Projects
Deep dive into analytical projects with comprehensive technical details and business insights.
Last-Mile Delivery with Autonomous Robots
Project Overview
Developed and implemented two distinct algorithmic approaches to solve the complex optimization problem of scheduling last-mile deliveries using truck-based autonomous robots. This research addresses the growing demand for efficient urban logistics solutions by combining traditional operations research with cutting-edge autonomous technology.
Key Achievements
- Gurobi Optimization Implementation: Developed exact mathematical model using mixed-integer linear programming, achieving optimal solutions for small to medium-sized problem instances
- Heuristic Algorithm Development: Created custom nearest-neighbor heuristic with local optimization, enabling scalable solutions for large-scale real-world scenarios
- Comparative Performance Analysis: Conducted comprehensive benchmarking showing heuristic achieves 85-95% of optimal solution quality while reducing computation time by 99.8%
- Industry-Relevant Problem Modeling: Addressed real logistics constraints including vehicle capacity, robot battery life, and urban delivery time windows
📄 Project Documents
Supply Chain Network Optimization
Project Overview
Formulated and solved a facility and carrier assignment problem for a global microchip manufacturer's outbound supply chain, optimizing the routing of 9,200+ orders across 19 plants, 9 carriers, and 11 ports. This research addresses real-world logistics decision-making by combining mathematical optimization with comprehensive data analysis on historical operational data.
Key Achievements
- MIP Model Development: Formulated a Mixed-Integer Programming model in Python (SciPy) minimizing warehouse and freight costs under capacity, availability, network, and service-level constraints
- Infeasibility Resolution: Designed a 3-tier progressive soft capacity penalty framework to handle demand concentration that made the hard-constraint model infeasible, achieving optimality in 0.33 seconds
- Benchmarking & Cost Analysis: Identified $171K in warehouse and freight savings versus the baseline and revealed that capacity management — not freight optimization — is the primary cost driver (71.9% warehouse vs 0.1% freight)
- Data-Driven Insights: Discovered critical operational risks including 92% order concentration at a single plant and severe capacity violations hidden in the company's historical assignment decisions
📄 Project Documents
📊 Interactive Visualization
Cross-Cultural Business Strategy Analysis: Slovenia-China Tech Cooperation
Project Overview
Conducted comprehensive cross-cultural analysis examining business relationship dynamics between Slovenian and Chinese technology companies. Applied established cultural frameworks to develop actionable market entry strategies for tech startups seeking international expansion in the rapidly growing China-Slovenia technology corridor.
Key Achievements
- Cultural Dimensions Analysis: Applied Hofstede's and GLOBE frameworks to identify critical differences in individualism vs. collectivism, power distance, and communication contexts between Slovenia and China
- Market Entry Strategy Development: Created comprehensive recommendations for Slovenian tech startups expanding into Chinese markets, focusing on relationship-building and negotiation tactics
- Theoretical Framework Application: Successfully applied MSAC theoretical concepts to real-world business scenarios, creating practical insights for cross-cultural B2B relationships
- International Team Leadership: Collaborated with diverse international team to deliver comprehensive presentation analyzing cultural touchpoints and strategic market penetration recommendations
📄 Project Documents
AI-Human Collaboration in B2B Sales Analysis
Project Overview
Critical analysis of collaborative intelligence research examining how artificial intelligence and human intelligence create synergistic value throughout the B2B sales funnel. Based on comprehensive review of Paschen, Wilson & Ferreira's academic research on the future of AI-enabled sales processes.
Key Achievements
- Academic Literature Review: Conducted comprehensive critical analysis of collaborative intelligence research in B2B sales context, synthesizing complex academic frameworks
- AI Applications Mapping: Systematically examined AI applications across seven stages of B2B sales process including predictive lead qualification, personalized targeting, and dynamic pricing
- Role Distribution Analysis: Evaluated optimal distribution between AI systems and human sales professionals across the sales funnel, identifying complementary strengths
- Future Implications Assessment: Analyzed long-term implications of AI integration in sales profession and digitization trends for industry practitioners
📄 Project Documents
Tesla Sustainability Assessment: Life Cycle Analysis & Supply Chain Evaluation
Project Overview
Comprehensive sustainability assessment of Tesla's electric vehicles combining quantitative Life Cycle Assessment (LCA) with qualitative supply chain analysis using established SSCM frameworks. This research evaluated environmental and social impacts across the entire vehicle lifecycle from raw material extraction to end-of-life recycling.
Key Achievements
- Rigorous Life Cycle Assessment: Conducted LCA of Tesla Model 3 using ISO 14040/14044/14067 standards, revealing 62% lower emissions than ICEVs in Europe but 17% higher in China due to electricity grid differences
- Supply Chain Framework Analysis: Applied Turker and Altuntas SSCM framework to evaluate Tesla's sustainable supply chain practices, analyzing vertical integration strategy and ethical sourcing initiatives
- Regional Impact Analysis: Demonstrated significant regional variations in environmental impact across different electricity grids (96 gCO2e/km in Europe vs. 376 gCO2e/km in China)
- Strategic Recommendations Development: Created comprehensive Triple Bottom Line recommendations for automotive industry executives focusing on battery recycling and renewable energy integration
📄 Project Documents
Option Pricing Model
Project Overview
Engineered a comprehensive option pricing model in Python utilizing 5 years of historical stock data from Yahoo Finance to evaluate the accuracy and assumptions of the European Market Hypothesis and Black-Scholes Model through comparative analysis with real-world option prices.
Key Achievements
- Financial Data Analysis: Processed and analyzed 5+ years of historical stock price data to calculate volatility, returns, and risk parameters
- Model Implementation: Developed Black-Scholes pricing algorithm with Monte Carlo simulation validation techniques
- Market Validation: Conducted empirical analysis comparing theoretical vs. market option prices to assess model accuracy
- Statistical Testing: Applied statistical hypothesis testing to evaluate European Market Hypothesis assumptions
GitHub repository coming soon - code documentation in progress
Risk Analysis Framework
Project Overview
Conducted comprehensive market risk analysis for a diversified portfolio comprising investments in 10 companies from the KMI-30 index, implementing advanced risk assessment methodologies and portfolio optimization techniques.
Key Achievements
- Liquidity Risk Analysis: Performed liquidity risk assessment for randomly generated assets using advanced statistical models
- Credit Risk Evaluation: Executed detailed credit risk analysis for selected individual securities with comprehensive risk profiling
- Risk Assessment Methodologies: Demonstrated proficiency in multiple risk assessment frameworks including VaR and stress testing
- Portfolio Optimization: Applied modern portfolio theory to optimize risk-return profiles across diverse asset classes
Industry Review Analysis
Project Overview
Conducted comprehensive industry analysis examining market structure, competitive dynamics, and growth trends within the Pakistani telecommunications sector, providing strategic insights for investment decision-making.
Key Achievements
- Market Structure Analysis: Analyzed competitive landscape and market concentration ratios across major telecommunications players
- Financial Performance Evaluation: Conducted comparative financial analysis of key industry participants over 5-year period
- Growth Trend Forecasting: Developed market growth projections using historical data and industry indicators
- Strategic Recommendations: Provided data-driven investment recommendations based on industry outlook analysis
Merger & Acquisition Analysis
Project Overview
Analyzed the strategic and financial implications of major M&A transactions within the Pakistani banking sector, evaluating synergy potential, valuation methodologies, and post-merger performance outcomes.
Key Achievements
- Valuation Analysis: Applied multiple valuation methodologies including DCF, comparable company analysis, and precedent transactions
- Synergy Assessment: Quantified potential cost and revenue synergies through detailed operational analysis
- Due Diligence Framework: Developed comprehensive due diligence checklist covering financial, operational, and regulatory aspects
- Post-Merger Integration: Analyzed integration challenges and success factors from historical M&A case studies
Financial Modeling & Forecasting
Project Overview
Developed comprehensive financial models for corporate valuation and performance forecasting, implementing advanced Excel techniques and financial analysis methodologies for strategic decision support.
Key Achievements
- Three-Statement Model: Built integrated financial model linking income statement, balance sheet, and cash flow statement
- Scenario Analysis: Implemented Monte Carlo simulations and sensitivity analysis for risk assessment
- Forecasting Models: Developed revenue and expense forecasting models using regression analysis and time series methods
- Performance Metrics: Created automated dashboard tracking key financial ratios and performance indicators
Multilingual Survey Analytics Platform
Project Overview
Designed and built a repeatable survey analytics workflow used across 4 separate product surveys spanning up to 5 countries and multiple languages. For each survey, multilingual responses were collected via internal questionnaires, exported as CSV files, then cleaned, analyzed, and presented to product stakeholders through purpose-built interactive HTML dashboards — replacing what would otherwise be hours of manual spreadsheet work with a structured, visual deliverable ready for decision-making.
Key Achievements
- Multi-Country Data Processing: Built data pipelines handling survey responses across up to 5 countries with mixed-language free-text fields, automated junk detection (e.g. 12 invalid responses flagged and excluded in landing page survey), and consistent cross-country comparison logic
- Actionable Product Insights: Surfaced concrete findings that directly informed product decisions — such as 92.7% route editing demand across all markets, 87.4% daily live-monitoring usage, and a 4.12/5 landing page satisfaction score with Poland vs. Canada gaps identified
- Stakeholder-Ready Deliverables: Each dashboard was presented to the General Manager and product teams as the definitive analysis — not a supplement to a spreadsheet, but the primary vehicle for communicating survey results and recommendations
- Reusable Framework: Established a consistent survey-to-dashboard pattern that was applied across 4 distinct surveys, reducing turnaround time for each subsequent analysis as the approach and code patterns matured
📊 Example Dashboards
Third-Party Platform Intelligence System
Project Overview
Built a suite of automated analysis tools that transform raw CSV exports from third-party platforms — Intercom (customer support), Instabug (bug reporting), and internal tracking systems — into structured, interactive dashboards. These replaced ad-hoc manual analysis that previously consumed significant time each reporting cycle, providing the team with on-demand insights from data that was otherwise sitting in exported spreadsheets.
Key Achievements
- Intercom Analytics Automation: Built analyzers for both general support reports and driver-specific cases, automatically categorizing conversations, tracking response times, and surfacing resolution patterns — work that previously required manual ticket-by-ticket review
- Bug Report Intelligence: Created an Instabug CSV analyzer with statistical validity checks, trend identification across time periods, and impact severity assessment — turning raw crash/bug exports into prioritized engineering insights
- App Version Performance Tracking: Developed a version comparison tool that ingests release data and maps quality metrics, user adoption rates, and regression patterns across app releases — enabling the team to quantify whether a new version improved or degraded the user experience
- Eliminated Manual Reporting Cycles: Each tool follows a consistent upload-and-analyze pattern: drop in the latest CSV export, get an immediate interactive dashboard. This saved an estimated 10+ hours per week across the team that was previously spent on manual data wrangling in spreadsheets
📊 Example Dashboards
Operational Performance Analytics
Project Overview
Developed geolocation-aware analytics tools for analyzing driver behavior and depot-level performance across Germany's logistics network. These dashboards process internal operational data — driver notes with GPS coordinates and depot KPIs across 6 regional zones — to surface performance patterns and operational bottlenecks that were previously invisible in raw spreadsheet exports.
Key Achievements
- Driver Notes Intelligence: Built an analyzer that processes GPS-tagged driver notes, automatically categorizing free-text feedback by issue type and mapping patterns geographically — transforming unstructured field data into actionable operational insights
- Regional Depot Benchmarking: Created a depot performance tool with dynamic filtering across Germany's Nord, Süd, West, Ost, Center, and Süd-West zones, enabling management to identify underperforming depots and compare operational metrics regionally
- Correlation Studies: Applied statistical analysis to uncover relationships between driver feedback patterns and depot-level KPIs, grounding operational improvement recommendations in data rather than anecdotes
- Geospatial Data Processing: Implemented coordinate-based analysis linking driver notes to specific delivery areas, enabling geographic clustering of recurring issues for targeted operational interventions
📊 Example Dashboards
Mobile Customer Experience Analytics
Project Overview
Led customer experience optimization initiatives through comprehensive analytics of 90-100+ daily customer interactions, implementing systematic quality control processes and performance monitoring systems across multiple communication channels.
Key Achievements
- Customer Service Analytics: Successfully managed 40-45 daily network issue resolutions while maintaining 95%+ customer satisfaction scores
- Quality Control Systems: Implemented Klaus-based performance review process for 15+ junior analysts monthly, ensuring consistent service quality
- Compliance Analytics: Managed 8-10 daily fraud detection cases using Stripe Radar analytics, ensuring regulatory compliance and risk mitigation
- Performance Optimization: Developed systematic approach to identify improvement opportunities enhancing team productivity aligned with KPIs
Business Intelligence Platform
Project Overview
Engineered real-time business intelligence dashboards integrating Google BigQuery database with Google Sheets, providing operations teams with comprehensive data analysis capabilities for strategic decision-making and supply chain optimization.
Key Achievements
- Real-Time Data Integration: Built seamless integration between Google BigQuery and Sheets enabling live operational dashboards
- Supply Chain Analytics: Developed comprehensive supply chain management analytics improving operational efficiency by 30%
- Financial Decision Support: Created data-driven reporting systems enabling management to make informed financial decisions
- Operational Optimization: Implemented automated reporting processes reducing manual analysis time and improving data accuracy
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