Kimia Farma : Performance Dashboard & Analysis

This project analyzes Kimia Farma's business performance using SQL and Python. It covers net sales trends, net profit margins, top-selling products, branch performance distribution, and strategic recommendations. Built as a final project for the Rakamin Academy x Kimia Farma program.

Retail pharmacy chains need to identify which products, branches, and regions drive profit so they can allocate resources and adjust strategy accordingly.

Extract actionable insights from sales data to support business decisions : product mix optimization, branch investment, and marketing strategy.

• SQL queries for data extraction and aggregation.
• Python analysis pipeline for metrics calculation.
• Visualization dashboard for key performance indicators.
• Recommendation framework based on data findings.

• Net sales and profit trend analysis.
• Top-performing products and categories.
• Branch performance ranking and regional distribution.
• Actionable business recommendations.

1. 1Raw sales data loaded into SQL database.
2. 2SQL queries aggregate metrics by time period, branch, and product.
3. 3Python scripts calculate derived KPIs and statistical summaries.
4. 4Visualizations present findings for stakeholder review.

• Data quality issues and missing fields in raw records.
• Making analysis actionable rather than just descriptive.
• Balancing depth with clarity for non-technical stakeholders.

• Prioritize stakeholder-facing clarity over analytical complexity.
• Use SQL for data transformation and Python for visualization.
• Keep recommendations grounded in data with clear confidence levels.

Complete business analysis with visualizations and strategic recommendations. Presented as final project deliverable.

• Add interactive dashboard with drill-down capability.
• Include forecasting for future sales and profit trends.
• Add competitor benchmarking if data becomes available.