Restaurant Management System Project Plan

1. Executive Summary

Product Vision

To revolutionize restaurant operations by providing an integrated, intelligent, and intuitive platform that streamlines every aspect of the dining experience, from reservation to payment, inventory to staff management. Our vision is to empower restaurant owners and staff with real-time insights and automation, enhancing efficiency, profitability, and customer satisfaction, ultimately fostering sustainable growth in a competitive industry.

Value Proposition

The "DineFlow Pro" system offers a unified digital ecosystem that eliminates operational silos and manual inefficiencies. It provides:
* Enhanced Operational Efficiency: Automates table management, order routing, and inventory tracking, drastically reducing human error and wait times.
* Superior Customer Experience: Facilitates seamless online reservations, personalized loyalty programs, and efficient service delivery, leading to higher customer retention.
* Data-Driven Decision Making: Delivers comprehensive analytics on sales, inventory, staff performance, and customer trends, enabling owners to make informed strategic choices.
* Reduced Costs & Waste: Optimizes inventory levels, minimizes food waste, and improves staff scheduling, directly impacting the bottom line.
* Scalable & Adaptable: A modular architecture designed to grow with the restaurant, easily integrating new features and adapting to evolving business needs.

2. User Personas & Pain Points

Persona 1: Restaurant Owner (Sophia Chen)

• Background: Owns a mid-to-high-end casual dining restaurant. Focused on profitability, brand reputation, and operational excellence.
• Goals: Maximize revenue, minimize costs, ensure high customer satisfaction, retain staff, expand business.
• Pain Points:
  • Lack of real-time visibility into sales performance, inventory levels, and staff productivity.
  • Difficulty tracking customer preferences and managing loyalty programs effectively.
  • Manual reconciliation of finances from different systems (POS, inventory, payroll).
  • High staff turnover due to inefficient scheduling and communication.
  • Struggles to identify peak hours and optimize staffing accordingly.

Persona 2: Restaurant Manager (David Miller)

• Background: Oversees daily front-of-house and back-of-house operations. Manages staff, resolves issues, ensures smooth service.
• Goals: Efficient service delivery, effective staff deployment, seamless communication, quick problem resolution.
• Pain Points:
  • Inefficient table management leading to bottlenecks and inaccurate wait times.
  • Manual scheduling and shift swaps are time-consuming and prone to errors.
  • Poor communication between kitchen and front-of-house, leading to order mistakes and delays.
  • Difficulty tracking server performance and tip distribution.
  • Cumbersome process for handling customer feedback and complaints.

Persona 3: Head Chef (Maria Rodriguez)

• Background: Responsible for kitchen operations, menu development, food quality, and inventory.
• Goals: Maintain food quality and consistency, minimize food waste, manage kitchen staff efficiently, ensure timely order fulfillment.
• Pain Points:
  • Lack of real-time ingredient inventory, leading to last-minute stock-outs or over-ordering.
  • Inefficient order display system (KDS) causing confusion and delays during peak hours.
  • Difficulty tracking food costs per dish and identifying waste.
  • Challenges in standardizing recipes and ensuring adherence across kitchen staff.
  • Manual tracking of preparation times for different dishes.

Persona 4: Server/Waiter (Ethan Johnson)

• Background: Direct customer interaction, takes orders, serves food, handles payments.
• Goals: Provide excellent customer service, process orders accurately, maximize tips, efficiently manage assigned tables.
• Pain Points:
  • Manual order taking is slow and prone to errors, especially for complex customizations.
  • Running back and forth to the POS terminal, reducing time with customers.
  • Difficulty checking table status (e.g., waiting for food, ready for bill) without direct interaction.
  • Complex split bill requests are cumbersome and time-consuming.
  • Unclear communication from the kitchen regarding dish availability or delays.

Persona 5: Host/Hostess (Sarah Lee)

• Background: Manages reservations, greets guests, assigns tables, manages waitlist.
• Goals: Optimize table utilization, minimize guest wait times, provide a welcoming first impression.
• Pain Points:
  • Manual reservation books are prone to double-bookings and illegibility.
  • Difficulty accurately estimating wait times for walk-ins.
  • Inefficient table assignment, leading to uneven server load or empty tables during peak times.
  • Lack of real-time updates on table status (e.g., cleaned, seated, paid).
  • Challenges in communicating waitlist status to guests remotely.

3. Product Backlog (EPICs)

EPIC 1: Order & Table Management

• US1.1: As a Server, I want to view an interactive floor plan with real-time table statuses so that I can efficiently manage my assigned tables.
• US1.2: As a Server, I want to take a customer's order using a handheld device so that orders are accurately sent to the kitchen instantly.
• US1.3: As a Server, I want to easily customize menu items (add modifiers, special instructions) so that customer requests are precisely communicated.
• US1.4: As a Server, I want to split a bill by item, by diner, or by custom amount so that I can accommodate diverse payment requests easily.
• US1.5: As a Host, I want to assign guests to available tables based on party size and server sections so that seating is optimized and balanced.
• US1.6: As a Host, I want to manage a digital waitlist with estimated wait times and SMS notifications so that guests are informed and wait times are transparent.
• US1.7: As a Kitchen Staff, I want to view new orders and their preparation status on a Kitchen Display System (KDS) so that I can efficiently prepare and track dishes.
• US1.8: As a Manager, I want to easily transfer tables between servers or merge tables for larger parties so that floor operations remain flexible.
• US1.9: As a Server, I want to mark an order item as "voided" or "returned" with a reason so that discrepancies are accurately recorded for accounting.

EPIC 2: Menu Management

• US2.1: As a Manager, I want to add, edit, or remove menu items with names, descriptions, and prices so that the menu is always up-to-date.
• US2.2: As a Manager, I want to categorize menu items (e.g., Appetizers, Entrees, Desserts) so that the menu is organized and easy to navigate.
• US2.3: As a Manager, I want to define modifiers (e.g., "extra cheese", "no onion") and their associated costs so that customers can customize orders.
• US2.4: As a Manager, I want to set item availability (e.g., "out of stock", "seasonal") so that servers and customers know what can be ordered.
• US2.5: As a Manager, I want to associate menu items with specific ingredients from inventory so that stock levels deplete automatically.
• US2.6: As a Manager, I want to create daily specials or limited-time offers so that I can quickly update the menu for promotions.

EPIC 3: Inventory & Supply Chain Management

• US3.1: As a Chef, I want to view real-time inventory levels for all ingredients so that I can plan daily prep and avoid stock-outs.
• US3.2: As a Manager, I want to set reorder points and receive automated low-stock alerts so that I can proactively place orders.
• US3.3: As a Manager, I want to manage a list of suppliers with contact details and pricing so that ordering is streamlined.
• US3.4: As a Manager, I want to create and track purchase orders (POs) for ingredients so that I can monitor incoming stock.
• US3.5: As a Chef, I want to record ingredient waste with reasons so that I can identify areas for cost reduction.
• US3.6: As a Manager, I want to conduct periodic inventory counts and reconcile physical stock with system records so that accuracy is maintained.
• US3.7: As a Manager, I want to track ingredient expiry dates and receive alerts so that food safety is ensured and waste is minimized.

EPIC 4: Staff Management & Scheduling

• US4.1: As an Owner, I want to add and manage staff profiles with roles, permissions, and personal details so that access control is maintained.
• US4.2: As a Manager, I want to create weekly staff schedules by assigning shifts and roles so that staffing levels are optimized.
• US4.3: As Staff, I want to view my upcoming shifts and request time off or shift swaps so that I can manage my work-life balance.
• US4.4: As a Manager, I want to approve or deny shift swap requests and notify relevant staff so that schedules are accurate and fair.
• US4.5: As Staff, I want to clock in and out using the system so that my work hours are accurately recorded.
• US4.6: As a Manager, I want to track staff performance metrics (e.g., sales per server, average table turn time) so that I can provide feedback and identify training needs.
• US4.7: As an Owner, I want to generate payroll reports based on clocked hours and tips so that payroll processing is simplified.

EPIC 5: Reservations & Customer Relationship Management (CRM)

• US5.1: As a Customer, I want to make an online reservation through the restaurant's website so that I can secure a table in advance.
• US5.2: As a Host, I want to view, confirm, or modify incoming reservations so that I can manage the reservation book effectively.
• US5.3: As a Host, I want to record walk-in customers and their preferences so that I can seat them efficiently when a table is ready.
• US5.4: As a Manager, I want to build a customer database with contact information, visit history, and dietary preferences so that I can offer personalized service.
• US5.5: As a Manager, I want to create and manage loyalty programs (e.g., points, discounts) so that I can reward repeat customers.
• US5.6: As a Customer, I want to receive automated confirmation and reminder messages for my reservation so that I don't miss my booking.
• US5.7: As a Manager, I want to send targeted marketing promotions to specific customer segments so that I can drive repeat business.

EPIC 6: Reporting & Analytics

• US6.1: As an Owner, I want to view daily, weekly, and monthly sales reports by item, category, and server so that I can track revenue trends.
• US6.2: As an Owner, I want to analyze peak hours and busiest days so that I can optimize staffing and inventory.
• US6.3: As an Owner, I want to generate cost-of-goods-sold (COGS) reports so that I can understand profitability per dish.
• US6.4: As a Manager, I want to track average table turn times and customer wait times so that I can improve service efficiency.
• US6.5: As an Owner, I want to view inventory valuation reports so that I can assess asset value and identify potential waste.
• US6.6: As an Owner, I want to track customer loyalty program engagement and effectiveness so that I can refine marketing strategies.
• US6.7: As an Owner, I want to integrate with external accounting software (e.g., QuickBooks) for automated financial reconciliation.

4. Database Schema

Table: Restaurants

• restaurant_id (UUID, Primary Key)
• name (VARCHAR(255), NOT NULL)
• address (TEXT, NOT NULL)
• phone_number (VARCHAR(20))
• email (VARCHAR(255))
• website (VARCHAR(255))
• cuisine_type (VARCHAR(100))
• capacity (INTEGER, DEFAULT 0)
• operating_hours_json (JSONB) -- e.g., { "Mon": "09:00-22:00", ... }
• created_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)
• updated_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)

Table: Users

• user_id (UUID, Primary Key)
• restaurant_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Restaurants(restaurant_id))
• first_name (VARCHAR(100), NOT NULL)
• last_name (VARCHAR(100), NOT NULL)
• email (VARCHAR(255), UNIQUE, NOT NULL)
• password_hash (VARCHAR(255), NOT NULL)
• role (ENUM('Owner', 'Manager', 'Chef', 'Server', 'Host'), NOT NULL)
• phone_number (VARCHAR(20))
• hire_date (DATE)
• is_active (BOOLEAN, DEFAULT TRUE)
• profile_picture_url (TEXT)
• created_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)
• updated_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)

Table: Tables

• table_id (UUID, Primary Key)
• restaurant_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Restaurants(restaurant_id))
• table_number (VARCHAR(50), NOT NULL)
• capacity (INTEGER, NOT NULL)
• location_description (VARCHAR(255))
• current_status (ENUM('Available', 'Seated', 'Occupied', 'Awaiting_Service', 'Awaiting_Bill', 'Awaiting_Cleaning', 'Reserved'), DEFAULT 'Available')
• is_active (BOOLEAN, DEFAULT TRUE)
• created_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)
• updated_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)

Table: Menu_Categories

• category_id (UUID, Primary Key)
• restaurant_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Restaurants(restaurant_id))
• name (VARCHAR(100), NOT NULL)
• description (TEXT)
• display_order (INTEGER, DEFAULT 0)
• is_active (BOOLEAN, DEFAULT TRUE)

Table: Menu_Items

• item_id (UUID, Primary Key)
• restaurant_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Restaurants(restaurant_id))
• category_id (UUID, FOREIGN KEY REFERENCES Menu_Categories(category_id))
• name (VARCHAR(255), NOT NULL)
• description (TEXT)
• price (DECIMAL(10, 2), NOT NULL, CHECK (price >= 0))
• image_url (TEXT)
• is_available (BOOLEAN, DEFAULT TRUE)
• preparation_time_minutes (INTEGER)
• allergens_json (JSONB) -- e.g., ["gluten", "dairy"]
• created_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)
• updated_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)

Table: Modifiers

• modifier_id (UUID, Primary Key)
• restaurant_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Restaurants(restaurant_id))
• name (VARCHAR(100), NOT NULL)
• price_adjustment (DECIMAL(10, 2), DEFAULT 0.00)
• is_active (BOOLEAN, DEFAULT TRUE)
• type (ENUM('Add_On', 'No_Option', 'Size'), DEFAULT 'Add_On') -- e.g., 'Extra Cheese', 'No Onions', 'Large'
• is_required (BOOLEAN, DEFAULT FALSE)

Table: Menu_Item_Modifiers (Many-to-Many through explicit linking)

• item_modifier_id (UUID, Primary Key)
• item_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Menu_Items(item_id))
• modifier_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Modifiers(modifier_id))
• created_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)
• UNIQUE (item_id, modifier_id)

Table: Orders

• order_id (UUID, Primary Key)
• restaurant_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Restaurants(restaurant_id))
• table_id (UUID, FOREIGN KEY REFERENCES Tables(table_id))
• customer_id (UUID, NULL, FOREIGN KEY REFERENCES Customers(customer_id)) -- For registered customers or reservations
• server_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Users(user_id))
• order_time (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)
• status (ENUM('Pending', 'Preparing', 'Ready_For_Pickup', 'Served', 'Cancelled', 'Paid'), DEFAULT 'Pending')
• total_amount (DECIMAL(10, 2), DEFAULT 0.00)
• discount_amount (DECIMAL(10, 2), DEFAULT 0.00)
• tax_amount (DECIMAL(10, 2), DEFAULT 0.00)
• payment_status (ENUM('Unpaid', 'Partially_Paid', 'Paid'), DEFAULT 'Unpaid')
• notes (TEXT)
• last_updated_by (UUID, FOREIGN KEY REFERENCES Users(user_id))
• updated_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)

Table: Order_Items

• order_item_id (UUID, Primary Key)
• order_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Orders(order_id))
• item_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Menu_Items(item_id))
• quantity (INTEGER, NOT NULL, CHECK (quantity > 0))
• unit_price (DECIMAL(10, 2), NOT NULL) -- Price at the time of order
• item_total (DECIMAL(10, 2), NOT NULL)
• special_instructions (TEXT)
• kitchen_status (ENUM('Pending', 'Preparing', 'Cooked', 'Served', 'Cancelled_Item'), DEFAULT 'Pending')
• created_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)
• updated_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)

Table: Order_Item_Modifiers (Links specific order items to chosen modifiers)

• order_item_modifier_id (UUID, Primary Key)
• order_item_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Order_Items(order_item_id))
• modifier_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Modifiers(modifier_id))
• price_adjustment (DECIMAL(10, 2), DEFAULT 0.00) -- Price at time of order
• created_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)

Table: Payments

• payment_id (UUID, Primary Key)
• order_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Orders(order_id))
• payment_method (ENUM('Cash', 'Card', 'Mobile_Pay', 'Gift_Card'), NOT NULL)
• amount (DECIMAL(10, 2), NOT NULL, CHECK (amount > 0))
• transaction_id (VARCHAR(255), UNIQUE) -- From payment gateway
• payment_date (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)
• server_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Users(user_id))
• tip_amount (DECIMAL(10, 2), DEFAULT 0.00)

Table: Reservations

• reservation_id (UUID, Primary Key)
• restaurant_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Restaurants(restaurant_id))
• customer_id (UUID, FOREIGN KEY REFERENCES Customers(customer_id))
• guest_name (VARCHAR(255), NOT NULL)
• guest_phone (VARCHAR(20))
• guest_email (VARCHAR(255))
• party_size (INTEGER, NOT NULL, CHECK (party_size > 0))
• reservation_time (TIMESTAMP WITH TIME ZONE, NOT NULL)
• duration_minutes (INTEGER, DEFAULT 90)
• status (ENUM('Pending', 'Confirmed', 'Seated', 'Cancelled', 'No_Show'), DEFAULT 'Pending')
• notes (TEXT)
• assigned_table_id (UUID, NULL, FOREIGN KEY REFERENCES Tables(table_id))
• created_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)
• updated_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)

Table: Waitlist_Entries

• waitlist_id (UUID, Primary Key)
• restaurant_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Restaurants(restaurant_id))
• guest_name (VARCHAR(255), NOT NULL)
• guest_phone (VARCHAR(20))
• party_size (INTEGER, NOT NULL, CHECK (party_size > 0))
• arrival_time (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)
• estimated_wait_minutes (INTEGER)
• status (ENUM('Waiting', 'Seated', 'Cancelled', 'No_Show'), DEFAULT 'Waiting')
• seated_time (TIMESTAMP WITH TIME ZONE)
• assigned_table_id (UUID, NULL, FOREIGN KEY REFERENCES Tables(table_id))

Table: Customers (CRM)

• customer_id (UUID, Primary Key)
• restaurant_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Restaurants(restaurant_id))
• first_name (VARCHAR(100), NOT NULL)
• last_name (VARCHAR(100))
• email (VARCHAR(255), UNIQUE)
• phone_number (VARCHAR(20), UNIQUE)
• loyalty_points (INTEGER, DEFAULT 0)
• total_visits (INTEGER, DEFAULT 0)
• last_visit (DATE)
• dietary_preferences_json (JSONB) -- e.g., ["vegetarian", "nut-allergy"]
• notes (TEXT)
• created_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)
• updated_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)

Table: Loyalty_Programs

• program_id (UUID, Primary Key)
• restaurant_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Restaurants(restaurant_id))
• name (VARCHAR(255), NOT NULL)
• description (TEXT)
• points_per_currency (DECIMAL(10, 2), DEFAULT 1.00) -- e.g., 1 point per $1 spent
• is_active (BOOLEAN, DEFAULT TRUE)
• created_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)

Table: Rewards

• reward_id (UUID, Primary Key)
• program_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Loyalty_Programs(program_id))
• name (VARCHAR(255), NOT NULL)
• description (TEXT)
• points_cost (INTEGER, NOT NULL, CHECK (points_cost > 0))
• discount_value (DECIMAL(10, 2)) -- e.g., $5 off
• item_id (UUID, FOREIGN KEY REFERENCES Menu_Items(item_id)) -- e.g., Free Dessert
• is_active (BOOLEAN, DEFAULT TRUE)
• created_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)

Table: Inventory_Items (Raw ingredients or sellable items)

• inventory_item_id (UUID, Primary Key)
• restaurant_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Restaurants(restaurant_id))
• name (VARCHAR(255), NOT NULL)
• unit_of_measure (VARCHAR(50), NOT NULL) -- e.g., 'grams', 'ml', 'pieces', 'kg', 'liter'
• current_stock (DECIMAL(10, 3), NOT NULL, DEFAULT 0.000)
• reorder_point (DECIMAL(10, 3), DEFAULT 0.000)
• average_cost (DECIMAL(10, 2), DEFAULT 0.00)
• supplier_id (UUID, NULL, FOREIGN KEY REFERENCES Suppliers(supplier_id))
• category (VARCHAR(100)) -- e.g., 'Produce', 'Dairy', 'Meat', 'Beverage'
• expiry_date_tracking_required (BOOLEAN, DEFAULT FALSE)
• created_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)
• updated_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)

Table: Menu_Item_Ingredients (Recipes - Many-to-Many for Menu_Items and Inventory_Items)

• recipe_id (UUID, Primary Key)
• item_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Menu_Items(item_id))
• inventory_item_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Inventory_Items(inventory_item_id))
• quantity_used (DECIMAL(10, 3), NOT NULL) -- How much of the ingredient is used per menu item
• unit_of_measure (VARCHAR(50), NOT NULL) -- The unit for quantity_used
• created_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)
• UNIQUE (item_id, inventory_item_id)

Table: Suppliers

• supplier_id (UUID, Primary Key)
• restaurant_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Restaurants(restaurant_id))
• name (VARCHAR(255), NOT NULL)
• contact_person (VARCHAR(255))
• phone_number (VARCHAR(20))
• email (VARCHAR(255))
• address (TEXT)
• notes (TEXT)
• created_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)

Table: Purchase_Orders

• po_id (UUID, Primary Key)
• restaurant_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Restaurants(restaurant_id))
• supplier_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Suppliers(supplier_id))
• order_date (DATE, DEFAULT CURRENT_DATE)
• expected_delivery_date (DATE)
• actual_delivery_date (DATE)
• status (ENUM('Pending', 'Ordered', 'Partially_Received', 'Received', 'Cancelled'), DEFAULT 'Pending')
• total_cost (DECIMAL(10, 2), DEFAULT 0.00)
• created_by (UUID, FOREIGN KEY REFERENCES Users(user_id))
• created_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)
• updated_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)

Table: Purchase_Order_Items

• po_item_id (UUID, Primary Key)
• po_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Purchase_Orders(po_id))
• inventory_item_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Inventory_Items(inventory_item_id))
• ordered_quantity (DECIMAL(10, 3), NOT NULL, CHECK (ordered_quantity > 0))
• received_quantity (DECIMAL(10, 3), DEFAULT 0.000)
• unit_cost (DECIMAL(10, 2), NOT NULL)
• line_total (DECIMAL(10, 2), NOT NULL)
• expiry_date (DATE) -- If tracking expiry dates for this specific batch

Table: Staff_Schedules

• schedule_id (UUID, Primary Key)
• restaurant_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Restaurants(restaurant_id))
• user_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Users(user_id))
• shift_date (DATE, NOT NULL)
• start_time (TIME WITH TIME ZONE, NOT NULL)
• end_time (TIME WITH TIME ZONE, NOT NULL)
• role (ENUM('Owner', 'Manager', 'Chef', 'Server', 'Host'), NOT NULL)
• notes (TEXT)
• created_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)
• updated_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)

Table: Time_Clocks

• clock_id (UUID, Primary Key)
• user_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Users(user_id))
• restaurant_id (UUID, NOT NULL, FOREIGN KEY REFERENCES Restaurants(restaurant_id))
• clock_in_time (TIMESTAMP WITH TIME ZONE, NOT NULL)
• clock_out_time (TIMESTAMP WITH TIME ZONE)
• status (ENUM('Clocked_In', 'Clocked_Out', 'Break'), DEFAULT 'Clocked_In')
• shift_date (DATE, NOT NULL) -- Derived from clock_in_time for easy lookup
• created_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)
• updated_at (TIMESTAMP WITH TIME ZONE, DEFAULT CURRENT_TIMESTAMP)

5. Business Logic & Constraints

General System Constraints

• All UUIDs must be unique.
• Timestamps created_at and updated_at are automatically managed by the system.
• restaurant_id is a mandatory foreign key for most operational tables, ensuring data segmentation per restaurant.
• User roles define access levels and UI permissions.

User & Authentication

• Password Policy: Minimum 8 characters, at least one uppercase, one lowercase, one number, one special character.
• Role-Based Access Control (RBAC):
  • Owner: Full access to all modules, including restaurant settings, financial reports, and user management.
  • Manager: Full access to staff, inventory, menu, table, order, reservation, and limited access to reporting. Cannot alter owner-level settings or user roles.
  • Chef: Access to KDS, inventory levels, recipe management. Limited view of orders (kitchen-relevant only).
  • Server: Access to specific table management (their assigned tables), order taking, payment processing. No access to inventory or staff scheduling.
  • Host: Access to table management, waitlist, reservation management. View-only access to floor plan.
• Session Management: Secure, token-based authentication with configurable expiry.

Order & Table Management

• Table Status Transitions:
  • Available -> Reserved (by reservation) -> Seated
  • Available -> Seated (by walk-in/host)
  • Seated -> Occupied (once order is placed)
  • Occupied -> Awaiting_Bill (order finalized)
  • Awaiting_Bill -> Awaiting_Cleaning (payment complete)
  • Awaiting_Cleaning -> Available (after cleaning)
• Order Status Transitions:
  • Pending (new order) -> Preparing (kitchen starts) -> Ready_For_Pickup (for takeaway/delivery) / Served (for dine-in) -> Paid -> Completed.
  • Orders can be Cancelled at any Pending or Preparing stage with Manager override and reason.
• Inventory Depletion: When an Order_Item is marked as Preparing or Cooked or Served, associated Inventory_Items quantities are automatically decremented based on Menu_Item_Ingredients.
• Price Calculation: Order_Item_total = Menu_Item.price * quantity + SUM(Order_Item_Modifiers.price_adjustment). Order.total_amount = SUM(Order_Items.item_total) + tax_amount - discount_amount.
• Split Bill Logic:
  • By Item: Each item can be assigned to a specific payment group.
  • By Diner: Total bill divided evenly by number of diners.
  • Custom Amount: Specific monetary amounts can be paid.
  • System must ensure all Order_Items are accounted for across split bills.

Menu Management

• Item Unavailability: If Menu_Item.is_available is FALSE or an Inventory_Item associated with it is below a critical threshold, the item cannot be added to new orders by servers.
• Modifier Application: Modifiers can only be applied to Menu_Items to which they are linked via Menu_Item_Modifiers.

Inventory & Supply Chain

• Reorder Alerts: Triggered when Inventory_Items.current_stock falls below Inventory_Items.reorder_point. Alerts sent to Manager/Chef roles.
• Purchase Order (PO) Processing:
  • Purchase_Order.status changes from 'Pending' to 'Ordered' upon confirmation.
  • Upon Purchase_Order_Item received_quantity updates, Inventory_Items.current_stock is incremented.
  • If Purchase_Order_Item.expiry_date is provided, it should be tracked.
  • Purchase_Order.status changes to 'Received' when all ordered_quantity equals received_quantity for all items, or 'Partially_Received' otherwise.
• FIFO/LIFO (Optional for v1, but good for complexity): For expiry-tracked items, when depleting stock, prioritize oldest stock (FIFO) for higher efficiency.
• Waste Tracking: Requires User role, Inventory_Item_ID, Quantity, and Reason for auditability.

Staff Management

• Clock-in/out: Users can only clock in if they are not already clocked in. Clock-out requires a prior clock-in.
• Shift Overlap: Scheduling module prevents overlapping shifts for the same user.
• Role-Specific Scheduling: Only roles relevant to a shift can be assigned (e.g., a "Server" cannot be scheduled as a "Chef").

Reservations & CRM

• Availability Check: Reservations are only possible for available slots, considering restaurant_id.capacity, Tables availability, and existing Reservations.
• No-Show Policy: After a grace period (e.g., 15-30 min past reservation_time), Reservation.status can be automatically or manually changed to No_Show.
• Loyalty Points: Customer.loyalty_points are updated when an order linked to a Customer is Paid. Points accumulation based on Loyalty_Programs.points_per_currency.
• Reward Redemption: Redeeming a Reward reduces Customer.loyalty_points and applies the corresponding discount or free item to the current order.

Reporting

• Data integrity for reports requires all operational data to be consistent and transactional.
• Real-time aggregation for dashboards.
• Historical data retained for configurable periods (e.g., 5 years) for long-term trend analysis.

6. Code Flow & Algorithms: Dynamic Table Assignment & Optimization

Feature: Intelligent Table Assignment for a Host/Manager, considering party size, table capacity, server load, and estimated wait times.

Scenario: A Host receives a walk-in party or needs to seat a confirmed reservation. The system suggests optimal tables.

Algorithm: SuggestOptimalTables(partySize, restaurantId)

Input:
* partySize: Integer, the number of guests in the party.
* restaurantId: UUID, the ID of the restaurant.

Output:
* A ranked list of Table_IDs with rationale (e.g., "Best Fit", "Optimal Server Load", "Next Available").

Steps:

1. Retrieve Available Tables:

   • Fetch all Tables for restaurantId where is_active is TRUE and current_status is 'Available' or 'Awaiting_Cleaning'.
   • Filter tables: table.capacity >= partySize AND table.capacity <= partySize * 1.5 (or partySize + 2 for flexibility, avoiding too large tables for small parties).
   • Store these as potentialTables.

2. Retrieve Current Server Load:

   • For each User with role = 'Server' working currently (Time_Clocks.status = 'Clocked_In') at restaurantId:
     • Calculate currentOpenOrders: Number of Orders where server_id = user_id and status NOT IN ('Paid', 'Cancelled').
     • Calculate currentTableCount: Number of Tables currently assigned to this server (via active Orders or Reservations).
     • Calculate totalCapacityServed: Sum of Tables.capacity for tables assigned to this server.
     • Store this as serverLoad[userId] = { openOrders, tableCount, totalCapacityServed }.

3. Score Each Potential Table:

   • Initialize scoredTables = [].

   • For each table in potentialTables:

     • score = 0
     • reasons = []

     a. Capacity Fit (Higher is better for fit, penalty for over-sizing):
     * capacity_diff = table.capacity - partySize
     * If capacity_diff == 0: score += 10; reasons.add("Perfect Capacity Match")
     * If capacity_diff == 1 or capacity_diff == 2: score += 7; reasons.add("Good Capacity Fit")
     * If capacity_diff > 2: score -= capacity_diff * 0.5; reasons.add("Slightly Oversized")

     b. Server Load Balancing (Aim for lower load, balanced distribution):
     * Identify serverCandidates: Servers whose sections include table.table_id (if floor plan sections are defined, else all active servers).
     * If serverCandidates is empty or not applicable, skip this step or use a default.
     * Else, for each server in serverCandidates:
     * Calculate loadFactor = serverLoad[server.user_id].openOrders + serverLoad[server.user_id].tableCount
     * Find minLoadFactor = minimum loadFactor among all active servers.
     * If loadFactor == minLoadFactor: score += 5; reasons.add("Balances Server Load")
     * Else if loadFactor > minLoadFactor: score -= (loadFactor - minLoadFactor) * 1 (penalty for assigning to busy server).

     c. Table Status Nuance:
     * If table.current_status == 'Awaiting_Cleaning': score -= 3; reasons.add("Requires Cleaning") (implies a short delay).
     * If table.current_status == 'Available': score += 2; reasons.add("Immediately Available")

     d. Proximity to Other Reserved/Seated Tables (Optional for complex layouts, to cluster service):
     * Could add logic to prefer tables near already active tables for a given server, reducing travel time. (Complexity TBD)

     e. Reservation Assignment (if applicable):
     * If table.table_id is already assigned to a Reservation for the current reservation_time and partySize matches, give highest score. score += 20; reasons.add("Pre-assigned Reservation Table")

     f. Historical Turnaround Time (More complex):
     * Fetch historical Table.turnaround_times for this table.capacity.
     * If table.current_status == 'Awaiting_Cleaning', estimate time until available and penalize if too long.

     • Add { table_id: table.table_id, score: score, reasons: reasons, capacity: table.capacity, current_status: table.current_status } to scoredTables.

4. Sort and Return:

   • Sort scoredTables in descending order by score.
   • Return the top N (e.g., 3-5) tables, including their score and reasons.

Pseudocode Example (Simplified SuggestOptimalTables function core):

FUNCTION SuggestOptimalTables(partySize, restaurantId):
    potentialTables = GET_AVAILABLE_TABLES(restaurantId)
                      .FILTER_BY(capacity >= partySize AND capacity <= partySize * 1.5)

    serverLoads = GET_CURRENT_SERVER_LOADS(restaurantId) // Map: {userId: {openOrders, tableCount}}

    scoredTables = []

    FOR EACH table IN potentialTables:
        score = 0
        reasons = []

        // Capacity Fit
        capacityDiff = table.capacity - partySize
        IF capacityDiff == 0:
            score += 10
            reasons.ADD("Perfect Capacity Match")
        ELSE IF capacityDiff <= 2:
            score += 7
            reasons.ADD("Good Capacity Fit")
        ELSE:
            score -= capacityDiff * 0.5 // Penalty for oversized
            reasons.ADD("Slightly Oversized")

        // Server Load Balancing
        assignedServerId = GET_ASSIGNED_SERVER_FOR_TABLE_SECTION(table.table_id)
        IF assignedServerId IS NOT NULL AND assignedServerId IN serverLoads:
            currentServerLoad = serverLoads[assignedServerId].openOrders + serverLoads[assignedServerId].tableCount
            minOverallLoad = MIN(load.openOrders + load.tableCount FOR load IN serverLoads.VALUES())

            IF currentServerLoad == minOverallLoad:
                score += 5
                reasons.ADD("Balances Server Load")
            ELSE IF currentServerLoad > minOverallLoad:
                score -= (currentServerLoad - minOverallLoad) * 1 // Penalize busier servers
                reasons.ADD("Server is Busier")
        ELSE: // Handle cases where server sections are not strictly defined or server not active
            // Default to minimal penalty or specific logic
            pass 

        // Table Status
        IF table.current_status == 'Awaiting_Cleaning':
            score -= 3
            reasons.ADD("Requires Cleaning")
        ELSE IF table.current_status == 'Available':
            score += 2
            reasons.ADD("Immediately Available")

        scoredTables.ADD({tableId: table.id, score: score, reasons: reasons, details: table})

    RETURN SORT(scoredTables, DESCENDING BY score)
END FUNCTION

Considerations for Production:
* Real-time Updates: Table statuses and server loads must be updated in real-time. WebSockets could be used for instant UI updates.
* User Overrides: Host/Manager should always have the option to override system suggestions.
* Learning Algorithm (Future Enhancement): The scoring weights could be dynamically adjusted based on historical data (e.g., if tables assigned by "Perfect Capacity Match" lead to faster turnarounds).
* Floor Plan Visualization: The UI would display these suggestions directly on an interactive floor plan.

7. UI/UX Style Guide

Vibe: Premium, Modern, Intuitive

• Premium: Clean lines, subtle animations, sophisticated color palette, high-quality iconography, ample whitespace. Focus on clarity and efficiency.
• Modern: Flat design principles with subtle shadows and depth where necessary. Responsive layouts, fast loading times, contemporary typography.
• Intuitive: Clear hierarchy, consistent navigation, natural user flows, instant feedback, minimal cognitive load. Features are where users expect them to be.

Color Palette

Using HSL (Hue, Saturation, Lightness) for flexibility and thematic consistency:

• Primary Accent (Calls to Action, Key Highlights):

  • --primary-accent-500: hsl(198, 86%, 53%) (Vibrant Teal/Cyan)
  • --primary-accent-400: hsl(198, 86%, 65%) (Lighter Teal)
  • --primary-accent-600: hsl(198, 86%, 40%) (Deeper Teal)

• Secondary (Complementary UI Elements, Subtler Actions):

  • --secondary-400: hsl(210, 20%, 65%) (Soft Grey-Blue)
  • --secondary-500: hsl(210, 20%, 50%) (Medium Grey-Blue)

• Neutrals (Backgrounds, Borders, Text):

  • --neutral-900: hsl(220, 15%, 15%) (Near Black, for primary text)
  • --neutral-700: hsl(220, 10%, 30%) (Dark Grey, for secondary text)
  • --neutral-300: hsl(220, 8%, 80%) (Light Grey, for borders, inactive elements)
  • --neutral-100: hsl(220, 10%, 96%) (Off-white, for main backgrounds)
  • --neutral-000: hsl(0, 0%, 100%) (Pure White, for cards, modals)

• Semantic Colors (Feedback, Statuses):

  • --success-500: hsl(120, 60%, 40%) (Green, for success messages)
  • --warning-500: hsl(40, 90%, 55%) (Amber, for warnings)
  • --danger-500: hsl(0, 70%, 55%) (Red, for errors)

Fonts (Google Fonts)

• Headings & Display (Bold, Modern Sans-serif):

  • "Montserrat": Used for all main headings (H1, H2, H3), prominent titles, and key statistics.
  • Weights: 600 (Semi-Bold), 700 (Bold), 800 (Extra-Bold).
  • Rationale: Provides a strong, confident, and professional presence, aligning with the "Premium, Modern" vibe.

• Body Text & UI Elements (Clean, Highly Readable Sans-serif):

  • "Inter": Used for paragraphs, labels, button text, table data, and all general UI text.
  • Weights: 400 (Regular), 500 (Medium), 600 (Semi-Bold).
  • Rationale: Excellent legibility across various sizes, neutral yet modern, ensuring intuitive understanding and comfortable reading for long periods.

• Monospace (Optional, for specific data display or code-like elements):

  • "JetBrains Mono": For unique identifiers, time codes, or specific inventory SKU displays where fixed-width characters are beneficial.
  • Weights: 400 (Regular).

Typography Scale (Example - will be adaptive/responsive):

• H1: 3.0rem (Montserrat Bold)
• H2: 2.25rem (Montserrat Semi-Bold)
• H3: 1.75rem (Montserrat Semi-Bold)
• H4: 1.25rem (Montserrat Medium)
• Body Large: 1.125rem (Inter Regular)
• Body Base: 1.0rem (Inter Regular)
• Body Small: 0.875rem (Inter Regular)
• Caption: 0.75rem (Inter Medium)