This page aims to explain the main differences between PO.AI and classic Machine Learning modelling and how PO.AI interacts with other Pricefx modules.
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Advantages
PriceOptimizerAI, once fully configured, combines powerful, transparent segmentation and guidance with AI-based multi-constraint full-price waterfall optimization.
Machine Learning | MAAI-based Optimization Engine |
ℹ Machine Learning is not part of the demo solution out-of-the-box. |
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Background
PriceOptimizerAI is built around Optimization Engine which is a Multi-Agent System to optimize prices, discounts, or any continuous values with respect to any user-defined criteria, such as margin targets, revenue targets, and custom business rules. Optimization Engine is basically 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 with each other 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 PriceOptimizerAI position within our cloud-native 360° pricing platform:
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Accelerator and Setup
The current PO.AI Accelerator deploys a set of Logics to tackle a “Waterfall Optimization” problem as described in PO.AI Accelerator Overview.
In order to set up PO.AI, you need to first understand the customer’s domain and requirements, and turn them into a 'problem description' that is fed to the Optimization Engine. Each instance of PO.AI has its own specific problem description file which is the result of problem modeling. It can be configured based on PO.AI Accelerator, documented in POAI Accelerator: an accelerator for PO.AI module.