Spare parts used in renewable energy systems differ in terms of lead times, failure impacts, and production loss risks, making criticality assessment an important decision problem. Identifying critical spare parts and evaluating their implications for inventory decisions is essential for maintaining operational continuity in wind and solar energy plants. This study proposes an integrated decision-support framework for spare parts criticality assessment and inventory cost evaluation. Uncertain expert evaluations are modeled using Picture Fuzzy sets, allowing decision makers to express acceptance, rejection, and hesitation simultaneously. The relative importance of evaluation criteria is determined using the SWARA method, and spare parts are evaluated based on criteria such as price, lead time, failure frequency, and production loss cost. The Taguchi loss function is then applied to quantify the economic impact of deviations from target inventory levels. The results indicate that such deviations can lead to considerable economic losses, particularly for highly critical spare parts.