1️⃣ Objective
Build a comprehensive, real-time dashboard leveraging SAP data (MM, WM/EWM modules) to provide operational visibility, predict stockouts, optimize inventory levels, and analyze warehouse efficiency metrics for supply chain decision-making.
Key Goals:
✨Visualize key inventory metrics (turnover, fill rate, obsolete stock) in near real-time.
✨Implement predictive models for stockout risk and demand forecasting.
✨Analyze warehouse operations: picking/putaway efficiency, cycle time, and labor productivity.
✨Develop a user-friendly, interactive dashboard for supply chain managers.
2️⃣ Problem Statement
Organizations running on complex ERP systems like SAP often struggle to extract timely, unified, and actionable insights from their immense volume of inventory and logistics data. This leads to suboptimal purchasing, frequent stockouts, high carrying costs, and inefficient warehouse processes. A consolidated, intelligent dashboard is necessary to transform raw SAP transactional data into strategic business intelligence.
3️⃣ Methodology
The project will combine data extraction, warehousing, time-series modeling, and visualization:
✨Phase 1 — Data Extraction & Staging: Extract key data from SAP modules (MARA, EKPO, LIKP, MSEG, etc.) using an appropriate connector (e.g., SAP OData/Hana or third-party ETL).
✨Phase 2 — Data Modeling & Feature Engineering: Create star/snowflake schema in a data warehouse; calculate derived features like inventory turnover ratio, days of supply, and historical lead times.
✨Phase 3 — Predictive Modeling: Build time-series models (ARIMA, Prophet, or deep learning) for short-term demand forecasting and classification models (XGBoost) for stockout prediction.
✨Phase 4 — Dashboard Development: Create interactive visual dashboards to display current stock levels, inventory performance, forecast vs. actuals, and warehouse efficiency metrics.
✨Phase 5 — Performance Evaluation: Validate model accuracy and measure the impact of the dashboard on key operational KPIs (e.g., reduction in out-of-stock events).
4️⃣ Dataset
Core Entities:
✨SAP Material Management (MM): Material Master (MARA), Inventory Movements (MSEG).
✨SAP Warehouse Management (WM/EWM): Transfer Orders, Picking/Putaway activities, Labor utilization logs.
✨SAP Sales and Distribution (SD): Sales Orders, Deliveries (VBAK, LIKP, LIPS).
✨External: Historical market data, seasonal indices, promotional calendars (for forecasting).
Patient Records Table (Sample):
5️⃣ Tools and Technologies
6️⃣ Evaluation Metrics
✨Forecasting Accuracy: MAE or MAPE for demand predictions.
✨Warehouse Efficiency: Average picking/putaway cycle time.
✨Stockout Rate: Percentage of orders that cannot be immediately fulfilled.
✨Obsolete Inventory Value: Total cost of stock with no movement for a defined period.
7️⃣ Deliverables
8️⃣ System Architecture Diagram
Source 1: SAP EWM (Execution)
Warehouse Tasks (WT), Handling Unit (HU) movements, Labor Management data.
Source 2: S/4HANA Inventory (MM)
Stock types (Unrestricted, Quality Inspection), Material Master, and GR/GI documents.
Source 3: Quality Management (QM)
Inspection Lots, Usage Decisions, and quality hold statuses affecting inventory availability.
SAP Landscape Transformation (SLT) / ODP
Change Data Capture (CDC) to replicate real-time EWM/MM tables to the central HANA DB.
Staging Area (SAP HANA)
Temporary storage for raw operational data before cleansing and transformation.
SAP BW/4HANA (Reporting Layer)
Data flow objects for historical inventory movements, stock levels, and KPI calculation.
SAP HANA Calculation Views
Calculation of real-time inventory on-hand, safety stock variances, and throughput rates.
Data Marts / Aggregates
Pre-calculated, summarized views tailored for specific dashboard requirements (e.g., Cycle Time).
SAP Analytics Cloud (SAC) Dashboard / Fiori Apps
Visualizes key metrics: Stock Valuation, Inventory Accuracy, Warehouse Throughput, and Labor Efficiency.
Source 1: SAP EWM (Execution)
Warehouse Tasks (WT), Handling Unit (HU) movements, Labor Management data.
Source 2: S/4HANA Inventory (MM)
Stock types (Unrestricted, Quality Inspection), Material Master, and GR/GI documents.
Source 3: Quality Management (QM)
Inspection Lots, Usage Decisions, and quality hold statuses affecting inventory availability.
SAP Landscape Transformation (SLT) / ODP
Change Data Capture (CDC) to replicate real-time EWM/MM tables to the central HANA DB.
Staging Area (SAP HANA)
Temporary storage for raw operational data before cleansing and transformation.
SAP BW/4HANA (Reporting Layer)
Data flow objects for historical inventory movements, stock levels, and KPI calculation.
SAP HANA Calculation Views
Calculation of real-time inventory on-hand, safety stock variances, and throughput rates.
Data Marts / Aggregates
Pre-calculated, summarized views tailored for specific dashboard requirements (e.g., Cycle Time).
SAP Analytics Cloud (SAC) Dashboard / Fiori Apps
Visualizes key metrics: Stock Valuation, Inventory Accuracy, Warehouse Throughput, and Labor Efficiency.
9️⃣ Expected Outcome
✨Reduction in stockout incidents due to accurate demand forecasting.
✨Lower carrying costs achieved through optimized safety stock and purchasing.
✨Improved warehouse throughput and labor efficiency via performance analytics.
✨A single source of truth for all inventory and warehouse key performance indicators.