Operating a fast-food establishment involves dealing with changing aspects of demand and supply. Therefore, good forecasting and capacity planning practices can be used to ensure that there is adequate management of resources. On the other hand, capacity and forecasting activities are responsible for ensuring that demand for all products “is met in the right amount, at the right time, and with the right quality” (Nahmias, 2011, p. 34). The business operations at McDonald’s are reflective of how capacity and forecasting factors can affect a business. Most business operations at McDonald’s are aimed at guaranteeing fast services for customers. Capacity and forecasting issues for McDonald’s are heightened by the fact that the business covers a wide area of operation and serves millions of customers around the world. This paper addresses capacity and forecasting issues within the operations at McDonald’s restaurants.
One capacity issue that applies to McDonald’s involves demand. The demand for fast food items peaks at certain times of the day. Consequently, it is quite difficult for the restaurant to meet customer demand during peak hours. Several competitors have taken note of this capacity problem and they have established their businesses close to McDonald’s restaurants with the view of capitalizing on this constraint (Rainbird, 2009). McDonald’s restaurants are known for fast service and this advantage attracts a big number of customers. Another capacity issue for McDonald’s involves space especially in regards to customer parking. Most of McDonald’s restaurants are located in busy locations. Therefore, there is a shortage of parking spaces in most of the franchised establishments. This capacity issue has made malls and drives through facilities prime locations in regards to the McDonald’s business model. On the other hand, it is quite expensive to set up a restaurant within these prime locations. The future of McDonald’s restaurants will be restricted by the need to target areas with high foot traffic and drive-in customers at the same time.
McDonald’s restaurants use both qualitative and quantitative forecasting methods. On most occasions, available customer data and sales statistics are used to forecast the input that can be able to satisfy the demand for products and services. On a short-term basis, these forecasting methods ensure that the oscillations in demand within several hours in a day can be matched by adequate and constant supply. In the long term, quantitative forecasting methods can be used to determine the areas with the highest demand for new restaurants (Makridakis, 2008). Forecasts can also be used to indicate the factors that can change the fortunes of non-performing restaurants. Long-term data from several restaurants across the world can also be used when the restaurant is developing new menu items.
To eliminate demand and supply constraints in the operations of McDonald’s, the company’s management needs to ensure that the input of employees matches customer queues. For instance, in places where peak demand is too much, the management can establish temporary counters and hire part-time employees. In regards to the parking space problem, the restaurant should establish a parallel between its foot and drive-in customers. The space constraint is severe where the restaurants seek to target both walk-ins and drive-through customers. Another method of handling the peak demand constraint is by reducing menu items during these periods. I would recommend the establishment of extra/temporary service counters and hiring of part-time employees during peak hours.
The operations at McDonald’s restaurants are subject to changing demand and supply factors. On the other hand, the operations of McDonald’s restaurants are dependent on the management’s ability to build capacity. The company’s strategy aims to eliminate capacity constraints through the utilization of qualitative and quantitative forecasting methods.
References
Makridakis, S., (2008). Forecasting methods and applications. New York: John Wiley & Sons.
Nahmias, S. (2011). Perishable inventory systems. New York: Springer Science & Business Media.
Rainbird, M. (2009). Demand and supply chains: the value catalyst. International Journal of Physical Distribution & Logistics Management, 34(4), 230-250.