Detailed Case Study

Background & Context

AI-driven predictive analytics is 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 demonstrates how Python AI skills complement ABAP/BRFplus technical implementation.

Traditional SAP EWM Labor Management uses BRFplus (Business Rule Framework Plus) for engineered labor standards (ELS) - rule-based calculations of work times. While effective for standard operations, it lacks predictive capabilities for future demand fluctuations.

Challenge Analysis

Key challenges addressed:

• Demand Volatility: Labor needs vary significantly by day, shift, and season
• Multi-Function Allocation: Workers need to be assigned across picking, packing, and receiving
• Overtime Prevention: Accurately predicting when extra workers are needed
• Productivity Optimization: Matching worker assignments to forecasted workload
• SAP Integration: Forecast outputs must complement existing BRFplus rules

Solution Architecture

The system follows a three-stage architecture:

┌─────────────────────┐
│ Historical Labor     │
│ Performance Data     │
│ (Daily/Shift Level)  │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│ Forecasting Engine  │
│ (Prophet/LSTM)      │
│ - Weekly Seasonality │
│ - Monthly Trends     │
│ - Holiday Effects    │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│ Task Allocation     │
│ Logic               │
│ - Workers per        │
│   function/shift    │
│ - Overtime Risk     │
└─────────────────────┘

Key Components:

1. Data Preparation: Aggregates historical daily/shift labor data
2. Forecasting Model: Prophet or LSTM for time series prediction
3. Allocation Engine: Simple heuristic-based task assignment
4. Dashboard: Streamlit interface for visualization and interaction

Implementation Details

Data Model

Single warehouse, daily granularity with fields:

• Date, Shift (Day/Night)
• Orders count, Lines to pick/pack/receive
• Workers available, Workers per function
• Actual productivity metrics

Forecasting Approach

For MVP, Prophet was chosen for faster implementation:

• Handles seasonality well (weekly patterns)
• Provides uncertainty intervals
• Easy to interpret and explain
• Built-in holiday support

Code Example: Prophet Forecasting

from prophet import Prophet
import pandas as pd

def forecast_labor_demand(df):
    """Forecast labor demand for next 14 days."""
    # Prepare data for Prophet
    prophet_df = df[['date', 'workers_needed']].rename(
        columns={'date': 'ds', 'workers_needed': 'y'}
    )
    
    # Initialize and fit model
    model = Prophet(
        yearly_seasonality=False,
        weekly_seasonality=True,
        daily_seasonality=False
    )
    model.fit(prophet_df)
    
    # Create future dataframe
    future = model.make_future_dataframe(periods=14)
    
    # Generate forecast
    forecast = model.predict(future)
    
    return forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]

Task Allocation Logic

Simple allocation algorithm:

• Given forecasted workers available and workload distribution
• Allocates workers across picking (50%), packing (30%), receiving (20%)
• Respects historical productivity ratios
• Flags overtime risk if workers_needed > workers_available

Results & Metrics

The system delivers measurable business impact:

Lessons Learned

• AI Complements BRFplus: Predictive analytics enhances rule-based systems
• SQL Skills Transfer: SQL proficiency transfers to SAP Open SQL for extracting EWM labor data
• Forward-Thinking: Demonstrates understanding of where SAP EWM is heading with AI integration
• Business Impact: Shows how analytics translates into operational decisions