Moawia Mohamed

<p>The problem: Raw financial statements contain hundreds of numbers but do not show whether growth is profitable, safely funded, or turning into cash. The question was: <em>Is Edita growing profitably, and is that growth sustainable?</em></p><p>The solution: I built a 6-page Power BI dashboard on 20 quarters of data (Sep 2021 to Jun 2026) covering the income statement, balance sheet, and cash flow. The data was collected with a Python ETL (requests, pandas, Regex) and stored in one long table, so a new quarter only adds rows. Each page answers one question: Overview, Profit & Loss, Balance Sheet, Liquidity & Leverage, Working Capital, and Risks & Takeaways. Every page has a headline finding, four KPI cards, one main chart, two supporting charts, and a “So what” strip. One Year slicer filters the whole report.</p><p>Key results (TTM Jun 2026): Revenue EGP 23.92B (+37.4%), operating margin 17.7%, net income EGP 3.39B, EBITDA EGP 4.76B, ROE about 54%, net debt/EBITDA 0.09x.</p><p>Insights:</p><ul><li>Growth is profitable: revenue grew 37% while the operating margin held near 17.7%, and OPEX moved only from 17.5% to 17.6% of revenue in 8 quarters.</li><li>It is a capital-heavy business: PP&E and inventory hold 56% of assets, and liabilities are 1.60x equity.</li><li>Liquidity is adequate: current ratio 1.55x, interest covered 6.3x. Cash is tied up for about 34 days.</li><li>Risk 1: Profit is not turning into cash. Q2 2026 operating cash flow was EGP -124M against net income of EGP 796M.</li><li>Risk 2: Short-term debt rose from EGP 0.70B to 1.97B in a year, and inventory rose 37%.</li><li>The risk is refinancing and liquidity timing, not solvency, because cash and short-term investments (EGP 5.23B) cover total debt (EGP 5.67B) almost fully.</li></ul><p>Recommendations: refinance short-term debt into longer terms, cut inventory days, and match dividends and capex to operating cash flow.</p>

<ul><li>Problem: Direct web queries to Egyptian Exchange (EGX) pages caused Power BI refresh timeouts, making the analysis slow and unreliable. </li><li>• Solution: Replaced the web query with a Python ETL script that scrapes, cleans, and normalizes quarterly financial statements for EGX-listed companies (e.g., Juhayna, Elsewedy Electric) using Regex and pandas into financials.xlsx, cutting page loads by 93% (84 → 6) and refresh time to under 5 seconds. </li><li>• Insight: Fintech and digital names trade at far higher revenue multiples (e-finance ~10.7x, Fawry ~6.5x market cap/revenue) than industrial and food companies (El Sewedy ~0.85x, Juhayna ~1.2x, Edita ~1.8x), so the market prices growth narratives well above asset-heavy earnings.</li></ul>

<p>The problem: Schools have student-level data but cannot easily see which factors (attendance, family support, study habits, resources) are associated with academic results.</p><p>The solution: I built a Power BI dashboard on 397 students from two schools, using Power Query for cleaning and DAX for measures. It compares schools, grades (math, reading, writing), absences, family background, guardians, internet access, paid classes, study time, and previous failures, with interactive slicers.</p><p>Key results: 397 students, average age 18, average absences 6, 355 students living with both parents, 242 receiving family support.</p><p>Insights:</p><ul><li>School GP has most of the students (~287, 72.29%).</li><li>Absences differ clearly: GP averages 5.97 versus 3.68 in MS, so attendance is worth monitoring at GP.</li><li>Average grades were 10.63 vs 9.31 by internet access and 11.09 vs 10.62 by paid classes, so extra resources are associated with higher grades. This is an association and needs further investigation, not proof of cause.</li><li>Among students who passed, the guardian was the mother in 69.44% of cases, the father in about 24%, and someone else in 6.48%.</li><li>152 students had no previous failures, and the number of students drops as failures increase (88, 54, 13).</li><li>The dashboard helps schools identify students who need early support.</li></ul><p></p>

<p>The problem: Management needed one view of sales, profit, customers, shipping, and regional performance to see where the business is growing and where profitability can improve.</p><p>The solution: I cleaned and transformed the data with Power Query, built the measures in DAX, and designed an interactive Power BI dashboard with KPI cards, year-over-year comparison, market and regional analysis, customer segmentation, shipping analysis, product and sub-category analysis, and monthly trends.</p><p>Key results: Sales $12.64M, profit $1.47M, margin 11.61%, 51,290 orders, 17,415 customers, average processing time 4 days.</p><p>Insights:</p><ul><li>Sales grew 47.28% versus last year ($8.58M), and profit grew from $991K to $1.47M. Growth is strong, but a margin of 11.61% means profitability must be watched as sales scale.</li><li>Asia Pacific is the strongest market (~$4.0M), followed by Europe (~$3.3M), USCA (~$2.4M), and LATAM (~$2.2M). Africa is the smallest (~$0.8M).</li><li>Phones, Copiers, and Chairs lead in sales. Binders, Storage, and Art lead in quantity, so high-volume products are not always the high-revenue ones.</li><li>Standard Class is the most used shipping mode (~31K orders) and carries the largest share of shipping cost.</li><li>The Consumer segment is the largest (51.6% of customers), then Corporate (29.98%) and Home Office (18.42%).</li><li>Orders peak in November, December, and September (~6.0K to 6.3K), which points to seasonal planning for inventory and campaigns.</li></ul><p></p>