Root Cause Analysis

OVERVIEW

FreshHarvest Foods Ltd. faced challenges with defective batches in its production line, impacting product quality and manufacturing efficiency. This project was initiated to conduct a thorough investigation into these defects. By analyzing various operational factors, ingredient quality, and supplier variations, the aim was to pinpoint the root causes and provide actionable recommendations to quality control managers and stakeholders, ultimately leading to a sustainable reduction in defective batch rates. 

OBJECTIVES

The primary objectives of this project were to:

• Identify the root causes of defects in food processing batches using comprehensive data analysis.

• Analyze historical production, quality, and sensor data to expose weaknesses in the production process.

• Evaluate the impact of ingredient quality, supplier variations, and operational factors on batch defects.

• Guide the implementation of targeted corrective actions to reduce defects.

• Improve overall product standards and optimize manufacturing efficiency.

KEY COLUMNS

The analysis involved a wide array of data points across the production process. While specific column names were not explicitly provided, the report indicates analysis based on:

• Batch Information: Total Batches, Defective Batches, Defective Rate.

• Quality Standards: Acceptable Quality Level (AQL) Standard, Defective Material Rate.

• Product Data: Product Names (e.g., MEAL002, VEG003, SOUP001), Product Defect Rates.

• Ingredient Data: Raw Materials (e.g., carrots, broccoli, peas, rice, chicken), Ingredient Defect Rates, Ingredient Quality Grades (A, B, C).

• Supplier Data: Supplier information, Supplier Impact on Material Quality.

• Production Line Data: Line Number (Line 1, Line 2, Line 3, Line 4), Line Defects.

• Shift Data: Shift (Night, Evening, Morning), Shift Defects.

• Machine Data: Production Duration (e.g., 2 hours, 3 hours, 5 hours, 6 hours), Machine operational parameters (temp, sensor, pressure, overheating etc.).

• Operator Data: Operator IDs (e.g., EMP001)

• Test Type: (e.g., Microbial).

• Batch Completion Status: (Pass/Fail).

TOOLS

The tool used include:

• Visualization & Reporting Tools: Power BI

APPROACH

The project followed a systematic root cause analysis methodology:

Phase 1: Data Collection & Defect Quantification

• Collected historical production, quality, and sensor data.

• Quantified overall defective batch statistics, including total batches, defective batches, defective rate (11.23%), and defective material rate (3.9%).

• Compared the defective rate against the Acceptable Quality Level (AQL) standard (2.5%).

Phase 2: High-Level Process Fault Area Identification

• Identified high-level process fault areas linked to defects: variations in ingredient quality, processing equipment issues, operator influence, and shift/production timing.

Phase 3: Detailed Observation & Analysis of Root Causes

• Product and Ingredient Defect Rates: Analyzed defective batch rates for specific products (MEAL002, VEG003, SOUP001) and individual ingredients (Carrots, Broccoli & Peas, Rice, Chicken).

• Ingredient Quality Grades: Investigated the link between ingredient quality grades (A, B, C) and defective rates, identifying Grade C as the highest contributor to defects.

• Supplier Impact: Assessed how supplier practices influenced material quality, noting uniform grade issues across suppliers.

• Shift and Line Number Defect Rates: Analyzed defect rates by production shift (Evening, Morning, Night) and individual production lines (Line 1, 2, 3, 4) to identify operational vulnerabilities.

• Production Duration and Defects: Examined the correlation between batch production duration (e.g., 3 hours, 6 hours) and defective rates, identifying optimal and high-risk timings.

Phase 4: Recommendation Development

• Formulated actionable recommendations categorized into:

  • Ingredients and Grades: Focusing on material quality and supplier collaboration.

  • Production Improvements: Addressing equipment maintenance, batch duration control, and supervision.

  • General Process Enhancements: Implementing tighter controls and monitoring operator performance.

KEY INSIGHTS

The Root Cause Analysis revealed several critical insights into the defective batches:

• High Defective Rate: A significant defective batch rate of 11.23% was observed, substantially exceeding the 2.5% AQL standard, indicating a pressing need for intervention.

• Material Quality Challenges: A 3.9% defective material rate highlighted issues with raw material quality.

• Product-Specific Vulnerabilities: MEAL002 (4.42%) and VEG003 (4.11%) exhibited the highest defective batch rates, despite sharing ingredients, suggesting product-specific process issues.

• Ingredient-Specific Defects: Carrots (4.37%) were identified as the ingredient with the highest defect rate, followed by Broccoli & Peas (4.02%).

• Grade C Material as Key Contributor: Grade C quality ingredients had the highest defective rate (4.62%), making low-grade materials a significant root cause. Even Grade A materials showed a moderate defect rate (3.95%).

• Supplier-Side Quality Issues: All suppliers showed similar grade defect patterns, indicating that supplier practices directly influence batch quality and require external solutions.

• Shift and Line Impact: The Evening shift (4.20%) and production lines 2 (4.63%) and 4 (4.37%) were associated with higher defect frequencies, pointing to potential operational or mechanical issues.

• Production Duration Risk: Batches lasting 3 hours showed the highest defective rate (4.89%), while 5-hour durations had lower defects (3.36%), suggesting optimal production times.

IMPACTS

This project delivered significant impacts for FreshHarvest Foods Ltd.:

• Reduced Defect Rates: By pinpointing specific root causes, the project enables targeted interventions to sustainably reduce defective batch rates.

• Improved Product Quality: Addressing issues related to ingredient quality, production lines, and operational timing leads to safer and higher-quality food products.

• Optimized Manufacturing Efficiency: Understanding and correcting process weaknesses improves overall manufacturing efficiency and reduces waste.

• Enhanced Supplier Management: Insights into supplier-side quality challenges facilitate stronger partnerships and intervention strategies for raw material quality improvement.

• Data-Driven Quality Control: Provides quality control managers with actionable insights to implement effective corrective actions.

• Cost Savings: Reducing defective batches directly translates to cost savings from reduced waste, rework, and potential product recalls.

DELIVERABLES

The key deliverables for this project included:

• Root Cause Analysis Report (PDF): A comprehensive report detailing the problem, objectives, production process overview, defective batch statistics, detailed observations on product/ingredient defects, quality grades, supplier impact, shift/line defects, production duration, and actionable recommendations.

• Power BI Reports (Appendix):  To provide access to interactive Power BI reports for visualizing the analysis.

• Root Cause Hypothesis Document (Appendix): To provide access to documentation of initial hypotheses.