Course Methodology
This course relies on exercises and workshops to engage participants and help them learn and apply new concepts and practices. Group and plenary discussions will be used, and participants will be required to debate and present their findings. In addition, videos with different case studies will be offered.
Course Objectives
By the end of the course, participants will be able to:
- Break down productivity concepts, types of organizational waste, and the role of AI
- Identify areas of poor organizational and personal effectiveness and efficiency and discuss practices and tools for improvement
- Analyze real-world case studies of organizations successfully implementing AI-driven productivity and quality improvement methodologies
- Apply quality improvement tools and techniques to improve processes, achieve faster cycle times, and reduce defects
- Explore effective cost-optimization strategies and understand how to leverage existing organizational culture to create a solid platform for adopting an AI-driven mindset
Target Audience
Managers, supervisors, and staff who are responsible for, or indirectly involved in, productivity and quality improvement initiatives using an AI mindset.
Target Competencies
- Problem-solving
- Change management
- Applying quality
- Understanding strategic cost optimization
- Applying Lean
- Understanding AI concepts
Introduction to Productivity, Organizational Waste, and AI
- Definitions of productivity, organizational waste, and AI
- Research on productivity
- Causes for poor productivity
- Productivity improvement approaches
- Productivity improvement focus areas: Staff, quality, and cost optimization
- Productivity factor
Organizational and Personal Effectiveness and Efficiency
- Improving staff productivity at the workplace
- Sleep and its impact on productivity
- Six tips from Robin Sharma to unleash your productivity
- Efficiency defined and the quality approach
- Identifying the 'Muda' factor and value-added analysis
- Lean principles and the eight types of waste
- SMART practices for increasing productivity
AI-driven Productivity and Quality Improvement Methodologies
- Introduction to AI within the context of quality improvement
- Overview of AI technologies and their applications in quality improvement
- Utilizing AI to collect, analyze, and interpret data for informed decision-making
- Predictive analytics for anticipating and mitigating quality issues
- Leveraging AI to identify bottlenecks and streamline workflows
- Lessons learned and best practices from real-world AI deployments
- Addressing ethical considerations and challenges in AI implementation
Productivity Improvement Tools and Methodologies
- Productivity improvement project road map
- Tool selection guideline
- The seven fundamental quality tools
- Why-Why and How-How tree tools
- Six Sigma methodology
- The 5 S program for improving efficiency
Cost Optimization Opportunities and Creating the AI Mindset
- Cutting cost strategically: Tips from the experts
- Setting up a cost-optimization program
- Barriers to cost optimization programs
- Resistance psychology in humans
- Getting commitment to a cost-optimization program
- One hundred and forty ideas to optimize costs
