Priyanka Tiwari

Managed end-to-end supply planning for 12,000+ SKUs across 40+ distribution centers, improving on-shelf availability by approximately 7% within network capacity limits using SAP IBP.

Achieved a 14% improvement in weekly forecast accuracy by building and maintaining SKU-level demand forecasts using Python and SQL, incorporating seasonality and promotion adjustments.

Reduced annual excess inventory by nearly $4M by establishing inventory segmentation and safety stock parameters aligned to service-level targets for ambient, refrigerated, and frozen categories.

Mitigated stockout risk by 18% during peak periods through conducting scenario analysis on transportation delays, labor constraints, and demand spikes to enable proactive mitigation actions.

Automated SQL-based data validation and refresh processes in a cloud data environment, cutting manual effort by 35% and improving planning reliability.

Identified 8-10% margin leakage across modern and general trade channels by developing SKU- and channel-level cost-to-serve models using SQL and Excel (Power Query, Pivot Tables).

Reduced average delivery lead times by ~12% without increasing logistics spend through network scenario analysis using Python and Excel, evaluating warehouse consolidation and hub-and-spoke structures.

Reduced line-haul transportation costs by ~7% by building transportation analysis models in SQL and Excel VBA to assess lane utilization, shipment frequency, and mode mix, enabling freight consolidation.

Achieved ~5% cost savings while improving On-Time, In-Full (OTIF) performance by collaborating with procurement and 3PL vendors to renegotiate rate cards and optimize contract SLAs.

Automated weekly logistics performance reporting using Power BI and SQL Server, reducing manual reporting effort by ~40% and improving visibility for regional and national supply chain leadership.

Improved forecast accuracy by ~18% for chronic and stable product lines by preparing demand forecasts in SAP APO (DP) using historical sales data, seasonality patterns, and commercial inputs.

Reduced excess and slow-moving inventory by ~14% while maintaining service levels above 98% by managing inventory planning activities for 3 manufacturing plants and 6 regional distribution centers.

Translated demand plans into feasible supply plans using SAP APO (SNP), incorporating production capacity, lead times, and safety stock parameters across multiple manufacturing sites.

Reduced manual reporting effort by ~30% through developing Excel-based planning and exception reports (Pivot Tables, Power Query) to track capacity utilization, supply gaps, and expedite requirements.

Ensured master data accuracy and stability of planning outputs post-implementation by assisting with planning system data migration and validation during SAP enhancements.