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# System prepended metadata

title: Allergenius PRD

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### Product Requirements Document (PRD)

**Product Name: AllerGenius**

**Prepared by: Madhusudhan Anand**

**Date: 31/May/2024**

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#### 1. Executive Summary

**Product Name:** AllerGenius  
**Purpose:** AllerGenius is a demand forecasting tool designed to predict the demand for allergy medications like the Anti-Allergy Medicine using Ambee's environmental data. The tool aims to optimize inventory management, enhance revenue opportunities, and provide actionable insights to Pharma company by leveraging climate intelligence and various weather parameters.

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#### 2. Product Overview

##### 2.1 Vision
To provide Pharma company with a powerful tool that uses real-time environmental data to forecast demand for allergy medications, optimize inventory, and improve revenue potential, outperforming existing solutions like the AAN Program.

##### 2.2 Goals
- Accurately forecast demand for the Anti-Allergy Medicine using real-time environmental data.
- Provide actionable insights to optimize inventory and enhance revenue.
- Demonstrate the capabilities of Ambee’s data and AI technology to secure a proof of concept pilot with Pharma company.

##### 2.3 Key Features
- **Data Integration:** Real-time integration with Ambee’s environmental data APIs.
- **Interactive Dashboard:** Visualize key metrics and forecasts.
- **Demand Forecasting:** Predict future demand using advanced machine learning models.
- **Insights Generation:** Provide recommendations for inventory management.
- **User Management:** Simple access control for different users.
- **Age Segmentation:** Provide age-based trends and insights.
- **Customization:** Allow customized reports by region, trading area, market, sales territory, or retailer geography.

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#### 3. Market and Audience

##### 3.1 Target Market
- **Pharmaceutical Companies:** Specifically targeting Pharma company for optimizing their allergy medication distribution.
- **Pharmacies and Retailers:** Interested in efficient inventory management and demand forecasting.
- **Marketing Teams:** Planning media and advertising campaigns for allergy medications.

##### 3.2 User Personas
- **Pharmacy Manager:** Needs to ensure optimal stock levels to meet demand without overstocking.
- **Marketing Executive:** Requires data-driven insights to plan effective marketing campaigns.
- **Sales Manager:** Focuses on setting realistic sales targets and strategies based on forecasted demand.

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#### 4. Features and Requirements

##### 4.1 Data Integration

###### 4.1.1 Description and Priority
Integrate with Ambee’s APIs to fetch relevant environmental data. This feature is of high priority as it forms the basis of the forecasting model.

###### 4.1.2 Functional Requirements
- **FR1.1:** Connect to Ambee APIs to fetch pollen levels, air quality, temperature, and humidity data.
- **FR1.2:** Allow manual upload of historical sales data in CSV format.
- **FR1.3:** Update the data at regular intervals (e.g., hourly or daily).

### 4.2 Interactive Dashboard

#### 4.2.1 Description and Priority
The interactive dashboard is designed to provide a comprehensive and intuitive visualization of key metrics and data, which is critical for user understanding and informed decision-making. This feature is of medium priority because it directly enhances the usability of the demand forecasting tool, making it easier for Pharma company users to interpret data and derive actionable insights.

#### 4.2.2 Functional Requirements

- **FR2.1:** Display historical sales data in line charts.
- **FR2.2:** Display forecasted demand data in bar charts.
- **FR2.3:** Provide filters for date range and geographical location.
- **FR2.4:** Display environmental data (pollen levels, air quality, temperature, humidity) alongside demand data.

#### 4.2.3 Detailed Breakdown

1. **Historical Sales Data in Line Charts**
   - **What to Show:** 
     - Sales data for the Anti-Allergy Medicine over selected time periods.
     - Trends and patterns in sales volumes.
   - **How to Show:** 
     - Use line charts to display historical sales data with time on the x-axis and sales volume on the y-axis.
     - Include markers for significant events (e.g., peak pollen seasons, major marketing campaigns) to correlate with sales spikes or drops.
   - **Why It's Useful:**
     - Helps users identify historical trends and patterns in sales data.
     - Assists in understanding the impact of past events on sales, aiding in better planning and strategy formulation.

2. **Forecasted Demand Data in Bar Charts**
   - **What to Show:** 
     - Predicted demand for the Anti-Allergy Medicine over future periods.
     - Comparison between forecasted demand and historical sales data.
   - **How to Show:** 
     - Use bar charts with time on the x-axis and forecasted demand on the y-axis.
     - Overlay forecast data on historical sales data for visual comparison.
   - **Why It's Useful:**
     - Provides a clear visualization of future demand, helping users plan inventory and marketing activities.
     - Allows users to identify potential periods of high or low demand and adjust strategies accordingly.

3. **Filters for Date Range and Geographical Location**
   - **What to Show:**
     - Date range selection (e.g., last month, last quarter, custom range).
     - Geographical filters (e.g., state, city, ZIP code).
   - **How to Show:**
     - Interactive date picker and dropdown menus for geographical filters.
     - Allow users to dynamically update the charts based on selected filters.
   - **Why It's Useful:**
     - Enables users to drill down into specific time periods and locations for detailed analysis.
     - Supports more granular insights, making the tool adaptable to different use cases and needs.

4. **Environmental Data (Pollen Levels, Air Quality, Temperature, Humidity)**
   - **What to Show:**
     - Real-time and historical environmental data relevant to allergy incidence.
     - Correlation between environmental factors and allergy medication demand.
   - **How to Show:**
     - Use multi-line charts to display environmental data alongside sales and demand data.
     - Color-code different environmental factors for easy distinction.
   - **Why It's Useful:**
     - Provides a comprehensive view of how environmental conditions impact allergy medication demand.
     - Helps users understand the rationale behind demand forecasts, enhancing trust and decision-making.
   
#### Value to Pharma company Users

1. **Optimized Inventory Management**
   - **How It Helps:** By accurately forecasting demand, Pharma company can ensure that stores have the right amount of the Anti-Allergy Medicine in stock, reducing both overstock and stockouts.
   - **Value Derived:** Improved inventory turnover rates, reduced storage costs, and better customer satisfaction due to availability of the product.

2. **Enhanced Marketing and Sales Strategies**
   - **How It Helps:** Insights from the dashboard allow marketing teams to plan campaigns around peak allergy seasons and adjust strategies based on forecasted demand.
   - **Value Derived:** Increased effectiveness of marketing spend, higher sales during peak periods, and improved ROI on promotional activities.

3. **Informed Decision-Making**
   - **How It Helps:** The ability to visualize historical data, forecast future demand, and correlate with environmental factors empowers users to make data-driven decisions.
   - **Value Derived:** Strategic advantage in responding to market changes, better planning for future demand, and increased confidence in decision-making processes.

4. **Age-Specific Insights**
   - **How It Helps:** By providing age-segmented data, Pharma company can tailor its strategies to different demographic groups, understanding which segments drive demand.
   - **Value Derived:** More targeted marketing efforts, better product positioning, and improved engagement with specific age groups.

5. **Customization and Flexibility**
   - **How It Helps:** Customizable reports and filters allow users to generate insights specific to their needs, whether by region, sales territory, or retailer geography.
   - **Value Derived:** Greater flexibility in analysis, ability to cater to specific business requirements, and enhanced user experience.

By incorporating these features into the interactive dashboard, AllerGenius will provide Pharma company with a powerful tool to optimize inventory, enhance revenue opportunities, and make informed decisions based on comprehensive data analysis. This will not only demonstrate Ambee’s capabilities but also provide tangible value to Pharma company, setting the stage for a successful proof of concept pilot.

###### 4.2.2 Functional Requirements
- **FR2.1:** Display historical sales data in line charts.
- **FR2.2:** Display forecasted demand data in bar charts.
- **FR2.3:** Provide filters for date range and geographical location.
- **FR2.4:** Display environmental data (pollen levels, air quality, temperature, humidity) alongside demand data.

##### 4.3 Demand Forecasting

###### 4.3.1 Description and Priority
Use machine learning to predict future demand. This feature is of high priority as it provides the core functionality of the tool.

###### 4.3.2 Functional Requirements
- **FR3.1:** Implement advanced machine learning models to forecast demand based on historical sales data and environmental factors.
- **FR3.2:** Update forecasts daily based on the latest data.
- **FR3.3:** Allow users to download forecast data in CSV format.
- **FR3.4:** Provide long-range projections up to 11 months in advance.

##### 4.4 Insights Generation

###### 4.4.1 Description and Priority
Generate actionable insights from forecasted data. This feature is of medium priority to provide additional value to users.

###### 4.4.2 Functional Requirements
- **FR4.1:** Analyze forecast data to identify trends and anomalies.
- **FR4.2:** Provide recommendations for inventory management.
- **FR4.3:** Generate reports summarizing key insights.
- **FR4.4:** Include age-based segmentation to identify trends in different age groups.

##### 4.5 User Management

###### 4.5.1 Description and Priority
Manage user access and permissions. This feature is of low priority but necessary for user access control.

###### 4.5.2 Functional Requirements
- **FR5.1:** Create user accounts with different roles (e.g., admin, viewer).
- **FR5.2:** Provide secure authentication and authorization mechanisms.

##### 4.6 Customization

###### 4.6.1 Description and Priority
Allow customization of reports by various geographical and market segments. This feature is of medium priority to enhance the tool's usability for different users.

###### 4.6.2 Functional Requirements
- **FR6.1:** Allow users to generate reports by region, trading area, market, sales territory, or retailer geography.
- **FR6.2:** Provide filters and customization options for generating tailored insights and reports.

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#### 5. User Experience (UX) and Design

##### 5.1 User Interface
- **Dashboard:** A clean, intuitive interface displaying key metrics and visualizations.
- **Data Upload Form:** Simple form for uploading historical sales data.
- **Settings Page:** Interface for configuring user settings and preferences.

##### 5.2 User Flow
1. **Login:** User logs into the system.
2. **Dashboard:** User views the main dashboard with key metrics and visualizations.
3. **Data Upload:** User uploads historical sales data if needed.
4. **Forecasting:** User views and analyzes forecasted demand.
5. **Insights:** User accesses insights and recommendations.
6. **Customization:** User generates customized reports based on specific needs.
7. **Settings:** User configures settings and manages account details.

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#### 6. Technical Requirements

##### 6.1 Architecture
- **Frontend:** HTML, CSS, JavaScript (React.js or Angular for a dynamic UI).
- **Backend:** Node.js or Python (Flask/Django) for server-side logic.
- **Database:** SQL (PostgreSQL/MySQL) or NoSQL (MongoDB) for storing data.
- **APIs:** Integration with Ambee’s environmental data APIs.

##### 6.2 Performance
- The system must handle at least 50 concurrent users.
- The system should return forecast results within 3 seconds.

##### 6.3 Security
- Use HTTPS for all data communications.
- Implement user authentication and authorization.
- Ensure compliance with data protection regulations (e.g., GDPR, HIPAA).

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#### 7. Launch and Marketing Plan

##### 7.1 Pre-Launch
- Develop a beta version of AllerGenius.
- Conduct internal testing and gather feedback.
- Create marketing materials and a landing page.

##### 7.2 Launch
- Announce the launch through a press release.
- Offer demos to potential clients (e.g., Pharma company).
- Engage in social media marketing and email campaigns.

##### 7.3 Post-Launch
- Monitor user feedback and usage analytics.
- Release regular updates and improvements based on feedback.
- Expand marketing efforts to reach a broader audience.

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#### 8. Success Metrics

##### 8.1 Key Performance Indicators (KPIs)
- **User Adoption:** Number of active users within the first three months.
- **Forecast Accuracy:** Accuracy of demand forecasts compared to actual sales.
- **Customer Satisfaction:** Feedback and satisfaction scores from users.
- **Market Penetration:** Number of pharmaceutical companies and pharmacies using the tool.

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#### 9. Risks and Mitigation

##### 9.1 Risks
- **Data Accuracy:** Inaccurate or incomplete environmental data could affect forecasts.
- **Technical Issues:** Potential technical issues during integration or usage.
- **User Adoption:** Risk of low user adoption due to usability or functionality issues.

##### 9.2 Mitigation
- **Data Accuracy:** Regularly validate and update data sources.
- **Technical Issues:** Implement robust testing and monitoring systems.
- **User Adoption:** Conduct user training sessions and gather feedback for continuous improvement.

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#### 10. Appendices

##### 10.1 Glossary
- **API:** Application Programming Interface
- **CSV:** Comma-Separated Values
- **GDPR:** General Data Protection Regulation
- **HIPAA:** Health Insurance Portability and Accountability Act

##### 10.2 References
- Ambee environmental data API documentation
- Historical sales data for the Anti-Allergy Medicine
- Climate impact studies on allergy medication demand
- AAN Program Overview Document

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**Prepared by:** [Your Name]  
**Title:** CTO, Ambee  
**Date:** [Current Date]

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This PRD for AllerGenius outlines the key requirements and features for developing a demand forecasting tool for the Anti-Allergy Medicine, leveraging environmental and climate data to optimize inventory management and enhance sales strategies. The additional features and focused scope of this MVP will allow a small team to build it in 2-3 days, making it ideal for a hackathon mode development and providing a competitive edge over the AAN Program.

##### 10.2 References
- Ambee environmental data API documentation
- Historical sales data for the Anti-Allergy Medicine
- Climate impact studies on allergy medication demand

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I will add some UI to this here shortly. Please keep watching this screen, stay tuned or ping me / reach out to me for any updates.

UI details: 

**Login Screen:**
User authentication with secure login.
**Dashboard**:
Interactive dashboard with line charts for historical sales and bar charts for forecast demand.
Filters for different metrics including date range and geographic location.
Scenario analysis tools with "if" and future prediction capabilities.
**Environmental Data Overview:**
Ambee environmental data visualization updated hourly/daily.
**Inventory Management:**
Stock level management based on predictive analytics.
Manual upload of historical sales data in CSV format.
**Revenue Opportunities & User Management:**
Insights on sales potential and trends.
Features for creating and deleting user profiles (integrated user management).
**Settings/Preferences & Billing:**
Customization of alert thresholds and app settings.
Access to billing and subscription management.

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Sample screens

![image](https://hackmd.io/_uploads/SkYK3zw4R.png)


# Sample screen 2

![image](https://hackmd.io/_uploads/SyT93GwNR.png)

# Sample screen idea 3

![image](https://hackmd.io/_uploads/H16h2fP4C.png)

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# Login 

![image](https://hackmd.io/_uploads/ryWgTGwVA.png)

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# Selection

![image](https://hackmd.io/_uploads/rJ0zpGPE0.png)


# Data Forecast

![image](https://hackmd.io/_uploads/Bya4azPNC.png)

# Data Upload

![image](https://hackmd.io/_uploads/Sko8pMDV0.png)

# Forecast on demand - Create new forecast

![image](https://hackmd.io/_uploads/HkfupGDER.png)




![image](https://hackmd.io/_uploads/BJ4xCzv4C.png)



