This page aims to explain the main differences between Optimization Engine and classic machine learning modeling and how Optimization Engine interacts with other Pricefx modules.
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Advantages
Optimization Engine, once fully configured, combines powerful, transparent segmentation and guidance with AI-based multi-constraint full-price waterfall optimization.
Machine Learning | MAAI-based Optimization Engine |
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ℹ Machine learning is not part of the demo solution out-of-the-box. |
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Background
Optimization Engine module is built around Optimization Engine backend which is a Multi-Agent System to optimize prices, discounts, or any continuous values for any user-defined criteria, such as margin targets, revenue targets, and custom business rules. Optimization Engine backend is a giant computation graph representing all the variables, computations, and criteria in the problem and their relationships. Each node of the computation graph is an autonomous agent. At runtime, agents cooperate to gradually converge towards the values that satisfy all the criteria, or towards the best compromise in case of conflicting criteria (such as increasing margin but also keeping prices as stable as possible).
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Position within Pricefx Platform
The following diagram focuses on Optimization Engine position within the Pricefx cloud-native 360° pricing platform:
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Accelerator and Setup
To get started, the easiest way is to use an optimization accelerator using the Optimization Engine (see Optimization Engine). It deploys a set of logics to tackle a given optimization problem.
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