Photo: Bloomberg
Photo: Bloomberg

Handling dine-in restaurant queues

Design challenges include handling variability in customer demand and food-processing times, skill levels of order takers and chefs, etc.

The size of the food services market in India is estimated to be $48 billion in 2013, as per the Food Services Report 2013 commissioned by consultancy firm Technopak for lobby group National Restaurant Association of India (NRAI).

This market is projected to grow at a compound annual growth rate of 11% over the next five years to reach $78 billion by 2018. With an increase in the consumer’s discretionary expenditure on dine-in services, the demand for better service quality at restaurants is also gaining much attention. Specifically, managing restaurant queues and improving table revenues are an active area of research in service operations.

Restaurant queues typically provide two-sided cues to a consumer. Some may perceive the queues as a reflection of a restaurant’s popularity in terms of cuisine or aesthetics, while others may perceive it as an outcome of a restaurant’s operation principle. While the former group of customers may visit a restaurant after observing long queues as a symbol of its popularity, the latter group may be demotivated after observing a large number of waiting customers and skip the restaurant.

With growing stress on improving customer satisfaction, managing restaurant queues is gaining importance for increasing table turnover and revenues. Prior research establishes that although consumers are highly price-sensitive, they place high importance on the speed of service. Further, there is an agreement among researchers that reducing queues can improve customer patronage and repeat restaurant visits.

The design and operational parameters of a restaurant affect customer waiting times and other restaurant performance measures such as table capacity utilization. The design parameters include: 1) physical layout parameters such as the orientation of tables, aisles and cross-aisle space; 2) dine-in table parameters such as the number of tables, the seating capacity of each table and the number of order takers; and 3) kitchen parameters, such as the number of burners and chef resources.

Further, other restaurant parameters such as number of items on the menu, length (number of pages) of the menu, item preparation time and number of reservation no-shows affect the customer waiting time at different phases of the dine-in process.

The design challenges include accommodating the variability in customer demand, variability in kitchen food-processing times, skill levels of the order takers and chefs, etc. These uncertainties around dine-in times, kitchen preparation times and customer arrival patterns make the queue length estimation process difficult. The first and foremost step to optimize the design is to estimate the total time spent by a customer, from his arrival time to departure, including the waiting times at various steps in the dine-in process.

We have developed a waiting line model for a dine-in restaurant in Ahmedabad to capture the dynamic interactions among customers, table resources and kitchen resources. The model obtains reliable estimates of resource utilization and the customer’s total dine-in time, which includes the waiting time components. Such models can rapidly identify the bottlenecks and quantify the effect of varying resource levels on customer waiting times and table turnover. We have also developed an analytical model to determine the optimal mix of tables for a restaurant to minimize queues and maximize revenues.

At present, we are investigating the effect of reservation policies and pre-ordering on customer service. The strategy calls for an arrangement where patrons place an order while waiting for a table. We believe that patrons spend significant time in deciding an order, which, most of the time, consists of items which can be partially prepared while patrons are waiting for a table.

Thus, our intuition is that overlapping part of the activities (placing an order and partial preparation of items for which the order has already been placed) will reduce the total time for which a patron occupies the table, thus increasing table turnover, especially during peak load. Other strategies such as introducing reservation no-show fees and having flexibility in reservation times can potentially improve table turnover.

Debjit Roy’s area of study includes production and quantitative methods.

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