Synopsis of the Case
Oliver’s Diner is a restaurant in Grand Bend, Ontario. Currently, the Oliver’s Diner is staffed with ten people, with the owner actively helping kitchen staff and front-of-restaurant coordination. Upon opening, it was successful in securing a steady customer flow and quickly became popular. However, it soon became apparent that the establishment does not have enough capacity to serve all of the customers without delay. On particular hours, especially on weekends, a queue of up to twenty people formed outside the diner. According to the owner’s expectations, the estimated time of serving one customer was 60 minutes or less. In reality, serving took at least 75 minutes on the average, largely because of the buildup of orders in the kitchen.
Analysis
Inventory in the System
The main inventory in this system is the order filed by the customers, although along the supply chain it takes several forms. At the reception, the inventory is in the form of customers who are escorted to their table by the host (satisfying their first need). Here the inventory is created because the subsequent stations (restaurant space and the rate of meal preparation by the kitchen staff) can not accept customers at their arrival rate, creating shortages of tables (Vrat, 2014). After this, the servers take orders for meals and beverages from the customers, at which point the inventory (in the form of meal descriptions) piles up in the kitchen. At this point, the inventory is present in the system because the kitchen is not productive enough to process orders at the rate of their arrival (Jerden, 2015).
Variability in the System
Variability in the system can be categorized into predictable and unpredictable (Das, 2015). The most evident predictable variability in the case of Oliver restaurant is the arrival of customer depending on the time of the day. As can be seen, the highest number of meals served occurs during 12 a.m. to 1 p.m. and 6 p.m. to 7 p.m. It is also higher on weekends. Therefore it is reasonable to expect a similar effect on holidays (National Restaurant Association, n.d.). Next, it is possible to expect seasonal variability of arrivals e.g. caused by weather conditions and traffic in winter. Finally, the relatively low number of employees introduces a predictable variability caused by their working schedule. Unlike the previous two, the latter variability originates in servers rather than arrivals (Abilla, 2012).
The dependence on small staff also introduces unpredictable variability. For instance, the restaurant employs only one host and two bussers, any of whom can become ill, leaving the establishment understaffed and, in the former case, deprived of important position. It is also likely that while the average time of meal preparation is 2.5 minutes per meal, there is the possibility that certain meals take more time to prepare than the others. Thus, a situation where more orders are placed for meals that require more time is another unpredictable variability. Finally, Oliver is said to spend roughly half of his time helping in the kitchen. Since this schedule cannot be controlled precisely, it introduces unpredictable variability of staffing for both kitchen and the front area. Of these variabilities, only the first is server-based while the others are arrival-based (Abilla, 2012). With the exception of winter conditions, all of these variabilities will put additional strain on the restaurant staff, contribute to the buildup of inventory, and, by extension, the queue of the customers waiting to be admitted. Therefore, they will decrease customer satisfaction and may result in depletion of clientele (Palawatta, 2015).
References
Abilla, P. (2012).Impact of variability on process performance and the queue. Web.
Das, A. (2015). An introduction to operations management: The joy of operations. New York, NY: Routledge.
Jerden, L. (2015). Process variability affects inventory levels. Web.
National Restaurant Association. (n.d.). Increase revenue during off-peak hours. Web.
Palawatta, T. M. B. (2015). Waiting times and defining customer satisfaction. Vidyodaya Journal of Management, 1(1), 15-24.
Vrat, P. (2014). Introduction to integrated systems approach to materials management. New Delhi, India: Springer.