STEPHEN CHINENYE ANYALEWECHI

Designed Power BI dashboards for stock, delivery, and supplier KPIs, enabling real-time tracking. Identified bottlenecks with logistics teams, improving delivery accuracy by 15%. Built Python web scrapers for supplier data, cutting procurement research time by 40%. Analyzed vendor performance to guide procurement strategies. Automated weekly reports in Excel and Power BI. Verified supplier and delivery data accuracy across systems.

Increased order efficiency by 20%, inventory accuracy to 99%, and cut accidents by 30%. Managed logistics for expansion from 0 to 14 stores. Created KPI tracking systems for drivers and warehouse staff. Automated reporting with Python and Excel, reducing prep time by 50%. Coordinated with store managers on deliveries and issues. Optimized transport schedules, lowering fuel costs.

Reduced retrieval times by 15% and achieved 99.8% order accuracy. Led a team of 30 in adopting data-driven processes. Planned and assigned deliveries for timely dispatch. Monitored outbound KPIs daily. Implemented stock tracking to reduce misplacement. Coordinated supplier and transport issue resolution.

Managed data for 100+ daily shipments, boosting accuracy and efficiency. Suggested protocols reducing damage by 30% and improving service by 25%. Participated in quarterly stock audits. Supported inbound/outbound data recording. Provided real-time data updates during peak hours.

Used sales data to raise cross-sell revenue by 18% and sales by 15% Reviewed daily operations to spot purchase trends Reduced trolley losses by 40%, saving $180k annually Logged daily write-offs and monitored waste trends Supported merchandising decisions with sales insights

Performed data analysis on fluid dynamics to provide recommendations that enhanced drilling efficiency and safety. Contributed to process optimization initiatives by applying analytical rigor to complex technical challenges, utilizing data to inform solutions.

Cleaned and transformed inconsistent transactional data in Excel to prepare for modeling. Used pivot tables and Excel formulas to uncover top-performing products, trends, and revenue drivers. Designed visual dashboards to support business decisions regarding promotions and stock prioritization.

This end-to-end project involved data cleaning, feature engineering, and advanced analysis of a global supply chain dataset. Data Analysis & Modeling: Used Pandas and NumPy in a Jupyter Notebook to prepare the data. Developed a Linear Regression model to understand how variables like Shipping Costs and Product Type influence Revenue, and used the output to drive strategic recommendations. Business Intelligence & Visualisation: Created a multi-page, interactive Power BI Dashboard that visualizes KPIs across key areas: Executive Summary: High-level performance metrics. Inventory & Production Insights: Stock levels, lead times, and manufacturing costs. Logistics Deep Dive: Analysis of carriers, transportation modes, routes, and shipping costs/profit margins. Outcome: Delivered a strategic analysis identifying bottlenecks (e.g., high-cost routes, slow lead times) and providing targeted recommendations for cost reduction and operational efficiency.

RETAILGO is an ongoing, market-ready mobile self-checkout solution developed as a capstone project at AltSchool Africa. Built through a collaboration with students from the Schools of Product, Engineering, and Data, this Althub Project is designed to solve the real-life problem of long retail queues and inefficient payment systems in Nigeria. The platform enables shoppers to scan and pay for items with their phones, providing a seamless and secure experience while helping retailers reduce costs. This project is on track to go live, showcasing my ability to contribute to a production-focused, interdisciplinary development cycle.

Applied SQL queries to an online bookstore dataset to uncover key trends in sales, customer behavior, and product performance, demonstrating strong analytical and reporting capabilities.

This project details the development of a simple Food Ordering Application built with Python. It demonstrates core programming concepts and practical application development skills by allowing users to select food items, specify quantities, and receive a detailed order receipt. The application handles menu display, user input validation, price calculation, and order finalization, showcasing proficiency in creating interactive and functional software solutions.

Recognized and promoted for: → Achieving 99.8% order accuracy in fast-paced environments, ensuring customers always received exactly what they needed. → Innovative Problem-Solving: Used data analytics to tackle a persistent trolley loss issue, slashing misplaced carts by 40% and boosting customer convenience.