Cambridge
Academy
In London

Introduction

Quality and productivity are at the forefront of sustaining competition in the global market. High customer expectations propel the demand for consistent quality in products and services. The quality control process is intrinsic to this, involving measuring and managing process variations that affect the final product.

Statistics is pivotal in quality management. The SPC statistical process and control for the decision-making seminar provide a quantitative foundation for managers to monitor and control production processes and evaluate the output quality, aligning closely with the ideals of Total Quality Management.

Managers responsible for product delivery or service provision must know about statistical tools that analyze process outputs that impact quality. Additionally, they should hone quantitative reasoning abilities to interpret statistical process control (SPC) findings or critically examine interpretations provided by others.

Course Objectives

By the end of this course, participants will be able to:

·        Grasp the concept of variation in work processes and how it is measured.

·        Recognize the significance of data quality in SPC.

·        Utilize the normal distribution in SPC effectively.

·        Differentiate and employ various control charts for diverse SPC processes.

·        Apply suitable statistical tools for quality control data analysis.

·        Translate statistical output into actionable management decisions.

·        Comprehend process capabilities concept, purpose, and assessment.

Target Audience

This course is designed for:

·        Managers, Supervisors, and Team Leaders.

·        Management support professionals.

·        Analysts who encounter data/analytical information in their jobs.

·        Individuals seeking to enhance decision-making through data analytics.

Course Outlines

Day 1: Setting the Statistical Scene for SPC

·        Overview of Statistical Process Control (SPC).

·        Fundamentals of Process Analysis and its Connection to Quality and Variation.

·        Implementing SPC with Six Sigma.

·        Statistics and Data Analysis in Quality Control.

·        Differentiating Data Types and Emphasizing Data Quality.

·        Introduction to Basic Statistical Concepts and SPC Tools.

·        Utilizing Summary Tables and Graphs to examine data distribution.

·        Exploring Descriptive Statistical Measures.

·        The critical role of the Normal Probability Distribution (Z Statistics).

·        Practical Excel analysis of sample QC datasets using essential statistical tools.

Day 2: Exploring SPC Tools

·        Framework of SPC Tools: Terms and Definitions.

·        Sub-group formation strategies.

·        Deep dive into Control Charts:

·        Different Types.

·        Data Requirements.

·        Methodology.

·        Interpretation.

·        Applications and Benefits.

·        Variable Control Charts for Continuous Data.

·        Detailed analysis of each Control Chart in Excel with sample datasets.

Day 3: Advancing SPC Tools

·        Investigation of Control Charts for Individual Data.

·        Study of Attribute Control Charts for Discrete/Countable Data.

·        Applying Excel to analyze datasets for each Control Chart.

Day 4: Validity Tests and Process Capability

·        Understanding Valid SPC Analysis Conditions.

·        Assumptions of Control Charts.

·        Using Curve Fitting to test for Normality.

·        Implementation of Run Chart and Run Test Rules in SPC.

·        Detailed Overview and Calculation of Process Capability Indices.

·        Utilizing Excel for validity tests and process capability analysis.

Day 5: Advancedx'satistics in SPC

·        Statistical Methods and inferences about Process Behaviour.

·        The role of Sampling and its distributions.

·        Confidence Limits and Hypothesis Tests: Interpretation and Use.

·        Analysis of Variance (ANOVA) and Regression Analysis.

·        Integrating SPC into the work environment using Excel.