Introduction
Artificial Intelligence is redefining supply chain planning, enabling companies to predict, adapt, and optimize their operations with unprecedented precision.
In this article, we present NPLAN's proprietary prioritization for AI applications in planning, developed based on real projects and client learnings. We use public references as market context (e.g., Gartner), but the focus here is the practical path we prioritize to generate results.
You'll see the current adoption landscape, understand how we prioritize initiatives considering impact and feasibility, and discover how NPLAN can transform your planning into a competitive advantage through AI.
AI Adoption Status
Maturity overview — numbers inspired by public market readings for educational reference.
Demand Planning
in production
Supply Planning
in production
Note: numbers inspired by public market readings (e.g., Gartner) and organized here for educational reference.
These numbers show that there are still few companies using practical AI applications in production environments, especially in supply chain. The technology is still in the early stages of adoption in real operations.
However, many companies are already exploring AI with pilot projects. If your company is not doing anything in this direction, you may fall behind while competitors gain competitive advantage testing and learning from these technologies.
AI with NPLAN
NPLAN is a Supply Chain Planning SaaS platform that connects data, processes, and teams in a single environment, transforming planning into a competitive advantage. Through the combination of Artificial Intelligence, optimization, and automation, the platform delivers more accurate forecasts, faster decisions, and execution aligned with strategic objectives.
Designed to handle complex operations, NPLAN is flexible to adapt to different technological environments and secure to handle critical business information. This combination enables its application in companies of various sizes and segments, ensuring operational efficiency, supply chain resilience, and governance over data used in decision-making.
Opportunities: NPLAN's Proprietary Prioritization
NPLAN brings together a set of applications prioritized based on two main criteria: the potential for generating results and implementation feasibility. In the chart, the Result axis considers the impact on productivity, service, and revenue, while the Feasibility axis evaluates technological maturity, data availability, and cultural alignment for adoption.
NPLAN prioritization matrix: Result vs Feasibility
Detailed Use Cases
Predictive Demandhigh impact
Models that combine history, seasonality, and external variables to predict volumes accurately and reduce stockouts.
Inventory Optimizationquick payback
Algorithms dynamically adjust inventory policies and safety stock, balancing cost and service level.
Order Promising
Delivery promise based on real ATP/CTP, integrating production capacity and material availability.
Distribution Optimization
Automatically decides between DCs/routes, reducing logistics cost without sacrificing OTIF.
AI-Driven Capacity
Rebalances machines, shifts, and setups according to demand variation and manufacturing constraints.
Planner Co-Pilot
Assistant that suggests scenarios, highlights risks, and recommends actions based on data and business heuristics.
Scenario Explorer
'What-if' simulation of events (price, lead time, stops), quantifying operational and financial impact.
Platform Assistant
Unifying layer of data and workflows for collaborative and auditable decisions.
Actionable Insights
Detects patterns and anomalies and transforms them into alerts prioritized by business value.
Root Cause
Analyzes causes of deviations (demand, production, logistics) and recommends countermeasures.
Workforce Optimization
Sizes and allocates teams in critical operations, reducing overtime and idle time.
Supplier Response
Intelligent responses to supplier constraints, prioritizing critical items and replanning purchases.
Disruption Resilience
Mitigates risk events (stockout, delay, weather) with alternative plans and contingency stocks.
Supply Chain Twin
Mirrors processes and constraints in a digital twin to test changes before execution.
Data Agent
Data orchestration and continuous quality: integrates, validates, and versions critical planning inputs.
Emission Reduction
Optimizes supply chains for lower carbon footprint, considering cost/service/CO₂ in the same objective function.
How we apply in practice
We start with two sprints: (1) data and constraints diagnosis, (2) proof of value with one of the priority use cases. From there, we expand with scenarios and financial KPIs.
Technological Flexibility
NPLAN was designed to be versatile and compatible with various technologies for digital transformation and AI adoption, such as Python, Superset, Streamlit, N8N, and embedded web apps, allowing it to act as a central platform for planning and innovation.
This compatibility enables the creation of intelligent dashboards, automation flows, AI applications, and integrations with legacy or corporate systems. Additionally, the platform can be customized on demand, transforming specific client requirements into exclusive solutions while maintaining robustness, scalability, and adherence to market best practices.
Security and Compliance
We preferably use Azure OpenAI, ensuring that data is processed and stored in Brazil and not used to train public models.
We adopt encryption in transit and at rest, access controls, continuous monitoring, and security testing performed by specialized companies. This approach ensures confidentiality, integrity, and availability of information, meeting regulatory and compliance requirements.
References and Context
We use public market readings as context input — for example, the Supply Chain Top 25: AI Edition article.
The prioritization and roadmap presented here are NPLAN's intellectual property, refined in real projects.