title: Investigating landside congestion at bulk cargo terminals in forestry supply chains: a role for information systems
Creators: name: Neagoe, M
Publisher: name: University of Tasmania
Year: 2021

description: This research investigates landside congestion at bulk cargo marine terminals in forestry export supply chains and explores the role of information systems in understanding congestion and mitigating its impacts. Through the conduct of three qualitative case studies supported by quantitative modelling, this research contributes to a more holistic understanding of congestion factors, their interactions, and mechanisms for congestion mitigation at bulk cargo terminals in forestry supply chains. Contemporary approaches to understanding and mitigating congestion, both in the research literature and in practice, have primarily focused on the supply chain's individual components rather than on how these components interact. These approaches are often disconnected from the underlying factors that contribute to the emergence of congestion in the system as a whole and focus on congestion symptoms and their resolution at pinch-points along the supply chain. Many congestion mitigation approaches prioritise technical solutions that address narrowly defined technical, economic and regulatory metrics. For example, digital tools in the form of terminal appointment systems (Huynh, Smith and Harder, 2016; Schulte et al., 2017) and automation technologies (Heilig and Vos, 2017) are regularly promoted to manage congestion. While these tools are undoubtedly useful, their promotion is often primarily for terminal efficiency or cost considerations (Chang Guan and Liu, 2009), in isolation from other factors that may be equally important. More broadly, evidence supporting infrastructure, technology and regulatory instruments as impacting positively on congestion, are too frequently only measured through narrowly defined metrics at specific points in a supply chain exhibiting congestion. This raises questions relating to what extent positive evaluations of congestion mitigation are partly a consequence of shifting the congestion problem to other parts of the supply chain. This issue has remained under-explored, as have the mechanisms through which congestion mitigation approaches are chosen and how their effects are experienced by various stakeholders involved in bulk cargo supply chains. Improving understanding of factors contributing to congestion is important, as is a better understanding of the adoption, use and application of information systems as part of approaches to mitigate the effects of congestion. Two of the most influential and highly cited papers in the domain of landside congestion management are empirical investigations (Giuliano &amp; O'Brien, 2007; Morais &amp; Lord, 2006). The issues highlighted by these papers regarding the ineffectiveness of appointment systems and other congestion mitigation methods in practice have been the primary driver for this work. Although these papers are more than a decade old, the extant research literature has, to date, failed to answer the question of how theoretical benefits derived from congestion mitigation be achieved practice. This research provides enhanced insights into factors contributing to congestion and into mechanisms for its mitigation. The research also presents insights into selecting and calibrating mechanisms to enhance their effectiveness for the entire supply chain. Landside congestion is conceptualised as a 'wicked' problem to sensitise this research to the socio-technical factors and their interactions in forest products export supply chains. 'Wicked' problems as described by Rittel and Webber (1973) are characterised by a plurality of perspectives on the problem, stakeholder objectives and potential problem resolutions. Already research has identified novel technologies such as remote sensing, networked embedded sensors operating in the Internet of Things (IoT) (Scholz et al., 2018), blockchain (Jabbour et al., 2020) artificial intelligence (AI), machine learning and deep learning as well as big data and cloud computing (Muller, Jaeger and Hanewinkel, 2019) are perceived as both disruptors and potential solutions to the many challenges faced by modern supply chains including forestry. However, most research focuses on technical aspects of these technologies and to a lesser extent on understanding of the importance and impact of social and behavioural components. Indeed, whilst there are large numbers of research papers advocating for the use of these novel technologies, few, if any, provide detailed insights into the mechanisms for their implementation or metrics to evaluate their impact on congestion. To address this limitation, this research adopted a participatory design approach to capture the multiple perspectives from the diverse set of supply chain stakeholders grappling with the congestion. More specifically, the participatory design approach used focused on facilitating solution development by participants in ways sensitive to the role of digital tools and techniques along the supply chain (Bodker, Kensing and Simonsen, 2004, 2011). The methodology adopted in this research involved the conduct of three participatory design case studies. Each case study focused on an Australian bulk-cargo marine terminal and its users' supply chains. The research strategy consisted of three stages deploying both qualitative and quantitative data collection and analysis techniques. The three stages were: exploration, design workshops and evaluation. This investigation was underpinned by a subjective ontology and an interpretive epistemology. Using multiple case studies was designed to overcome the perceived shortcomings of a single case concerning generalisability, the causal relations identified (Cavaye, 1996), and the possibility that findings result from case idiosyncrasies (Miles and Huberman, 1994). In terms of research design, Stage 1: Exploration aimed to provide a baseline understanding of the participants' perceptions of congestion factors, implications and potential mitigation mechanisms. During Stage 1: Exploration, qualitative data were collected through 13 site visits and 30 semi-structured interviews. These data were coded using a process drawing on grounded theory principles and led to insights that guided the subsequent stages. Quantitative data consisting of more than 250,000 truck arrival records and over 16,500 truck geo-positioning entries were also collected. These data were analysed in the next stage of the research to prepare the workshops. Stage 2: Design Workshops aimed to capture the joint understanding of the participants' perceptions, facilitate the alignment of perspectives, and develop a common vocabulary among participants. Furthermore, the workshops included a design component in which participants could develop congestion mitigation approaches for their supply chains. Four workshops involving 25 participants across the three case studies were conducted. The quantitative data were analysed using simulation modelling and exploratory data analysis to improve understanding of the impact of stochastic components on the terminal's operational performance and evaluate the truck unloading operations' sensitivity at the terminal to changes in these stochastic components or the terminal setup. The quantitative data analysis results were presented during the workshops and directly contributed to a common understanding of options' implications. The qualitative data emerging from this stage were coded using a process drawing on grounded theory principles. Finally, Stage 3: Evaluation aimed to explore the effectiveness of the participatory design process on the participants' understanding of congestion and where possible, to evaluate the impact of developed and implemented solutions on congestion. It could not be assumed at the outset of the research that the supply chain stakeholders would implement the designs emerging from the workshops. However, when this did occur, the second component of Stage 3: Evaluation aimed to capture the impact of the designs on congestion. Stage 3: Evaluation consisted of 11 semi-structured interviews and approx. 10,000 truck arrival records. Qualitative data from the workshops were also used during Stage 3: Evaluation. This research has been approved by the Human Ethics Research Committee (Tasmania) under ref: H0016718. The key findings of this research pertain to a better, more holistic, understanding of congestion factors, mitigation design alternatives and impact evaluation. The research has also highlighted the utility of a participatory design approach in achieving these results and has explored in detail the role information systems can play in better understanding and mitigating congestion at bulk cargo marine terminals for forest products. KF-1. Social, technical and behavioural factors and processes pertaining to the terminal, the marine- and landside supply chain interact to contribute to the appearance and severity of landside congestion. Therefore, congestion can be considered an 'emergent' property of intersecting supply chains. As a result, congestion mitigation is often perceived to fall outside individual organisations' responsibility. The factors and processes identified in this research include: • limited coordination of logistics flows within organisations, and within and between forest products supply chains, • misaligned incentives within organisations, and within and between forest products supply chains, • excessive interdependence of operations within supply chains and technical limitations to flexibility, • infrastructure capacity or performance limitations • behavioural responses associated with operational disruptions and congestion, • misinterpretation of performance expectations • a plurality of perspectives on congestion within and between supply chains; KF-2. Congestion, particularly with increased recurrence, affects the costs, compliance and fatigue risks of truck operators and creates operational uncertainty and the generation of significant frustration for participants across supply chains. Congestion is not only an operational problem. Failure to conceptualise and respond to congestion as a supply chain problem has consequences for the competitiveness and resilience of individual organisations and supply chains. KF-3. The participatory design approach utilised in this research enhanced the researcher's and participants' understanding of congestion and facilitated the xxii emergence of contextually relevant congestion mitigation mechanisms. A key component of the participatory design approach was the interplay between the qualitative and quantitative data and analysis. Qualitative techniques permitted identifying aspects pertaining to congestion that do not easily lend themselves to quantification. Quantitative techniques allowed the validation of or challenging of participants' perceptions and beliefs underlying their conventional responses to congestion. An outcome of the approach was that the participants designed and implemented mechanisms to mitigate congestion and initiated the deployment of digital tools to support coordination efforts and also attempted to apply by themselves the same approach in other similar circumstances. As a result, the congestion factors previously discussed in KF-1 were identified and congestion was defined as "An emergent symptom of logistics systems, characterised by higher-than-expected delays, generally manifesting at marine terminals, caused by a plurality of factors and their interactions and a multitude of stakeholders' perspectives and associated individual response behaviours". Furthermore, the participants designed and implemented mechanisms to mitigate congestion and investigated the deployment of digital tools to support coordination efforts. These were evaluated, and their positive impact on congestion confirmed. KF-4. Conventional measures for congestion (e.g. average truck turnaround times), used in isolation from other indicators, tend to misrepresent congestion. Performance metrics based solely on average measures may obscure the uncertainty and variability of measurements. Stakeholders may have unrealistic expectations as the average is often confused with the maximum. Congestion mitigation measures aimed at addressing average measures for congestion may, in fact, fail to address congestion even if successful at reducing the average measures. KF-5. Information systems can contribute to better understanding and mitigation of congestion. Exploratory data analysis and simulation modelling highlighted the congestion-related bottlenecks and helped challenge the participants' assumptions on congestion factors and frequency of occurrence. Furthermore, the simulation scenario analyses helped direct the participants' attention towards designing for the most promising congestion mitigation approaches. Information sharing supported the supply chain coordination mechanisms designed by participants. Information sharing, both at the operational and tactical levels pertaining to truck and vessel schedules, was instantiated to enhance coordination between the supply chains intersecting at the terminal. In one case study, the participants also commenced the procurement process for a terminal appointment system to facilitate truck arrivals' coordination at the terminal. The initiation of information sharing was partially contingent on addressing information asymmetry between participants and a mutual definition of each party's behavioural responses following information sharing. KF-6. The participants' perceptions of the expected benefits of digital tools to mitigate congestion were not grounded in evidence or a clear understanding of the mechanisms through which information technology would address congestion. As a result, the way in which technology was adopted and utilised by users was rarely closely correlated to congestion mitigation. Indeed, there were numerous examples of where individual organisations had justified investment in IT tools by reference to landside congestion management but had not subsequently analysed the data produced by these systems or utilised it to address congestion-related challenges proactively. These key results have also led to the production of a model for identifying and understanding interactions among factors contributing to congestion and a framework to support holistic responses to congestion mitigation.