# Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain
## 6 Conclusions  
The financial data supply chain is of huge importance to the banking sector. It impacts financial institutes\({}^{\prime}\) performance and customer service. Therefore, it is of great necessity to manage the scheduling of the financial data supply chain. The main tool utilized in the financial data supply chain is data batch processing. Batch scheduling and processing are extremely important because they are widely used in service industries to track tasks and data continuously. However, there is a lack of an efficient scheduling solution for data batch scheduling which creates a major issue for the financial sector. The goal of this work is to develop an iterative Dynamic cost scheduling for DBP (DCSDBP) algorithm that includes all aspects of DBP. Different types of costs associated with the batch process were taken into consideration to develop the iterative scheduling algorithm. While the algorithm worked towards minimizing these costs, it aimed at the same time to allocate files based on their weight and priority without violating network predecessors\({}^{\prime}\) relations. Also, the algorithm tries to satisfy the time limit specified in the SLA. The developed algorithm proved its effectiveness in allocating data files to available resources while satisfying priority and predecessors constraints in addition to maintaining the minimum possible cost, keeping in mind the SLA time limit. After coding the developed algorithm using Lingo, a number of networks were used to test it. It was concluded from the results that it is more effective to include all types of costs along with priority, weight, predecessor, and time factors, which led to a more effective allocation and a lower total batch process cost. It can also be seen from the results that renting more processors does not necessarily mean that the batch process will be performed in a shorter time because network logic relations or 'predecessors\({}^{\prime}\) relations\({}^{\prime}\) govern the process\({}^{\prime}\)s total time. The decision of whether or not to \begin{table}
\begin{tabular}{c c c} \hline \hline
**No. of Network Activities** & **Complexity Index I2** & **Run Time (s)** \\ \hline \multirow{3}{*}{15} & 0.2 & 2 \\  & 0.5 & 3 \\  & 0.8 & 3 \\ \hline \multirow{3}{*}{25} & 0.2 & 5 \\  & 0.5 & 2 \\  & 0.8 & 4 \\ \hline \multirow{3}{*}{50} & 0.2 & 11 \\  & 0.5 & 13 \\  & 0.8 & 14 \\ \hline \multirow{3}{*}{100} & 0.2 & 33 \\  & 0.5 & 34 \\ \cline{1-1} \cline{2-3}  & 0.8 & 34 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Run times for variant network sizes with different complexities. rent a new processor and when to do so is very important since it will affect the whole batch process in terms of file allocations to processors and total processing cost and time. Our research has positive implications on the performance and customer service of financial institutes and banks that choose to adopt it. It optimizes the scheduling of the data batch process which reflects on a more efficient and reliable financial data supply chain and through better management. The algorithm was developed under certain assumptions; while we tried to generalize it to cover batch processing, there are some limitations that could be addressed in future researches. For instance, it was assumed that resources costs are the same for leased processors, this could be generalized to assume different costs or even different costs as a future research direction. Also, additional research could be done on cases where renting additional processors has varying costs. Future researchers might also work on developing the additional processors renting mechanism to allow more than one processor to be rented for each time unit in case that serves the total completion time and maintains a low cost.