Modeling Sri Lanka’s Fastest-Growing Restaurant Franchise Amid the Pandemic

Challenge: Addressing Underutilized Kitchen Capacity

At the tail-end of the COVID-19 pandemic, restaurants and cafés in Colombo, Sri Lanka faced a significant operational challenge. Due to social distancing protocols, dine-in sales volume had dropped drastically. At the time, in the early years of the food delivery business, most restaurants were modeled to focus on dine-in revenue. This left many food business operators struggling to operationalize idle kitchen capacity effectively.

These businesses required a strategy to bridge the revenue shortfall without increasing operational costs or disrupting their primary business. Identifying a solution that leveraged existing infrastructure while aligning with market demand was essential.

Solution: Implementing a Data-Driven Ghost Kitchen Strategy

Cyaniq developed Meal Mesh, a structured approach to help restaurants optimize their kitchen capacity by introducing "Second Menus," delivery-only brands designed to meet existing cuisine gaps in the market. This model was informed by consumer behavior data from PickMe Food, one of the leading food delivery businesses in Sri Lanka, which provided insights into unmet demand in densely populated geographic pockets.

Key components of the strategy included:

• Market Analysis – Identifying underrepresented cuisines based on real-time ordering trends.
• Operational Integration – Implementing new menus within existing restaurant workflows without requiring significant changes to kitchen operations.
• Brand Development – Establishing virtual restaurant concepts tailored for delivery platforms.

This approach allowed restaurants to introduce new offerings using existing, idle resources, enabling revenue diversification with minimal investment.

Results: Increased Utilization and Revenue Growth

Within the initial three months of implementation, the Meal Mesh initiative achieved the following outcomes:​

• 14 virtual restaurant brands launched through Second Menu integration.
• $50,000 in additional revenue generated for partner restaurants.
• Improved kitchen utilization without affecting core business operations.

By leveraging data-driven insights and efficient operational models, restaurants increased output while maintaining cost efficiency.

Key Learnings and Future Applications

The project provided valuable insights into optimizing restaurant operations:

• Market intelligence enhances decision-making – Using real-time consumer data reduces risk and improves menu performance.
• Existing resources can be optimized for additional revenue. Underutilized kitchen capacity can support multiple revenue streams without adding infrastructure.
• Scalability depends on operational flexibility – The Second Menu model demonstrated adaptability for diverse restaurant formats.