I build automated forecasting models, inventory diagnostics, and supplier scorecards in Python and SQL — turning messy operational data into decisions that cut cost and stockouts.
Eight end-to-end case studies applying Python, SQL, predictive modeling, and strategic frameworks to logistics, demand, supplier, and inventory challenges.
End-to-end diagnostic on 2,500 shipments across carriers, zones, and routes. Used Pandas for cleaning and K-Means clustering to segment carrier performance by OTD — surfaced an estimated 15% cost-reduction lane.
Time-series forecasting across 20 SKUs over 24 months. Cleaned 14,620 daily demand rows and deployed interactive 3-month SMA models with MAPE computed live from a held-out test split — no resume-bait hardcoded numbers.
Supplier scorecard built on SQL aggregations evaluating cost variance, delivery reliability, and quality. Used as a benchmarking layer to drive 98% compliance and surface vendor-renegotiation opportunities.
ABC-XYZ classification driving differentiated turnover targets — 6–12x for Class A, 1–3x for Class C — with reorder-point optimization and a strategic recommendations panel for action items.
Diagnostic study comparing inventory performance across industries in India and the USA. Trend analysis on turnover and DIO, with financial-impact sizing on working capital tied up by tier-one operators.
Multi-criteria supplier scoring model evaluating cost, lead time, and geopolitical risk across global Li-Ion cell vendors. Output: a defensible sourcing recommendation framework.
Integrated value-chain analysis spanning upstream, midstream, and downstream operations with competitive positioning and KPI benchmarking against global majors.
24-slide deep dive on India's pharmaceutical sector covering growth and risk drivers, competitive intensity, and end-to-end value chain mapping from API sourcing to formulations export.
Three deployed front-ends turning raw operational data into decision support — open any one in a new tab.
2,500 shipments and 14,620 demand records unified into carrier scorecards, route cost analysis, and a 3-month SMA forecast.
Open ↗Quality metrics, on-time delivery KPIs, cost-variance trends, and risk scoring — combined into a single supplier scorecard.
Open ↗ABC-XYZ segmentation with realistic 6–12x Class A turnover, reorder-point optimization, and a Strategic Recommendations action panel.
Open ↗I work at the intersection of operations and analytics — translating supply chain problems into models that decision-makers can actually use.
Supply Chain Analyst
My work spans demand forecasting, inventory optimization, and supplier intelligence — the three levers that quietly decide whether a supply chain costs too much or breaks at the wrong moment. I'm most useful when the data is messy, the question is fuzzy, and someone needs a defensible answer by Friday.
I lean on Python (Pandas, NumPy, scikit-learn) and SQL for the heavy lifting, then surface results in Tableau, Power BI, or — as on this site — purpose-built front-ends so the analysis stays accessible after the slide deck closes.
I hold certifications in Supply Chain Analytics (Rutgers) and Supply Chain Data Analytics (Unilever), and I'm currently completing a BBA from Manipal University Jaipur. These combined with hands-on projects are how I bridge theory and real operational problems.
Outside the dashboards: I read research on procurement strategy, write up case studies on SCData.ai, and keep a running list of supply chain failure modes that should never have made it past the planning phase.
Core competencies bridging data engineering, predictive modeling, and strategic research.
Logistics optimization, carrier benchmarking, route analysis, cost-per-kg modeling, OTD tracking.
SMA, WMA, exponential smoothing, seasonality decomposition, MAPE / RMSE accuracy diagnostics.
Vendor scorecards, multi-criteria selection (MCDM), risk assessment, quality KPI tracking.
ABC-XYZ classification, safety stock, reorder points, turnover analysis, stock-out prevention.
Analytics: Python (Pandas, NumPy), SQL, Excel (pivot tables, solver, scenario analysis).
BI: Tableau, Power BI.
RFQ management, multi-criteria vendor evaluation, cost negotiation basics, supplier risk assessment, and sourcing strategy frameworks.
Competitive intelligence mapping, value-chain diagnostics, and market-entry frameworks.
Real-world supply chain and analytics work delivered for clients across markets.
Conducted supply chain and market intelligence research for a UK-based SaaS platform serving dental practices. Mapped vendor and procurement decision patterns across 300+ dental clinics — evaluating software adoption readiness, operational workflows, and switching barriers. Delivered a segmented intelligence report used to sharpen go-to-market targeting and vendor outreach strategy. Applied multi-criteria scoring to rank market segments by adoption likelihood and cost sensitivity.
Designed and implemented end-to-end supply chain systems for a US retail business operating cross-border inventory between India and the US. Led transition from push-based to pull-based replenishment — eliminating dead stock while maintaining delivery timelines. Built a multi-variable demand forecasting model covering seasonality, inflation, promotional lift, and customer retention. Implemented live logistics tracking across the India–US corridor and managed supplier relationships with a limited vendor pool, prioritising lead-time reliability and procurement continuity.
Feedback from founders and teams I've worked with.
Tamarai provided sharp analytical clarity on a complex compliance challenge for our product. His research helped us identify a legally viable path to launch our platform. Highly recommend his work.
Open to Supply Chain Analyst, Demand Planner, and Analytics roles — or a coffee chat about messy data.