This repository contains a comprehensive set of data analysis and optimization strategies for Telcar Cocoa. The project aims to enhance various aspects of the business, including production, marketing, sales, inventory management, and employee performance, using advanced data analytics and machine learning techniques.
Telcar Cocoa is a leading cocoa producer seeking to optimize its operations and improve profitability. This project focuses on analyzing historical data and applying optimization techniques to achieve these goals.
- Data Types: Production costs, marketing spend, sales, inventory levels, employee performance.
- Time Period: 2010-2025.
- Sales Forecasting: ARIMA models used to forecast future sales and identify trends.
- Key Insights: Identification of high-performing and underperforming periods.
- Customer Segmentation: K-Means clustering to identify distinct customer segments.
- Benefits: Improved targeting and personalized marketing strategies.
- Linear Programming: Optimization of inventory levels to balance supply and demand.
- Importance: Reduction of costs and improvement of operational efficiency.
- Performance Analysis: Visualization and analysis of employee performance data.
- Actionable Insights: Identification of areas for training and development.
- New Markets: Identification of high-potential markets based on demand and growth.
- Digital Marketing: Analysis and forecasting of engagement metrics for 2025.
This project provides valuable insights and actionable recommendations to enhance Telcar Cocoa's business operations. Future directions include further refinement of models and exploration of additional data sources.