Zade runs a small flower shop. He places orders for the flower bunches and those orders are to be placed in quantities of twenty (orders must be multiples of 20. He can place orders for 20, 40, 60, 80 bunches but not for 25 or 30 bunches). The cost per bunch is $10, if he orders 20 bunches. The cost per bunch is $8, if he orders 40 bunches. The cost per bunch is $6, if he orders 60 bunches, and it costs her $4, if he orders 80 bunches. One bunch of flowers would be sold at $15 each. Any bunch left over at the end of the day can be sold at $3 each the next morning. If Zade runs out of flower bunches (demand is more), he will miss on the goodwill of the customer, and he estimates the loss to be $5 per bunch (from his experience). Zade estimates that the demand for flower bunches would be 10, 20, 50, or 80 with probabilities of 0.1, 0.3, 0.4, and 0.2 respectively. Calculate the payoff table for each possible order (20,40,60 and 80) across all possible demand estimates (10, 20, 50 and 80). How much should Zade stock using Maximax principle? Come up with decision tree and use the demand probabilities to compute total Expected revenue for each possible order (10,20,50,80). Does your decision change when compared to Maximax criterion?