# OffshoringDSS: An Automated Tool of Application Maintenance Offshoring
## 3 Proposed Method  
The current section discusses the research method of developing an automated tool for application maintenance. Furthermore, the four steps as shown in Figure 1, are used to create the suggested tool. The most important factors of application maintenance were derived by conducting two systematic literature reviews, as reported in [12; 13]. Both literature reviews presented a list of 15 influencing factors, as given in Table 1, that will be used to assess the projects for making decisions regarding application maintenance. The identified list of influencing factors was further reviewed and validated by 93 outsourcing experts using an empirical study, as published in [14]. Table 2 shows that among the 15 factors, 10 factors were identified as critical success factors. The critical factors will be used to evaluate the projects for sourcing decisions. Using the AHP approach, an MCDM model was created based on identified critical success factors, as published in [15]. The model gives the ranking of alternatives for the application maintenance based on the provided priorities by the decisions makers to the factors and sourcing models. The proposed sourcing frameworks [14; 15] are employed to assess projects that help the experts to choose one of the suitable alternatives. However, using the frameworks, projects are evaluated on the basis of \"Yes\" and \"No\" or \"Low\", \"Medium\" and \"High\"; for example: is \"legal requirements\" high in the project (Yes or No)? Similarly, the decision is taken whether the impact of the legal requirements is high or medium or low. However, \begin{table}
\begin{tabular}{l l l l} \hline \hline  & \multicolumn{3}{c}{**Influencing Factors**} \\ \hline
1. Employee skills & 5. Infrastructure & 9. Cultural diversity & 13. Domain knowledge \\ \hline
2. Cost & 6. Maturity level & 10. Project management & 14. Service scope \\ \hline
3. Legal requirements & 7. Language barrier & 11. Knowledge transfer & 15. Project duration \\ \hline
4. Poor communication & 8. Frequent requirements changes & 12. Time zone difference & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of influencing factors. Figure 1: Proposed method of the OffshoringDSS. it is not always possible to make appropriate sourcing decisions on the basis of \"Yes\" or \"No\" and \"Low\", \"Medium\" or \"High\", as shown in these presented frameworks. More importantly, sufficient domain expertise is necessary to evaluate the projects based on the presented frameworks. Furthermore, an MCDM model was presented [15] to solve the afore-mentioned limitations of the suggested frameworks. Although the presented MCDM model ranks the alternatives based on the weights assigned to factors by experts but the model lacks automation. It does not automatically calculate the various options according to the input provided. Furthermore, it is based on complex formulas and equations that need to be solved for decision making. The current study intends to improve the process of decision-making concerning application maintenance by addressing the limitations of the previously proposed frameworks, as presented in [14; 15]. Furthermore, the present study also introduces automation into the previously developed MCDM model [15]. Thus, a novel and automated tool for offshoring decisions using the JavaScript language is developed. The proposed automated tool is developed using the JavaScript language by implementing the AHP techniques. It would be used as a decision support system for the sourcing decision making of application maintenance. It automatically ranks the three alternatives, namely the onshore model, nearshore model and offshore model, according to the provided input. It can be adopted by both vendors and clients to make suitable sourcing decisions. Vendors may utilize the developed tool to analyze the projects in order to adopt an effective sourcing strategy that increases the likelihood of future business success.