Hi, I'm Islam Yasser

Fragmented transport logs and lack of unified visibility into fleet safety, driver behavior, and route cost structures created operational blind spots for supply chain management.

Structured a normalized Star Schema connecting operational trip facts to driver, vehicle, and route dimensions. Automated ETL data transformations in Power Query, formulated custom DAX measures, and created dynamic What-If parameters to simulate fuel and route cost variations.

Delivered a comprehensive 9-page interactive dashboard empowering fleet managers to evaluate route profitability, track driver safety scorecards, and forecast operating expenses under changing logistics scenarios.

Disparate, non-standardized sales records lacked relational integrity, causing redundant customer data, missing product category hierarchies, and an inability to track sales rep quota performance across regional markets.

Designed a 5-table normalized Star Schema in MySQL connecting a central sales fact table to customer, product, category, and employee dimension tables. Engineered complex analytical queries using multi-table INNER/LEFT JOINs, Common Table Expressions (CTEs), DENSE_RANK() window functions, correlated subqueries, and conditional revenue aggregations.

Delivered 18 verified business analytical reports uncovering top-performing customer deciles, category revenue contributions (> 20,000 threshold), discount sensitivity across payment methods, and benchmarked sales representative productivity with sub-second execution speeds.

The massive volume of e-commerce transactions obscured critical financial leakage stemming from late customer deliveries, customer installment delays, and payment underpayment checkout anomalies.

Audited 99,441 real orders across 3 dedicated analytical views (Financial Overview, Order Breakdown, Delivery/Logistics) in Excel. Built relational data models in Power Pivot to calculate delivery turnaround vs. estimated promise dates and correlate payment installments with order retention.

Uncovered $1.35M in revenue affected by 7,826 late orders (averaging 10 days of delay) and pinpointed 11,277 underpayment checkout anomalies, establishing actionable recommendations for carrier SLA renegotiation.

Insufficient visibility into trip completion rates, cancellation root causes, and lost revenue from unfinished bookings left mobility operations without clear levers to improve driver retention and platform efficiency.

Engineered a Star Schema joining ride booking facts to trip status, vehicle category, and calendar dimensions. Centralized all KPIs into a dedicated DAX measure repository, evaluating trip durations, ride fare distributions, and cancellation patterns.

Constructed a 5-page operational report (Home, Overview, Revenue, Losses, Cancellations) that segmented driver cancellations vs. customer drop-offs and quantified lost distance earnings to optimize surge matching.

Balancing warehouse inventory levels against variable supplier lead times and high freight costs across regional fulfillment hubs posed continuous risk of stockouts and bloated shipping expenses.

Standardized complex multichannel sales and transportation records using Power Query. Mapped multimodal logistics modes (Road, Rail, Air, Sea) against supplier lead times, calculating transit duration variances and freight spend per unit sold.

Analyzed $577.60K in revenue and 46K units sold, benchmarking an average freight cost of $5.55 and transit duration of 5.75 days to guide regional warehouse rebalancing and curb high-cost air freight reliance.

Aggressive discount strategies were severely eroding net margins across core product categories and geographic zones without generating sustainable customer repeat purchases.

Constructed an in-depth dimensional model in Power Pivot linking order transactions with product lines, regional manager assignments, and return logs. Implemented interactive timeline filters and dynamic multi-level slicers.

Evaluated $2.29M in total sales across 9,994 orders (12.47% overall profit margin). Isolated top profitable regional markets while revealing that deep discounts in Furniture and Tables produced net margin losses and elevated return rates.

Lack of centralized visibility into multi-regional retail sales, customer demographic segments, product margin profitability, and return rates hindered strategic decision-making and inventory resource planning.

Engineered a robust relational data model combining Star and Snowflake Schemas connecting sales facts to customer demographics, products, dates, and territories. Built automated ETL pipelines in Power Query (M) and developed specialized DAX measures for dynamic MoM revenue growth, customer lifetime value, and return rate ratios across a multi-page interactive view.

Analyzed $24.9M in total revenue, $10.5M net profit across 25.2K orders and 17K unique customers, identifying high-margin product categories (Bikes & Accessories) and pinpointing high-return items to optimize supply chains across North America, Europe, and Pacific regions.

Disparate sales reporting across hundreds of independent resellers and retail channels prevented leadership from monitoring distributor sales quotas, regional margin health, and product category demand cycles.

Engineered a centralized Star Schema linking the primary Sales fact table to 6 dimensional entities (Product, Reseller, Salesperson, Region, Targets, and SalespersonRegion). Built custom DAX calculations to benchmark actuals versus target sales quotas, margin percentages, and channel volume distributions.

Audited $77.55M in total sales and 204K quantity sold across 701 resellers and 4K orders, benchmarked top-performing sales representatives, and isolated regional margin variations to strategically guide distributor wholesale allocations.

Point-of-sale operational logs lacked unified relational structuring, preventing store managers from identifying peak ordering hours, top revenue-generating menu combinations, and payment fulfillment channels.

Cleaned and standardized raw transaction records with Power Query, structuring a relational 1-to-many model in Power Pivot linking transaction facts with item price dimensions. Configured dynamic slicers, timeline selectors, and automated Pivot calculations for multi-channel sales breakdown.

Evaluated $141,713.50 in revenue across 14,928 transactions and 45,240 items sold, revealing an even 50/50 fulfillment split (In-store $71.5K vs. Takeaway $70.2K), top revenue drivers in Sandwiches & Smoothies ($29.75K each), and payment behavior distribution across credit cards and digital wallets.

Fragmented HR records, lack of centralized retention KPIs, and absence of root-cause diagnostics created organizational blind spots around workforce flight risk and overtime burnout across 1,470+ personnel.

Architected a 4-page Power BI model in Developer Mode (PBIP) with TMDL semantic modeling. Formulated centralized DAX measures for dynamic headcount, cohort retention longevity, and gender pay equity, integrating Power BI's AI Decomposition Tree to interactively diagnose multi-tiered attrition pathways across departments, job roles, and salary tiers.

Evaluated 1,470 total employees (1,233 active, 237 exits; 16.1% baseline attrition), isolating a critical 30.5% attrition rate among overtime workers (nearly 2x company baseline) and pinpointing Sales Representatives and Laboratory Technicians as high-priority retention risk cohorts.

Fragmented monthly telephony logs obscured queue congestion, agent workload imbalances, and a severe 28.4% demand forecasting gap across high-volume customer support operations.

Consolidated multi-month data using Power Query Append Queries to preserve 90-day time-series continuity. Formulated Weighted-Average DAX measures (Weighted ASA, SLA %, Forecast Variance) to prevent average-of-averages distortion. Transformed standard Power BI reporting into a bespoke operational UI using Dynamic HTML with DAX, CSS Grid & Flexbox layouts, custom SVG graphics, keyframe CSS Animations, interactive 3D Flip Cards, and Deneb (Vega-Lite) visualizations with bookmark-driven slide-out filter drawers.

Evaluated 1,744,885 inbound calls (98.7% answer rate, 91.1% service level, 14.3s ASA) across 16 agents; uncovered a 692,186-call over-forecasting gap (-28.4%) to calibrate staffing schedules, and isolated agent quality disparities—revealing that top-volume handler (Dina, 171.8K calls) had lower SL compliance (78.1%), whereas top-quality agent (Ali) achieved 97.7% SL with an ultra-fast 3.5s answer speed.

Multi-store retail operations spanned 45 regional branches with volatile weekly revenues, complex holiday surges, and fluctuating macroeconomic indicators (Fuel Price, CPI, Unemployment), creating forecasting blind spots and delayed inventory adjustments.

Automated multi-sheet data ingestion, cleaning, and weekly rollups with Google Sheets formulas & Google Apps Script, implementing an automated data pipeline and ETL synchronization. Engineered responsive interactive BI dashboards with Chart.js, dynamic cross-filtering, 3D Flip KPI Cards, Sales Decomposition Trees, and Pearson correlation / linear regression modeling for macroeconomic variables.

Audited $6.74B in total sales across 6,435 store-week records ($1.05M weekly average); ranked top store (#20 at $301.4M), quantified major holiday sales lifts (Thanksgiving and Christmas weekly surges surpassing $80M), and evaluated economic elasticity across fuel price ranges ($2.47 to $4.47/gal) to optimize regional replenishment schedules.