Forecast structure terms and concepts

This page describes the critical concepts involved in RMS forecast structures:

Key Business Indicators (KBIs)

Key Business Indicators (KBIs) are the foundations upon which all staffing guidelines are built. They are countable factors, which, either alone, or in various combinations, assist in determining staffing needs when combined with appropriate work standards. Examples of KBIs are covers for food and beverage units, occupied rooms or arrivals and departures for rooms, cars for valet, and so on. There is no limit to the number of KBIs that might be used on a property, but keep in mind that these factors must be able to be tracked and recorded if they are going to be used. For example, 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.

When a revenue center or market segment is created, RMS automatically generates KBIs that you configure and later attach to specific jobs. KBIs can also be manually created for special instances. There are four types of KBIs:

Calculated KBIs

When you create a calculated KBI, you use algebraic formulas and existing KBIs to calculate a value. These formulas are created by inserting KBI values, KBIs, day offsets, and operators. Formulas can be entered manually or by using the Formula buttons.

Formulas can be as simple as a sum of two other KBIs, or they can be very complex. 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 example, the fine-dining restaurant behaves more like a freestanding 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.

Input KBIs

Input KBIs define factors that are not forecast by RMS but need to be available for use in calculated or statistical KBIs. Input KBIs are not dependent upon, or related to, any other KBIs.

The most common input KBIs are related to activities within the Banquet department. Other common input KBIs account for hotel guests who are not available to food outlets or rooms that are out of service. Events that affect the available guest market are also considered common input KBIs. For example, you might need to use information about guests at a nearby convention or a local theater to determine your available guest market; or if you have a nearby golf course and your guests are out playing golf (and not available for lunch), you can create an input KBI for the golf guests. Within the appropriate revenue center period KBI, you could indicate whether the counts entered for golf guests should be subtracted from the available guest market for lunch or analyzed as an independent factor to determine whether there is any impact on the lunch period for the available guest market.

With RMS, you can establish all of these variables as input KBIs, which are then available for planning and reporting standards and configuring labor standards.

Note: KBIs are critical when creating Labor Standards. For example, if you create a KBI for a morning coffee break, this KBI is a relevant variable for the Stewarding job that performs this task. When you create the labor standards for this job, you will attach this KBI and any others that are relevant.

Percent of base KBIs

Percent of base KBIs are calculated KBIs based on the relationship between itself and another KBI. Entries for percent of base KBIs are done as percentages.

Statistical KBIs

Statistical KBIs use statistical (regression) formulas to forecast future business volumes by analyzing historical information. Most statistical KBIs are created automatically by the system when you create revenue center periods and market segments. All revenue center periods, market segments, rooms, and arrivals are statistical KBIs. However, you must complete their regression formulas. The only way to manually create a statistical KBI is to select Separate as the Type in the General tab of the KBI Set Config section in Forecast Structure.

Statistical KBIs assess the relationship between dependent (actual data) and independent (forecast data) factors. 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 upon the number of guests in the hotel. (If the restaurant received most of its business from local patrons, regression analysis would not be the appropriate methodology to select, as was mentioned above). 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 also allows you to use multiple independent factors. In other words, when forecasting meal covers, you could have the software analyze hotel guests, banquet guests, and any other independent factor that might show a relationship. 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 of zero (0). Keep in mind that the system needs approximately 20 weeks of past history to accurately determine the relationship between the dependent and independent factors. Occasionally, the system will return values for many of the forecast days and zeros for the others. That means that there is a relationship on some days of the week and not 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.

Frequently, the KBIs for transient rooms in a hotel are examples of statistical KBIs. These rooms can be statistically projected by tracking the relationship of actual rooms (actual data) to booked rooms (forecast data).

When you create a statistical KBI, you select one or more related KBIs, which are used to generate a forecast, and then you choose an appropriate operation.

Statistical KBIs use the following mathematical operators:

  • Independent—Used to indicate that the associated KBI name is an independent value to be used in the regression calculation.

  • Add—Used to add to the current value of the independent value.

  • Subtract—Used to subtract from the current value of the independent value.

For example, if you selected four related KBIs and assigned modifiers as follows:

  • KBIA Independent

  • KBIB Add

  • KBIC Subtract

  • KBID Independent

You would produce the following formula, where A, B, and C are the regression coefficients and KBIX represents the current value of the KBI:

KBIX = A(KBIA + KBIB -- KBIC) + B(KBID) + C

RMS lets you create KBIs for factors whose impact you want to assess without assuming a perfect one-to-one relationship between one guest factor and the available guest market. For example, local guests attending AM meetings might use a restaurant for breakfast, but it is difficult to determine what percentage will do so. For this reason, the local guest market is more difficult to measure than in-house guests. In this case, you can create a statistical KBI to assess the impact of local guests as an independent variable.

Market groups and segments

Market groups are used as a reporting mechanism to help you track and forecast categories of rooms and guests. Market groups are defined by the market segment types you associate with them.

Most properties create market groups to define every part of their room and guest business. Your property probably has an established set of market groups that are used to report and forecast rooms sold.

You should understand your property's structure before completing this task in RMS. Once created, you will associate these market groups with market segment types.

A market segment is a category of rooms and guests. You decide which type(s) of market segment(s) to use and how you want to use them. Within each of the market segment types you can create multiple segments which helps you more accurately track reservations and actual occupied rooms.

For example, you want to track how much of the Group market segment comes from Conventions vs. Motor Coach vs. Junket. To do this you, create three market segments with Group as the type and then name the segments accordingly.

Revenue centers and revenue center periods

A revenue center is any outlet that generates revenue. Revenue centers are defined by periods that are associated with KBIs. Most properties create revenue centers to define the outlets that generate revenue or volume. Typical revenue centers are restaurants, gift shops, lounges, and so on. Revenue centers let you gather information about a specific outlet for the purpose of generating a forecast. When you create a revenue center, you can specify:

  • The days it is open for business.

  • The time of day it is open for business. For food outlets, you can specify the time period for each meal period.

  • Its periods and their associated KBIs and standard sets.

  • The capacity, retention, and utilization for each one of its periods.

Based upon the actual data (historical events) and forecast data (future events) that you gather and enter, the system can generate forecasts for every revenue center, by hours of operation and by period. For example, RMS can forecast restaurant covers for a given day by analyzing what happened on that day last week, last month or last year (actual data). As a general rule, the more data you make available the more accurate RMS will be.

When configuring revenue centers you perform the following tasks: