PRD: AI-Driven Labor Forecasting & Dynamic Task Assignment for SAP EWM (MVP)

Project Priority: 🥈 SECOND MOST IMPORTANT
Version: 1.0 MVP
Status: Ready for Development

1. Product Vision (MVP)

Deliver a simple but credible AI engine that forecasts labor demand at the shift/day level and recommends a basic task allocation plan, illustrating how AI-driven labor optimization boosts productivity and reduces overtime. This demonstrates understanding of where SAP EWM is heading with AI integration.

Why This Matters: “AI-driven predictive analytics dominate in warehouse management” was named the #1 supply chain trend for 2025. SAP is actively embedding predictive labor planning with AI into EWM, and the industry is shifting toward “skill-based task matching and dynamic allocation”. This project shows you’re ahead of the curve.

2. Users & Jobs-to-be-Done

Primary User: Warehouse Operations Manager

• Need: Predict labor requirements and optimize worker assignments
• Job-to-be-Done: “I want to know how many people I need tomorrow, and roughly where to assign them (picking vs packing vs receiving).”

Secondary User: SAP EWM / Labor Management Consultant

• Need: Demonstrate AI capabilities that complement SAP EWM Labor Management
• Job-to-be-Done: “I want to show how AI can complement SAP EWM Labor Management by feeding predicted capacity and demand.”

3. MVP Scope (Must-Haves)

3.1 Data Model (Simplified)

Single warehouse, daily granularity:

Required Fields: - date - Date of shift - shift - Shift identifier (e.g., Day, Night, Evening) - orders_count - Number of orders processed - lines_to_pick - Total picking lines - lines_to_pack - Total packing lines - receiving_lines - Total receiving lines - workers_available - Number of workers scheduled - workers_picking - Workers assigned to picking - workers_packing - Workers assigned to packing - workers_receiving - Workers assigned to receiving - actual_picks_completed - Actual productivity metric - avg_lines_per_worker_per_shift - Historical productivity baseline

Data Requirements: - Minimum 6 months of historical data - Daily granularity (can aggregate from hourly if needed) - At least 2 shifts per day - Include seasonality indicators (day of week, month, holidays)

Initial Data Source: - Synthetic data generation based on realistic warehouse patterns - Or aggregated from logistics datasets (Kaggle warehouse datasets) - Include realistic patterns: weekly seasonality, monthly trends, holiday spikes

3.2 Forecasting (Core)

Model Selection (MVP - Pick One): - Option A: Prophet (Recommended for MVP - faster to ship) - Handles seasonality well - Provides uncertainty intervals - Easy to interpret

• Option B: Simple LSTM (More impressive, but more complex)
  • Better for complex patterns
  • Requires more data preprocessing
  • More “AI” credibility

Forecast Targets: - required_workers_next_shift - Total workers needed - forecast_orders - Expected order volume - forecast_pick_lines - Expected picking workload - forecast_pack_lines - Expected packing workload - forecast_receiving_lines - Expected receiving workload

Model Features: - Weekly seasonality (Mon–Fri vs weekend patterns) - Monthly trends - Known peak periods (e.g., Black Friday, month-end) - Holiday effects - Day-of-week patterns

Output Format: - Table with next 14 days × shifts: - forecast_workers_needed - forecast_orders - forecast_pick_lines - forecast_pack_lines - forecast_receiving_lines - lower_bound / upper_bound (confidence intervals)

3.3 Simple Task Allocation (MVP-level)

Inputs: - Forecasted workers available (configurable slider or static value) - Workload distribution: % of effort by function - Default: 50% picking, 30% packing, 20% receiving - Configurable per shift/day

Allocation Logic: Given forecasted workload and available workers, compute: - workers_picking - Recommended picking workers - workers_packing - Recommended packing workers - workers_receiving - Recommended receiving workers

Constraints (Simple, no heavy OR-Tools for MVP): - Sum of workers assigned ≤ available workers - Minimum 1 worker per function (if workload > 0) - Respect historical productivity ratios (e.g., 100 lines/worker/shift)

Output: - Per-day/shift allocation table - Overtime risk flag if workers_available < workers_needed - Productivity efficiency score

3.4 UI / Visualization (MVP)

Streamlit App or Notebook Output:

Dashboard Components: 1. Forecast Visualization: - Line chart: forecasted workers needed over time (14-day horizon) - Confidence intervals shown as shaded area - Historical actuals vs forecast comparison

2. Allocation Table:
   • Per-day/shift allocation recommendation
   • Columns: Date, Shift, Workers Picking, Workers Packing, Workers Receiving, Total, Overtime Risk
3. KPIs:
   • Predicted overtime risk flag (if workers_available < workers_needed)
   • Average workers needed per day
   • Peak day identification
   • Cost savings estimate (overtime avoided)
4. Model Performance:
   • Simple accuracy metrics (MAPE, RMSE) on holdout period
   • Forecast vs actual comparison chart

Optional: - Interactive date range selector - Shift-specific views - Export to CSV functionality

4. Out-of-Scope for MVP

• Per-worker LSTM productivity modeling (keep at aggregate level)
• Complex task routing between zones
• Real-time reallocation logic (keep it planning-level, not real-time)
• Skill-based matching (workers have different skill levels)
• Multi-warehouse support
• Integration with actual SAP EWM APIs
• Advanced optimization algorithms (OR-Tools, genetic algorithms)

5. Tech Stack (MVP)

Core: - Python 3.9+ - Pandas / NumPy (data manipulation) - Prophet (time series forecasting) OR Keras/TensorFlow (for LSTM) - Scikit-Learn (for baseline models and metrics)

SAP-EWM Alignment: - Labor data structures aligned with SAP EWM Labor Management concepts - Understanding of engineered labor standards (ELS) used in BRFplus - Task allocation logic that complements SAP EWM resource management - Forecast outputs compatible with SAP EWM planning interfaces - SQL queries for extracting labor performance data from EWM transaction tables

UI: - Streamlit (recommended for quick MVP) OR - Jupyter Notebook with matplotlib/plotly visualizations

Data: - Synthetic data generation (using numpy/pandas) - Or aggregated from existing logistics datasets (Kaggle) - CSV/Parquet file storage

Validation: - Time series cross-validation - Holdout period testing - MAPE, RMSE metrics

6. Success Criteria (MVP)

Functional Requirements:

• ✅ Can run 1 script/notebook and:
  • Load historical daily/shift data
  • Train forecasting model
  • Produce a 14-day labor forecast with uncertainty intervals
  • Print a table with recommended workers per function per shift
  • Generate visualizations

Model Performance:

• ✅ MAPE < 20% on holdout period
• ✅ Captures weekly seasonality patterns
• ✅ Provides reasonable confidence intervals

Interview Readiness:

• ✅ You can say:
  • “This is how AI can support EWM Labor Management – predictive staffing and high-level allocation”
  • “AI-driven labor optimization is the #1 supply chain trend for 2025”
  • “Complements SAP EWM’s BRFplus-based engineered labor standards (ELS) with predictive analytics”
  • “Forecast outputs can feed into SAP EWM Labor Management module for proactive planning”
  • “Demonstrates understanding of where SAP EWM is heading with embedded AI capabilities”
  • “Shows how Python analytics skills complement ABAP/BRFplus technical implementation”

7. MVP Deliverables

1. Source Code:
   • labor_forecast.py - Main forecasting script
   • data_preparation.py - Data loading and preprocessing
   • allocation_engine.py - Task allocation logic
   • app.py - Streamlit dashboard (if using Streamlit)
   • requirements.txt - Dependencies
2. Data:
   • data/historical_labor_data.csv - Sample historical data
   • data/synthetic_data_generator.py - Script to generate synthetic data
3. Documentation:
   • README.md with setup instructions
   • Model explanation document
   • Sample outputs and visualizations
4. Outputs:
   • Forecast CSV file
   • Allocation recommendations CSV
   • Visualization charts (PNG/HTML)

8. Development Timeline (MVP)

Week 1: Data & Foundation - Create synthetic data generator - Implement data loading and preprocessing - Basic exploratory data analysis

Week 2: Forecasting Model - Implement Prophet model (or simple LSTM) - Train on historical data - Generate forecasts with confidence intervals - Validate model performance

Week 3: Allocation Logic - Implement simple allocation algorithm - Add overtime risk detection - Create allocation output format

Week 4: Visualization & Polish - Build Streamlit dashboard (or notebook visualizations) - Add KPIs and metrics - Create documentation - Prepare demo

9. SAP-EWM Technology Complementarity

How Python AI Skills Complement SAP Technologies:

While ABAP and BRFplus are core for SAP EWM Labor Management (engineered labor standards, rule-based task assignment), this Python-based AI forecasting demonstrates complementary skills:

1. BRFplus Complement:
   • BRFplus handles rule-based labor standards (ELS)
   • Python AI adds predictive capabilities on top of rule-based logic
   • Forecast outputs can inform BRFplus decision tables
2. SQL & Data Extraction:
   • SQL skills for extracting labor performance data from EWM transaction tables
   • Understanding of EWM labor management data structures
   • Ability to analyze historical productivity patterns
3. Integration Readiness:
   • Forecast outputs compatible with SAP EWM planning interfaces
   • Understanding of how predictive analytics enhances standard SAP functionality
   • Demonstrates forward-thinking about SAP’s AI integration roadmap
4. Analytics & Problem-Solving:
   • Python ML skills complement ABAP/BRFplus technical implementation
   • Shows understanding of where SAP EWM is heading (AI integration)
   • Demonstrates ability to bridge analytics and operations

Key Differentiator: Most SAP consultants understand current EWM capabilities; fewer understand where SAP is heading with AI integration. This project demonstrates awareness of industry trends and SAP’s strategic direction.

10. Key Interview Talking Points

1. Industry Relevance:
   • “AI-driven predictive analytics is the #1 supply chain trend for 2025”
   • “SAP is actively embedding AI into EWM Labor Management”
   • “Complements SAP’s BRFplus-based engineered labor standards with predictive analytics”
2. Technical Depth:
   • “Uses Prophet/LSTM for time series forecasting with uncertainty quantification”
   • “Demonstrates understanding of warehouse operations and labor patterns”
   • “SQL skills transfer to SAP Open SQL for extracting EWM labor data”
3. SAP Technology Alignment:
   • “Forecast outputs can feed into SAP EWM Labor Management module”
   • “Shows how Python AI skills complement ABAP/BRFplus technical implementation”
   • “Understanding of EWM labor management data structures and concepts”
4. Business Impact:
   • “Reduces overtime costs by predicting labor needs accurately”
   • “Enables proactive workforce planning vs reactive scheduling”
5. Forward-Thinking:
   • “Demonstrates understanding of where SAP EWM is heading with embedded AI capabilities”
   • “Shows ability to bridge analytics and operations”

11. Future Enhancements (Post-MVP)

• Per-worker productivity modeling with LSTM
• Skill-based task matching
• Real-time reallocation based on actual vs forecast
• Integration with SAP EWM APIs
• Multi-warehouse support
• Advanced optimization (OR-Tools for optimal allocation)
• Explainable AI (SHAP values for model interpretability)
• Mobile dashboard for operations managers
• Integration with workforce management systems