Default Case Study Guide: Apex Electro-Tech

Context: Apex Electro-Tech produces smart IoT hubs. Due to rising customer complaints and internal rework loops, profitability has declined. Follow the guided sequence below to see how each tool is used:

Step 0 (Symptom & COPQ): Apex selects *High defects*. They analyze their COPQ (Revenue: ₹100 Cr). Visible External Failures are ₹4.2 Cr, while hidden Internal Failures reach ₹6.8 Cr. Appraisal costs are ₹2.5 Cr and Prevention is ₹0.8 Cr.
Step 1 (Check Sheet): The team logs counts for 5 main defects over 4 process stations. "Solder Joint Fracture" is logged with 65 total occurrences, the highest on the list.
Step 2 (Pareto): The compiled Check Sheet is sorted. Solder Fractures represent 40.9% of defects. The "Vital Few" cutoff points to Solder Joint Fracture, Calibration Drift, and Enclosure Scratch (accounting for 81.1% of total errors).
Step 3 (Fishbone & 5-Why): Focus is set to *Solder Joint Fracture*. A 5-Why trace reveals reflow oven zones are shifting due to an unstandardized thermocouple calibration schedule.
Step 4 (Histogram): Peak reflow oven temperature is measured. LSL is 235°C, USL is 245°C. With a Mean of 238.4°C and Std Dev of 1.48°C, the process is found **Not Capable** ($C_{pk} = 0.77$).
Step 5 (Scatter): Conveyor speed is plotted against Peak temperature, revealing a strong negative correlation ($r = -0.98$) between conveyor speed and thermal joints.
Step 6 (Control Chart): Tracking hourly oven peak temperatures reveals that the process is out of control at hour 9 and hour 19 due to external electrical loading fluctuations.
Step 7 (Flowchart): The process map identifies hand-soldering assembly (Station 2) as generating 75.6% of solder fractures.

Lean Diagnostic & Quality Toolkit

A practitioner's utility to quantify quality costs, isolate process vulnerabilities, and select targeted Lean interventions based on operational symptoms.

Select Workspace Mode

Load Default Case Study

Apex Electro-Tech IoT Smart Hub defects case. Automatically populates all steps with tutorial data.

Clear & Enter My Own Data

Clear all fields and begin a clean operational quality study with your own process parameters.

Step 0: Diagnose the Symptom First

Cost of Poor Quality (COPQ) Calculator

Step 1: Check Sheet

Tally & Log
Check Sheet Rule: Define the process steps (stations) and failure categories before starting the logs to establish stratification metrics at the point of occurrence.

Check Sheet Configurations

Define Defect Categories

Define the defect types your operators will tally. You can add new rows or modify names.

Category Name Action

Data Tally Form

Input tally counts for each defect type across periods.

Step 2: Pareto Chart

Prioritise Vital Few
Pareto Principle: Separate the vital few from the trivial many. Quantify the cumulative impacts to focus operational focus.

Cumulative Defect Analysis

Pareto Priorities

Rank Defect Type Count % Cum %

Vital Few Analysis

Calculate to prioritize.

Step 3: Fishbone & 5-Why Diagram

Diagnose Root Causes
Ishikawa Guideline: Ensure representation across all 6M categories to counter confirmation bias or process preconceptions.

Visual Ishikawa 6M Model

Effect (#1 Pareto Defect) Man (People) Machine (Equipment) Method (Process) Material (Inputs) Measurement Mother Nature (Env)

Add Causes to 6M Categories

Select a category and input a potential cause, then look at the list below to perform a 5-Why analysis on it.

5-Why Drill-Down Tool

Confirmed Causes & 5-Why Root Diagnoses

Category Brainstormed Cause Ultimate Root Cause (5-Why) Action
No root causes added yet. Use the fields above to brainstorm and drill down.

Step 4: Histogram & Process Capability Analysis

Cp & Cpk Math
Capability Assessment: Use histograms to evaluate process distribution skewness, centering, and spread. Standard capability threshold is set at $C_{pk} \ge 1.33$.

Variable Configurations

Capability Distribution Histogram

Parameters & Calculations

Step 5: Scatter Diagram

Correlation Analysis
Correlation Analysis: Leverage bivariate data mapping to investigate process dependencies and verify root-cause hypotheses before designing engineering interventions.

Scatter Axis Configurations

Bivariate Correlation Plot

Input Paired Data (X, Y)

Input values formatted as: X_value, Y_value (one pair per line).

Step 6: SPC Control Chart

Process Stability
Statistical Process Control: Control limits represent internal process capability. Compare actual runs with standard $\pm3\sigma$ thresholds to detect out-of-control conditions.

Control Chart Settings

Process Control Chart (X / Run Chart)

Control Limits Settings

Input time-series measurements (one value per line) to calculate Mean, UCL, and LCL.

Step 7: Flowchart & Stratification Mapping

Locate Defect Birthplace
Stratification Principle: Sub-divide aggregated error logs based on manufacturing cell stations, operator teams, or shifts to locate exactly where defects are generated.

Interactive Process Flowchart

Visualize the process steps and allocate where defects are generated.

Defect Allocation by Station

These ratios are calculated automatically from the Step 1 Check Sheet tallies.

Station Name Defects Born Here Percentage (%)

Step 8: Lean & Quality Decision Engine

RAG Knowledge Base & Actionable Advice
Strategic Alignment: Convert statistical signals into shopfloor action. This engine uses theoretical principles from Deming, Juran, Montgomery, and Liker to synthesize qualitative recommendations based on your current workspace measurements.

Select Quality Tool

Click on any tool to load its theoretical foundations and qualitative decision-making model:

Syncs the simulator with values from Step 0 to 7 automatically.

Cost of Poor Quality (COPQ) Foundation

Qualitative Advisor Simulator