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Reactive BBQ Case Study

The Reactive BBQ restaurant chain has determined that their existing restaurant operations system is not suitably meeting their needs. They have hired a high technology consulting company to provide guidance on how they can improve their customer service, tracking, and reliability.

The Challenge

The original system was designed for a single restaurant. As the chain expanded to 500+ locations across 20 countries, they moved to the cloud. However, the monolithic architecture hasn't scaled well:

  • Performance degradation during peak hours
  • Cascading failures that affect multiple or all locations
  • Deployment difficulties requiring system-wide downtime
  • Resistance to change - new features require "major refactors"

Personnel Interviews

To understand the domain and identify requirements, interviews were conducted with key personnel at Reactive BBQ. Each interview reveals challenges and requirements that inform the RIDDL model.

Executive & Corporate

Role Key Challenges
CEO System reliability, scaling, feature additions
Corporate Head Chef Menu distribution, supply chain coordination

Front of House

Role Key Challenges
Host Slow reservations system, frequent failures
Server Order entry bottlenecks, terminal contention
Bartender Server notification for ready drinks

Kitchen

Role Key Challenges
Chef Lost orders when system fails, handwritten fallback
Cook Illegible handwritten tickets, frustrated servers

Delivery & Online

Role Key Challenges
Delivery Driver App connectivity, payment collection
Online Customer Website/app unreliability

Common Themes

Across all interviews, several themes emerge:

  1. Reliability - The system fails too often, forcing manual workarounds
  2. Responsiveness - Performance degrades under load
  3. Isolation - Failures cascade across the entire system
  4. Flexibility - New features are difficult and risky to add

These themes directly inform the reactive architecture principles that RIDDL helps model: resilience, elasticity, message-driven communication, and bounded contexts.

Next Steps

After reviewing the interviews, proceed to the domain model: