PLAN | Supply Chain Analytics

13

Demand Forecasting

Predicting future customer demand

What Is It?

Demand Forecasting uses historical data, statistical algorithms, and market analysis to predict future customer demand.

  • Historical analysis - Past sales data, trends, and patterns
  • External factors - Market conditions, seasonality, promotions
  • Quantity estimation - Products/services consumers will purchase
  • Decision support - Inventory, production, and sales strategies

Business Objectives

Inventory Optimization

Balance stock without overstocking or stockouts

Resource Allocation

Efficient workforce and capacity planning

Cost Reduction

Minimize excess inventory and lost sales

Customer Satisfaction

Ensure product availability

Competitive Advantage

Anticipate customer needs better

Forecasting Layers in Planning

Strategic (Long-term)

Network planning, capacity investments

Tactical (Mid-term)

Master planning, procurement

Operational (Short-term)

Production and distribution scheduling

Real-time

Consolidated orders, immediate adjustments

Forecasting Approaches

Univariate

Single variable over time as input

  • Sales data of one product
  • Classical time series methods
  • ARIMA, Exponential Smoothing

Multivariate

Multiple input variables considered

  • Sales + promotions + price + weather
  • Machine learning approaches
  • Random Forests, Neural Networks

Analytics Methods

ARIMA

Capture trends and seasonal effects

Machine Learning

Random Forests, Gradient Boosting

Deep Learning

RNN, LSTM for sequential patterns

Facebook Prophet

Open-source tool for seasonality

Key Performance Indicators

Forecast Accuracy

MAPE, RMSE metrics

Inventory Levels

Optimal stock management

Stockout Rate

Reduce out-of-stock events

Related Use Cases

Inventory Optimization Sales & Operations Planning Vendor Managed Inventory
All PLAN Cases

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