General Overview
Predictive and prescriptive modeling is one of the most strategic foundations of artificial intelligence applied to decision-making. It enables organizations to forecast likely future events (predictive modeling) and determine the optimal actions to take to influence these possible futures (prescriptive modeling) based on probabilistic analyses, advanced simulations, alternative scenarios, or constrained optimizations.
At NeuriaLabs, we design and deploy these systems to allow our clients to better anticipate, plan, and decide in environments often characterized by volatility, complexity, uncertainty, or information density. Our models are based on a combination of advanced statistical algorithms, machine learning, causal analysis, and mathematical optimization, and are systematically designed within a concrete business operation framework.
Conceptual Distinction
• Predictive modeling aims to provide a probabilistic estimate of a forthcoming phenomenon, based on historical, contextual, or behavioral data. It answers the question: "What is likely to happen?"
• Prescriptive modeling, on the other hand, incorporates the results of prediction to propose, simulate, or recommend optimal decisions, taking into account constraints, multiple objectives, risks, or limited resources. It answers the question: "What should we do to achieve this objective or avoid this risk?"
Applications of Predictive Modeling
Our predictive systems are deployed in contexts where anticipation is a critical lever of competitiveness, security, or performance:
• Demand forecasting: estimating consumption, sales, resource needs, or production in the short, medium, and long term.
• Risk or event forecasting: detecting likely failures, predicting quality defects, estimating the probability of default or claims.
• Behavioral scoring: predicting churn (customer attrition), marketing conversion, product uptake, or service engagement.
• Logistics and industrial forecasting: anticipating flow, delays, overloads, or disruptions in supply or distribution chains.
• Financial and economic forecasting: projecting margins, profitability, market trends, or asset values.
The algorithms used include linear or logistic regression, ARIMA or SARIMA models, random forests, neural networks for time series (LSTM, TCN), hierarchical Bayesian models, and transformers adapted for sequences.
Applications of Prescriptive Modeling
Our prescriptive models are designed to simulate optimal decisions in constrained or uncertain environments:
• Supply chain optimization: calculating the optimal transport plan, inventory distribution, or dynamic resource allocation.
• Pricing and commercial optimization: determining differentiated pricing strategies, personalized promotions, or optimal budget allocation.
• Strategic planning: simulating investment scenarios, portfolio allocations, or risk coverage.
• Operational optimization: automating the management of schedules, maintenance, tenders, or prioritization of corrective actions.
• Intelligent production management: balancing between lean flow and stock, between pace and cost, between quality and timing.
These models are based on linear or nonlinear programming, operational research, evolutionary algorithms, heuristic methods, or reinforcement learning approaches.
NeuriaLabs Approach
Our predictive and prescriptive models are designed to be operationalized and used daily by decision-makers, planners, or business units. They are always accompanied by:
• Clear visualization interfaces for forecasts or recommendations, with uncertainty indicators, alternative scenarios, and justification of proposed choices;
• Automatic recalibration mechanisms based on incoming data, ensuring relevance in a dynamic environment;
• Interactive simulation features that allow users to explore the impacts of their decisions themselves;
• A robust governance framework documenting assumptions, limitations, data sources, and rules for model use.
Our models are designed to integrate into existing decision-making systems via APIs, microservices, or business dashboards and can be deployed on cloud, edge, or on-premise according to client constraints.
