Degree Name

Doctor of Philosophy


School of Management & Marketing


The core challenge within supply chain management is to simultaneously align business operations, with the supplier’s objectives and customer demand. Under specific demand patterns, strategies have been developed, such as lean and agile manufacturing, to coordinate operational practices. The numerous success stories reported in the popular press (e.g. use of lean manufacturing in Toyota, and the importance of an agile supply chain at Zara) has increased the appetite among peer supply chains to adopt these strategies. However, questions on the implementation and feasibility of these strategies in different contexts have not been answered. These questions are particularly relevant for process industries where operational inflexibilities and equipment availability uncertainties impose quite different constraints to those found in traditional mass production practices.

While the development of lean and agile manufacturing strategies has been widely discussed in ‘discrete industries’ where production is characterised by discrete unit production, the operationalisation of these strategies into process industries ─ where economies of scale dominate due to the capital-intensive equipment required ─ remains largely underdeveloped. This study aims to address this gap by; (i) designing three operational representations of supply chain strategy, (ii) measuring the extent to which they induce desired supply chain behaviour, and (iii) quantifying the robustness of these supply chains against equipment disruptions.

To perform these activities, mass production, lean and agile supply chain strategies are operationalised as a series of management policies. Each policy was developed using accepted definitions found in the literature, coupled with case material on a large Australian steelmaking supply chain. In addition, a simulation model was developed to capture supply chain performance under various management policies, demand conditions, and equipment availability. The results reveal that supply chain performance is adversely affected by equipment disruptions, but the severity of these effects is conditional upon management policies and demand conditions. These results imply that there is no ‘silver bullet’ strategy for all demand conditions and equipment uncertainties. Instead, strategy selection decisions should be based on the degree of alignment with both demand and production equipment uncertainties. Therefore, it is important for practitioners to consider maintenance reliability spending levels against strategic choice decisions.

This study provides several scholarly contributions; (i) an operationalisation of three common supply chain strategies with a process industry context, (ii) an empirically grounded case analysis that provides validity for the operational constraints used to model process industry supply chains, and (iii) empirical evidence that reveals how demand conditions, equipment disruptions and supply chain strategies influence supply chain performance. Insights drawn from these contributions have implications for researchers and practitioners, these include; (i) empirical evidence of a clear relationship between disruption magnitude and supply chain performance, (ii) proof that alternative management policies exhibit different levels of robustness, and (iii) that differences in robustness levels among management policies are statistically significant.