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Pyplan Documentation

website·Indexed 33 days ago

https://pyplan.com/

An AI-native planning platform featuring an Economic Decision Engine that integrates Sales, Operations, HR, and Finance into a single model. The platform utilizes a four-layer architecture and agentic AI capabilities grounded in economic logic.

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What's inside Pyplan

  1. Understand the Economic Decision Engine Architecture

    Pyplan's Economic Decision Engine uses a four-layer architecture to convert data into business decisions:

    1. KNOW (Semantic Planning Ontology): Encodes business relationships, constraints, and rules once to be applied platform-wide.
    2. REASON (Economic Decision Engine): Models the P&L consequences of trade-offs, making margin, working capital, and cash impact visible alongside operational KPIs.
    3. EXECUTE (Operational Intelligence Layer): Synchronizes decisions across the organization so that changes in one team are instantly reflected across all functions in the shared model.
    4. AUTOMATE (AI Agents): Handles routine decisions autonomously based on the Economic Decision Engine, remaining explainable and within human-defined boundaries.

  2. Pyplan Technical Architecture Overview

    Pyplan is a Python-native planning platform designed for mid-market enterprises ($50M–$5B revenue). It utilizes an in-memory computation engine to allow real-time propagation of changes and concurrent planning without lock contention. The platform supports multi-dimensional modeling across time, geography, product, customer, and channel at any granularity.

  3. Pyplan Platform Overview

    Pyplan is an AI-native planning platform designed for mid-market enterprises ($50M–$5B revenue) to integrate Sales, Operations, HR, and Finance into a single shared model. It utilizes a Python-native architecture with in-memory computation and is cloud-agnostic, supporting AWS, Azure, GCP, and OCI. The platform provides RESTful APIs and a UI design tool for creating planning applications with tables, charts, maps, and user controls.

  4. Understand Pyplan AI capabilities and design principles

    Pyplan is an AI-native planning platform designed with the following core principles:

    • Agentic AI grounded in economic logic: Agents reason across demand, supply, inventory, capacity, and finance simultaneously rather than in isolated silos.
    • Explainability: Every AI recommendation includes the supporting data, the logic used, and the projected business impact to avoid "black box" results.
    • Human-in-the-loop: AI proposes and planners interrogate. Users can override any input or assumption, and downstream impacts are recalculated instantly in memory without requiring re-runs.
    • Data Privacy: Customer data is never used to train Pyplan's models.

  5. Utilize Pyplan AI Capabilities for Planning

    Pyplan integrates multiple AI techniques depending on the specific business problem:

    • Statistical Forecasting: Used for stable demand patterns and seasonality.
    • Optimization Engines: Used for capacity, inventory, and allocation problems involving constraints.
    • Machine Learning: Used for anomaly detection, demand sensing, and pattern recognition.
    • Generative AI: Used for natural-language explanations, scenario narratives, and assumption reviews.
    • Agentic AI: Used for autonomous monitoring, recommendations, and actions within defined boundaries.

  6. Extend Pyplan Models using Python

    While Pyplan provides a visual interface for business users to manage dashboards, simulations, and AI agents without coding, the platform allows advanced teams to incorporate Python to build or extend complex models. Because the platform is Python-native, it avoids proprietary modeling languages and vendor lock-in.

  7. Deploy Pyplan to Cloud Infrastructure

    Pyplan is cloud-agnostic and can be deployed on the following infrastructure providers:

    • AWS (Amazon Web Services)
    • Microsoft Azure
    • Google Cloud Platform (GCP)
    • Oracle Cloud Infrastructure (OCI)

  8. Integrate Pyplan with External Systems

    Pyplan provides RESTful APIs for native integration with various enterprise systems, including:

    • ERPs: SAP, Oracle, NetSuite, Microsoft Dynamics
    • Logistics: WMS (Warehouse Management Systems), TMS (Transportation Management Systems)
    • Customer Relations: CRM systems
    • Analytics: Data warehouses and BI tools

  9. Compare Pyplan with other planning platforms

    Pyplan positions itself as an AI-native alternative to several legacy and finance-focused planning tools:

    • vs Anaplan: Pyplan is built on Python and is AI-native. It offers faster implementation (weeks vs months), requires fewer specialist modelers, and provides agentic AI with operational integration.
    • vs Board: Pyplan offers more flexibility than Board's BI-first stack.
    • vs Vena: Pyplan provides deeper capabilities than Vena's Excel-front model.
    • vs Jedox: Pyplan is more AI-native.
    • vs Pigment: Pyplan combines agentic AI with operational integration, whereas Pigment is primarily a finance-only platform.

  10. Pyplan Security and Governance Features

    Pyplan implements the following security and governance controls:

    • Role-Based Access Control (RBAC)
    • Single Sign-On (SSO) and Multi-Factor Authentication (MFA)
    • Full audit trails
    • Version control for planning models
    • IQNet-certified information security

  11. Map Pyplan Functional Solutions and Modules

    Pyplan organizes its capabilities into functional areas where the output of one module serves as the input for the next:

    • Demand Planning: Includes Demand Planning (DP), Demand Sensing, Demand Control, and Quota Planning. Focuses on statistical forecasting and AI-driven anomaly detection.
    • Inventory Planning: Includes Tactical Inventory Management, Inventory Optimization, Distribution Requirements Planning (DRP), Vendor-Managed Inventory (VMI), and CPFR.
    • Supply Planning: Includes Capacity Planning (RCCP), Master Production Scheduling (MPS), Master Materials Planning, Advanced Planning and Scheduling (APS), and MRP.
    • Financial Planning & Analysis (FP&A / xP&A): Includes Closing & Reporting, Budget & Management Control, Labor Cost Planning, OPEX/CAPEX Budgeting, Margin Planning, Financial Statements Planning, and Financial Consolidation.
    • S&OP and IBP: Coordinated supply/demand planning and executive trade-off analysis with P&L impact.
    • S&OE and ITP: Real-time replanning and deviation monitoring.

  12. Pyplan AI Data Privacy Policy

    Pyplan maintains a firm policy that customer data is never used to train Pyplan's AI models.