Abstract
This paper demonstrates the feasibility of the application of self-organizing mapping neural networks (SOMs) to examine the financial performance of the U.S. lean production firms. Recently, one of the most important evolutions of management practice is lean production. Lean production includes programs such as customer satisfaction, just-in-time (JIT), total quality management (TQM), employee empowerment, and other organizational learning and improvement activities (Kaplan 1992).The ability of lean firms to strengthen its competitive posture, through dramatic improvements in operation, has been documented in numerous business studies (Drucker 1989; Johnson 1992). However, the operating improvements and reduction of resource consumption are not automatically translated into better financial performance (Kaplan and Norton 1992; Young and Selto 1991). Balakrishnan et al. (1996) study confirms “the alleged linkage between operating performance and financial success is actually quite tenuous and uncertain”. Using control group design, we test the ability of SOMs to distinguish the financial performance between members of the target group (lean firm) and control group (non-lean firm).The financial performance we investigate includes return on assets (ROA), current ratio, and the ratio of cost of goods sold to sale, gross profit ratio, asset turnover, and inventory turnover ratio. Results show that the SOMs models successfully identify the financial performance of the lean firms from non-lean firms with quantization error of less than 0.01. The article is organized as follows. Section 2 explains the financial impact of lean production. Section 3 explores the theoretical framework of SOMs models and the application of these models in accounting research. Section 4 describes the sample selection procedure and data. Section 5 analyzes empirical results. Section 6 provides a summary and conclusions.