 On
October 4th, 2006, Carnus Systems was invited to speak at an HFTP Educational
Session about new artificial intelligence (AI) forecasting technologies
for the hospitality sector, and how these technologies can be used to improve
service quality and reduce labor costs. The benefits of artificial
intelligence within hospitality are particularly evident in F&B departments,
environments that are notoriously challenging to staff due to radical daily
fluctuations in demand.
Where is Artificial Intelligence Used? AI has many current applications. New elevator systems in high-rise buildings, including the one at the New York Times headquarters in New York City, implement artificial intelligence technologies to make passenger transportation more efficient. During peak times, the elevator systems �learn� where to park elevator cars, how to group passengers together, and which floors to stop on to minimize transportation time. In medicine, artificial intelligence has been implemented to detect the presence of prostate cancer with results that exceed those of technique in place prior to its adoption. Additionally, artificial intelligence has successfully been used to determine which patients are optimal candidates for a prostate biopsy, a fairly invasive procedure. In many instances, artificial intelligence has been shown to exceed the reasoning ability of humans. Such was the case in 1997, when IBM-designed AI �Deep Blue,� defeated chess champion Garry Kasparov in a series of matches. A similar outcome was observed with NASA-designed AI technologies that have the ability to distinguish distance stars from galaxies with superhuman accuracies. The Hospitality Challenge The hospitality sector is an area that demands a high level of accuracy in order to staff labor, control costs, and efficiently manage operations. F&B departments are particularly challenging areas to forecast and schedule due to radical daily fluctuations in demand. Despite the need for strong analytical performance, the hospitality sector has generally lagged behind in its adoption of technologies to serve the management. Currently, the use of managerial experience and guesswork, capture ratios, and regression techniques are commonplace in determining upcoming F&B demand. However, all of these techniques are noticeably flawed, especially when compared to forecasting technologies available today, and frequently lead to poor labor efficiency results which directly impacts profitability. Hotels are complex environments that are affected by the interactions between large numbers of variables. Distinguishing all the intricate interactions between weather, city-wide events, market segmentation, and many other variables is nearly impossible, even for the most experienced managers. Ironically, even veterans to F&B departments consider cover forecasting and scheduling to be weekly �crap-shoots,� due to their apparent unpredictability. Capture ratios, another technique common to F&B managers, is the use of a static percentage of occupancy or some other variable to estimate F&B demand. Unfortunately, the assumption that F&B demand is exclusively based on one variable is incorrect, and fails to take into account many other variables that can be tested and shown to be important predictors, such as city-wide events, season, day of week, weather, market segmentation, and many others. Linear regression, a modeling technique that assumes F&B demand is linearly related to other variables, is also flawed. By simply plotting F&B demand in the form of restaurant covers with any other hospitality variable, such as occupancy, it becomes apparent that many variables are not linearly-related to F&B demand. Therefore, linear regression techniques will yield poor results. How Can Artificial Intelligence Help The Hospitality Sector? The Hyatt Regency Riverfront in Jacksonville, FL, one of the largest hotels on the U.S. East coast with 966 rooms, is an early adopter of artificial intelligence. Using an artificial intelligence system designed by Carnus Systems, the hotel is now able to forecast F&B demand in addition to generating on-the-fly staffing schedules that both demonstrate superhuman accuracy. Similar to the Hyatt Regency, the Pan Pacific in San Francisco, CA, was on track to save an estimated 4% of labor cost during the first year of artificial intelligence implementation. The Pan Pacific achieved a daily overall average restaurant accuracy of 90%, with similar accuracy being achieved for room service and banquet forecasting. The improved accuracy allowed the hotel to appropriately staff and reduce costs. What is the Financial Impact of Artificial Intelligence in Industry? With labor accounting for around 50% of operational costs in the hospitality sector, the need for efficient labor controls are essential to control costs and maintain consistent service quality. Despite a lag in the hospitality sector�s adoption of intelligent technologies, other industries have successfully implemented such tools to improve forecasting and reduce costs. Unilever, a food manufacturer, reduced forecasting error by 15% by implementing artificial intelligence, resulting in multi-million dollar savings. Additionally, Covenant, a non-profit healthcare delivery system, reduced costs by $200,000 - $300,000 per year and reduced scheduling time by 15 hours per month after implementing artificial intelligence technologies. Industry-wide implementation of similar technologies in the hospitality sector will likely have a similar impact. It is time for the hotel sector to join so many other industries already making use of user-friendly artificial intelligence technology to accurately forecast demand and reduce labor costs. About the Speakers: Director of Data Analytics: Timothy D�Auria
CEO: Vicky Lee Bradshaw
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