Prescriptive Analytics in Demand Forecasting

Prescriptive analytics enhances demand forecasting by:

• Translating forecast insights into optimal actions (e.g., ordering, allocation, pricing).

• Accounting for constraints, costs, risks, and service level targets.

• Generating "what-if" scenarios to support decisions under uncertainty.

Key Techniques in Prescriptive Analytics for Forecasting

A. Optimization Models

Used to determine optimal inventory levels, pricing, production plans, etc., based on forecasted demand.

• Linear Programming (LP) & Mixed-Integer Programming (MIP)

  • E.g., minimize cost of stockouts + holding + procurement given forecasted demand.

• Stochastic Optimization

  • Considers demand uncertainty: optimizes actions under probabilistic scenarios.

  • Use case: choose safety stock levels for variable demand forecasts.

• Robust Optimization

  • Deals with worst-case scenarios instead of average-case (when data is noisy or unreliable).

B. Simulation-Based Optimization

• Simulates different demand scenarios to test policies (e.g., replenishment or production).

• Monte Carlo simulation + optimization to find best strategies under uncertainty.

C. Inventory Optimization (Based on Forecasts)

• Determine:

  • Economic Order Quantity (EOQ)

  • Reorder Point (ROP)

  • Safety Stock

• Inputs come from forecasts + service level targets.

D. Multi-Echelon Inventory Optimization (MEIO)

• Prescribes inventory policies across entire supply chain network.

• Inputs: demand forecast, lead times, variability, cost parameters.

• Outputs: optimal stock levels per node.

E. Dynamic Pricing Optimization

• Adjust prices dynamically based on forecasted demand curves to:

  • Maximize revenue or profit

  • Balance demand and inventory

F. Assortment and Allocation Optimization

• Decide which SKUs to stock where based on forecasted local demand.

• Optimize product mix to maximize revenue given space/capacity.

G. Reinforcement Learning (RL) & GenAI Approaches

• RL: Learns inventory/promotion decisions over time via trial & error, using forecasts as part of state space.

• GenAI:

  • Generate demand scenarios from unstructured data (e.g., social media, news).

  • Summarize forecasts or recommend actions in natural language to planners.

How It All Fits Together – A Typical Workflow

1. Forecasting Layer (Predictive)

   • Generate probabilistic or point forecasts for demand (time series, ML, etc.)

2. Prescriptive Layer (Optimization & Decision)

   • Input: Forecasts, constraints, business rules, objectives

   • Output: Actions like optimal ordering, allocation, pricing, capacity plans

3. Feedback Loop

   • Evaluate outcomes → feed back into models to refine forecasts & decisions

Use Cases

Tools and Technologies

• Solvers: CPLEX, Gurobi, Google OR-Tools

• Forecasting + Optimization Frameworks: Python (SciPy, Pyomo), R, Azure ML, SAP IBP, Kinaxis, o9

• GenAI Integration: Use LLMs for narrative explanations, unstructured signal ingestion, or scenario generation.