Ernst & Young · Bangalore · June 2023 – June 2025 · Status: ✅ Shipped
📌 TLDR
Directed cross-functional pods across multiple concurrent global engagements, owning product strategy, roadmap, and delivery of AI-driven planning solutions across 12+ global markets — lifting forecast accuracy 40%, feature adoption 30%, and cutting forecast bias 15%.
🏷 Context
Company: Ernst & Young — Global consulting, Technology Consulting practice
Clients: Global FMCG and enterprise clients across 12+ markets
Team: ~10 data scientists, 14 data engineers, 5 visualization specialists per product line
My role: Senior Consultant — Product Management
Timeline: June 2023 – June 2025
🔴 The Problem
Global enterprise clients were running demand planning on a patchwork of siloed, manual workflows across dozens of geographies:
- No consistent planning view across geographies — each market ran its own process with different tools and assumptions
- Chronic forecast inaccuracy with high bias, leading to simultaneous stockouts and excess inventory across markets
- Planning teams spending the majority of time on data gathering and reconciliation, leaving almost no time for actual analysis
- No repeatable product operating model — every market was a one-off project with no way to scale what worked
👩💼 My Role
- Product strategy & roadmap: Owned end-to-end strategy, roadmap, and delivery across 12+ global markets simultaneously
- Cross-functional leadership: Directed pods of ~10 data scientists, 14 data engineers, 5 visualization specialists per product line
- User research & discovery: Ran structured interviews with planning directors, supply chain leads, and regional heads
- PRDs & user stories: Translated research into detailed PRDs, user stories, and measurable success criteria