Normative Lean Performance Score Model Based on Financial and Accounting Metrics

This paper introduces the Normative Lean Performance Score (NLPS) model designed to evaluate lean operational performance using publicly available financial and accounting metrics, without requiring advanced analytics for practical implementation. The study applies an empirical research design based on a longitudinal dataset, where firms are first classified into lean-oriented groups, followed by logistic regression to identify significant indicators and Random Forest models to estimate their relative importance. The resulting index provides an objective, interpretable, and easily implementable performance measure suitable for cross-firm benchmarking and managerial decision support. Empirical testing using automotive manufacturers demonstrates strong alignment with lean classification and efficiency outcomes, providing evidence for the model’s relevance as an accounting-based benchmarking tool. In addition to its practical applicability, the framework contributes to lean performance measurement by translating machine learning insights into a reproducible index that can be applied in data-constrained environments. This approach ensures that the resulting index remains both empirically grounded and practically interpretable, while avoiding reliance on arbitrary or expert-assigned weighting schemes and qualitative assessment-based approaches. The model therefore offers a scalable and transparent alternative for practitioners, analysts, and researchers seeking robust lean performance evaluation when advanced modelling resources are unavailable. The study contributes a transparent, accounting-based normative index that reframes lean performance as a financial configuration rather than an operational maturity construct. The empirical analysis uses quarterly financial data from 17 publicly listed automotive manufacturers over the period 1994Q1–2024Q4.