muzamil Jamil Channa

Supply Chain Performance Dashbaord

Business Challenge:
AtliQ Mart, a fast-growing FMCG retailer, faced frequent stockouts and delayed deliveries due to supply chain inefficiencies across multiple regions.

Approach:
Using Power BI, I monitored daily performance KPIs like OT%, IF%, and OTIF% for all City. A detailed breakdown by region, product category, and customer helped identify the root causes of service gaps.

Key Insights & Impact:
Six key customers contribute over 53% of total orders—indicating priority focus for service and strategy.
Dairy category dominates with 79.3% of total orders—highlighting it as a critical product and inventory area.
OT%, IF%, and OTIF% are below targets—indicating delivery inefficiencies affecting customer satisfaction and loyalty.
Line Fill Rate is low for major customers—signaling inefficiencies in fulfilling complete order lines.
Suggested inventory optimization and tech integration to improve fulfillment accuracy and delivery consistency.

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Sales Performance Dashboard

Business Challenge:
To support Maven Market’s growth in the U.S., I conducted an in-depth sales performance analysis after noticing uneven profit trends in 1998. Despite a major revenue increase, profitability wasn’t consistent across all store types and products.

Approach:
Using Excel, I cleaned and structured raw sales data, built a relational model, and developed a dynamic dashboard with calculated metrics for revenue, profit, and transactions. The analysis emphasized seasonal patterns, store-level performance, and product-specific trends.

Key Insights & Impact:
• Revenue grew 56% YoY, but profit remained flat, signaling inefficiencies in store and product-level performance.
•Supermarkets led in revenue and profit, with higher average transaction value—ideal for premium pricing and bulk promotions
•Transactions increased 36%, revealing strong customer activity but low margin contribution from some product categories.
•Q4 showed the highest revenue, highlighting a seasonal opportunity for targeted marketing and inventory planning.

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Credit Card Financial Dashbaord

Business Challenge:
The objective was to develop a weekly credit card financial dashboard that offers real-time insights into customer behavior, revenue patterns, and transactional trends—helping stakeholders monitor and optimize credit card operations effectively.

Approach:
Using Power BI & SQL, I created calculated columns and custom DAX measures to segment customers by age, income, and weekly performance. The model included dynamic metrics like current vs. previous week revenue, card category contribution, and satisfaction scores. I built two interactive dashboards—one focused on transactions and the other on customer behavior.

Key Insights & Impact:
• Generated a total revenue of $55M YTD, with interest earnings of $7.84M and transaction volume of 656K+
• Week 52 snapshot: $933K in revenue, 11K+ transactions, and an improved customer satisfaction score of 4.21
• Identified that male customers contributed 30M vs. 25M from females, with the 40–50 age group as top spenders

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House Price prediction

Business Challenge:
Built a house price prediction model using regression techniques on Kaggle’s housing dataset. Focused on clean data prep, smart feature engineering, and model optimization.

Approach:
The project followed a 3-phase pipeline: Data Analysis, Feature Engineering, and Feature Selection. Handled missing values, transformed skewed data, and encoded categorical variables. Selected key features using Lasso regression to boost model performance.

Key Insights & Impact:
• Identified key predictors such as Overall Quality, Living Area, and Garage Area that strongly influence house price predictions.
• Reduced model complexity by selecting 30+ impactful features using Lasso regression, improving performance and reducing overfitting risk.
• Filled over 15% missing data using tailored imputation strategies, ensuring data integrity and enhancing model training quality.
• Boosted model accuracy and interpretability by engineering meaningful features and removing noisy or less relevant variables.

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About me

I'm Muzamil Jamil

Recents Works

Supply Chain Performance Dashbaord

Sales Performance Dashboard

Credit Card Financial Dashbaord

House Price prediction

Skills

Power BI

SQL

Excel

Python

Data Cleaning

Communication

Certificate & workshop

Thinking Like an Analyst

Power Query, Power Pivot & DAX

Data Literacy Foundations

Launching your Data Career

Data Analysis using Advance Excel & SQL

Business Analytics workshop

Data Science & Machine Learning workshop

Data Analytics Essentials

Contact Me

Talk to me

Email

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Messenger

Write me your Project