Evaluate Manufacturing Process Using Statistical Process Control

Project Overview

This project focuses on enhancing the monitoring and control of manufacturing processes using a methodical approach known as Statistical Process Control (SPC). The main objective is to ensure that the manufacturing process operates within an acceptable range, which is essential for maintaining consistent high-quality production.

Key Concepts

• Statistical Process Control (SPC): A strategy that uses data to determine the efficiency of a process, making adjustments only when necessary.
• Control Limits: The process is considered acceptable if it operates within the Upper Control Limit (UCL) and Lower Control Limit (LCL).

Goals

• Analyse historical manufacturing data.
• Define acceptable range using UCL and LCL.
• Identify points in the process that require adjustments.

Data Description

The analysis is based on data from the manufacturing_parts table in a PostgreSQL database and a corresponding parts.csv file.

Data Fields

• item_no: The item number.
• length: The length of the item.
• width: The width of the item.
• height: The height of the item.
• operator: The operating machine.

Tasks and Analysis

1. Database Connection

   • Connect to the PostgreSQL database.
   • Load data from the table into a Pandas DataFrame.

2. Exploratory Data Analysis (EDA)

   • Assess dataset integrity (no missing values).
   • Evaluate dataset composition (500 items).
   • Analyse data variation (mean values and standard deviations for length, width, and height).

3. Control Limit Calculation and Alert System

   • Use UCL and LCL formulas to establish control limits.
   • Create an alert system to flag deviations from the control limits.
   • Focus on the height measurement, employing a specific windowing technique for analysis.

Conclusion

This notebook serves as a practical application of SPC in manufacturing, demonstrating how data-driven approaches can significantly enhance process monitoring and quality control.

Skills Applied in the Project

In the course of this project, a variety of technical skills and tools were utilized to achieve the objectives. These include:

• SQLalchemy: Used for connecting to the PostgreSQL database to retrieve the necessary data.
• SQL: Used for querying the PostgreSQL database to retrieve the necessary data.
• Python: The primary programming language for analysis and algorithm development.
• Pandas: Employed for data manipulation and analysis, providing the means to handle the data efficiently.
• Matplotlib & Seaborn: These Python libraries were used for data visualisation, helping in the interpretation of results and trends in the data.
• Statistical Analysis: Fundamental statistical methods were applied to determine control limits and assess the manufacturing process.
• Jupyter Notebook: Served as the development environment, facilitating an interactive approach to coding, documenting, and presenting the analysis.