Program Overview
Operations teams today face the pressure of driving productivity while managing variability, unplanned downtime, compliance, and cost efficiency. This program helps operations professionals understand how AI can be applied across daily workflows—automation, maintenance, quality control, safety, manpower planning, and cross-functional coordination. Through hands-on exercises using real operational data, participants learn how AI can analyze logs, detect anomalies, forecast failure patterns, optimize resources, and streamline reporting. The course equips teams to shift from reactive firefighting to predictive, insight-driven operations while building a governance and deployment roadmap to responsibly scale AI across the function and beyond.
Features
- Use AI tools to automate daily operational tasks, reporting, and administrative workflows
- Apply AI for predictive maintenance, quality analysis, and process optimization
- Strengthen safety, compliance, and manpower planning using AI-driven insights
- Build a 90-day roadmap for AI adoption within the operations function
Target audiences
- Operations & Manufacturing Teams
- Maintenance & Reliability Teams
- Safety & Compliance Teams
- Digital Transformation Teams
Curriculum
- 10 Sections
- 73 Lessons
- 2 Days
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- The New Operations Landscape - AI as an Enabler7
- 1.1Evolution of operations: Manual → Digital → Predictive → Autonomous
- 1.2What AI means for Ops leaders: ML, GenAI, Process Mining, Digital Twins
- 1.3Where AI fits in operations: production, maintenance, quality, safety, compliance, reporting
- 1.4Role of Ops teams in enterprise-wide AI enablement
- 1.5AI readiness model for Operations functions
- 1.6Exercise: Identify top 10 time-consuming operational processes suitable for AI
- 1.7Demo: AI summarizing operations logs or plant reports
- AI for Daily Operations Automation8
- 2.1Objective: Reduce operational workload through intelligent automation.
- 2.2AI for SOP automation, shift planning & resource allocation
- 2.3AI-based checklists, inspections & compliance workflows
- 2.4Automating repetitive admin tasks: reports, emails, approval notes
- 2.5AI-enhanced communication between shop floor & leadership
- 2.6RPA + AI for end-to-end workflow automation
- 2.7Exercise: Convert a manual SOP/inspection checklist → AI-automated version
- 2.8Auto-generate daily operations reports using AI
- AI for Operational Data, Dashboards & Reporting7
- 3.1AI for KPI reporting (OEE, downtime, output, yields, cycle time)
- 3.2AI-driven insights from production logs, shift books, QC data, and incident records
- 3.3Control tower capabilities: visibility, alerts, anomaly detection
- 3.4AI for RCA (Root Cause Analysis) from production issues
- 3.5Automated stakeholder updates & executive summaries
- 3.6Exercise: AI-generated “Operations Performance Summary”
- 3.7Transform messy logs into automated insights
- AI in Maintenance, Reliability & Asset Management7
- 4.1AI for predictive maintenance — sensors, algorithms, patterns
- 4.2Predicting failure patterns through ML models
- 4.3Spare parts forecasting & maintenance scheduling
- 4.4AI for lubrication analysis, vibration monitoring & equipment diagnostics
- 4.5Digital twins for asset health & scenario testing
- 4.6Exercise: Predictive maintenance simulation using sample asset data
- 4.7AI recommending preventive tasks & spare part needs
- AI for Quality Control & Process Optimization7
- 5.1AI for defect detection — image analytics & pattern recognition
- 5.2Process optimization using AI-driven parameter adjustments
- 5.3Quality prediction & preventive alerts
- 5.4Reducing rework, scrap & yield losses
- 5.5AI for process variance & capability analysis
- 5.6Exercise: AI-based RCA of a sample quality issue
- 5.7Exercise: Generate an AI-driven process improvement plan
- AI for Resource, Manpower & Capacity Planning7
- 6.1AI-generated shift planning & manpower optimization
- 6.2Dynamic scheduling based on demand, maintenance, & capacity
- 6.3AI for material requirement & resource forecasting
- 6.4Smart allocation for bottleneck operations
- 6.5Linking AI planning tools to ERP/MES
- 6.6Exercise: Create an AI-assisted manpower or shift plan
- 6.7AI-generated capacity plan considering constraints
- AI for Safety, Compliance & Risk Management7
- 7.1AI-based incident prediction (near misses, unsafe acts)
- 7.2Vision AI for PPE, safety violations & hazard identification
- 7.3Predictive risk scoring for plant operations
- 7.4AI for regulatory documentation, safety manuals, audits
- 7.5AI-generated compliance reports & SOP updates
- 7.6Exercise: AI-generated safety audit report
- 7.7Exercise: Analyze a simulated incident using AI for preventive actions
- AI for Supply Chain & Cross-Functional Operations7
- 8.1AI-driven synchronization of production & material planning
- 8.2AI for procurement demand (RM, PM, indirect materials)
- 8.3AI for dispatch scheduling, loading & logistics visibility
- 8.4Linking Ops dashboards with SCM, finance & quality KPIs
- 8.5Multi-functional decision loops with AI support
- 8.6Exercise: Create an AI-enabled cross-functional dashboard
- 8.7Exercise: Simulate a production bottleneck & AI-powered resolution plan
- GenAI for Documentation, SOPs, Trainings & Internal Communication7
- 9.1Auto-generating SOPs, WI (Work Instructions), job aids
- 9.2Automated training content for new operators
- 9.3Meeting notes, reports, escalation templates using AI
- 9.4Digital knowledge base creation & retrieval
- 9.5AI for daily review meetings and production summaries
- 9.6Exercise: Convert a process description → AI-generated SOP
- 9.7Exercise: Create a training-ready “How-To” guide using AI
- Responsible AI, Governance & Implementation Roadmap for Operations9
- 10.1Data governance & security for sensitive operational data
- 10.2AI safety: model accuracy, human oversight, error prevention
- 10.3IT–Ops integration for AI deployments
- 10.4How to pilot, deploy & scale AI in plant operations
- 10.5Building an internal Ops AI COE (Center of Excellence)
- 10.6KPIs to track AI-enabled performance
- 10.7Exercise: Define a governance checklist for Ops
- 10.8Exercise: Build an AI Roadmap for Operations
- 10.9Exercise: Choose 3 high-impact use cases to pilot immediately
Instructor
