The Measurement Model of Supply Chain Uncertainty and Risk in the Australian Courier Industry


Author

Michael Wang
School of Business IT and Logistics, RMIT University, Melbourne, Australia

Ferry Jie
School of Business IT and Logistics, RMIT University, Melbourne, Australia

Ahmad Abareshi
School of Business IT and Logistics, RMIT University, Melbourne, Australia


Content

Courier is a booming industry in today’s world. Courier company is one of the most significant 3PLs modules. With the development of globalisation, e-business, shorter product and technology life cycles and higher customer expectations, courier service providers face more supply chain uncertainty and risk than ever before. The supply chain uncertainty and risk has significant impacts on logistics performance in the courier service providers. Uncertainties and risks are interchangeable and inseparable. Moreover, in a real world environment, managers have to face and manage both supply chain uncertainty and risk simultaneously. The purpose of this paper is to present the results for development of an empirically validated measurement of supply chain uncertainty and risk in the Australian courier industry. The empirical data was collected in the Australian courier industry. A measurement model of structural equation modelling is used to identify the underlying factors of supply chain uncertainty and risk in the Australian courier industry. They are (1) companyside uncertainty and risk, (2) customer-side uncertainty and risk, and (3) environment uncertainty and risk. The results indicate high levels of reliability and validity of the measurement. This measurement model contributes to the supply chain uncertainty and risk literature. In addition, it provides an insight into assess supply chain uncertainty and risk in an industry.

Keywords: supply chain uncertainty and risk, risk management, risk assessment, courier, logistics

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Supply chain uncertainty and risk have been widely recognised as an issue in today’ supply chain and logistics (Miller, 1992; Davis, 1993; Prater, 2005; Lee, 2002; Rodrigues et al., 2010). Both supply chain uncertainty and risk influence decision makers in the supply chain resulting in ineffectiveness and inefficiency (Vorst and Beulens, 2002), which ultimately affect the organisational performance. In this paper, supply chain uncertainty and risk is defined as the impacts, consequences, unexpected outcome and/or problems, they were caused by incidents, disaster and/or errors may harm the logistics performance of logistics and transport service providers. In a real world environment, managers have to deal with supply chain uncertainty and risk simultaneously. It is important to consider and manage them together.

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Based on extensive literature review on logitsics and supply chain management, four categories of supply chain uncertainties and risks were proposed (Figure 1). These four categories include: 1) logistics uncertainty and risk, 2) information uncertainty and risk, 3) customer-related uncertainty and risk and 4) environmental uncertainty and risk (Murugesan et al., 2013; Simangunsong et al., 2012; Sanchez-Rodrigues et al., 2010) Measure of supply chain uncertainties and risks is one of the important parts of supply chain uncertainty and risk management (Aven, 2011). Supply chain uncertainty and risk in transport is a part of contingent uncertainty and risk models (Sanchez-Rodrigues et al., 2008). It is ineffective and inefficient to investigate and measure every single source of contingent uncertainties and risks in logistics and transport service providers, due to different companies may have different uncertainties and risks. However it is possible to measure the impacts under the same category of supply chain uncertainties and risks in the separate companies, because the same category of supply chain uncertainties and risks may cause common problems in companies. It is easy for managers to monitor and assess these uncertainties and risks. Therefore, the supply chain uncertainties and risks are measured by their impacts of uncertainties and risks, which obstruct the logistics performance of logistics and transport service providers.

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Over the past decade, some authors started paying attention to supply chain risk and uncertainty in transport and logistics operations (Sanchez-Rodrigues et al., 2010; Rodrigues et al., 2008). Supply chain risk and uncertainty in logistics and transport can broadly be categorised as the potential disturbances to the flow of goods, information and money (Ellegaard, 2008). For example, Sanchez-Rodrigues et al. (2010) state transport-related uncertainty, and the main drivers impacting the sustainability and transport operations are delays, variable demand / poor information, delivery constraints and insufficient supply chain integration. Vorst and Beulens (2002) identify sources of uncertainty for supply chain redesign strategies. Rodrigues et al. (2008) develop a logistics-oriented uncertainty model – the logistics uncertainty pyramid model, which includes five sources of uncertainty related to suppliers, customer, carrier, control system and external environment. Sanchez-Rodrigues et al. (2010) evaluate the causes of uncertainty in logistics operations. Most studies focus on the identification of supply chain uncertainty and risk. Literally, we need a reliable and appropriate measurement for assessing the supply chain uncertainties and risks in logistics and transport industries.

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About Author

Michael Wang is a Casual Academic in the School of Business IT and Logistics, RMIT University, Melbourne, Australia. He has a Bachelor degree of Commerce at University of Auckland, Auckland, New Zealand, a Master degree of logistics and supply chain management from Massey University, Auckland, New Zealand. He is currently doing a PhD research at RMIT University. His main research areas are supply chain optimisation, reverse logistics, supply chain uncertainty and risk management, urban freight transport and logistics.

Ferry Jie is a Senior Lecturer in Supply Chain and Logistics Management and Deputy and Program Director Master of Supply Chain and Logistics Management in the School of Business IT and Logistics, RMIT University, Melbourne, Australia. His main research areas are supply chain management, logistics, operations/production management, quantitative management/operations research/management science, decision making, quality management, lean six sigma, strategic management, project management.

Ahmad Abareshi is a Senior Lecturer in Supply Chain and Logistics Management and Program Director OUA Logistics & SCM in the School of Business IT and Logistics, RMIT University, Melbourne, Australia. He has a range of research interests including the IT/IS capabilities, Green Logistics, Supply chain management, Operations management, Artificial Neural Networks, Quality Management.