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Quest Training

Excel for Quality Data Analysis Training Course

Helps quality professionals use Excel's data, statistical and charting tools to turn inspection and production records into actionable insight.

SPC · Statistical Process Control & Quality AnalyticsAll Levels#2921ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Quality Management & Operational ExcellenceCategories:Statistical Process Control & Quality Analytics

Course Overview

Excel is a practical and widely used tool for analyzing quality data, monitoring operational performance, identifying defects, and supporting continuous improvement. When applied effectively, its data management, statistical, and visualization capabilities help quality professionals turn inspection and production records into meaningful insights that support informed decisions and more consistent processes. This course focuses on the practical use of Microsoft Excel for quality data analysis, covering data preparation, descriptive statistics, conditional formulas, lookup functions, PivotTables, charts, defect analysis, trend monitoring, and quality performance indicators. Participants will learn how to organize inspection records, calculate rejection rates, analyze defect patterns, compare performance across production lines, and use statistical functions to evaluate process variation. Through hands-on exercises and realistic quality datasets, participants will develop the skills to automate routine calculations, build interactive quality reports, monitor key performance indicators, and communicate findings clearly. The course emphasizes techniques that can be applied directly to quality assurance, manufacturing, inspection, supplier quality, and continuous improvement activities without requiring advanced programming skills.

Learning Objectives

  • Apply Excel tools to organize and analyze quality-related datasets.
  • Clean, validate, and structure inspection and production records.
  • Use formulas and functions to calculate quality metrics accurately.
  • Apply descriptive statistics to evaluate process performance and variation.
  • Use conditional formulas and lookup functions to classify and investigate quality issues.
  • Create PivotTables and PivotCharts to summarize and compare quality results.
  • Analyze defect frequencies, rejection rates, and recurring quality problems.
  • Develop charts and dashboards to monitor quality performance over time.
  • Use Excel's statistical functions to support data-driven quality investigations.
  • Produce standardized quality reports that support corrective actions and continuous improvement.

Who Should Attend

Quality assurance and quality control managers and specialists. Quality inspectors and data analysts. Manufacturing, production, and process engineers. Production supervisors and operations coordinators. Continuous improvement and operational excellence professionals. Six Sigma practitioners and process improvement specialists. Supplier quality and incoming inspection personnel. Laboratory and testing professionals managing measurement records. Professionals responsible for quality reporting and performance indicators. Employees seeking to improve their Excel skills for practical quality analysis.

Learning Outcomes

  • Import, organize, clean, and validate quality data in Excel.
  • Apply formulas and functions to calculate defect counts, rejection rates, and quality indicators.
  • Use conditional logic, lookup functions, and error-handling techniques in quality analysis.
  • Calculate averages, standard deviations, ranges, and other descriptive statistics.
  • Build PivotTables to summarize quality data by product, process, supplier, or production shift.
  • Create charts that reveal defect patterns, performance trends, and unusual results.
  • Apply Pareto analysis to identify the most significant defect categories.
  • Develop dashboards that present key quality indicators clearly.
  • Use Excel's statistical capabilities to compare performance and investigate variation.
  • Prepare consistent, evidence-based quality reports with practical improvement recommendations.

Course Outline

Day 1

Excel Fundamentals and Quality Data Preparation

  • Understanding Excel's role in quality management and data analysis.
  • Organizing quality records using structured tables and consistent data formats.
  • Importing and consolidating inspection, production, and defect data.
  • Cleaning datasets by handling duplicates, missing values, and inconsistent entries.
  • Applying sorting, filtering, data validation, and conditional formatting.
  • Using essential formulas and functions, including IF, COUNTIF, SUMIF, and AVERAGEIF.
  • Practical Exercise: Prepare and validate an inspection dataset containing product measurements, defect records, and production quantities.
Day 2

Statistical Analysis and Quality Indicators

  • Calculating mean, median, range, variance, and standard deviation.
  • Using COUNTIFS, SUMIFS, AVERAGEIFS, and logical functions for quality calculations.
  • Calculating defect rates, rejection percentages, and first-pass yield where applicable.
  • Applying lookup functions such as XLOOKUP or VLOOKUP to connect quality records.
  • Identifying unusual measurements, outliers, and performance variations.
  • Using Excel statistical functions to summarize process performance.
  • Practical Exercise: Calculate and interpret quality indicators for multiple products, production lines, and inspection periods.
Day 3

Defect Analysis and Data Visualization

  • Analyzing defect frequency, severity, and distribution.
  • Applying Pareto analysis to prioritize recurring quality problems.
  • Creating histograms, bar charts, line charts, scatterplots, and box plots where supported.
  • Using trend charts to monitor changes in quality performance.
  • Applying conditional formatting to highlight deviations and exceptions.
  • Interpreting charts to distinguish recurring patterns from isolated observations.
  • Practical Exercise: Conduct a defect analysis and develop visualizations to identify the main contributors to quality losses.
Day 4

PivotTables, Reporting, and Quality Dashboards

  • Creating and customizing PivotTables for multidimensional quality analysis.
  • Summarizing results by product, supplier, shift, department, and defect category.
  • Building PivotCharts and using slicers and filters for interactive analysis.
  • Designing dashboards with key quality performance indicators.
  • Applying formulas and conditional formatting to support automated reporting.
  • Establishing consistent reporting layouts and data refresh procedures.
  • Practical Exercise: Build an interactive quality dashboard showing defect rates, rejection trends, and performance by production line.
Day 5

Advanced Quality Analysis and Continuous Improvement

  • Using advanced conditional formulas and nested functions for quality investigations.
  • Applying Excel's statistical tools to compare process performance and evaluate variation.
  • Introducing control charts and process capability calculations using appropriate data and assumptions.
  • Comparing quality results before and after corrective actions.
  • Automating recurring reports through reusable templates and structured workflows.
  • Translating analytical findings into corrective actions and continuous improvement recommendations.
  • Practical Exercise: Complete an end-to-end quality data analysis project and present a dashboard with findings, performance indicators, and improvement priorities.

Upcoming Dates

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Madrid · 11 – 15 Oct 2027 · Classroom

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