Tea Sales Analysis – Power BI Project

Project Overview

This project analyzes tea production and employee performance for September 2025. The dataset was manually recorded in Excel and contains daily entries for each employee, including:

• Employee number and name
• Date and week
• Day of the week
• Weight of tea collected (Kgs)
• Amount paid (Ksh)
• Attendance records

The analysis focuses on tracking cumulative production, weekly performance, and total payments using Power BI. Interactive dashboards provide insights into overall productivity and individual contributions throughout the month.

Data Collection and Preparation

The dataset was manually compiled and entered into Excel to ensure accuracy and a structure suitable for analysis in Power BI. The data represents tea production records for September, including key details such as employee information, cumulative tea weight (kgs), and weekly payments.

Steps followed:

1. Performed manual data entry and cleaning to remove inconsistencies.
2. Verified and included important details such as employee names, weekly totals, and payment rates.
3. Calculated the total amount paid (Ksh) from Week 1 through Week 4 for each employee.
4. Reformatted the dataset into a tabular structure ready for import into Power BI.

Data Analysis and Visualization

The analysis was carried out in Power BI Desktop. Key measures and visuals were created to highlight production and performance trends.

Key components:

• Overview Dashboard – Displays total produce, employee population, and amount paid, along with gender-based distribution.
• Employee Performance Dashboard – Shows attendance patterns and total weight harvested by each employee.
• Daily & Weekly Analysis Dashboard – Tracks production trends across days and weeks.

Power BI Dashboard Screenshots

Overview

Employee Performance

Daily & Weekly Analysis

Tools Used

• Microsoft Excel – Data collection and cleaning
• Microsoft Power BI – Data modeling, visualization, and dashboard design

Insights

• Female employees contributed the highest proportion of total produce.
• Production varied significantly across weeks, peaking around the middle of the month.
• Attendance strongly correlated with total weight harvested and amount paid.

Author

Linda Cheruto
Data Analyst | Excel | Power BI | SQL
Nairobi, Kenya