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Looking for a more specific outcome? We’ll build a solution to get you there.
Looking for a more specific outcome? We’ll build a solution to get you there.
We partnered with a North American apparel retailer facing frequent stockouts, slow Excel-based planning cycles (1–3 days), and fragmented inventory visibility across 120+ stores and ecommerce. Instead of replacing their existing systems, we implemented a side-by-side planning and decisioning layer on SAP BTP, enabling near real-time inventory visibility and faster allocation decisions. The result was a 32% reduction in stockouts and 90% faster planning cycles, achieved without disrupting core transactional systems.
A large North American apparel retailer operating in a highly seasonal, trend-driven environment.
Apparel & Fashion Retail
Omnichannel (Retail + Ecommerce)
US & Canada
US & Canada
In seasonal retail, decision latency directly translates to lost revenue. The client’s existing planning approach was not designed for short demand cycles or rapid shifts in buying behavior.
Short product lifecycles and external signals (weather, trends, promotions) caused frequent deviations from historical forecasts. Existing planning relied heavily on lagging indicators.
Merchandising teams operated on Excel-based workflows with planning cycles taking 1–3 days. By the time decisions were executed, demand conditions had already changed.
Inventory data across stores, warehouses, and ecommerce systems was not synchronized. Reallocation decisions were based on partial visibility.
Stock distribution was driven by historical averages and predefined rules, with limited ability to respond dynamically to intra-season demand changes.
Replacing SAP ECC was neither practical nor necessary. It continued to function reliably as the transactional backbone for finance, order management, and inventory accounting. The real constraint was the lag in decision-making and the lack of unified visibility across planning workflows.
To address this, we implemented a side-by-side intelligence layer on SAP BTP that sits above the existing landscape, augmenting it with near real-time insights and faster decisioning capabilities without disrupting core operations.
This approach enabled a clear decoupling between transaction processing and planning. While SAP ECC continued to manage execution and record-keeping, SAP BTP was leveraged to aggregate data from multiple systems, process demand signals, and generate allocation recommendations.
At the core of this solution is a demand–supply control tower that continuously ingests data from POS, ecommerce, and warehouse systems, processes it with seconds-to-minutes latency, and surfaces prioritized actions to planners. This ensures that decisions are based on the most current demand and inventory signals, significantly reducing response time while maintaining stability in the underlying transactional systems.
1
Inventory and sales data from POS, ecommerce, and warehouse systems are ingested using a hybrid model:
This enables a seconds-to-minutes latency view of inventory across all channels.
2
Instead of relying solely on historical forecasts, the system incorporates:
This is implemented using statistical smoothing and rule-based adjustments, with scope for ML-based forecasting in future phases.
3
A constraint-aware engine that recommends stock movements using:
Planners retain control through approvals and overrides.
4
A centralized SAP Analytics Cloud dashboard that provides:
Planners can evaluate demand, validate recommendations, and execute allocation decisions within a single workflow.
5
The solution follows SAP’s recommended side-by-side extension model, ensuring:
| Layer | Component | Technical Role |
| Source | SAP ECC, SFCC, POS | Systems of Record & Transactional Engines. |
| Integration | SAP Integration Suite | Event Mesh for real-time sales signals; Cloud Connector for secure ECC access. |
| Persistence | SAP HANA Cloud | High-speed data federation and real-time inventory aggregation. |
| Logic | SAP BTP (CAP/Node.js) | The “Brain” – executes the Dynamic Allocation Engine and constraint checks. |
| Consumption | SAP Analytics Cloud | The “Face” – provides the What-If simulation and executive visibility. |
We followed a phased, low-risk implementation model:
DSW’s platform enhancements required a strong, flexible technology stack to support product discovery, sizing workflows, personalization, and integrations with third-party retail systems. Below is the technology ecosystem used:
To enhance product discovery and deliver a sleek, responsive fashion experience
React
HTML5
CSS3
Angular
To power search, sizing, promotions, and secure ordering workflows
Python
Django
Flask
Spring Boot
Java
To maintain accurate product, inventory, and customer data
PostgreSQL
MySQL
Oracle DB
Connects fashion analytics, personalization tools, and customer rewards in real time
Analytics Integration
Loyalty Systems
custom Python scripts
To ensure performance and fast rollouts during high-volume retail periods
Docker
Kubernetes
To streamline releases and support continuous improvement
Git
Jenkins
GitHub Actions
Planning cycle time reduced by ~90% Manual effort reduced by ~85%
Improved inventory visibility across channels Better stock balancing across regions Faster response to demand fluctuations
Stockouts reduced by 32% (measured over peak seasonal period) Reduction in markdown losses on seasonal inventory
Transform inventory challenges into real-time, data-driven decisions with our advanced AI, analytics, and intelligent planning capabilities—empowering faster allocations, unified visibility across stores and ecommerce, and scalable optimization without disrupting your existing systems.
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