# An Integrated Model for the Harvest, Storage, and Distribution of Perishable Crops
## 4 Case Study
### Computational Experiments and Managerial Insights--Tactical Level
#### 4.2.1 Single-Supplier Model
When \(\gamma\in\{5,6,7\}\), the benefits obtained can no longer offset the additional costs of the harvesting, therefore the average profit significantly reduces. Note that the routing costs remain constant because all the parameters related to the distribution activities remain unchanged. Next, we analyze how KPIs change, by fixing \(\gamma\) and varying \(\theta\). Here, we have two main contrasting effects: the reward insured by the main customer, and the routing costs. The reward pushes towards a reduction of the daily time limit, but this entails a significant increase in the routing costs because the flexibility in the choice of the routes is reduced (observe that, as expected, the number of trips reduces as \(\theta\) increases). Basically, we can say that when \(\theta=10\) h, the additional revenue from the main customer is enough to cover the increase in routing costs and to guarantee the best profitability. At the tactical level, the use of \(M_{SS}\) referring to \(S_{1}\), suggests the following setting: \(\gamma=3\) and \(\theta=10\). Observe that, when \(\gamma\in\{3,4\}\) the average profit is very similar. However, when the harvesting frequency is three days per week, the average quality of the product shipped is higher, and this aspect could favor customer's satisfaction and loyalty in the mid term. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{2}{c}{**KPI**} & \multicolumn{4}{c}{\(\gamma\)} \\ \hline  & 2 & 3 & 4 & 5 & 6 & 7 \\ Revenue Main [EUR] & 119.336.37 & 123.304.78 & 124.484.04 & 125.568.56 & 125.946.92 & 125.724.96 \\ Reward Main [EUR] & 3282.25 & 3287.55 & 3274.44 & 3277.89 & 3270.17 & 3252.99 \\ Revenue Spot [EUR] & 11,920.63 & 12,287.85 & 13,014.80 & 12,961.07 & 13,696.76 & 14,153.24 \\ Inventory Cost [EUR] & 13,585.96 & 7049.99 & 4152.71 & 2550.25 & 1360.49 & 678.69 \\ Fuel Cost [EUR] & 22,897.35 & 22,894.17 & 22,894.26 & 22,894.17 & 22,894.26 & 22,894.17 \\ Driver Cost [EUR] & 7172.04 & 7170.87 & 7171.14 & 7100.87 & 7171.14 & 7100.87 \\ Production Cost [EUR] & 36,341.34 & 36,341.34 & 36,341.34 & 36,341.34 & 36,341.34 & 36,341.34 \\ Harvesting Cost [EUR] & 9600.00 & 14,400.00 & 19,200.00 & 24,000.00 & 28,800.00 & 33,600.00 \\ Number of trips [unit] & 75.10 & 75.10 & 75.10 & 75.10 & 75.10 & 75.10 \\ Average product age [day] & 21.8 & 1.62 & 1.37 & 1.22 & 1.10 & 1.20 \\ \hline Profit [EUR] & 44,942.56 & 51,023.81 & 51,013.84 & 48,850.89 & 46,019.62 & 42,446.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Average KPIs for the case of \(\theta=9\) h (company \(C_{1}\)). In Tables 12 and 13, the computational results of the experiments conducted on the 10 instances related to \(C_{2}\) are shown. When the service level is fixed, the profit varies by varying \(\gamma\). As the harvesting frequency increases, most of the shipments concern the fresh product, therefore the storage costs decrease drastically. The overall revenue increases because the product delivered is on average \"younger\" and then better paid. In particular, profit is maximized when \(\gamma=3\). If the service level is varied, a reward must be taken into account when \(\delta=0.85\). In this case, the larger amount of product to be guaranteed daily to the main customer implies the loss of some market opportunities offered by spot customers. However, the revenue increase from the main customer is higher than the revenue decrease from spot customers, therefore this solution is more convenient, whatever the harvesting frequency. It should be noted that in any case the routing costs do not vary, as parameters related to distribution remain unchanged. Basically, with reference to the second supplier, our optimization model \(M_{SS}\) suggests the setting \(\gamma=3\) and \(\delta=0.85\).