Managerial Challenge New York City was at a turning point in the early 2000s. The real estate boom of the past decade had pushed up property values, and many family-owned busi- nesses were facing increasing pressure to sell their buildings, potentially close their businesses. One of Manhattan's most iconic Jewish delis wanted to build a 10-year forecast of the restaurant's sales, make recommendations on how to improve the yield of the operations, and give a determination on whether selling the business' real estate to reinvest in the operations would be financially lucrative, and what the growth potential of the restaurant would look like. The restaurant takes customer orders via little paper tickets and handed guests their orders for pickup at numerous service counters in the store. There was very lim- ited customer seating inside, and the bulk of the store's orders were taken to-go. The operation had changed very little since its inception 120 years ago, and despite the introduction of the computer and Internet age, the restaurant still took orders with physical note pads, with no historical demand or revenue-tracking system to speak of. Though bookkeeping records recorded gross sales dollars and check counts on a daily basis, no reports existed on customer demand or spending patterns. Question Whatever information used for the analysis would have to be painstakingly coded and tabulated by hand - there simply would not be enough time to compile all the restaurant's data in time for key deadlines. If you were in charge of this task, what information would you ask for from the restaurant owners? Think about the type of data (for example, sales by item, time of day, day of week), and how far back would you want for these data (for example, one year's worth? Two? Ten? One-hundred?!) What method(s) would you use to forecast revenue for the next 10-year period? What are the strengths and weakness of method(s) that you have chosen? You have to be strategic and selective in the information you request - asking only for the data that would be most helpful in forming a robust demand forecast.