## 4 Discussion  
SCRM requires dealing with uncertainty, interrelations, and subjectivity inherent in the risk assessment process. Although several quantitative/qualitative tools and techniques have already been developed for SCRM, there is a limited focus on introducing holistic frameworks that not only integrate all stages of the RM process but also consider prior analysis of SC processes and strategic management focus. Existing frameworks generally focus on optimizing a single objective, generally a performance measure, without capturing the correlation effects of common risk triggers. Existing frameworks also do not consider the trade-off between multiple and interdependent strategic objectives at the same time. The proposed approach in this study deals with capturing this complexity by integrating all of features in a holistic framework that combines qualitative and quantitative techniques for effective SCRM. A mathematical model with a fuzzy inference system was developed to aggregate risk values taking into account the correlations of agents and risk events associated with the achievement of objectives. The intra-and inter-dependencies among the categories were considered in the form of a risk network that provides a tool to visualize the position of a risk within the network and its influence on the key performance measures. The risk network also helps in identifying potential mitigation strategies and control measures which allows for a proactive management of the strategy (see Table 13). The fuzzy risk assessment method proposed in this study estimates the uncertainty inherent in the input values and allows users to adequately describe the uncertainty. The use of fuzzy logic allows for the treatment of vague and ambiguity data from different domains and scales. In addition, this fuzzy method can easily be transformed into traditional probabilistic methods when sufficient information and knowledge is available. \begin{table}
\begin{tabular}{c c c c c} \hline
**Objective** & \(A_{\#}\) & **Impact Score** & **Events** & **Checkpoints** \\ \hline \multirow{4}{*}{1} & \(A_{11}\) & & P\({}_{1}\), P\({}_{3}\), P\({}_{4}\), P\({}_{10}\), S\({}_{1}\), S\({}_{5}\) & 1, 3, 4, 9, 13 \\  & \(A_{21}\) & B & P\({}_{1}\), P\({}_{3}\), P\({}_{4}\), P\({}_{6}\), P\({}_{8}\), P\({}_{10}\), D\({}_{3}\)-D\({}_{6}\), D\({}_{9}\), R\({}_{6}\) & 1, 3, 4, 7–9, 28–30, 34, 44 \\  & \(A_{31}\) & & S\({}_{3}\), D\({}_{11}\), D\({}_{16}\) & 12, 33, 40 \\  & \(A_{41}\) & & P\({}_{1}\), P\({}_{3}\), P\({}_{7}\)–P\({}_{9}\) & 1, 4, 6, 7, 9 \\ \hline \multirow{4}{*}{2} & \(A_{52}\) & & P\({}_{7}\), S\({}_{10}\) & 6, 21 \\  & \(A_{62}\) & C1 & R\({}_{2}\) & 42 \\  & \(A_{72}\) & & S\({}_{6}\), S\({}_{6}\), S\({}_{6}\), R\({}_{1}\)–R\({}_{7}\) & 18, 22, 23, 41–45 \\ \hline \multirow{4}{*}{3} & \(A_{23}\) & & P\({}_{4}\), S\({}_{1}\), S\({}_{5}\), S\({}_{5}\)–S\({}_{11}\) & 3, 13, 20, 21, 23, 24 \\  & \(A_{73}\) & C2 & P\({}_{6}\), P\({}_{8}\), S\({}_{5}\), S\({}_{5}\)–S\({}_{11}\), R\({}_{5}\) & 8, 7, 13, 20, 21, 23, 24, 44 \\  & \(A_{83}\) & & D\({}_{3}\) & 28 \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{\(A_{24}\)} & & P\({}_{5}\), P\({}_{6}\), P\({}_{8}\), P\({}_{9}\), P\({}_{11}\), S\({}_{2}\), S\({}_{6}\)–S\({}_{9}\), S\({}_{12}\), D\({}_{1}\), &The results of the study in the selected telecommunications industry show the identification and analysis of 18 sources of risk, which are translated into 26 risk agents and 54 risks events associated to eight objectives. The analysis and assessment of risk scores made it possible to identify, for the strategic period evaluated, that objectives 8 and 1 present higher risks. The risk sources that cause higher levels of risk and; therefore, require greater focus were agents 2 (aggregate objectives 1, 3, 4 and 5) and agent 17 (objective 8), followed by agent 15 (objective 8). The associated checkpoints facilitate proactive analysis in the achievement of activities.