My Published Projects

Access my full suite of work here: interact with my data dashboards, review my published projects, and dive into my latest business insights.

Supply Chain Health & Supplier Risk Analysis

An end to end Power BI tracking System designed to bridge the gap between procurement lead times and sales velocity. This analysis identified critical “stockout” risks for high-revenue items while flagging supplier inefficiencies that negatively impacts revenue.

Note: This dashboard is interactive. Filter by Product Type, Location, or hover over charts for detailed metrics.

About the Project

Automated Logic Engine (DAX)

I developed complex, nested measures to categorize 100 SKUs in real-time. This includes a dynamic ABC Analysis that re-ranks products based on revenue shifts and an Economic Order Quantity (EOQ) formula that calculates optimal purchase volumes.

Prescriptive “Strategic Action”

I programmed the dashboard to cross-reference Stock Levels, Lead Times, and ABC Ranks. This transforms raw data into specific instructions, flagging whether a manager should Audit Supplier, Reduce Stock, or Prioritize Order.

Key Takeaway

By moving the business logic into the DAX layer, I reduced manual data preparation by 100%, allowing the supply chain team to focus on execution rather than spreadsheet management.

The Business Problem (The "Why")

Supply chain managers often struggle with Lead Time Variance. If a high selling product has a long lead time, a minor shipping delay can cost thousands of dollars in revenue. Conversely, holding too much stock for slow-moving items increases the carrying costs. I developed this dashboard to prioritize action based on ABC Ranking. The goal was to ensure that we never run out of our A-Rank revenue drivers.

The Technical Core: 100% Power BI

Unlike standard reports that are typically based in Excel, SQL, or Python, I built this entire model and engine of this dashboard directly within Power BI using Power Query and Advanced DAX. This approach ensures that the dashboard is a living tool, not just a static snapshot.

Current Problems

The "Stockout" Crisis vs. "A-Rank" Revenue

Any item classified as A-Rank that also triggers a Reorder Now status represents a critical revenue setback. Because Rank A items drive the vast majority of total revenue, maintaining stock levels is non-negotiable. A stockout in this category is significantly more costly than a stockout for a Rank C item.

Inefficient Procurement & Supplier Misalignment

There are two strategic issues within our current procurement model that require immediate correction:

  • Priority Stock Bottlenecks: A significant portion of our Class A inventory (13 out of 49 items) suffers from high sales volume paired with excessively long lead times. Seven of these products require immediate reordering to avoid a total stockout.

  • Low Velocity Capital Tied Up: We are currently holding inventory for slow-movers that have low sales volume but high lead times. Forcing the warehouse to hold stock that takes 30 days to arrive just to sell a few units is an inefficient use of capital and space.

Solutions

Mitigating the "Stockout" Crisis for A-Rank Revenue

To protect our primary revenue drivers, we must prioritize fulfillment for any Rank A item with a "Reorder Now" status.

  • Immediate Action: Expedite shipping or initiate direct supplier communication to bypass standard queues for the 10 skincare SKUs currently flagged for reorder.
  • Risk Assessment: Eight of these items are high-performing priority stock. Failing to secure them immediately represents a significant threat to total revenue.
  • Proposed Buffer: Implement a dynamic Safety Stock calculation for these top 10 SKUs to account for shipping volatility.

Inefficient Procurement & Supplier Misalignment

  1. Optimizing Priority Stock: Our priority stock consists of high-velocity items hampered by extended lead times, representing approximately 27% (13/49) of our Class A inventory.
    • Supplier Partnerships:​​​ We will negotiate with current vendors to establish fast-track fulfillment protocols for these items. If lead times remain static, we will begin a competitive bidding process to source a more agile supplier.
    • Inventory Logic:​​ To prevent reoccurring stockouts, we will adjust the Reorder Point and increase the standard order quantity to better align with high sales velocity and current vendor delays.​​​​
  2. Strategic Audit & Stock Reduction: We are currently misallocating warehouse resources to "slow mover" products with high lead times but negligible sales volume.
    • Efficiency Audit: We must question the logic of 30-day lead times for low-demanded products that tie up capital and physical shelf space.​
    • Agile Sourcing: For these items, we will transition to local suppliers who can also provide shorter lead times. While the per-unit cost may be slightly higher, the reduction in holding costs and improved liquidity will provide a better net return.
    • SKU Rationalization: Any SKU that fails to meet a minimum sales threshold over two consecutive audit periods will be considered for total phase-out to streamline the portfolio.

This project utilizes a supply chain dataset sourced from Kaggle. You can view the raw data and original context here.

SKU Rationalization & Revenue Concentration Analysis

An interactive Power BI dashboard designed to identify revenue drivers and "long-tail" inefficiencies across 7,000+ SKUs. This analysis provides actionable insights for inventory optimization, helping the business reclaim warehouse capacity by identifying underperforming product categories.

Note: This dashboard is interactive. Filter by category or hover over charts for detailed metrics.

The Business Problem (The "Why")

Retailers often struggle with carrying too many products that don't actually sell. I developed this analysis to apply the Pareto Principle (80/20 Rule) to the product catalog, identifying which 20% of products drive 80% of the revenue and which products are simply taking up space.

The Recommendation (The "Value)

  • Rationalization: Implement a 15% SKU reduction in the 'Bottom' and 'Dupatta' categories to lower overhead.
  • Strategic Reinvestment: Reallocate the reclaimed warehouse space and budget toward 'Set' and 'Kurta' categories, which show the highest return on investment.
  • Inventory Turnover: Focus on a learner inventory model to improve cash flow and reduce the risk of dead-stock.

Deep Dive: Insights & Methodology

  • The Pareto RealityOnly 27.6% of SKUs are driving the bulk of the revenue. This confirms that the catalog is highly concentrated.
  • The Efficiency Star: The 'Set' category is the powerhouse, contributing 43% of total revenue. This suggests a "double-down" strategy for marketing this specific category.
  • Operational Risk: Over 5,000 SKUs (72.4%) fall into the "Tail" tier. These are high-maintenance, low-return items that increase storage costs and complicate logistics.
  • Category Nuance: Categories like 'Bottom' and 'Dupatta' have almost no 'Core' performers, making them prime candidates for immediate inventory cuts.

Key Metrics & Tech Stack

Tools Used

  • Power BI, Excel, Pivot Tables

Data Size

  • 7.14k Unique SKUs

Total Revenue

  • 78.59M

Primary Analysis

  • Pareto Analysis, Category Performance, Tail-End Rationalization

This project utilizes an e-commerce sales dataset sourced from Kaggle. You can view the raw data and original context here.