Enhancing Inventory Throughput by 30% with a React-Based Data Platform

Introduction

Greenleaf Pharmacy is a trusted community pharmacy located in Hastings-on-Hudson, New York, USA. The pharmacy provides prescription medications, over-the-counter products, health consultations, and wellness services to local residents.

Known for its personalized care and commitment to community health, Greenleaf Pharmacy serves as a reliable healthcare partner for families in the region. The pharmacy also offers services such as prescription refills, vaccinations, medication synchronization, and delivery options, ensuring convenience and continuous care for its customers.

Challenges

Before engagement with Top Remotely, GreenLeaf faced these main challenges:

Their inventory, sales and supply-chain data were scattered across multiple systems: on-premises SQL servers, CSV exports from older ERP, point-of-sale databases, warehouse management system and supplier portals.
Because of fragmentation, they lacked a coherent view of inventory throughput: no unified view of stock flows, duplications or mismatches between supply receipts and pharmacy dispense transactions.
Manual processes (spreadsheets, reconciling warehouse and store inventory, matching supplier shipments) were labour-intensive, error-prone, and inhibited responsiveness of operations teams.
Throughput for inventory (i.e., how fast stock turns, how quickly items move from receipt to dispense) was difficult to monitor, making it hard to identify bottlenecks, slow-moving SKUs or expired product risk.

GreenLeaf needed a solution scalable for a midsize pharmacy chain (multiple stores + warehouse) without the cost or complexity of very large enterprise-grade solutions. Top Remotely’s remote deployment model, modular architecture and lean data-aggregation approach were selected to address these constraints.

Objectives

GreenLeaf engaged Top Remotely with the following key goals:

Build a centralised inventory-throughput platform to ingest, clean, normalise and unify data from multiple sources (POS, warehouse, supplier shipments, legacy ERP) to create a coherent master-data foundation.
Achieve a unified inventory view and throughput metric: link each receipt, each SKU movement, each dispense transaction, across stores and warehouse; reduce duplicate or mismatched records; enable visibility into throughput (e.g., days-on-hand, stock-turn, time-to-dispense).
Reduce manual overhead of reconciling spreadsheets and ad-hoc reports, enabling operations and supply-chain teams to focus on analysis rather than data grunt-work.
Build scalability and flexibility: support additional data sources (supplier portals, expiry tracking, returns), automate ingestion pipelines, maintain data-governance and cost-control suitable for a midsize pharmacy chain.
Enable faster, more accurate insights: better SKU-turn monitoring, store-level throughput dashboards, identification of slow-moving or expired stock, optimisation of reorder points, replacement of low-throughput SKUs — all via a unified, trustworthy data platform and React-based dashboard.
Top Remotely helped define the architecture, choose appropriate ingestion/ETL tools, design the data model, and train GreenLeaf’s internal teams for remote operations and continuous data-pipeline maintenance.

Solutions

The inventory-throughput platform implemented for GreenLeaf included:

Data Ingestion & ETL: Pipelines to pull data from on-premises SQL (warehouse), POS databases (store sales/dispense), supplier portals (shipment receipts), Excel/CSV exports (legacy ERP). These were normalised and ingested into the unified platform.
Data Cleaning, Deduplication & Transformation: Custom logic to match supplier shipments with warehouse receipts, reconcile warehouse stock with store dispatches, standardise SKU codes, normalise units of measure, identify duplicate/mismatched records.
Master Data Model & Unified Inventory View: A single “Inventory Item” master entity was built that linked receipt, warehouse, store-dispatch, shelf-dispense transactions. Key objects (Receipts, Transfers, Store Dispenses, Returns) were aligned into the model.
Throughput & Analytics Layer: On top of the unified data foundation, React-based dashboards (with Material-UI or similar) and BI reports were created: e.g., inventory-turn trend, days-on-hand by SKU and store, slow-moving items, expiry risk, movement latency (receipt → store → dispense) and cross-store transfers.
Scalable Architecture: The solution used a scalable cloud architecture (for example AWS S3 + Redshift or Snowflake) for staging and data-warehouse; low-code pipelines (e.g., AWS Glue or Apache Airflow) for ingestion; modular design enabling new sources (e.g., returns, expiry logs) without major re-architecture.
Training & Roll-out: Role-based training for warehouse operations, store operations, supply-chain analytics users; establishment of inventory-data governance (ownership, data-quality monitoring, alerting).

Deployment Process

Top Remotely executed the rollout in a phased, remote-first, collaborative way:

Initial Assessment: Mapped GreenLeaf’s existing data sources (warehouse SQL server, POS databases, legacy ERP CSV exports, supplier portals), catalogued key pain-points (mismatched receipts vs dispenses, manual spreadsheets, slow throughput insight).
Data Source Integration & Pipeline Setup: Built initial ingestion pipelines for warehouse SQL, POS databases, major supplier portals; set up staging area in cloud; defined mapping/transformation logic for SKUs, units, store codes.
Model Design & Cleaning Logic: Defined master-data model for Inventory Item, Receipt, Store Dispense, Return; implemented deduplication and reconciliation logic; standardised SKU codes and units across sources.
Analytics & Dashboard Build: Created initial React-based dashboard prototypes for inventory-turn, days-on-hand, store comparisons, SKU-growth curves; connected unified data to visualization layer and iterated with user feedback.
Pilot and Training: Rolled out pilot to a subset of stores / one region; trained warehouse and store teams on new dashboards and data flows; collected feedback on throughput metrics, cleaning logic, dashboards; refined accordingly.

Full Launch & Ongoing Support: Rolled out across all stores and warehouse; onboarded additional data sources (returns, expiry logs); established data-governance processes (data-owners, monitoring, alerting for data-quality); handed off operations and provided remote support for continual improvement.

Results

Following deployment of the inventory-throughput platform, GreenLeaf achieved:

Manual reconciliation effort (warehouse vs stores vs supplier logs) was reduced dramatically — operations teams spent more time on insight and less on spreadsheet reconciliation.
Inventory throughput improved: for example, inventory days-on-hand reduced by ~30% (i.e., throughput up by ~30%) across the store network, enabling faster movement from receipt to dispense, less capital tied up in slow-moving stock.
Better insights: supply-chain and operations teams could quickly identify slow-moving SKUs, stores with high days-on-hand, stock that neared expiry, and could take targeted actions (discounting, transfer to faster stores, reduce reorder).
Scalability and flexibility achieved: adding new data sources (e.g., returns, expiry logs, supplier lead-time tracking) was accomplished with minimal overhead; dashboard users across operations, supply-chain and finance had unified, trusted data.
Improved data quality and trust: fewer mismatches between receipts and stores, consistent SKU codes and units, fewer manual corrections — leading to more confident decision-making around inventory investments and SKU rationalisation.

These gains illustrate how a mid-sized pharmacy chain, working with a remote-deployment partner like Top Remotely, can transform fragmented inventory data into actionable throughput insights — unlocking faster movements, cleaner data, improved operational agility — all without the heavy cost or complexity of large enterprise systems

Technology Stack

The solution was built using a modern, scalable architecture fit for mid-sized pharmacy operations:

Backend / ETL: Node.js or Python (for ingestion scripts); micro-services for data ingestion pipelines.
Data-store: Cloud-data lake / staging (AWS S3 or similar), Data-warehouse / BI layer (AWS Redshift, Snowflake) for unified inventory and movement data.
ETL/ELT Tools: Apache Airflow or AWS Step Functions for scheduling ingestion, IoT/external supplier APIs; connectors for SQL servers, CSVs, supplier portals.
Master Data Management (MDM) Layer: Custom logic for SKU standardisation, deduplication, transformation, reconciliation of receipts/dispenses.
Frontend Visualization: React with Material-UI (or Ant Design) for dashboards; BI tools (Power BI, Looker) for detailed reports.
Security & Governance: Role-based access, encryption at rest and in transit, data-quality monitoring and alerting, data-governance policies.
Cloud Hosting: AWS public-cloud (auto-scaling, monitoring, logging, secure VPC, region replication where needed).
Modules: Data Ingestion, Transformation & Cleaning, Master-Data Model, Analytics & Dashboard, Governance & Monitoring.

With this architecture, GreenLeaf now has a future-ready, scalable inventory-throughput platform that enables their operations and supply-chain teams to leverage clean, unified data — eliminating silos, reducing manual burden and driving insight-oriented inventory movements.

By partnering with Top Remotely, GreenLeaf Pharmacy Solutions transformed its fragmented, multi-system inventory landscape into a robust, unified throughput platform — unlocking faster stock movement, cleaner data, improved operational agility and inventory capital efficiency — at a cost and complexity level suitable for a midsize pharmacy operation.