Pooja

• Led inventory planning, allocation control, and replenishment execution for sourcing hubs by analyzing demand, availability, and safety buffers using Excel models and reconciling ATP vs Reserved inventory via SQL‑driven data extracts, reducing mismatches by ~20% across 2,700+ SKUs
• Built and maintained central inventory dashboards by integrating SQL data into Power BI / Tableau, enabling real‑time visibility of stock health, IWIT, FC closures, and reservation risks
• Streamlined daily MIS and variance tracking using advanced Excel (Power Query, pivots, automated checks), cutting manual reporting effort by ~30%
• Analyzed historical inventory movement, closures, and dispatch timelines to identify cut‑off breach patterns and slow‑moving SKUs, improving on‑time dispatch performance
• Used data‑driven controls to manage SKU reservations and closure actions, contributing to 10–12% reduction in inventory losses

• Maintained >99.5% inventory accuracy by reconciling ERP stock vs physical counts using Excel‑based variance and root‑cause analysis
• Improved manpower utilization by planning rosters using Excel productivity dashboards (volume vs headcount vs SLA)
• Increased pick productivity and accuracy by analyzing pick‑rate, error trends, and ageing data through Excel pivot‑based reports, achieving 15–18% efficiency gains
• Ensured on‑time cut‑off closures by tracking operational and system exceptions using SQL/ERP reports and Excel trackers, reducing dispatch delays by ~20%
• Planned product movement using historical sales and ageing analysis in Excel to support peak readiness
• Optimized picker paths by clustering SKUs using velocity and category analysis, reducing travel distance by ~40% and improving pick rate by ~25%
• Executed cycle counts using ABC classification, reducing shrinkage and inventory variance
• Reduced good‑damage losses by identifying damage‑prone touchpoints through data‑led process analysis (~15% reduction)

• Managed inbound operations including goods receiving, scheduling, and GRN processing, supported by Excel‑based accuracy and TAT dashboards
• Controlled expiry, damage, and quality losses by tracking RTV/PRN performance using structured Excel loss‑analysis reports
• Reconciled GRNs vs invoices (CP, tax, SKU) using Excel validation logic, resolving discrepancies with Finance teams
• Identified and corrected high‑value GRN/GDN/PRN errors using SQL queries on transaction value thresholds
• Tracked FTD / MTD / YTD inventory movement through Excel summaries to support replenishment and liquidation decisions
• Managed ₹15+ Cr inventory across 19,000+ SKUs, maintaining high fill rates and availability
• Monitored FEFO compliance, bin audits, manpower productivity, and dispatch accuracy using operational control reports
• Reduced short/excess picking and handling damage through data‑based process reviews (~15% reduction)

Kaizen – Near Expiry Tracker (PAN-India) -

• Designed and implemented a Near‑Expiry Inventory Tracker for 17,000+ SKUs using Excel automation (expiry buckets, ageing logic), delivered in 2 months
• Operationalized FEFO execution through shelf‑life‑based SKU classification (0–30 / 31–60 / 61–90 days)
• Segregated inventory into Food / Non‑Food / RTV / NRTV to accelerate liquidation
• Enabled buyer‑led liquidation via standardized Excel reports, reducing near‑expiry losses by 20–25%
• Tracker scaled and adopted across multiple facilities PAN‑India