Executive Summary
In summary: This case study documents successful implementation of fatigue AI in a Peruvian mining operation under DS 024-2016-EM, achieving 73% reduction in drowsiness-related incidents and 340% ROI over 18 months through preventive pre-work monitoring and in-cabin DMS systems.
Key Points:
- Problem: 23 fatigue incidents in 2024, $180,000 USD SUNAFIL fines, poor DS 024 compliance
- Solution: 3-layer Logifit ecosystem with smartbands, ProVision AI DMS and predictive analytics platform
- Impact: 73% incident reduction, $620,000 USD annual savings, 98% DS 024 compliance
A DS 024 case study analyzes practical implementation of anti-fatigue technology under Peru's mining safety regulatory framework. In Latin America's energy and mining sector, where night shift operations represent 67% of serious incidents according to SUNAFIL 2024, AI systems for fatigue prevention have demonstrated measurable ROI and verifiable regulatory compliance. (Source: OSHA — Commonly Used Statistics)
DS 024 Regulatory Context: Legal Framework for Fatigue Prevention
Supreme Decree DS 024-2016-EM establishes specific requirements for fatigue management in energy mining operations. According to SUNAFIL 2025 data, 89% of DS 024 non-compliance sanctions relate to inadequate fatigue monitoring systems.
Solutions like Logifit Pre-Work assessment identify risks before each shift begins, measuring sleep phases and generating real-time fitness status.
DS 024 Article 47-A Requirements
Establishes mandatory fatigue detection systems for mobile equipment operators in mining. Includes pre-shift assessment, continuous monitoring and documented records for SUNAFIL audits.
The operation analyzed in this case study faced recurring sanctions for DS 024 compliance deficiencies. Between January-October 2024, it recorded 23 incidents classified as "fatigue-related" by internal investigation, representing 34% of total safety events.
Critical Data: Mining operations without fatigue AI systems register 2.7x more night incidents vs. operations with automated monitoring (ICMM 2024).
The DS 024 regulatory framework requires specific documentation of fatigue-related safety KPIs:
- Documented pre-shift assessments: Record of physical and mental fitness of critical operators
- Real-time monitoring: Automatic alert systems for microsleep or distraction
- Predictive analytics: Risk pattern identification before critical events
Case Study Methodology: Phased Implementation
Implementation followed structured 3-phase methodology over 18 months, with continuous safety KPIs measurement and documented ROI by operational area.
Systems like Logifit In-Cabin DMS system detect microsleeps and distractions in under 300 milliseconds using infrared computer vision.
Phase 1: Pre-Work Assessment (Months 1-6)
Deployment of Logifit Band 9 smartbands for 340 critical operators, including mining truck operators, shovels and auxiliary equipment. The system generates APTO/NO APTO classification based on sleep phase analysis and PVT reaction time tests.
Smartband Integration Protocol
Integration protocol that synchronizes biometric data with existing SCADA systems. Enables automatic work fitness classification according to DS 024 parameters with 94.3% accuracy.
| Pre-Work Metric | 2024 Baseline | Post-Implementation | Improvement |
|---|---|---|---|
| Assessment time/operator | 12 minutes | 3 minutes | 75% reduction |
| Severe fatigue detection | 23% missed cases | 2% missed cases | 91% improvement |
| False positives | 34% | 6% | 82% reduction |
Phase 2: In-Cabin DMS Monitoring (Months 7-12)
Installation of ProVision AI Cam systems in 85 critical units, including CAT 793F trucks, P&H 4100 shovels and D10T tractors. Microsleep and distraction detection operates with <300ms latency according to technical specifications.
Key Fact: Logifit DMS systems detect fatigue events 7.2 seconds earlier than traditional visual supervision methods (Universidad Nacional de Ingeniería study 2024).
DMS system safety KPIs showed significant improvements:
- Detected microsleep reduction: From 156 events/month to 41 events/month in 6 months
- Alert response time: Average 4.7 seconds vs. 23 seconds from previous method
- Stop protocol compliance: 94% vs. 67% in previous manual system
Phase 3: Predictive Analytics (Months 13-18)
Full implementation of Logifit Ops platform with machine learning for risk forecasting. The system analyzes historical patterns, weather conditions, rotating shifts and biometric data to generate preventive alerts.
Predictive Risk Scoring
Proprietary algorithm that assigns 0-100 risk score per operator/shift. Integrates 47 variables including sleep history, environmental conditions, workload and rotating shift patterns.
Measurable Results: Safety KPIs and Documented ROI
Quantitative results of the case study demonstrate direct impact on corporate safety KPIs and DS 024 compliance verifiable by external audit.
Tools like Logifit Ops Platform integrate biometric data, DMS alerts, and predictive analytics in a centralized dashboard.
Operations with Logifit anti-fatigue AI achieve 73% reduction in incidents related to drowsiness, according to data audited by SGS Peru during 18 months of implementation.
Safety KPIs: Quantifiable Safety Indicators
Systematic measurement of safety KPIs provided objective evidence of fatigue prevention improvements:
- Total Incidents: Reduction from 23 events (2024) to 6 events (2025) = 73.9% improvement
- Fatigue Near Misses: Decrease from 89 reports to 34 reports = 61.8% reduction
- Lost Time: From 2,340 hours/year to 890 hours/year = 62.0% improvement
- Cost per Incident: Average $27,000 USD to $11,200 USD = 58.5% reduction
ROI Analysis: Documented Return on Investment
ROI calculation included direct implementation costs versus measurable savings in incident prevention, regulatory compliance and operational optimization. (Source: McKinsey — Mining Insights)
Total Investment (18 months):
- Logifit software licenses: $145,000 USD
- Hardware (smartbands + DMS cameras): $89,000 USD
- Implementation and training: $34,000 USD
- Total: $268,000 USD
Annualized Savings:
- Incident cost reduction: $387,000 USD/year
- SUNAFIL fines avoidance: $180,000 USD/year
- Insurance premium reduction: $53,000 USD/year
- Total: $620,000 USD/year
ROI Calculation Formula
ROI = (Annual Savings - Total Investment) / Total Investment × 100. In this case: ($620,000 - $268,000) / $268,000 = 131% annual, equivalent to 340% over 18 months of measurement.
DS 024 Compliance: Evidence of Regulatory Conformity
The March 2025 SUNAFIL audit rated the system as "DS 024 compliance model" with 98% conformity in fatigue management requirements.
Verified DS 024 compliance elements included:
- Article 47-A Pre-shift Assessment: Automated system with audited digital records
- Article 47-B Continuous Monitoring: DMS with documented <300ms automatic alert
- Article 47-C Event Recording: Database with complete 24/7 traceability
- Article 47-D Training: Logifit Academy module with digital certification
Key Fact: 94% of Peruvian mining operations do not fully comply with DS 024 fatigue monitoring requirements, according to SUNAFIL 2024 survey.
Compliance documentation included:
- Biometric records: 127,340 documented pre-shift assessments
- DMS events: 2,847 processed alerts with average response time
- Predictive analysis: 156 preventive interventions with documented outcome
- Operator training: 340 completed Logifit Academy certifications
Implementation Challenges in LATAM Energy Sector
Implementation faced specific challenges of the Latin American energy sector, including intermittent connectivity, cultural resistance and budgetary limitations typical of emerging markets.
Connectivity and IT Infrastructure
Remote energy operations require solutions with robust offline functionality. Logifit system operates 72 hours without connectivity, synchronizing data when communication is restored.
Edge Computing Architecture
Local processing architecture that enables full functionality without internet connection. X1 modules process AI directly in-cabin, reducing dependence on external connectivity.
Cultural Adoption and Change Resistance
Initial operator resistance required structured change management strategy:
- Transparent communication: Clear explanation of personal and organizational benefits
- Active participation: Operators as safety ambassadors in the process
- Measurable incentives: Bonuses for improvements in individual safety KPIs
Adoption results showed positive evolution:
| Adoption Metric | Month 3 | Month 9 | Month 18 |
|---|---|---|---|
| Daily smartband usage | 67% | 89% | 96% |
| Assessment compliance | 78% | 94% | 98% |
| Operator satisfaction | 54% | 81% | 87% |
Cost Considerations for Emerging Markets
Rollout strategy considered typical LATAM budgetary limitations through phased implementation and flexible financing models.
"The key to success in LATAM is demonstrating tangible ROI in short timeframes, adapting technology to local operational realities without compromising effectiveness"
— Safety Manager, Analyzed Mining OperationGradual implementation options included:
- 90-day pilot: 50 critical operators, $45,000 USD investment
- Selective phase: Highest risk equipment, scaling based on results
- Full rollout: Complete operation once initial ROI validated
Lessons Learned and Case Best Practices
Retrospective analysis of the case study identified critical success factors and replicable elements for future implementations in the energy sector.
For more on this topic, see our article on related case study strategies.
Critical Success Factors
Success determinants included visible executive leadership, systematic communication and rigorous safety KPIs measurement from the start.
- Executive sponsorship: General Manager as visible project champion
- Defined KPIs: Specific success metrics agreed beforehand
- Structured training: 40 hours of training per supervisor
- 24/7 support: Logifit call center with <15 minutes average response
Change Management Framework
Change management framework based on early communication, active operator participation and continuous satisfaction measurement. Includes escalation protocol for specific resistances.
Replicable Elements in Other Operations
Identified best practices are applicable to similar Latin American energy sector operations:
- Quantitative baseline: Rigorous safety KPIs measurement pre-implementation
- Controlled pilot: Validation in reduced group before massive rollout
- Gradual IT integration: Progressive connection with existing systems
- Practical training: Training on real equipment, not simulation
Identified Avoidable Errors
Main errors identified in similar implementations include underestimating cultural resistance, inadequate DMS alert configuration and lack of systematic post-implementation follow-up.
Critical Data: 67% of anti-fatigue technology implementations fail due to inadequate alert sensitivity configuration, generating alarm fatigue (MIT 2024).
Replicate This Case Study in Your Operation
The safety KPIs and methodology documented in this case are replicable in similar energy operations. Logifit offers free baseline assessment and specific ROI projection.
Request Assessment →2026 Projection: Regulatory and Technological Evolution
2026 regulatory trends in LATAM indicate tightening of DS 024 requirements and expansion to renewable energy sectors, making anti-fatigue AI a standard operational requirement. (Source: ISO 45001 — Occupational Safety)
Expected developments include:
- Modified DS 024: Probable update including solar and wind energy
- NR-17 Integration: Harmonization with Brazilian norms for regional operations
- Mandatory certification: Monitoring systems with SUNAFIL technical validation
- Automatic reporting: Direct safety KPIs transmission to authorities
Energy operations implementing anti-fatigue AI in 2026 will avoid $2.3 million USD average in fines and incident costs over the next 3 years (McKinsey Energy 2025 Projection).
Proactive Preparation for New Regulations
Leading organizations are anticipating regulatory requirements through voluntary implementation of advanced fatigue management systems.
Preparation includes:
- Regulatory gap audit: Current evaluation vs. projected 2026 requirements
- Technology roadmap: 24-month staggered implementation plan
- Preventive training: Internal competency development before mandatory compliance
- Proactive documentation: Recording systems that exceed current requirements
This DS 024 case study demonstrates that structured implementation of anti-fatigue AI generates measurable ROI, improved safety KPIs and verifiable regulatory compliance. In the context of projected regulatory tightening for 2026, energy organizations that adopt preventive technology early will obtain significant competitive advantages in operational costs, risk management and regulatory compliance.
The quantified results—73% incident reduction, 340% ROI over 18 months, 98% DS 024 compliance—provide empirical evidence of the effectiveness of integrated fatigue prevention systems in critical Latin American energy sector operations.