Propagation effect based on Markov process

In this section, the maritime disruption propagation effect model based on the Markov process is explored which continues the previous works of Kim et al. (2007), Kim et al.

(2006), Blackhurst, Wu and O‟Grady (2004). This propagation model proposes four steps as a comprehensive process: risk-state definition, disruption state transition matrix, initial mitigation scenario with its results of the expected frequency and probabilities, and finally the risk propagation evaluation. A more detailed description is presented in the following subsections.

Three processes are explored in defining disruptive states: the gathering of occurrence data of disruptions, disruption analysis, and the defining of a set of disruption states for

a fixed mitigation policy are applied (Howard 1960 in Jensen 2010; Dreyfus and Law 1977). To assist understanding and defining the propagation effect evaluation problem, it is useful to categorise propagation effects into the various types that may occur. The various propagation effects characteristics are explained in Table 3-14 below.

Table 3-14. Classification of propagation effect

CLASSIFICATION CHARACTERISTICS DEFINITION OF THE TYPE

TYPE I Internal

Occurring within the boundaries of maritime entities where the disruptive risks propagate

(Scope)

External Occurring outside the maritime boundaries where the disruptive events propagate as a direct

or indirect result

TYPE II Direct Occurring as a direct impact of the previous

(Consequences) disruptive event

Indirect Occurring as an indirect impact of a preceding

disruptive event

TYPE III Delay and deviation

Occurring with delay and deviation effects from its original plans and targets

(Character) Stoppage Occurring with unavailability of dominant services

Stoppage Occurring with a disaster event TYPE IV

Serial Occurring as one of several simultaneous impact links of interruptive chain caused by preceding events

(Process)

Parallel

Occurring as one of several simultaneous impact links of interruptive chain caused by preceding

events

Source: Adapted from Reiners et al. (2009)

Four different parameters are used to unambiguously identify the nature of the propagation effect under this consideration. By using the enumerative approach in categorising the effects into four types, maritime providers may select effective and acceptable mitigating plans appropriately. Undertaking a similar direction to the exploration, the mitigation scenario assessment attempts to propose the application of the Markov approach in analysing the disruption propagation including impacts of maritime operations in a supply chain using wheat transport between Australia and Indonesia.

3.8

Summary

This chapter has comprehensively reviewed the literature on supply chain risk management and has started to configure the concept of disruption, the possible types of disruptions in the maritime leg of a supply chain.Thus, this research proposes a new continuum concept of supply chain disruption based on uncertainties incorporating four different types of maritime disruptions from various direct and indirect factors namely delay, deviation, stoppage of service, and loss of service platform. Next, this chapter reviewed the concept of mitigation approach. Whilst this approach has been well explored in literature, it is found that the networking-based mitigation plans have been researched rarely and seemingly not concentrated on operational dimension of mitigations among entities in a supply chain when managing maritime disruptions. Hence, this research has reconfigured the mitigation strategies by linking together the application in a supply chain and wheat trade.

Overall, four major supply chain mitigation approaches are investigated from a broad mitigation perspective, in that entity interaction in a supply chain is explored and then operational disruption strategies are conceptualised and identified. Next, the chapter has explored stochastic methods applied to manage the individual and networking chain wide factors in the maritime disruptions by the concept of Markov decision process. This analysis approach allows insights into how entities and their disruption management strategies impact on the enacted wheat supply chain performance. In addition, the Markov decision method is also adopted to explore the propagation effect of disruptive events in a supply chain, and how the consequences of disruption affects the performance of a supply chain. Finally, this chapter has demonstrated that supply chain disruptions are dominated by various unwanted driving factors from entities along a supply chain process on the one hand, and the internal factors of maritime operations on the other. In Chapter Four, the description of disruption and mitigation response will be now incorporated into a research and data collection method.

CHAPTER FOUR

4.1

Introduction

This study uses a research approach that combines elements of quantitative and qualitative methods. This mixed-mode is deemed a suitable way to proceed as the research questions require not only a multi-modal approach to investigate the existence of disruption risk but also to assess the effectiveness of previous mitigation scenarios. This mixed-mode approach has been applied by Handfield et al. (as discussed in 2008), Gaonkar and Viswanadham (2007), Lewis et al. (2006), MacDonald (2008), McCormack (2008) and Martin and Hau (2004) to enable the exploration of various risk factors, consequences, and mitigation development in a supply chain. Under this mixed-mode approach in the current study, the perceptions of senior managers are investigated in relation to maritime disruption risks, the stages and parametric values of various disruptive events such as probabilities and occurrence rates in the supply chain, including the mitigation strategies used in managing maritime disruptions.

This chapter outlines the data collection process by implementing an interview approach using a structured questionnaire to survey views on maritime disruption in the Australian-Indonesian wheat supply chain. As indicated earlier, this data collection process is an intermediate step to address the primary research question: Does the maritime leg contribute to disruptions in the wheat supply chain between Australia and

Indonesia? More specifically, this study applies a telephone interview methodology as

a data collection technique to explore the extent of the consequences of maritime disruptive events in a wheat supply chain, and how the effectiveness of mitigation responses can be explained by the quantitative rate of disruption risks and the qualitative risk perception of maritime disruptions when they occur.

Further, the procedures of preparing, pre-testing, and administering the telephone interviews, including the error control process of the interviews, are discussed. Statistical tests such as the t-test, p and mean values relating to the reliability and validity of the data gathering process, and the subsequent generalisability of the findings are also discussed later in this chapter.