Sugo Bryanpark

Abstract

Work scheduling in the restaurant industry is an often neglected task due to the abnormal complexities of having to allocate multi-skilled staff over multiple overlapping shifts to meet dynamic demand. Client satisfaction is a significant aspect of the industry, in which the time to produce is almost as important as the quality of the food. Thus it is vital for a restaurant to be able to anticipate and meet demand. It is found that the use of quantitative (formula driven) methods does not ensure higher accuracy in both the forecasting and work-scheduling, but rather when used in combination with qualitative (experience input) is the accuracy majorly increased. The forecast model operates on data gathered from the point of sale system, and is then reworked into 100 di↵erent time intervals over the course of each day in a week. The output is then validated and adjusted before it is imported into the work-schedule function. The work-schedule function is able to output the ideal work schedule per staff member while also illustrating the shortages and surplus staff per time interval. This then allows the manager to ensure that service delivery is met as efficiently as possible.