Introduction
Operational performance depends on the ability to measure efficiency, identify bottlenecks, and improve processes continuously. Operations Analytics for Process Improvement equips professionals with the analytical tools and thinking needed to evaluate operational data and turn it into practical improvements. The course focuses on using metrics, workflows, and performance data to strengthen productivity, service quality, and cost efficiency across operations.
Course Objectives
- Understand how analytics supports operational improvement
- Measure process performance using relevant operational metrics
- Identify inefficiencies, bottlenecks, and variation
- Use data to improve productivity, quality, and service delivery
- Support continuous improvement with evidence-based analysis
- Strengthen decision-making in operations management
Target Audience
- Operations managers and supervisors
- Professionals involved in process improvement initiatives
- Supply chain, service, and production staff
- Business analysts supporting operational reporting
- Team leaders responsible for productivity and efficiency
- Executives overseeing operational performance
Course Outline
- 5 Sections
- 0 Lessons
- 5 Days
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- Day 1: Analytics in Operations Management• What operations analytics is and why it matters
• Key operational data sources and measures
• Understanding process flow and performance
• The role of analytics in efficiency and service improvement
• Examples of operational decisions informed by data0 - Day 2: Measuring Operational Performance• Cycle time, throughput, utilization, and quality metrics
• Tracking cost, delays, and service levels
• Using dashboards for operational visibility
• Aligning operational KPIs with business objectives
• Workshop: Measuring a business process0 - Day 3: Diagnosing Operational Problems• Identifying bottlenecks and waste
• Analyzing variation and recurring issues
• Using root-cause methods with operational data
• Recognizing patterns in process breakdowns
• Practical activity: Diagnosing a process improvement case0 - Day 4: Data-Driven Process Improvement• Prioritizing improvement opportunities
• Comparing alternative solutions using evidence
• Monitoring the impact of changes
• Balancing efficiency, quality, and customer outcomes
• Case study: Analytics-led process transformation0 - Day 5: Embedding Analytics into Operations• Building continuous improvement routines
• Supporting accountability through operational reporting
• Improving collaboration between teams and functions
• Creating an operations analytics roadmap
• Final group project: Presenting a process improvement plan using data0
Instructor
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