Some guys, decided to make some money, by selling lemonade in different public places. Each guy has a fix spot on which she sell the lemonade. In every morning, they go to a lemonade maker warehouse and buy the stock for the entire day, base on their forecasts. At any moment of the day, they can ask for an instant delivery, which will cost more and we don't wana this to happen. Also, if they are buying more than it was needed for that day, they will make smaller profit.
The thing is that every guy knows the places of their business. They begin to see patterns in customers habits, the consume based the day of the week, the weather, the events from the area, etc. All of those variables, the forecasts and real consumptions data are stored in a database.
My wish is to implement a software approach which will help lemonade sellers for a better forecast their lemonade needs. Do you have any idea about which machine learning method can help me in forecasting lemonade sellers needs?
Sounds like a regression problem. Try linear regression and advance to more sophisticated methods (such as SVM regression) if it doesn't work.
There's no telling which method will work best. Experiment with various, try different sets of features and algorithmic settings, and measure, measure, measure.