Product Requirement Document (PRD): Inventory Forecast Planner

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black blue and yellow textile
black blue and yellow textile

Key Components

1. Product Vision

The Inventory Forecasting System aims to transform inventory management across retail sectors by providing AI-driven, highly accurate demand forecasts, enabling businesses to optimize their inventory levels, reduce costs, and improve customer satisfaction.

2. Business Requirements

  • Revenue Model: Subscription-based pricing with tiered plans based on business size and feature set

  • Pricing Strategy: Value-based pricing, emphasizing ROI through inventory optimization

  • Marketing Plan: Targeted campaigns for different retail sectors, case studies highlighting success stories, partnerships with major ERP providers

3. Target Users and Categorization

  • Apparel retailers

  • Electronics stores

  • Supermarket chains

  • Specialty shops

User Categorization: a) Inventory Managers: Primary users, responsible for day-to-day inventory decisions b) Purchasing Managers: Use forecasts to optimize purchasing decisions c) C-level Executives: Access high-level insights and reports for strategic decision-making

4. Product Scope

Modules and Features:

  • AI-Powered Predictive Analytics

  • Dynamic Dashboard

  • What-If Scenario Planning

  • Data Integration and Processing

  • Reporting and Analytics

5. User Journeys

Example: An inventory manager uses the system to generate a 3-month forecast for winter apparel, adjusts the forecast based on a planned marketing campaign using the What-If scenario feature, and shares the results with the purchasing team.

6. Technical Requirements

  • Cloud-based solution hosted on AWS

  • Microservices architecture for scalability

  • Real-time data processing using Apache Kafka

  • Machine learning models: Ensemble of ARIMA, Prophet, and LSTM

  • RESTful API for integration with existing systems

7. UI/UX Design Requirements

  • Intuitive, customizable dashboard with drag-and-drop functionality

  • Consistent color scheme and typography aligned with brand guidelines

  • Responsive design for desktop and tablet devices

  • Clear data visualizations with drill-down capabilities

8. Project Timelines

  • Month 1-2: User research and product definition

  • Month 3-4: Core algorithm development and initial UI design

  • Month 5: Integration and testing

  • Month 6: Beta testing with select clients and final refinements

9. Acceptance Criteria

  • Forecast accuracy improvement of at least 25% compared to traditional methods

  • System capable of processing and analyzing data for up to 100,000 SKUs

  • Dashboard load time under 3 seconds for standard views

  • Positive user feedback from beta testing (minimum 8/10 satisfaction score)