For a plan to be effective, it must be based on a prior assessment of work required or supplies needed. If a manager forecasts two hundred (200) covers during a meal period, it is imperative that they know how many server hours will be required to meet this demand. The plan then helps the manager make decisions about how to use the labor resources needed to meet the expected demand. The plan should directly relate key business indicators (covers, in this case) to labor needs in terms of hours. With the completed planning report, the manager is then able to schedule the employees needed.

Planner produces a series of reports that display the work requirements for each department which establishes the parameters to be followed when the department manager completes their weekly schedule. In cases where departmental staffing should be related to guest demand patterns (for example, the Front Desk or Restaurant), the system will produce a Planning Report that clearly indicates the time periods during which staff is needed at the various stations.

What are KBIs?

KBIs are the Key Business Indicators on which all staffing guidelines are built. They are countable factors that assist in determining staffing needs when combined with appropriate work standards. Examples of KBIs are covers for food and beverage units, occupied rooms, and arrivals and departures for rooms. There is no limit to the number of KBIs that might be used on a property, but the user must keep in mind that these factors have to be able to be tracked and recorded if they are going to be used. For instance, you cannot use group arrivals as a KBI if the front desk system does not actually record group arrivals as a separate number on a daily basis.

There are three primary types of KBIs:

• Input
• Calculated
• Statistical

The system treats these different types in a different manner when using them to forecast.

Input KBIs

Input KBIs are those business indicators or volumes that cannot be predicted in the system through the use of past historical data. In other words, someone must tell the system what the volume will be for the upcoming short-term forecast period. An example would be banquets. Someone must tell LMS how many banquet covers there will be for next week since LMS would have no way of knowing how to predict banquets based on past history. Any volume that doesn't have a relationship to past history would be classified as an input KBI.

Calculated KBIs

Calculated KBIs use formulas to determine short-term volume forecasts. The system administrator develops these formulas with input from others at the property. For example, the KBI for room service breakfast covers might be 25% of yesterday's guests in the hotel minus those guests not available for breakfast due to a banquet. Calculated KBIs can also use a past average method for predicting volumes. This is done through the Formula Builder function that is built into

RMS

. Past averages are used primarily when it is apparent that there is a trend in volume but that trend does not seem to be related to any other factors at the property. For instance, the fine dining restaurant behaves more like a free standing restaurant due to a consistent amount of business from outside the property. Therefore, a past average or 4 or 5 weeks of the same day might serve as a good predictor of the future volume.

Statistical KBIs

When you decide to use statistical methods to forecast a KBI in

RMS

, the program uses regression analysis. Regression analyzes the relationship between dependent and independent factors. In turn, the relationship between the two is then used to predict a future dependent factor based on the projected independent factor. For example, the number of guests in a hotel is an independent factor. Assuming that a hotel restaurant gets most of its business from hotel guests, the number of people who eat in the hotel restaurant is dependent on the number of guests in the hotel. (If the restaurant got most of its business from local patrons, regression analysis would not be the appropriate methodology to select). Therefore, regression uses the historical relationship between the dependent and independent factors to take a forecast for a future independent factor and use it to arrive at a forecast for the dependent factor.

Regression uses multiple independent factors. In other words, in forecasting meal covers you could ask the software to analyze hotel guests, banquet guests, and any other independent factor that may show a relation. Thus, the statistical model determines how effective an independent factor is in predicting a dependent factor and, based on the relationship, predicts it again. If the prediction of the independent factor is inaccurate, the subsequent prediction of the dependent factor will be inaccurate. In this case, 

RMS

will return a predicted volume forecast zero (0). Keep in mind that the system needs approximately 20 weeks of past history to begin to accurately determine the relationship between the dependent and independent factors. Occasionally, 

RMS

may return values for many of the forecast days and return zeros for the others. This simply means that there is a relationship on some days of the week and no relationship on others. This does not mean the use of the statistical model is wrong. It might suggest the need for additional past history to return a value.

Defining and Setting Up KBIs

The department manager should consider all those countable factors that can be tracked and recorded that contribute to the process of scheduling labor for the upcoming week. Thought should be given to combinations of factors that might influence forecasts such as guests in-house yesterday minus those in banquets this morning. Once a list has been compiled, the system administrator is able to configure these KBIs in the LMS system. This process enables the KBIs to be forecasted and, in most cases, edited.

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Related topics:

• Configuring KBIs
• KBI Distribution