I'd recommend that you split up each business day iibunto sections (hour, half-hour etc) and then use different distributions for customer, food, and beverage counts.
From this you can add up all the random variables (note that sum of Poisson is also a Poisson) and get the expectation or confidence intervals.
If you want to resort to more detail then I suggest you look into applied probability and queuing theory to model incoming customers, beverages, food, and even money and turnover if necessary. This is more complicated, but it allows you to make more complex models if need be.
Without knowing any more about your problem, the domain, and its context, I can't really give you any more advice.
One thing though is that if your model is really complex, you might want to look at using R or something like WinBUGS (both are free) where WinBUGS allows you to use really complex models and MCMC techniques to get distributions through simulation for these models (think in terms of making a complex model where you have tonnes of random variables with basically anything you want).