SCM Projects

This portfolio showcases a selection of supply chain analytics and business intelligence projects designed and developed by Edith Acosta, focused on inventory optimization, procurement analytics, demand variability, and operational efficiency.

Through the use of data visualization and analytics tools such as Power BI, these solutions were created to transform complex operational data into actionable insights, supporting strategic decision-making across manufacturing and supply chain environments.

The projects presented include initiatives related to inventory reduction, reverse logistics, procurement optimization, demand trend analysis, and inventory health monitoring.

Power BI No demand Inventory control

This dashboard was developed to identify and visualize no-demand inventory components across the Americas region, enabling a strategic analysis by plant, value stream, procurement type, and material category.

The objective of the tool was to improve inventory visibility, support excess and obsolete material reduction initiatives, and identify opportunities for rework, redeployment, reverse logistics, or end-of-life mitigation actions.

Through dynamic segmentation and allocation analysis, the dashboard allowed cross-functional teams to prioritize high-value no-demand materials, understand concentration by operational area, and accelerate decision-making for inventory optimization projects.

Power BI Reverse Logistics Recovery Tracker

This dashboard was developed to track reverse logistics and material recovery opportunities across suppliers and manufacturing sites.

The tool supported decision-making related to excess and no-demand inventory by identifying materials eligible for supplier return, rework, redeployment, or inventory recovery initiatives.

Through supplier allocation analysis and savings visualization, the dashboard enabled cross-functional teams to prioritize recovery actions, monitor progress, and support cost avoidance strategies across procurement and supply chain operations.

Power BI SPQ / MOQ / Lead Time Optimization Dashboard

This dashboard was developed to identify SPQ (Standard Pack Quantity), MOQ (Minimum Order Quantity), and Lead Time optimization opportunities to improve inventory efficiency across suppliers and operational sites.

The objective of the tool was to reduce excess inventory, accelerate inventory turns, and improve procurement planning through data-driven analysis of supplier conditions and replenishment parameters.

By visualizing savings opportunities related to MOQ reductions, lead time optimization, and procurement adjustments, the dashboard enabled teams to prioritize high-impact initiatives focused on inventory reduction, cost avoidance, and supply chain flow optimization.

ABC / XYZ / FMR Inventory Classification Dashboard

This dashboard was developed to analyze inventory behavior and demand variability through ABC, XYZ, and FMR classifications across multiple operational sites.

The tool enabled visibility into inventory allocation based on material value, demand consistency, and consumption frequency, helping evaluate inventory health and demand trends throughout the supply chain network.

FMR analysis was used to classify materials according to demand movement (Fast, Medium, and Rare), while ABC and XYZ segmentation supported strategic inventory prioritization, risk identification, and planning optimization initiatives.

Demand Fluctuation & Inventory Risk Dashboard

This dashboard was developed to monitor weekly demand fluctuations through FMR classification analysis, enabling early detection of demand drop-offs and potential inventory exposure risks.

By tracking changes in material movement behavior (Fast, Medium, and Rare) over time, the tool helped identify negative demand trends, anticipate future inventory impacts, and support proactive supply chain decision-making.

The solution provided visibility into demand volatility across operational sites, allowing teams to react in advance to potential excess inventory, declining consumption patterns, and forecast-related risks.

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