Overview

The Manufacturing Process Optimization script focuses on enhancing production performance, supply chain efficiency, and quality control through advanced data analysis and optimization techniques. This script integrates data from various sources, such as production, sensor readings, shipping, quality control, maintenance, and raw materials, to identify key areas for improvement and implement optimization strategies.

Key Features

  • Production Performance Optimization:

    • Analyzes production data to calculate defect rates, uptime, and on-time delivery.

    • Identifies correlations between production performance metrics and machine operating conditions.

    • Utilizes Pearson correlation coefficient and p-values to determine statistical significance of relationships.

  • Supply Chain Optimization:

    • Integrates demand, inventory, production, transportation, supplier, and financial data.

    • Builds an optimization model using the pulp library to minimize total costs.

    • Defines decision variables for production quantities, inventory levels, transportation quantities, and ordering decisions.

    • Implements constraints to ensure demand is met, inventory is balanced, and orders are placed only when necessary.

  • Quality Control Optimization:

    • Analyzes data from sensors, machines, and tests to identify trends and patterns.

    • Uses advanced analytics techniques, including machine learning algorithms, to identify areas for improvement.

    • Implements adjustments to the manufacturing process to improve quality control and reduce defects.

Data Integration and Analysis

The script loads data from multiple CSV files and merges them into a comprehensive dataset. Key calculations and analyses include:

  • Defect Rates:

    defect_rates = all_data.groupby("defect_type")["num_units_produced"].sum() /     all_data["num_units_produced"].sum()

  • Uptime Calculation:

    uptime = all_data["machine_temp"].count() / all_data["num_units_produced"].sum()

  • On-Time Delivery:

    on_time_delivery = all_data[all_data["delivery_date"] <=     all_data["ship_date"]]    ["num_units_produced"].sum() / all_data["num_units_produced"].sum()

Optimization Models

Production Performance Optimization

  • Correlation Analysis:

    • Identifies correlations between machine operating conditions (temperature, pressure, vibration) and production performance metrics (defect rates, uptime).

    • Utilizes scatter plots and pairplots to visualize correlations.

    • Calculates Pearson correlation coefficients and p-values to assess statistical significance.

  • Optimized Parameters for Maximum Uptime:

    optimal_params[machine_id][material_id] = {

    "temp": optimal_temp,

    "pressure": optimal_pressure,

    "vibration": optimal_vibration}

Supply Chain Optimization

  • Optimization Engine:

    • Minimizes total production, inventory holding, transportation, and ordering costs.

    • Defines decision variables and objective function.

    • Implements constraints for demand fulfillment and inventory balance.

    • Solves the optimization model using the pulp library.

  • Exporting Optimal Plan:

    optimal_params.to_csv(r"optimal_production_plan.csv", index=False)

Quality Control Optimization

  • Sensor Data Integration:

    • Collects data from various sensor types (temperature, pressure, flow rate, vibration, position).

    • Analyzes sensor data to identify trends and patterns in machine performance and material quality.

    • Implements adjustments based on sensor data analysis to improve quality control.

Conclusion

This comprehensive script leverages data integration, advanced analytics, and optimization techniques to enhance manufacturing process efficiency, supply chain management, and quality control. By continuously monitoring and analyzing data, the script identifies key areas for improvement and implements optimization strategies to maximize production performance, minimize costs, and reduce defects.

 

Link to Code