Introduction

In today's competitive landscape, making informed decisions is crucial for maintaining quality standards. This 5-day professional course is designed to equip participants with the skills and tools needed to harness data for effective decision-making and drive continuous quality improvement across their organizations.

Objectives

• Understand the fundamentals of data-driven decision making.
• Learn how to collect, analyze, and interpret data effectively.
• Apply data-driven strategies to improve product and service quality.
• Utilize statistical tools for quality improvement processes.
• Develop a culture of continuous improvement within an organization.

Course Outlines

Day 1: Introduction to Data-Driven Decision Making

• Overview of data-driven decision making concepts
• Importance of data quality and integrity
• Key components of a data-driven quality strategy
• Case studies of successful data-driven approaches
• Interactive session: Identifying decision-making challenges

Day 2: Data Collection and Analysis Techniques

• Methods for effective data collection
• Introduction to data analysis tools and software
• Data visualization techniques
• Hands-on workshop: Data set analysis using Excel/Google Sheets
• Interactive session: Sharing best practices in data collection

Day 3: Applying Statistical Methods for Quality Improvement

• Introduction to statistical quality control
• Using control charts for process monitoring
• Applying regression analysis for predictive insights
• Workshop: Building and interpreting control charts
• Discussion: Integrating statistical methods into decision-making

Day 4: Advanced Tools for Data-Driven Quality Management

• Exploring Six Sigma methodologies
• Introduction to Lean principles
• Using software tools for quality management
• Interactive case analysis: Implementing Lean Six Sigma
• Group activity: Designing a quality improvement project

Day 5: Building a Data-Driven Culture

• Strategies for fostering a culture of continuous improvement
• Role of leadership in data-driven decision making
• Balancing data insights with human judgment
• Developing a roadmap for organizational change
• Closing session: Presentations of quality improvement project plans