability across the asset base.
Sources
- Gopalakrishnan, M., & Skoogh, A. (2018). Machine criticality based maintenance prioritization: Identifying productivity improvement potential. International Journal of Productivity and Performance Management, 67(4), 654–672. https://doi.org/10.1108/IJPPM-07-2017-0168
- Smith, R., Gpallied, C., & Martin, D. (n.d.). 10 Maintenance and Reliability KPIs You Need to Know and Use. CMRP Louisiana Offshore Oil Port.
- Teixeira, H. N., Lopes, I. S., & Pires, R. N. (2024). Maintenance Strategy Selection: An Approach Based on Equipment Criticality and Focused on Components. Lecture Notes in Mechanical Engineering. https://doi.org/10.1007/978-3-031-38165-2_1