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Chain Sampling Analysis Project

Producer risk in manufacturing is the probability that a good product will be rejected by a consumer because a bad batch was observed in a sample that has been tested. Due to the cost of manual quality checks, often times we rely on checking the quality of products in samples, and consecutive samples and make a decision whether a lot of produced/manufactured items are acceptable to market/consumption.

Here we replicate the contributions in chain sampling plans from research in academia and make them available for consumption.

Overview

In this project, we delve deep into the chain sampling method, a pivotal technique in quality control. Using synthetic data, we simulate two distinct real-world scenarios:

  1. Manufacturing of sneaker insoles
  2. Production of LED bulbs

Our objective is to decode the criteria for batch acceptance or rejection based on defect or failure rates.

Contents

  • Chain Sampling Analysis Notebook: This Jupyter notebook contains a detailed analysis, from generating synthetic data to visualizing results. It's equipped with explanations at each step to ensure clarity of the approach and findings.

  • Sampling Code: The core sampling functions and methodologies are derived from the code provided in the sampling_min_sum.py file.

Key Highlights

  • Synthetic Data Generation: We create realistic synthetic datasets for both scenarios, providing a foundation for our analysis.

  • Sampling Plans: Utilizing the single sampling plan function, we determine the acceptance or rejection of batches.

  • Visualization: A visual representation of defect rates and decisions, aiding in a clearer understanding of the results.

Getting Started

  1. Clone the repository to your local machine.
  2. Navigate to the directory and open the chain_sampling_analysis.ipynb notebook.
  3. Execute the notebook cells sequentially to understand the analysis flow.

Requirements

  • Python 3.8+
  • Libraries: pandas, numpy, matplotlib

Contributions

Feel free to fork this project and enhance it. Pull requests with improvements and optimizations are always welcome.