Workforce Scheduling: A Guide for the Hospitality Industry

Creating a workforce schedule that ensures appropriate service levels is a key management function. The many complexities of scheduling can be captured through a process that comprises the following four major steps: (1) forecasting demand, (2) translating the demand forecast into employee requirements, (3) scheduling the employees, and (4) controlling the schedule as the day unfolds. Each of those steps involves its own set of tasks. To create a forecast, a manager must determine what needs to be done to meet the expected demand for a given planning period. While a planning period may be of any duration, a 15-minute period is an effective one to use. In particular, the manager must identify the demand drivers and assess whether they are time variant (that is, variable over short periods) or time invariant (relatively stable over short periods). Another part of the forecasting step is determining the tasks to be done in a given period. Some of the tasks (notably, those involving direct customer service) are uncontrollable, because they must be done on the spot. Other tasks, though, such as side work, are controllable because they can be performed at any time (within reason). Having created a fairly reliable estimate of demand, the manager must next translate that demand into the number of workers needed, using an economics-based labor standard. At this point, the manager is ready to construct a schedule that will do the best job of deploying the staff to achieve the desired economic standards without overstaffing and inflating costs. Scheduling is subject to hard constraints, or factors that must be addressed (such as the number of hours an employee can work in a day), and soft constraints, or factors that are desirable in a schedule but not essential (such as employees’ desires for when they work and what tasks they perform). Having created a schedule that will meet the economic standards within the constraints, a manager must finally monitor and fine tune the schedule as the day goes on. Most critically, the manager must decide early on whether the demand estimate for the day is correct—meaning the staffing levels will be sufficient—or whether the actual demand is different from the estimate. If the demand estimate proves incorrect, the manager must further decide whether to take such long-lived actions as calling in workers to take care of a big day (or send them home if business has died off ) or merely take a short-lived action (such as sending employees on break) to account for momentary fluctuations in actual demand. Computer applications can assist managers in most of the workforce-scheduling tasks, but a manager needs to understand the process if only to judge whether the application in question is providing solutions that are reasonably close to the optimal schedule

Dedicated or Combinable? A Simulation to Determine Optimal Restaurant Table Configuration

Using a computer simulation, one can determine what the optimum table arrangement would be for restaurants of various sizes that accept walk-in customers only and take no reservations. At issue is whether the restaurateur can gain more revenue when its tables are dedicated to seating parties of specific sizes (for example, parties of one and two people would be served at 2-tops, while parties of one to four people would be served at 4-tops) or whether the restaurant should use tables that can be combined as needed according to party size. The simulation predicted that combinable tables would prove most useful in a small restaurant with a small average party size. Combining tables in that situation increased revenue per available seat hour by about 2 percent compared to having only dedicated tables. In a large restaurant or any restaurant with a large average party size, the simulation found that dedicated tables were superior to combinable tables. A loss in productivity occurs when some number of tables are held out of service until adjacent tables become available (so that the tables can be combined to seat a large party). The simulation found that the most efficient approach is for a restaurant’s table-size mix to match its customer party size mix, since doing so increases the restaurant’s effective customer-service capacity. However, that customer mix cannot always be known before a restaurant is constructed, and that mix might change during different dayparts. Moreover, the simulation makes certain assumptions that may need further examination, and it does not take into account such aesthetic factors as customers’ reactions to a particular restaurant layout

Restaurant Capacity Effectiveness: Leaving Money on the Tables

In fall 2005 The Center for Hospitality Research (CHR) at Cornell University released the Restaurant Table Mix Optimizer (or RTMO), which I developed. This tool identifies the best mix of tables for a restaurant, based on a variety of inputs. The tool itself is web-based, with the CHR storing users\u27 data anonymously in a database. As of mid March 2007, a total of 1,543 people had registered to use the RTMO. However, not all of those registrants created a valid table-mix scenario. With unusable scenarios eliminated, the final study analyzed the table mixes of 68 restaurants. While eight of the restaurants had the actual optimum table mix for peak operating times, the other 60 restaurants were leaving some money on the table. That is, most restaurants could improve their table mix. On average, the restaurants in this sample could increase their peak revenue by almost 15 percent by implementing a more effective table mix. Almost one-fifth of the restaurants in this sample could improve revenue by more than 20 percent just by having the appropriate mix of right-size tables

Optimizing a Personal Wine Cellar

This report takes what we believe to be the first scientific approach to optimizing a personal wine cellar. We identify the key factors related to optimizing a personal cellar: performance metrics, such as drinking the best possible wine; constraints, such as budget and cellar capacity; and decisions, specifically what to buy and when to consume the purchased wines. We describe the Personal Wine Cellar Optimizer, which is a tool designed to identify the optimum cellar management plan. Using scenarios differing in cellar capacity, cellar life, and wine budget, we examine how the constraints affect the optimal cellar management plan. Using an example of a real cellar, we also illustrate how the recommendations can be used to improve the cellar management. This report is cosponsored by The Vance A. Christian Beverage Management Center, Cornell University School of Hotel Administration

Social Media Use in the Restaurant Industry: A Work in Progress

A survey of 166 restaurant managers reveals a mixed picture in their use of social media and its impact on operations. Although many restaurants are using social media, the study found that many restaurateurs lack well-defined social media goals, both in terms of the purpose of the restaurants’ social media activities and the target of their social media messages. Although the restaurant operators in this convenience sample were generally supportive of the use of social media, well over half were not certain that social media met one or more of three specific goals, namely, increasing customer loyalty, bringing in new customers, and boosting revenues. The respondents generally rely more heavily on non-financial metrics than on actual financial numbers to measure the return on their social media investment, due to the large degree of uncertainty surrounding how to measure the financial returns of social media on operations. On balance, independent restaurants made more use of social media than did chains. The study’s findings suggest that restaurateurs should reevaluate their social media approaches to ensure that they are strategically designed and executed

An Analysis of Bordeaux Wine Ratings, 1970-2005: Implications for the Existing Classification of the Médoc and Graves

The French châteaux producing Bordeaux wines were classified in 1855, creating a taxonomy that continues in force to the present day. An analysis of the ratings of vintages from 1970 to 2005 from three popular rating sources—Robert Parker, Stephen Tanzer, and Wine Spectator—provides a lens into the status of that 1855 Classification, as well as allows a comparison of those three raters. The analysis found considerable internal consistency in the three rating sources and a high degree of correlation between those experts’ ratings. However, the raters differ systematically in the scores they assign. This study is based on 339 combinations of château and vintage for the “classified growths” for which we were able to find ratings from all three sources. We identify the top-rated years and top-rated châteaux, and compare this information to the 1855 Classification. Given our findings we propose an update to the 1855 Classification that incorporates the ratings we examine. To begin with, several châteaux showed remarkable staying power over the intervening 150 years. However, some châteaux had advanced to in the rankings, while others have faded, at least based on this sample of vintages. Notable changes include Château Leoville-Las-Cases (Saint-Julien) moving from second to first growth, replacing Château Mouton-Rothschild (Pauillac), and two châteaux moving from the fifth growth to the second growth: Château Lynch-Bages (Pauillac) and Château Pontet-Canet (Pauillac). Market prices of the 2005 vintage tend to support our findings. For example, as of early May 2008, the price of the Château Leoville-Las-Cases (Saint-Julien) was about three times that of the other nominally second-growth wines. While we believe it is unlikely that the classification will be changed, we believe that our proposed classification update (and our rank-ordering of the châteaux) can help guide wine purchase decisions of consumers and the restaurant industry

Managing a Wine Cellar Using a Spreadsheet 2.0

Using examples from a new Wine Cellar Management Tool, this report describes the many spreadsheet-based analyses in this tool that can assist an individual, restaurant, or bar to manage a wine cellar. If one is disciplined about recording the inflows and outflows to and from the cellar, the spreadsheet tool will provide several cellar analyses. In addition to providing insight into the key questions of what to consume and what to promote, the tool shows such interesting and informative analyses as appellations, vintages, and types of wine. In the tool described in this report, the spreadsheet itself incorporates form-based sets of data entry fields. The Wine Cellar Management Tool, which is available at no charge from The Center for Hospitality Research at Cornell University, does not require actual knowledge of how to construct a spreadsheet. It does require diligent data entry regarding wine purchases and withdrawals

Multi-unit Restaurant-productivity Assessment: A Test of Data-envelopment Analysis

This report describes a three-step process for performing a data envelopment analysis (DEA) to compare restaurants’ efficiency and to examine their best practices. To start with, prospective efficiency factors must be analyzed to ensure that they are relevant. Secondly, to put restaurants on an equal footing the first DEA should consider only managerially uncontrollable (nondiscretionary) factors as inputs. With uncontrollable factors accounted for, managerially controllable factors can then be assessed in terms of their effect on productivity. Best practices can be isolated and assessed in this manner. To illustrate this three-step approach, data from 60 full-service restaurants are analyzed. From a large number of prospective input factors, the analysis considers a short list of uncontrollable inputs namely, hourly server wage, number of restaurant seats, and a coding variable representing whether the restaurant is a stand-alone facility. The output variables for this analysis were daily sales and tip percentage. Just over 20 percent of the restaurants operated with maximum efficiency, with the chain’s average efficiency hitting 82 percent-good, but leaving room for improvement. However, the two discretionary factors that were proposed as differentiating the restaurants’ efficiency-server hours and number of servers-proved not to be significant factors, inviting further analysis of the efficiency effects of additional discretionary factors

Why Customers Shop Around: A Comparison of Hotel Room Rates and Availability across Booking Channels

As hotel chains and their would-be guests confront the plethora of electronic distribution channels, they face a complex picture of rates and availability of hotel rooms. In an attempt to sort out which channels offer consistently low room rates, this study found that chains have made considerable progress in fulfilling a stated goal of offering lowest-cost last-room availability on their own websites, in competition with sites operated by third parties. However, a check of 137 possible booking dates in four different hotel segments also revealed that the third-party providers, notably Travelocity, still frequently offer the lowest rate. The old standby of telephoning the hotel for a booking yields the lowest rate less often than does booking on the website or with a third party. However, telephoning the hotel is the most accurate channel for ascertaining room availability. The chains\u27 websites were reasonably good at ensuring room availability, while third-party providers, notably, Expedia, often showed rooms as unavailable at a given rate, when, in fact, the room was available through other channels. The findings demonstrate the relative consistency of the chains\u27 own websites in offering customers the lowest rate (and thereby gaining the booking), but the fact remains that customers who shop around may find even lower rates. In terms of ensuring that customers repeatedly look to book a particular chain, what makes sense for hoteliers is to maintain consistent rates across all channels, so that price becomes less of a consideration in the booking decision

Accurately Estimating Time-Based Restaurant Revenues Using Revenue per Available Seat-Hour

By calculating revenue per available seat-hour, or RevPASH, restaurant managers can implement revenue management approaches to build their restaurant’s profitability. The key to making this work is the appropriate calculation of RevPASH, in a way that captures accurately the effects of revenue, time, and capacity. Most RevPASH analyses are based only on the time at which a check is opened. Since the time needed for most meals crosses analysis periods (whether those periods are an hour, a half-hour, or less), assigning the entire RevPASH to the analysis period when the check is opened can create inaccurate analyses. Instead, as demonstrated in this report, a better approach would be to calculate RevPASH according to both check open and close times. The resulting revised RevPASH calculation accounts for the demand that customers place on restaurant capacity for the entire duration of their meals (and the revenue therefrom). Using eight months of data from one restaurant’s POS, we find that the traditional approach works fine when RevPASH is calculated for the entire day part (in this case, the entire lunch period). The approach based only on check-opening times become less accurate, however, as the analysis periods are shortened. Even when the analysis periods are two hours long, the inaccuracy of the traditional approach exceeds 40 percent. Understanding the nature of this inaccuracy (and how to overcome it) is essential for managers who use RevPASH to guide their revenue enhancing decisions