Forecasting at Hard Rock Café

Question 1 (a) 

Three types of information (data) used by Hard Rock Café to forecast 

Hard Rock Café operates hotels, cafes, and bars across more than 60 countries globally, with over 145 restaurants. This huge size of this hotel chain has prompted the need to use real-time data for sales forecasts, as well as forecasting on manager’s bonuses. These forecasts are based on data collected from the sales. There are four major types of information or data that Hard Rock Café uses in forecasting for its sales and other activities. The first type of information is monthly guest counts data (Yüksel, 2007). Using the monthly guest count data is quite essential in forecasting for the future capacities to be expected within the cafes. As such, such patterns like the investigation on whether the number of guests visiting the cafes has been fairly consistent over the months or there have been notable increases or decreases will aid the people in charge of analysis to make forecasts. These kinds of patterns can be quite useful when time series data is used, where a determination on whether this data is stationary or not will identify (Couto, 2012). Additionally, such data will be essential in helping the people concerned with handling the forecasts to identify whether there is a special day that should be considered such as a public holiday. 

With such information, employing the regression models will be a profound choice, since it allows for the inclusion of dummy variables. Additionally, monthly guest count data helps to offer an in-depth view on other aspects such as daily and weekly factors, especially when there were various menus served and their effect on guests. A deeper analysis of this kind of data becomes fundamental for the professionals involved in the process of forecasting since the choice of a forecasting model such as the regression model will be based only on count data (Yüksel, 2007). Count data will also determine whether to use the Poisson distribution or not since the Poisson distribution handles independent counts and not the case of returning guests. 

The second type of information that Hard Rock Café uses in forecasting is retail sales data. It has come to the realization of the management that, failure to use retail sales data in forecasting is likely to plunge the company into stock-outs and overstock inventories. Such occurrences will lead to the company realizing losses (Makridakis & Wheelwright, 1989). It is therefore critical for the company to employ sound forecasting techniques to salvage it against such inconsistencies or poor budgeting and predictions on sales. The Hard Rock Café depends highly on retail sales for the highest margin of its revenue, and therefore effective production of such sales will be imperative for it to realize its financial goals (Couto, 2012). In fact, there is a chain in such forecasts where the company has to start by predicting the retail sales for it to forecast on profits effectively. 

After profit forecasts have been done effectively, the company will be at a profound position to forecast for the retail cashflows and identify any needs to consult its banks for a loan advance or other forms of financial agreements. Daily sales forecasting mostly employs exponential smoothing or regression models. With such statistics, it will be possible for the management to identify products, which are highly demanded, and enable it to make price adjustments that may trigger a higher demand on other products that are provided for within the café. 

Lastly, there is information that is acquired through banquet sales data and concert sales data. Banquets which are large meals that feature several courses accompanied by desserts and both alcoholic and non-alcoholic drinks often are made for given gathering or ceremonies. Such events bring a lump sum revenue collection for Hard Rock Café; hence, they highly sort after. These banquets are requested by several companies who often hold employee training, government ministries, which are holding various conferences amongst other forms of celebrations and gatherings. Such data is often very essential in the forecasting department since it allows Hard Rock Café’s management to have enough funds to finance such occasions (Couto, 2012). The management together with the marketing team often works closely to identify their clients for such meals, planning depending on the event calendar of such clients. 

When the clients have many events, and Hard Rock Café has entered into a deal with them, they can budget for the financial and human resource requirements for such events. This will also be essential in forecasting on the profits of the hotel chain within that period. Trends in other kinds of banquets are also essential in forecasting (Pereira, 2016). Various methods of forecasting are used in forecasting such events such as regression analysis, moving averages and exponential smoothing. 

Another major source of forecasting data that Hard Rock Café uses in the concert sales data. This kind of data is collected from previous concerts and the volume of sales that the cafes made, helping the forecasting department to forecast on resource requirements and revenue forecasts for the financial year. Concerts are often advertised in advance, such as national holidays, musicals, various competitions such as games among others. With a calendar of events and time series data on the volume of sales from such events, the forecasting department is always at a profound position to make sales forecast that does not deviate much from the actual sales. 

Question 1 (b) 

Three Other Areas That Hard Rock Could Use Forecasting Models 

The areas that have been described above in which Hard Rock can use forecasting models are in sales forecasting as well as in human resource forecasting. The company can as well use forecasting models in several another areas such as in menu planning. Menu planning is essential since there are times when an adjustment in the proportions used in the menu has to be adjusted depending on customer behavioral data. Such data from customers may include a tendency to have leftovers from a certain component included in a given dish, which may prompt a reduction in that component’s proportions and an increase in the proportion of the other components. In other cases, use of forecasting models may help the management to decide on which menus to replace depending on demand. With the forecasting models, it is also possible for the management to forecast on the customer behavioral changes (Yüksel, 2007). Adjusting to the trends shown by the customers will be essential since it places the company at a competitive advantage. 

Customer-focused forecasting makes the consumers feel attached and recognized by the cafes, increasing their tendency to visit the cafes more frequently. Another area that forecasting will be an effective tool to use is the area of employees retention rates as well as attrition. Inflation of employee salaries within the hospitality industry can also be an element of focus on the forecasts, as observed through interviews on the expected salaries (Pereira, 2016). This will be essential especially in the financial planning where salary increments are made at the right time to avoid instances of industrial unrest through unprecedented strikes and lack of morale in the employees, reducing their productivity. The use of customer satisfaction surveys will also be essential in forecasting on consumer satisfaction, which will determine the level of training on customer care that the employees require. Blending this data with the global industry customer satisfaction expectation will enhance the analysis of the company’s score (Hanke & Wichern, 2014). 

Question 2 

The Role of POS System in Forecasting at Hard Rock 

The point-of-sale (POS) system is an important tool in the collection of data and forecasting process in Hard Rock Café. The POS system aids in recording every sales entry made in the different cafes, where this information is essential in both the long-range forecasting and intermediate-term forecasting processes (Couto, 2012). The long range forecasting is used in the development of a capacity plan for the entire Hard Rock Café chain. Intermediate-term forecasting is used in getting contracts that are critical in the supply of leather goods and food supplies. Every entrée that is made per sales is used to represent one customer via the POS system. As such, these sales data is then transmitted to the corporate headquarter on a daily real-time basis, adding to the database that is used by experts in forecasting. Data that the POS system can send to the headquarters appears in different t forms such as the guest counts, the banquet sales data, the concert sales data and the retail sales made by the different cafes. Based on this data, the management analyzes the long-term and medium-term forecasts. More so, the café managers are capable of accessing this data from the headquarters which they can as well sue in their respective weekly and monthly forecasts. 

The POS system is also essential since it is well connected to make comparisons with previous year’s sales data on that particular day. This kind of data when it is linked with information from credible sources such as the tourist board becomes vital to enhancing more focused forecasting. As such, the team assigned to the role of making projections and forecasts can project on the occurrence of a major sporting event or a concert, with the projected capacity (Couto, 2012). Therefore, with such forecasts, the financial department will be able to prepare in advance for the resource requirements. It is through the data collected from the POS system that the team at the headquarters can use forecasting methods such as the three years weighted average in forecasting sales. More so, such information is critical for the managers since they can view the data on sales for the past periods, and then work towards hitting or exceeding their targets for the awarding of bonuses. 

Additionally, this data becomes essential in calculating the impact on demand that various items in the menu will have when price change on one or some of the items happens. In the planning of menu changes, the experts in the data warehouse use regression analysis. More so, when handling analysis on the frequencies of sales of certain menu items, the experts are capable of identifying the price elasticity of a demand for a particular item, especial when the price is moved on that item (Couto, 2012). The effect that change in the price of that particular item will have on other items will also depict their elasticity. 

This can be seen when there is an increase in the price of a burger, which will be projected to have a rise in the demand for another item such as the sandwich. Therefore, it is fundamental to assert that the POS system is essential in the collection of data regarding sales in Hard Rock Café and that any sales that will be captured through an entrée are taken as one guest to the café. Eventually, the data that is collected is used by the managers at the headquarters in structuring individual cafes, as well as to schedule their ordering processes. 

Question 3 

Justification of the Use of the Weighting System in Evaluating Managers for Annual Bonuses 

The use of the weighted moving averages in determining the productivity of the managers and using the forecasts to accord them bonuses has both positive and negative effects on their motivation. Apparently, it is fundamental to point out that the POS system is the major source of information that is used in the forecasting method since it offers data for comparing the current sales and previous year’s sales (Couto, 2012). Various factors that should be considered when using the comparisons such as the dynamics in the economy, the changes in input prices, and the influx of competitors within the particular café areas, inflationary pressures, among other social issues that would affect the clientele base. 

Some of these effects are grave enough to affect the sales margins that the managers are expected to achieve. For example, in the case where the café was previously operating alone in given locality then a competitor opens a similar café that offers similar products, it is highly likely that the two cafes will share the market which does not change in its size. I, therefore, find it unfair to judge the performance of the manager based on the previous data, when such considerations have not been captured (Waters, 2011). 

According to Todd Lindsey who is the senior director of finance at the Hard Rock Café corporate headquarters, the company applies the weighing system pattern of 40:40:20 (Couto, 2012). 40% is applied to the most recent year’s sales, the next 40% is applied to the sales of the year before, and 20% is applied to the sales of two years that have past. This weighing pater is the one that helps them in reaching the moving average forecasts. If the actual sales depict that they are beyond or above the forecasting, the managers of the respective cafes are awarded their bonuses. When the actual sales tend to fall below the forecasting, the management may decide to revise the model as well as other variables causing such results, especially when due research has indicated no major issues with the management style employed by the manager at that particular café (Couto, 2012). The other variables that are considered are the performance of other similar businesses within the locality that that particular café is operating. 

The good thing about this weighing to determine manager’s bonuses is that it focuses on a 3-year weighted moving average, which does not have a major effect on the economic dynamics. Any major shift in demand that is beyond the manager’s control within such a short time can be detected and the desired action is taken to adjust the forecasts. Therefore, such a weighing process seems quite effective since it pushes the managers to work hard and smart to maximize their future earning as well as the future earnings of the company. 

Question 4 

Variables (Types of Information/Data) Besides the Previously Mentioned Ones that Can Predict Daily Sales in each Café 

The cafes are situated in different geographical areas and therefore there different events, different economic conditions, different traditions and different weather conditions in these areas. Therefore, every café should collect data on the climatic seasons that affect the area and how such climatic conditions such as summer and winter affect the sales of the café. When the café compiles such information, the managers will be able to determine the products that are in high demand during a given season, prompting the forecast on supplies (Couto, 2012). For example, during summer, there might be a high demand for fizzy drinks and ice-cold drinks, while during winter most people may prefer coffee. Additionally, during summer the condition of the hotels may demand that the fans be operating effectively while the winter conditions may demand that the AC be functioning well. It may indicate that there may be higher power consumption during winter than during summer. 

Different traditions within the areas where these cafes are located may also be a profound source of information for forecasting. With an area where the population is highly westernized, several trends may be observed as of today. Most of the westernized areas are now shunning the contemporary western dishes which have caused major health issues such as obesity, demanding natural food supplies which may be a good predictor of the products that will be in high demand. On the other hand, there are other communities globally that are trying to adopt the western model of lifestyle, indicating that such an influx may render the area good for offering products such as sandwiches and burgers (Couto, 2012). An increase in sales of such products in the future may be a good forecast that will be derived from such data. 

Lastly, there is the need to collect information on the customers that visit the café as well as the community composition regarding ages, sexes and financial capabilities. Such information is essential in determining the kind of products to stick more and the type of marketing to employ within the area. Income of the population will determine how the company will forecast on the proportions it sells such as small pieces of pizza or concentrate on full pizza. 

References 

Couto, J. (2012).  Capítulo 4 Forecasting at Hard Rock Cafe . Retrieved from https://www.youtube.com/watch?v=Y237ed1a_bM 

Hanke, J., & Wichern, D. (2014).  Business forecasting . Harlow: Pearson Educated Limited. 

Makridakis, S., & Wheelwright, S. (1989).  Forecasting methods for management . New York: John Wiley & Sons. 

Pereira, L. (2016). An introduction to helpful forecasting methods for hotel revenue management.  International Journal of Hospitality Management ,  58 , 13-23. http://dx.doi.org/10.1016/j.ijhm.2016.07.003 

Waters, D. (2011).  Quantitative Methods for Business . Harlow, United Kingdom: Pearson Education Limited. 

Yüksel, S. (2007). An integrated forecasting approach to hotel demand.  Mathematical and Computer Modeling ,  46 (7-8), 1063-1070. http://dx.doi.org/10.1016/j.mcm.2007.03.008