Managing Profitable Food and Beverage Operations
In the UK, Mitchells and Butlers provide her customers with a wide range of food experiences through the pubs, bars, and restaurants it operates with the famous brands including O'Neill, Toby Carvery, Harvester among others. In the last three year period, the sector has seen a boost of about 3000 new entries into the industry catapulted by the ever increasing number of customers primarily the millennial willing and able to spend their significant proportion of their income on eating out (Crick, 2016). The increasing demand has created an opportunity for new players in the industry to acquire a proportion of the market making the other traditional market players to continuously strive for market share making each service provider to frequently review their brands and estates as well as how to handle their customers in relation to time they spend in the premises and the quality of the service and experience during the eating out.
Currently, the mid-market casual dining sector faces a lot of pressure as it has an increased number of the pizza and burger clientele. On the other hand, the sector has also experienced a boom in the grab and go. The sector also faces a disruptor from the delivery as in some restaurants it accounts for over 10% of the total sales. The delivery has posed a strong challenge for restaurants with high demands at peak periods (Hombas, 2015).
According to report by Jun and Arendt (2016), there are possibilities of continued growth in the casual dining sector. A business that adopts modern technology such as the use of social media, as well as the dining apps, shows higher chances of recording growth in their performances. A focus into customer demand, quality of service, food, and environment can help maintain a restaurants clientele and win other referrals.
Queuing time and Customer satisfaction
Time taken to be served is of great importance in the service industry. Queuing time or the waiting time is also of significant importance in the context of establishing customer satisfaction in the casual dining. The customer satisfaction is often impacted negatively in instances that the customer considers waiting for the service to be more than what they feel is normal. According to the study by LeMaster (2018), there is a clear relationship between the shorter time taken for the service to be provided and greater customer satisfaction. It is therefore of great weight that the casual dining restaurants put in place the procedures minimize the time spent queuing for services in order to provide a great service experience In another study by Liang (2017), the study emphasizes that restaurant managers can make use of modern technologies to scale down the waiting time in their establishments. The study postulates that the internet can also be used to take care of the day sales transactions, inventories as well as human resource activities. These, in turn, save time for customers' be served appropriately and with the utmost accuracy making then have a greater and improved experience and satisfaction. Restaurants can make their service delivery differently from their competitors by improving the speed of service, therefore, reducing the waiting time by their customers. This is achieved through the constant re-evaluation of service delivery within the industry (Shan, 2015).
According to the findings of Ting (2016), 30% of the top one hundred restaurants in the UK are making losses. A number of factors including higher business rates, restaurant oversaturation, a fall in customer's confidence as well as rising labor costs have together led to a significant reduction in the profit margins thereby causing the drop in their performance. The study also states that consumers have a fixed disposable income that can be spared for eating out. It also stresses that oversaturation of the restaurants in the market spells stiff competition and any that fails to adjust to the modern methods of actions can easily lose. Another challenge that has been posted by the government has also reviewed labor costs upwards making it difficult for many restaurants to add more staff. In attempts to remain in the businesses, several mid-market restaurants have plans to close at least thirty percent of their outlets (LeMaster, 2018).
A number of numerical modeling procedures can be used to deal with the waiting time or the queuing problem. Some of them include the simulation, queuing models among others. Simulation imitates the process and describes the system's behavior before resources are engaged in a real project. The method has proven effective in the system sufficiency and customer service. In trying to understand the already established systems with a view to improving customer wait time, simulations were used. In this, the model is made to appear as the system from where it is modified with a view to reducing time customers take to be served. The model is created in software known as the Arena which has the ability to build a model using defined modules. The software through its data analyzer identifies the best and appropriate distribution. It also has the ability to provide a comprehensive report. The model has three phases. In the initial phase, the arrival time of each customer is captured this is used to determine the busiest time for the premises. The analyzer then works on the time of arrival and the subsequent time taken to be served in the restaurant (Hombas, 2015). The second phase is where the model for simulation is developed and consequently validated. In the last phase, the modification of the model takes place where it is allowed to give room for the adjustment in the system.
For a period of one month, the customer's arrivals time, as well as their service time, are collected between 5:00 a.m and 11:00 p.m in the evening. The period includes weekends. During the week, the peak hours are between noon and 1:00 p.m as well as in the evening during dinner.
After the data is captured, the analyzer is used to establish the best applicable distribution. Kolmogorov-Smirnov (KS) and Chi-square are then used for validation. Since the data is continuous, the KS is used.
Operational Management Theory
Satisfaction of the customer at a particular instance will either make the customer repurchase or not (Hombas, 2015). Several factors affect customer satisfaction in a fast food restaurant out of which waiting time has appeared to be of great significant impact which affects the customers' subsequent purchases in the same place. It, therefore, qualify that there is a positive correlation between the customer waiting time and their satisfaction, that is, the shorter the waiting time, the higher the customer satisfaction. This plays a role in the customers' next visit to the restaurant or not.
Previous studies show that waiting time is considered differently among different types of customers with 3.9% of the leisure customers having the opinion that the extended waiting time gives them the opportunity to have varied repeat purchase decisions before making the final decisions while the 8.5% among the business customers have the same view. In the comparison between the two classes of customers, business customers care more on the time spent queuing.
Restaurants can easily lose their customers as a result of too much time they spent waiting time and at the queues in the restaurants. A number of restaurants could at times provide chairs for the waiting customers. These alone still could face a number of challenges as the cost f time spend waiting for the service to be provided may be too much that the customers can opt for an alternative service provider. These in the long run negatively affect the performance of the premise. There is, therefore, the need to improve on the service time. The management, therefore, needs a numerical model in order to picture the situation well. In the restaurant, cleanliness, taste, and layout are among the factor that makes the customer evaluate the place as a good or a bad one. Over and above these is the waiting time or the queuing time (Crick, 2016). Through the queuing theory, the average queue length, expected to queue time and the minimum time the system takes is obtained and the management can easily use these to make the necessary adjustment to meet the requirement of the customers at a particular point in time.