Purpose: The purpose of this paper is to develop a model for upstream supply chain risk management linking risk identification, risk assessment and risk mitigation to risk performance and validate the model empirically. The effect of a continuous improvement process on identification, assessment, and mitigation is also included in the model.
Design/methodology/approach: A literature review is undertaken to derive the hypotheses and operationalize the included constructs. The paper then tests the path analytical model using partial least squares analyses on survey data from 162 large and mid-sized manufacturing companies located in Germany.
Findings: All items load high on their respective constructs and the data provides robust support to all hypothesized relationships. Superior risk identification supports the subsequent risk assessment and this in turn leads to better risk mitigation. The model explains 46 percent of the variance observed in risk performance.
Research limitations/implications: This study empirically validates the sequential effect of the three risk management steps on risk performance as well as the influence of continuous improvement activities. Limitations of this study can be seen in the use of perceptional data from single informants and the focus on manufacturing firms in a single country.
Practical implications: The detailed operationalization of the constructs sheds further light on the problem of measuring risk management efforts. Clear evidence of the performance effect of risk management provides managers with a business case to invest in such initiatives. Originality/value: This is one of the first large-scale, empirical studies on the process dimensions of upstream supply chain risk management.
|Number of pages||23|
|Journal||International Journal of Physical Distribution and Logistics Management|
|Publication status||Published - 27 Jan 2012|
- Continuous improvement
- Manufacturing industries
- Partial least squares
- Structural equations model
- Supply chain risk management
- Survey data