Inter-Firm Linkages and Profitability in the Automobile Industry: The Implications for Supply Chain Management
Ramcharran, Harri, Journal of Supply Chain Management
The current studies on supply chain management are limited in their analysis of thc linkages between firms in related industries. This study estimates the degree of linkages between automotive parts suppliers and automobile manufacturers. Significant linkages are demonstrated by the high correlation coefficients of the P/E ratio of auto parts suppliers and auto manufacturers and by the results of regression analysis. Demand uncertainty in the automobile manufacturing industry, resulting from business cycles and unexpected labor disputes, is one of the major risks facing auto parts suppliers. Risk assessment, utilizing information on linkages, is important for demand management and developing profit-maximizing strategies.
Competitive pressures over the past decade have promoted supply chain management (SCM) as a corporate strategy and a timely topic for academic research. This function has expanded tremendously to include activities from other business disciplines, including finance, engineering, purchasing, accounting, and forecasting, with the objective of optimizing the overall activities of firms. Dobler and Burt (1996) noted that the activities comprising SCM constitute the largest component of the cost of goods sold for many large U.S. firms and, thus, could significantly impact profitability and returns. Cooper and Ellram (1993) argued that SCM is applicable to both integrated firms and independent organizations operating in the chain. From a research perspective, Mabert and Venkataramanan (1998) contended that fully integrated firms may have a better visibility of operations and activities in the chain, which may reduce the degree of uncertainty. This is often not the case for nonvertically integrated firms that use m ore aggregated data and focus more on risk analysis.
The relationship between SCM and business performance has been the focus of many studies using data from questionnaires and company reports. Armistead and Mapes (1993) investigated the extent to which greater integration along the supply chain improves quality and operating performance. Monczka, Petersen, Handfield, and Regatz (1998) noted that close buyer/supplier relationships offer strategic, financial, and technical advantages. Vickery, Calantone, and Droge (1999), using correlation analysis, found significant relationships among supply chain flexibility (product, volume, launch, access, and target market) and different measures of performance (return on investment, return on sales, and market share) in the furniture industry. Vonderembse and Tracey (1999) also found significant relationships between supplier selection criteria and manufacturing performance. Narasimhan and Jayaram (1998) argued that the research on SCM tends to focus on the individual functions (purchasing, logistics, and so forth) and f ails to examine the causal linkages that comprise the supply chain. They used a structural equation model to measure the strength of the linkages between identified factors and the performance outcome of 127 U.S. and Mexican firms.
The identification of volatility, disruption, and opportunities in the firm's supply and demand environments is an important aspect of SCM. Vickery, Calantone, and Droge (1999) and Carter and Carter (1998) referred to this as environmental uncertainty. As stressed by Narasimhan and Jayaram (1998), a significant contribution to the study of SCM would be to explicitly account for the strength of linkages between supplying firms and demanding firms. The magnitude of this dependency could serve as the measure of the potential demand for inputs and thus a determinant of the volatility of suppliers' profits. Swaninathan, Smith, and Sadeh (1998) emphasized the importance of demand forecasting in the supply chain dynamics.
This article modified the methodology of earlier studies (Vickery, Calantone, and Droge 1999; Vonderembse and Tracey 1999) to meet two objectives. …