Warehouse Management System Modernization with Big Data

Leveraging big data for a legacy WMS to automate planning and boost real-time decision-making. Integrating advanced analytics and live data streams for dynamic forecasting, inventory balancing, and resource coordination across distribution centers. Structuring the system to scale seamlessly while maintaining reliable performance under increasing data loads.

client

NDA Protected

Denmark
100-150 employees

A warehouse and logistics provider covering high-volume inventories across multiple sites needed sharper demand forecasting. The company runs several distribution centers supporting both B2B and B2C supply chains across Europe. Their legacy WMS (warehouse management system) lacked the data architecture to anticipate stock fluctuations, resulting in frequent overstock and stockouts.

Handling dynamic stock movements, they fulfill thousands of orders daily for diverse retail and wholesale partners. Consistency and uptime are critical as clients depend on timely deliveries and accurate inventory levels.

This initiative introduced a scalable big data framework and advanced analytics to power real-time inventory decisions. In this WMS modernization case study, we'll have a detailed look at the transformation from siloed spreadsheets to a unified, predictive system.

request background

Warehouse Management System Modernization for Accuracy & Efficiency

Inaccurate forecasts led to missed sales when items ran out and costly surpluses when demand dipped unexpectedly. Data scattered across ERP, sales platforms, and manual spreadsheets made it impossible to gain a clear, real-time view of stock.

As transaction volumes climbed, their old WMS crawled under the load, slowing query responses and causing decision delays. Attempts to bolt on third-party analytics tools failed due to incompatible data formats and inconsistent update schedules.

Managers spent hours reconciling reports instead of focusing on strategic initiatives. They needed a complete warehouse software upgrade that could ingest large volumes of transactional and sensor data simultaneously.

Immediate insights, rather than next-day summaries, were essential to react to sudden shifts in customer orders. The client envisioned a unified pipeline feeding predictive models and interactive dashboards for planners, aiming to leverage the power of big data.

modernized legacy warehouse management system interface

challenge

Tackling Data Silos & Forecasting Gaps of the Legacy WMS

Integrating multiple data sources proved daunting, as each system used distinct schemas and update frequencies, preventing seamless consolidation. The task was to replace legacy warehouse system, as it struggled to support peak-hour queries without timing out or returning incomplete results.

Real-time analytics were out of reach, where reports arrived hours after events had already impacted stock. Without a central data layer, machine learning algorithms could not train on historical trends or live inputs effectively. Forecast accuracy hovered too low to guide purchasing or replenishment decisions reliably.

Ad-hoc spreadsheet workarounds introduced human error and versioning nightmares, further eroding trust in numbers. Hence, the need for automated big data warehouse software rose.

Technology constraints forced planners to work in silence until batch jobs ran overnight. Data engineers also had their WMS legacy system challenges: they lacked automated pipelines, spending most of their time on manual ETL scripts and error handling.

The brittle infrastructure risked failure under growing data volumes, threatening entire operations during data spikes.

Compliance requirements for inventory records demanded robust audit trails — something the outdated warehouse management system could not guarantee. Balancing the need for continuous availability with a major architecture overhaul posed a unique technical puzzle.

goals

Improve forecasting by applying machine learning models trained on real-time and historical data to anticipate demand shifts more accurately.
Centralize all incoming data, regardless of source or format, into a unified pipeline that continuously updates without manual input.
Use predictive signals to automatically adjust stock levels and reduce both oversupply and empty shelf scenarios across distribution centers.
Replace slow batch reporting from the legacy WMS with real-time dashboards that generate business-critical reports in under five minutes.
Build the infrastructure on a scalable architecture that can handle higher data volumes and new sources without any rework.
Ensure every data event is traceable with automated logging, meeting regulatory requirements for transparency and audit readiness.
Implement auditing capabilities to meet compliance standards.

warehouse management system modernization case study

solution

Building Predictive, Big Data Warehouse Automation Software

Node.js, NestJS, Kafka, Apache Spark, TensorFlow, PostgreSQL, Redis, AWS (S3, EC2, CloudWatch), Presto, React.js, D3.js, Docker, Kubernetes, Helm, Grafana, Prometheus, OpenAPI/Swagger, OAuth 2.0, JWT
Ongoing
9 specialists

For this big data warehouse management system, we started by creating a centralized, cloud-based storage solution where all warehouse, order, and sensor data could land in one place automatically. Real-time data collection tools were added so new information, like sales transactions or stock movements, could update the system immediately without delay.

With this foundation in place, we trained machine learning models to analyze seasonal trends and patterns, helping planners prepare for expected shifts in demand.

The warehouse system digital transformation had to provide better visibility and faster decision-making. Thus, we developed user-friendly dashboards where managers could view up-to-date data in just a few clicks. We also restructured the backend using a modular setup that lets the system grow with future data demands and stay connected with its older tools.

Compliance and traceability were baked in from the start, so every data point could be tracked and reviewed as needed. Overall, our dedicated team delivered the following solutions:

Set up secure cloud storage to collect and manage structured and unstructured data.
Installed real-time data streaming tools to feed fresh information into the system 24/7.
Built machine learning models to forecast demand for each product line more accurately.
Created dashboards with visualizations tailored to planners and warehouse staff.
Optimized the system to run high-volume reports in minutes instead of hours.
Designed the backend using a container-based architecture to allow seamless scaling.
Added role-based access controls for data security as part of warehouse management system modernization.
Enabled logging for every data update or transformation to ensure compliance.
Trained both technical and non-technical staff to confidently use the new tools.

outcome

Tangible Success After Modernizing Legacy Warehouse Software

Forecast accuracy climbed by 30%, shrinking both stockouts and overstocks significantly.
Inventory holding costs fell by 20% thanks to smarter replenishment timing.
Operational efficiency rose by 25%, freeing planners from manual ETL tasks.
Report turnaround dropped from hours to under five minutes, speeding decision cycles.
Compliance audit readiness improved, with full lineage for all forecasting inputs.

WMS modernization case study by Acropolium

client feedback

We knew replacing our legacy WMS would be a challenge, but Acropolium made the transition surprisingly smooth. Their team helped us rethink how we handle data from the ground up. Now we catch demand shifts early, adjust stock automatically, and finally have reports we can trust in real time. It's a huge step forward for our operations.