Retail
Market Entry Analysis for D2C Consumer Brand
Conducted competitive landscape and consumer demand analysis across 12 markets, guiding a successful product launch.
Customer Overview
Our client operates a supermarket carrying thousands of SKUs across perishable and non-perishable categories. Like most grocery retailers, they were losing margin to two connected problems: stock expiring on the shelf before it could be sold, and reorder decisions that were based on gut feel rather than data — leading to a mix of overstocking (which fed the expiry problem) and understocking (which caused lost sales).
The Challenge
Before working with Analytics Station, the client's inventory process was largely manual and reactive:
- • No consistent, category-level visibility into which products were consistently going to waste and why.
- • Reorder quantities were set by store managers using rough intuition, with little regard to holding costs, ordering costs, or actual demand variability.
- • Expiry write-offs were tracked in spreadsheets after the fact, with no forward-looking way to flag at-risk stock before it became a loss.
- • Reporting was fragmented across outlets, making it hard for category and operations managers to compare performance or spot systemic issues.
Our Solution
Analytics Station partnered with the client's operations and procurement teams to rebuild their inventory decision-making around data:
1. Inventory & Expiry Analysis
We consolidated sales, stock, and expiry data across outlets to build a clear picture of shrinkage — identifying the specific categories, SKUs, and stores driving the majority of expiry losses, and the seasonal and promotional patterns behind them.
2. EOQ-Based Reorder Recommendations
Using the Economic Order Quantity (EOQ) method, we modeled optimal order sizes and reorder points for key SKUs, factoring in demand rate, ordering costs, and holding costs. This replaced ad hoc ordering with a repeatable, defensible framework that balanced the cost of ordering too often against the cost of holding excess (and expiring) stock.
3. Reporting & Dashboards in Power BI and Excel
We built a layered reporting suite:
- • Power BI dashboards for management, giving real-time, drill-down visibility into stock levels, expiry risk, and reorder recommendations across all outlets.
- • Excel-based working models for category and store-level teams, so staff who lived in spreadsheets day-to-day could apply EOQ recommendations directly to their ordering workflow without needing to learn a new tool.
4. Ongoing Insight Delivery
Beyond the initial build, we set up recurring reporting cycles so the client could continuously monitor expiry trends and refine reorder parameters as demand patterns shifted.
Results
- • Meaningful reduction in stock expiring unsold, concentrated in the highest-loss perishable categories identified during analysis.
- • More consistent, data-backed reorder quantities across outlets, reducing both excess holding and stockouts.
- • A single, standardized view of inventory health for management via Power BI, replacing fragmented, outlet-by-outlet reporting.
- • Store and category teams equipped with practical, Excel-based tools to apply EOQ logic in their day-to-day ordering, without disrupting existing workflows.