Forecasting Applications at Hard Rock Cafe - Case Study Example

Forecasting applications used at Hard Rock Café

Hard Rock Café, just like any other business enterprise has a system of evaluating the activities that take place at the business premise. The core objective of a business is to grow and do better, and there is, therefore, necessity to ensure that it is achieved. All the activities that take place at the premise ought to be monitored so that necessary changes are made to improve the situation. Hard Rock Café already has at least three forecasting applications that it uses to ensure the smooth running of the business. The first forecasting application is the point-of-sale-system (POS). This system includes data on nearly every day partaker of Hard Rock (Grushkin, Selvin, Routhier & Stone, 2001). The system enables the management to analyze data sales, improve the accuracy of pricing and maintain a history of sales that can help the enterprise in adjusting their buying decisions.

The second forecasting application that is used at Hard Rock is a three-year weighted moving average. The weighted moving average technique helps the enterprise to evaluate their managers determine their bonuses. The amount of sales made at the stores that they supervise is used as an indicator of the bonuses that they should receive. The managers who exceed their targets are subject to bonuses, depending on the total amount of sales. Three years are used for the evaluation process. The current and previous year and two other years from before the previous year. The target for the current and previous year is 40%, while that of the two previous years is 20%.

Lastly, Hard Rock uses multiple regression in menu planning. This technique enables managers to compute the effect of demand on related menu items if the price of the item changes. For instance, if the price of chicken increase by two dollars, the manager is able to determine the demand for menu items like beef and pork (Gilligan, 2016). Multiple regression also helps in reorganizing the menu to the sales and the expected impact. There are other areas of Hard Rock that need forecasting. They include:

• The effect that lives performances has in sales. Such events attract spectators who are potential customers.
• Predictions of operation costs like rent, lighting, and heating
• Analysis of cost and break-even for the planning and control of the business
• Customer behavior change predictions
• Booming areas where more Hard Rock restaurants can be set up
• Customer satisfaction at the café compared to customer satisfaction surveys from real data.

The role of POS system in forecasting

POS stands for point-of-sale, and it relates to the location where a transaction occurs. In Hard, Rock POS is majorly used to collect data on each person who walks through any Hard Rock cafés door. The data is then automatically transmitted to the Corporate Headquarters database that is based in Orlando. The data transmitted can be classified by teller and customer. The information gathered enables the management to know the customer preferences. The management is then able to compile statistics on the average customer (Heizer & Render, 2014). Statistics on weather, food/beverage costs and conventions are combined with customer preference to finalize forecasts. A single POS system is used on most of Hard Rock’s information thus it is used as a core in coming up with strategies and basics for forecasting.

Justification on the use of the weighing system to evaluate managers

The three-year weighted moving average is a system of Hard Rock that is used to evaluate managers based on the sales made in a period of three years. The target for the current and previous years is 40%, while that of the year before is 20%. Managers whose sales exceed the target are liable for bonuses, depending on the number of their café sales. Managers whose sales are below the targets are subject to other methods like a revision of the model and evaluation of the global performance of the café (Heizer & Render, 2014). Actual sales are compared with a normal behavior of the time series. This system takes into account recent trends, and old data is less emphasized on as recent trends determine future sales. The weighing system ensures that managers focus more on the business’s long-term as they want to maximize the future earnings. Maximum emphasis on future earnings will ensure a long appreciation of the business.

Moreover, hard work pays. The amount of effort that managers put in ensuring high sales is clearly manifested in the total amount of sales that their café make. Hardworking managers should not be subjected to the same treatment as those who make minimal or no effort. They ought to be appreciated and motivated through bonuses. The dormant managers are also motivated to work hard so as to earn recognition and bonuses. The evaluation process takes into account the records of three years because seasonal factors that do not result from the efforts of the managers could affect the sales. However, such factors may not be present for a long period; hence most of the sales are determined by the efforts of the managers.

Variables for predicting daily sales

Every Hard Rock café could do a customer survey to all their customers and acquire data on their age, sex, and preference on food, clothes, and drinks. The data obtained could help the business to determine the age of the customers who visit their café, their sex and the kind of items that most of the customers prefer or purchase (Douglas, 2009). Most items in the café are obtained from the local market, and others are purchased from different shopping outlets. The other variables that can be used include:

• Weather conditions- the daily weather conditions determine the number of customers that visit the cafe and the kind of food that they will partake. During s cold weather, customers are likely to consume foods and drinks that keep them warm and vice versa.
• Major events that take place in the city like sports events and cultural events. Major events attract an audience, and the audience is potential customers to the café.
• Birth rate- the rate of births is likely to have a long-term effect on the sales of the café.
• Public holidays-people tend to meet with friends and family at public eating places during public holidays so as to have a good time.
• The number of competitive businesses that surround the café-the more the business competitors, the less the customers (Douglas, 2009). However, the amount of customer satisfaction can retain more customers to the café.
• A number of visitors visiting the city-visitors in the city are likely to eat in public eating joints. There is thus a higher probability of an increase in the sales.
• Data on customer satisfaction-this is a very crucial aspect that managers should always monitor. A high customer satisfaction automatically means more customers hence increased business sales.

Apart from using another variable to improve the daily predictions, different forecasting methods like ARMA based models can be employed so as to obtain better forecasting results (Belloc, 1967). The ARMA based models exploit all the relevant information that is contained in the time series.

Use of least square regression to forecast expected guest count (Moscow location)

Y^=a+bx

Y^= computed value of the variable to be predicted (dependent variable)

a=y-axis intercept

b=slope of the regression line

x=the independent variable and is used to predict the value of the dependent variable

Y^

X

Month

Guest count (in thousands)

Advertising (in thousands)

1

21

14

2

24

17

3

27

25

4

32

25

5

29

35

6

37

35

7

43

45

8

43

50

9

54

60

10

66

60

11

?

65

X

Y^

Advertising (in thousands)

Guest count (in thousands)

14

21

17

24

25

27

25

32

35

29

35

37

45

43

50

43

60

54

60

66

Sum= 366

376

Average= 36.6

37.6

Xx

Xy

196

294

289

408

625

675

625

800

1225

1015

1225

1295

2025

1935

2500

2150

3600

3240

3600

3960

Sum=15910

15772

b=0.799554566

a=8.336302895

If advertising is 65:

x=65

Guest count, y=60.30734967

60.307 is, therefore, the expected guest count when advertising is $65.00

Conclusion

A normal business monitors the daily activities of its clients/customers and employees. Employee behavior has a huge impact on the sales of the business. The first and most influential employee is the manager who is followed closely by the supervisors. A manager who is business-oriented will be keen on every single activity that takes place in the business premise and the effect that it has on the sales of the business. The saying about the customer being always right is very fundamental and applicable to business enterprises. Customers are the ones who keep the business going and the higher their number, the more successful the business becomes. It is therefore important to make the customers feel welcome and to provide adequate services to them. Customers are also very influential promoters in the business. They are likely to recommend their colleagues, friends, and relatives to a business enterprise or against it. Referrals are also likely to influence the people that they interact with, and the chain continues.

Customer feedback should be provided for and analyzed and responded to accordingly. Future forecasts are influenced by past results and the response towards those results. Improper employee behavior should be addressed to avoid negative effects on the business. Most importantly is to get everyone involved in making the business better. Employees should be given a chance/platform to make a suggestion on ideas that can improve the business. Effort plus determination will eventually lead to successful business activities.