Manufacturing Quality Tableau Dashboard

Overview

Built an interactive Tableau dashboard for manufacturing-quality review, combining KPI overview, yield trend, defect Pareto, equipment concentration, retest behavior, and corrective-action validation in one analytical workflow.

The dashboard separates rapid signal review from engineering drill-down. The overview surfaces yield movement and dominant defect contributors, while the drill-down supports filtered analysis across product family, line, station, equipment, defect code, review period, and lot-level records.

My contribution covered dashboard information architecture, quality-metric definitions, data preparation, calculated analysis fields, filter and drill-down design, visual hierarchy, validation logic, and the supporting Python-to-CSV data pipeline.

Problem

Manufacturing-quality investigations require engineers to connect yield movement, defect contribution, equipment concentration, retest behavior, lot-level context, and corrective-action history. When these signals are reviewed in separate files or isolated charts, teams can identify that a metric moved without quickly determining where the issue concentrated or whether the response was effective.

The dashboard needed to support a practical investigation sequence: detect abnormal movement, prioritize defect contributors, inspect equipment and process concentration, assess possible false-fail behavior, and compare issue-period performance with the post-action validation window.

Data Used

• Daily manufacturing lot-level quality records
• Input, pass, fail, retest, and retest-pass quantities
• Product family, line, station, equipment, lot, and shift dimensions
• Defect codes and defect categories
• Baseline, issue, corrective-action, and validation-period markers

Review Scope

• Yield, defect PPM, retest-pass rate, and before/after comparisons must use consistent filter context and review-window definitions.
• Equipment concentration is an investigation signal and does not independently establish equipment causality.
• Elevated retest-pass behavior can indicate possible false-fail contribution, but it does not by itself exclude a repeatable product or process issue.
• Before/after corrective-action comparison documents association with recovery; it does not isolate the independent effect of every intervention.

Approach

Designed a two-level Tableau workflow: a Quality Overview for abnormal-signal detection and an Engineering Drill-Down for filtered investigation. The analysis connects KPI movement, time-series context, defect Pareto, equipment concentration, retest behavior, and before/after corrective-action validation across consistent review periods.

Investigation Focus

• Quality KPI overview
• Yield and defect-PPM trend analysis
• Defect Pareto prioritization
• Equipment concentration review
• Retest and retest-pass behavior
• Corrective-action before/after validation

Key Investigation Choices

Methods & Tools

• Tableau
• Python
• CSV
• Tableau Calculated Fields
• Dashboard Filters

Result & Impact

• 6 views
  Analytical coverage
• 2 dashboards
  Dashboard experience
• Signal → validation
  Review workflow

Delivered an overview dashboard and engineering drill-down that connect abnormal-signal detection, defect prioritization, equipment concentration, retest analysis, and post-action validation in one Tableau workflow. The resulting dashboard supports a consistent progression from identifying what moved to investigating where it concentrated and reviewing whether the quality signal recovered.

Notes

• A useful quality dashboard should help teams move from abnormal-signal detection to action validation.
• Defect Pareto and equipment concentration views are stronger when paired with trend context.
• Retest behavior helps separate possible false-fail contribution from repeatable product-quality risk.
• A clear Tableau dashboard can help engineering and quality teams communicate the same manufacturing signal from different angles.