EVALUATION THE PROCESS OF NESPRESSO INVENTORY SYSTEM

after we analyzed Nespresso's machine data in excel and calculated the revenue

for the last profit, inventory cost, and monthly profit for each month in each season, we found

that season 3 has the maximum profit, while season 2 has the least profit. The actual demand

is close to the actual quantity of the machines in season 4, so that led to having less inventory

cost, which will affect the profit more than having a high inventory cost. In addition, the actual

quantity of all the months was higher than the actual demand in season 3, so that led to having

many machines in the inventory. As a result, despite the high revenue during this season, the

profit was low due to the high cost of inventory. Also, the profit for month 3 is higher than for

months 1 and 2 in season 2 because the actual demand for month 3 is slightly closer to the

actual quantity. In this case, months 1 and 2 of this season have the least profit. Ultimately, we

understood that having good forecasting for machine quantity would lead to having a high

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profit. Our suggestion for the company is to re-review the actual quantity for seasons 2 and 3

in order to have less inventory cost

7. Literature Review

For the multi-objective inventory optimization issue, we suggest a simulation-based

solution methodology. The purpose is to discover optimal reorder point and order quantity

settings in order to minimize three objective functions at the same time: total inventory

cost, average inventory level, and frequency of inventory shortage. We create novel

algorithms that take use of statistically valid ranking and selection (R&S) methods as well

as the appealing mechanics of multi-objective optimization approaches. Two simulation

approaches are presented to be used in various circumstances based on preference

information disclosed either during or after the optimization process. The efficiency of the

created algorithms and other existing solution frameworks is evaluated using experimental

findings [2].