# Intelligent Procurement Scheduling System for Items Involving Public Procurement
## 3 Results and Discussion  
The proposed system begins by effectively processing input parameters, transforming them into fuzzy sets. These fuzzy sets serve as the basis for subsequent computations that determine the output fuzzy set, aligning with the system's design. The input parameters under consideration include buying frequency, competition, criticality, tender value and variants. Each of these parameters plays a crucial role in shaping the procurement landscape and is graphically depicted in Figure 3, which provides visual representations of the input variables in relation to procurement norms. The variable \"buying frequency\" (Figure 3a) exhibits non-linear behavior with an intriguing quasi-linear relationship observed within the range of 1 to 6 for the tendered items. This suggests that there exists a critical threshold for buying frequency, beyond which the procurement period experiences a significant change. The variable \"competition\" (Figure 3b) illustrates a reduction in the procurement period when a substantial number of vendors participate in the tender process. This reduction is attributed to an intensified rate of competition, which, in turn, diminishes the justification required for item procurement. In Figure 3c, the impact of inter-departmental follow-ups on the procurement process is evident. As the criticality level surpasses 15, a noticeable declining trend in the procurement period is observed. This underscores the importance of effective communication and coordination between departments to expedite procurement activities. Conversely, Figure 3d reveals that as the tender value exceeds 5 million rupees, the procurement period tends to increase. This counterintuitive relationship may be attributed to the complexity and scrutiny associated with high-value tenders, resulting in a longer procurement duration. The fifth input variable, the number of variants tendered, demonstrates a limited impact on the procurement period, with slight fluctuations occurring within the range of 6 to 12, as depicted in Figure 3e. This suggests that the number of variants, within a certain range, does not significantly alter the procurement timeline. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable Type** & **Variable Name** & **Fuzzy Set** & **DataRange** \\ \hline Input & Criticality & LOW & (1–20) \\ Input & Criticality & HIGH & (15–30) \\ Input & Variants & LESS & (1–10) \\ Input & Variants & MULTIPLE & (5–30) \\ Input & Competition & LOW & (1–7) \\ Input & Competition & HIGH & (5–15) \\ Input & Buying frequency & LOW & (1–5) \\ Input & Buying frequency & MEDIUM & (2–5–6-5-11) \\ Input & Buying frequency & HIGH & (7–5–12) \\ Input & Tender value & LOW & (1–40) \\ Input & Tender value & HIGH & (40–100) \\ Output & Finalization period & SHORT & (1–60) \\ Output & Finalization period & NORMAL & (50–75–120) \\ Output & Finalization period & LONG & (100–180) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Data range of the key variables. The findings from the proposed fuzzy logic-based procurement system offer several practical implications for organizations, particularly those involved in complex procurement processes, like enhanced decision-making, identification of critical thresholds, improved coordination and communication, including optimization of procurement processes. Accurate Procurement Duration PredictionsThe system's ability to predict procurement durations with high accuracy, which allows procurement managers to make more informed decisions. Knowing the estimated time from purchase request to contract finalization helps in planning and scheduling procurement activities effectively. Strategic Resource AllocationBy understanding the factors that most significantly influence procurement durations, organizations can allocate resources more strategically. For instance, they can prioritize resources for highcriticality items or those with high tender values, ensuring that these procurements are managed more efficiently.