[14] INTEGRATED FINANCIAL PERFORMANCE OPTIMIZATION FOR SALZER ELECTRONICS LTD. THROUGH COST STRUCTURE AND SALES TREND ANALYSIS

CrossRef DOI:https://doi.org/10.56815/ijmrr.v5si1.2026.113-121

Abstract

This study titled “Integrated Financial Performance Optimization for Salzer Electronics Ltd. through Cost Structure and Sales Trend Analysis” focuses on evaluating and optimizing the cost structure, analyzing sales trends and profit margins, and assessing the financial impact of customer segmentation. The research integrates financial and business analytics to provide data-driven
insights for performance enhancement in a competitive manufacturing environment. Using historical data from Salzer Electronics Ltd., the study applies descriptive and analytical research design to identify cost patterns, sales performance trends, and profitability variations across product categories and customer segments. Key findings reveal that specific product lines with high variable costs affect profitability, while certain customer segments demonstrate stronger financial returns. The research also emphasizes the predictive potential of sales trend analysis in forecasting revenue growth. Recommendations include adopting activity-based costing
(ABC), leveraging predictive analytics in Power BI for future sales forecasting, and restructuring customer segment strategies to optimize revenue. The study demonstrates that a data-integrated financial framework strengthens decision-making, enhances operational efficiency, and ensures This study titled “Integrated Financial Performance Optimization for Salzer Electronics Ltd. through Cost Structure and Sales Trend Analysis” focuses on evaluating and optimizing the cost structure, analyzing sales trends and profit
margins, and assessing the financial impact of customer segmentation. The research integrates financial and business analytics to provide data-driven insights for performance enhancement in a competitive manufacturing environment. Using historical data from Salzer Electronics Ltd., the study applies descriptive and analytical research design to identify cost patterns, sales performance trends, and profitability variations across product categories and customer segments. Key findings reveal that specific product
lines with high variable costs affect profitability, while certain customer segments demonstrate stronger financial returns. The research also emphasizes the predictive potential of sales trend analysis in forecasting revenue growth. Recommendations include adopting activity-based costing (ABC), leveraging predictive analytics in Power BI for future sales forecasting, and restructuring customer segment strategies to optimize revenue. The study demonstrates that a data-integrated financial framework strengthens decision-making, enhances operational efficiency, and ensures sustainable financial performance in the manufacturing sector.