Model Validation of Lean Critical Success Factors and Operational Performance Measures Using Artificial Neural Networks (ANN)

Abstract

This study employs Artificial Neural Networks (ANNs) to develop predictive models by analyzing the relationship between lean critical success factors and operational performance, offering quantitative insights for industry improvement. Using ANN
SPSS (Statistical Software for Social Sciences) version 23, the study applied multilayer perceptron (MLP) feed-forward and backward propagation algorithms, with identity transfer functions for input nodes and sigmoid activation functions for
hidden and output layers. The ANN architecture utilizes nonlinear modeling, where input data is processed through hidden layers via nonlinear transformations, and weights are adjusted during training to minimize prediction errors. The data set was
divided into 70% for training and 30% for testing, with normalization and optimization techniques applied to achieve an optimal local minimum. Findings highlighted top management commitment as a key driver of organizational performance, productivity, and customer satisfaction. These results enhance understanding of ANNs while offering actionable strategies for policymakers and managers in competitive environments. The study’s novelty lies in its quantitative approach, using ANNs to address challenges beyond traditional statistical methods. By leveraging MLP with feed-forward and backward propagation, the research
provides deeper insights into performance assessment, establishing a foundation for future studies. This methodological advancement expands knowledge in the field, demonstrating ANNs' effectiveness in modeling complex organizational
relationships. The outcomes serve as a valuable reference for decision-makers seeking data-driven strategies to optimize industry performance.