Demand Forecasting & Planning Engine

AI-driven demand prediction across SKUs, regions, and channels for beverage supply chain operations.

Priority: P0 — Immediate ROI
Time to Value: 4-6 weeks
Category: Planning & Forecasting

---

Business Problem

Beverage supply chains face volatile demand influenced by seasonality, promotions, events, and market trends. Manual forecasting leads to:

• Overstock — excess inventory tying up working capital and increasing spoilage risk
• Stockouts — lost sales and damaged retailer relationships
• Bullwhip effect — demand signal distortion amplified across the supply chain
• Reactive planning — inability to anticipate market shifts until they hit the P&L

---

Capabilities

SKU-Level Demand Forecasting

Predict demand at the granular SKU × Region × Channel level (e.g., Pepsi 500ml demand in South region through modern trade channel for the next 4 weeks).

Seasonal & Event-Based Modeling

Automatically detect and model seasonal patterns (summer peaks, festive demand) and overlay event calendars (sports tournaments, holidays, promotional windows).

Promotion Impact Simulation

Model "what-if" scenarios: "If we run a 10% discount on Mountain Dew across the West region, what is the expected volume lift and cannibalization impact on Pepsi?"

Cannibalization Analysis

Quantify cross-product demand transfer when promotions or new launches shift volume between SKUs within the portfolio.

Consensus Forecast Workflow

Combine statistical AI forecasts with sales team inputs and trade intelligence to produce a unified consensus forecast.

---

Data Sources & Ontology Mapping

flowchart LR
    subgraph Data Plane
        SF["Salesforce CRM"]
        SAP_SYS["SAP"]
        OE["Oracle ERP"]
        SL["Social Listening"]
    end

    subgraph Ontology Entities
        PROD["Product / SKU"]
        CUST["Customer / Channel"]
        ORDER["Order History"]
        INV["Inventory Positions"]
        MKT["Market Signals"]
    end

    subgraph AI Workflow
        FE["Feature Engineering"]
        MODEL["Forecasting Models<br/><i>Time Series + ML</i>"]
        SIM["Promotion Simulator"]
    end

    SF --> CUST
    SF --> ORDER
    SAP_SYS --> PROD
    SAP_SYS --> INV
    OE --> ORDER
    SL --> MKT

    CUST --> FE
    ORDER --> FE
    PROD --> FE
    INV --> FE
    MKT --> FE

    FE --> MODEL
    FE --> SIM

Ontology Entity	Source System	Key Fields
Product / SKU	SAP Material Master	Material ID, Category, Pack Size, Shelf Life
Customer / Channel	Salesforce Account	Account ID, Channel Type, Region, Tier
Order History	Salesforce Opportunities + Oracle Sales Orders	Order Date, Quantity, Revenue, Fulfillment Rate
Inventory Positions	SAP Inventory	Stock on Hand, Reorder Point, Safety Stock
Market Signals	Social Listening	Sentiment Score, Trend Keywords, Volume

---

AI Workflow

1. Feature Engineering — Combine historical sales, inventory movement, weather data, event calendars, and social sentiment into a unified feature set
2. Model Ensemble — Run time-series (Prophet/ARIMA) and ML (XGBoost/LightGBM) models in parallel; ensemble for best accuracy
3. Promotion Overlay — Layer planned promotions with learned elasticity curves to adjust base forecast
4. Cannibalization Adjustment — Apply cross-product substitution factors from historical promotion data
5. Consensus Integration — Merge AI forecast with manual overrides from sales and trade teams
6. Output — Publish SKU × Region × Week demand plan to SAP MRP and dashboard

---

Dashboard & Alerts

Key Metrics

KPI	Description	Target
Forecast Accuracy (MAPE)	Mean Absolute Percentage Error vs. actuals	< 15%
Bias	Systematic over/under-forecasting tendency	± 3%
Stockout Rate	% of SKU-locations with zero available inventory	< 2%
Overstock Days	Days of excess inventory above safety stock	< 10 days
Promotion Lift Accuracy	Predicted vs. actual promotional volume uplift	± 10%

Alert Rules

Alert	Trigger	Severity	Action
Demand spike detected	Forecast exceeds 2σ above baseline for any SKU-region	High	Notify supply planning; trigger expedited production review
Forecast degradation	MAPE exceeds 20% for 2 consecutive weeks	Medium	Retrain model; flag for manual review
Promotion conflict	Overlapping promotions on substitutable SKUs	Medium	Notify trade marketing team
Seasonal ramp alert	30-day lookahead shows >25% volume increase	Info	Pre-position inventory; alert warehouse operations

---

ROI Model

Metric	Before	After	Impact
Forecast accuracy (MAPE)	30-40%	12-18%	50-60% improvement
Overstock inventory value	$2.4M tied up	$1.8M tied up	$600K working capital freed
Stockout incidents / month	120 SKU-locations	85 SKU-locations	30% reduction → recovered sales
Expired / wasted product	4.2% of volume	2.8% of volume	33% waste reduction
Planning cycle time	5 days / week	1 day / week	80% time savings

Estimated Annual ROI

$1.2M - $2.5M annually from reduced waste, freed working capital, and recovered sales — across a mid-size beverage distributor with $150M revenue.

---

Implementation Notes

• Requires minimum 18-24 months of order history from Salesforce + Oracle ERP for baseline model training
• SAP inventory snapshots should be available at daily granularity
• Social listening data enhances accuracy by 3-5% but is optional for initial deployment
• Promotion calendar integration with trade marketing team is critical for lift modeling

---

← Back to Catalogue | Next: Inventory Optimization →