Martín Chávez shows how Claude can accelerate Demand Driven planning and DDMRP analysis

AI as an accelerator for adaptive planning

Martín Chávez demonstrates how Claude AI can support Demand Driven planning by helping practitioners build intelligent simulators in minutes.

The objective is not to replace Demand Driven methodology. It is to make important planning signals easier to calculate, visualize and interpret across changing demand and supply conditions.

Planning signals that can be modeled

Chávez highlights several operational elements that can be incorporated into an AI-supported DDMRP simulator:

• Net Flow Position.
• Qualified demand.
• Identification of qualified demand spikes.
• Recommended replenishment quantities.
• Minimum, average and maximum inventory positions.
• Product profiles based on variability.
• Classification by supply source and lead time.

The combination creates an accessible way for planners to explore scenarios, understand product behavior and accelerate replenishment analysis.

From static MRP toward adaptive planning

The post contrasts traditional MRP, designed for more predictable operating conditions, with DDMRP, which is intended for environments characterized by persistent volatility and uncertainty.

AI can reduce the effort required to prototype simulations and analyze planning signals. However, the quality of the result still depends on correct Demand Driven logic, reliable data and an understanding of the operational context.

The role of the planner

The planner remains responsible for validating demand assumptions, buffer settings, lead times, supply constraints and replenishment recommendations.

Claude can accelerate analysis and make the methodology easier to explore, but the resulting simulator should remain a Decision Support tool rather than an autonomous source of replenishment decisions.

The original contribution is available in Martín Chávez’s LinkedIn post.