Hi I'm
Mohadeseh
Arianrad

I have developed a particular specialization in Quantitative Methods in Finance. This area represents the intersection of my passion for statistical modeling and complex financial decision-making. My goal is to leverage my analytical rigor and engineering precision to provide high-level insights in quantitative analysis and financial management.

Academic Leadership & Instruction
Beyond my coursework, I was selected to mentor my peers and junior students in highly technical subjects:

• SolidWorks Instructor: I served as a primary instructor for junior students, teaching the complexities of 3D modeling, advanced assemblies, and technical drawings.
• Teaching Assistant – Industrial Design: I provided technical problem-solving support and assisted junior students with their project development and course materials.

For my thesis, I utilized Machine Learning and Data Mining methodologies to model and predict the Bullwhip (whiplash) effect across industrial supply chains, aiming to reduce inefficiencies through predictive analytics.

• Maintaining Standards: I successfully supported the quality team through three IATF 16949 certification audits during my time there. This involved coordinating with 5+ departments to ensure our daily work matched global automotive requirements.
• Comprehensive Auditing: I conducted internal audits covering 20+ different processes and 80+ products to ensure technical compliance across the production line.
• Documentation Support: I assisted in creating and updating 15+ procedures, forms, documents, and checklists to keep our documentation aligned with IATF standards. This effort helped reduce manual reporting errors by roughly 15%.

Managing the high-integrity data required for three successful IATF audits is what initially drew me to quantitative analysis. I learned how to identify patterns and risks within complex industrial systems—a skill I am now advancing through Quantitative Methods in Finance in my Master's program.

The Problem: A Fragile Manual Workflow
Before this automation, our quality tracking relied on a fragmented, manual chain that was extremely difficult to maintain accurately:

I identified a critical liability in our FMEA tracking: a complex, manual system of data silos that relied on transcribing hundreds of data points between Excel and Word. This manual approach not only slowed down the process but compromised the accuracy of our RPN values and overall risk reliability.

The Technical Solution: The Pwark Engine
I engineered Pwark to replace this manual chain with a single, validated digital workflow:

To solve these challenges, I built a centralized "Source of Truth" by migrating plant data into a Python-managed SQLite database. I developed a custom extraction tool to bridge the gap between legacy Word-based FMEAs and our new digital system, turning static documents into actionable data. Finally, I automated the RPN calculation process, allowing the system to instantly flag high-risk failures the moment data is entered.

The Practical Result

Engineered a Python-based automation system to centralize complex FMEA data silos, achieving a 99% reduction in reporting errors and a 40% increase in operational efficiency through real-time risk tracking.

The Quantitative Connection
Building Pwark taught me that in complex systems, the highest risks often come from manual data handling.

The Technical Approach

• Data Preparation: I cleaned and processed multi-stage supply chain datasets to remove reporting inconsistencies and outliers before modeling.
• Predictive Modeling: Using Scikit-learn, I developed a model to forecast demand and anticipate these "whiplash" effects.
• Performance: The model achieved a low RMSE (Root Mean Square Error), ensuring the accuracy needed to optimize safety stock levels.

The Impact

• Inventory Efficiency: My findings suggested that using predictive models could reduce inventory distortion and safety stock requirements.
• Optimization: This research demonstrated how data-driven demand forecasting can significantly lower holding costs and improve supply chain stability.