Welcome to the Intelligent Inventory Management project, designed to optimize inventory processes for a fabric retail company in Paraguay using machine learning. This project aims to enhance inventory forecasting and management through advanced data analysis and predictive modeling.
The project follows the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology, which consists of six key steps:
- Objective: Gain insights into the fabric retail market in Paraguay to understand inventory dynamics and business needs.
- Activities: Conduct market research, analyze sales trends, and identify key factors influencing inventory requirements.
- Data Collection: Gather historical sales data, inventory records, and other relevant datasets.
- Initial Exploration: Perform exploratory data analysis (EDA) to identify patterns, outliers, and data quality issues.
- Problem Identification: Recognize data-related challenges such as missing values, inconsistencies, and limitations in data coverage.
- Data Cleaning: Address missing values, remove duplicates, and correct inaccuracies in the dataset.
- Feature Transformation: Apply techniques to transform and normalize variables for better model performance.
- Feature Selection: Identify and select relevant features that significantly impact inventory forecasting.
- Data Splitting: Create training and testing datasets to evaluate model performance.
- Technique Selection: Choose appropriate modeling techniques based on the nature of the data and business goals.
- Model Training: Train various models to predict inventory needs and sales trends.
- Parameter Tuning: Optimize model parameters to enhance accuracy and efficiency.
- Cross-Validation: Perform cross-validation to ensure robust and reliable model performance.
- Model Assessment: Evaluate model performance using metrics such as accuracy, precision, and recall.
- Results Interpretation: Analyze the results to understand model strengths and limitations.
- Iteration: Refine models and processes based on evaluation results and feedback.
- Solution Implementation: Deploy the final model into a production environment for real-time inventory management.
- Monitoring and Maintenance: Continuously monitor model performance and update it as needed to adapt to changing market conditions.
The project employs various methodologies to achieve optimal results in inventory management:
- Predictive Models and Regression: Initially, we used scikit-learn to apply predictive modeling and regression techniques. While these methods provided valuable insights, they did not yield optimal results.
- Facebook Prophet: We subsequently implemented Facebook Prophet, a forecasting tool designed for time series data. This approach delivered significantly improved outcomes, demonstrating the effectiveness of Prophet in predicting inventory needs.
- Python: The primary programming language for data analysis and model implementation.
- scikit-learn: A library for applying machine learning algorithms and techniques.
- Facebook Prophet: A tool for forecasting time series data.
- Pandas: Used for data manipulation and analysis.
- Numpy: Employed for numerical operations and handling arrays.
For a detailed overview of the project, including code and implementation specifics, please refer to the project's Notion page.
Feel free to contact us for further details or inquiries related to this project.
Twitter: @nicoelingeniero
The Data provide must be use in a ethical way.