Academic journal article Journal of Supply Chain Management

Traceability and Normal Accident Theory: How Does Supply Network Complexity Influence the Traceability of Adverse Events?

Academic journal article Journal of Supply Chain Management

Traceability and Normal Accident Theory: How Does Supply Network Complexity Influence the Traceability of Adverse Events?

Article excerpt

INTRODUCTION

Thinking about complex supply networks rather than simple supply chains has become a preferred approach to the challenges of managing the flow of goods and information. Authors such as Lee (2002), Choi, Dooley and Rungtusanatham (2001) and Madhavan, Gynwali and He (2004) have proposed that in most cases the supply chain metaphor leads managers and researchers to oversimplify the problems of managing flows of goods, services and information. This over-simplification has prevented scholars and managers from fully understanding the causes of defects and adverse events that occur in supply relationships. This is because the causes of some of the defects and adverse events that occur in supply networks are complex because the network itself is complex. For example, defects and adverse events that result from interactions between a changing mix of participants in supply systems that depend on both relational and transactional governance mechanisms can be exceptionally difficult to understand. Shifting the unit of analysis from the simple supply chain to the complex supply network should enable managers and researchers to engage quality and control problems that have thus far proved intractable.

One of these problems has to do with traceability. Traceability, which we define as the ability to identify and verify the components and chronology of events at all stages of a process chain, has long been an important construct in control-oriented theories of quality (Juran 1979; Cheng and Simmons 1994; Ramesh 1998; Linderman, Schroeder, Zaheer and Choo 2003). Complete information about process chains is necessary in order to verify conformance to specifications on one hand, and to trace the causes of failures and adverse events on the other. As processes and relationships become more complex, achieving traceability becomes an elusive goal. For example, efforts to build traceability into design processes, so that features in the end product can be traced back to the requirements they fulfill, have sometimes broken down as requirements and development processes have become more complex (Griffin 1992; Ramesh 1998). As a result traceability is an issue in the management and improvement of processes in both designed and emergent systems. As defects or adverse events occur, traceability is called into play to uncover the causes of variation, which may in turn lead to the development of improved controls or improved processes. What we propose in this study is that as processes (supply processes in particular) become more complex, there are greater barriers to traceability. Overcoming these barriers is critical for quality management in complex supply networks (Roth, Tsay, Pullman and Gray 2008).

The purpose of this study is to make progress toward a more encompassing theory of barriers to traceability, based on recent work advancing the idea that supply network complexity changes the range of possibilities for managing material and information flows. In this paper we develop the idea that traceability depends on the degree of tight coupling in information flows and the transparency of buyer supplier relationships, in addition to supply network complexity. All of these play a role in the ability of users to identify and verify the sources and sequence of events in supply networks (Roth et al. 2008). Our theory development is informed primarily by normal accident theory (Sagan 1993; Perrow 1999; Weick 2005), which is best known for the proposition that accidents are nearly inevitable in complex, tightly coupled production systems. We take the normal accidents approach because, while catastrophic failures may not be inevitable (Roberts and Rousseau 1989), less critical adverse events, defects and near misses (Sagan 1993) are common enough to be the basis of widely applied theories of continuous improvement and methodologies like Six-Sigma (Linderman et al. 2003). Our use of normal accident theory departs from prior work on normal accidents because we are concerned with the problem of how network structure obstructs tracing the causes of adverse outcomes after the fact, rather than with showing how catastrophic system accidents could (or could not) be prevented. …

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