Decision Support System for Financial and Accounting Performance Assessment in Manufacturing Industries

Abstract The increasing intricacy of financial and accounting decisions within manufacturing sectors necessitates comprehensive, data-driven assessment tools. This study examines the assessment of financial and accounting performance in manufacturing firms amidst uncertainty, emphasizing the importance of reliable and transparent decision support. A novel decision support system is proposed, integrating advanced multi-criteria decision-making techniques. Picture fuzzy sets are utilized to represent uncertainty and hesitation in expert evaluations by depicting positive, neutral, and negative assessments with different levels of indeterminacy. A dual weighting approach is utilized, employing the logarithmic percentage change-driven objective weighting method to quantify the dispersion and relevance of criterion data, while the ranking comparison method systematically integrates expert preferences. The MARCOS method is employed to assess alternatives and rank firms according to compromise solutions. A case study of manufacturing firms demonstrates the model’s applicability, revealing that profitability, liquidity, and efficiency of costs are the primary financial and accounting measures. The automotive part supplier has been recognized as the best option due to its emphasis on liquidity ratios and efficiency in operations, enabling it to fulfill supply commitments and mitigate risks related to profit margin limitations and quality compliance costs. The sensitivity and comparative analyses illustrate the system’s endurance and adaptability under different circumstances and stakeholder perspectives.