Intelligent Decision Support for Logistics and Supply Chain Management

Permanent URI for this collectionhttps://hdl.handle.net/10125/107435

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    Critical Spare Parts Management in Maritime Shipping: Selection and Control
    (2024-01-03) Patzke, Matthis; Saeed, Saber; Pahl, Julia
    Maritime cargo is essential for the worlds economy with thousands of ships transporting it year round over the sea. Through rough seas and weather conditions, maintaining ships operational and safe for the crew and cargo is vital. The maritime industry is challenged by planning and executing maintenance jobs including the allocation of critical spare parts in a cost-sensitive manner. Spare parts are generally defined as interchangeable parts that are kept in inventory and used as a material source of maintenance activities. In fact, the selection of those parts to carry on board and those to allocate where it is most beneficial to assure continuous ship operations is paramount. In this paper, we use an ABC/XYZ analysis for categorizing critical spare parts for vessels and entire fleets. We further aim to evaluate strategies for controlling them. Our analysis is based on data of a representative case in maritime bulk shipping aiming at identifying spare parts to be allocated such that they are available at the right time in the right place to lowest costs. This is a difficult task as bulk shipping is comparable to taxi driving where port calls cannot be foreseen much time in advance.
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    Digital Supply Chain Resilience: Analyzing the Literature Using a Topic Modeling Approach
    (2024-01-03) Kazeroonimonfared, Atiyeh; Vatrapu, Ravi
    The Covid-19 pandemic highlighted digitalization's crucial role in creating resilient supply chains. Academics believe that technologies like Big Data Analytics, the Internet of Things, Blockchain, and Cloud Computing enhance supply chain visibility and transparency. Leveraging these technologies helps firms proactively anticipate disruptions, adapt swiftly, and cultivate resilient and agile supply chains. Although the pandemic accelerated the adoption of digital technologies in supply chain management, comprehensive academic research on digital supply chain resilience is lacking. The primary objective of this study is to thoroughly explore the existing body of literature in this field and analyze it to capture the knowledge that has been neglected in the previous research. Through text mining and topic modeling, we identified the research topics and trends. Finally, we identified digital transformation, viable supply chain, Internet-of-Things, Blockchain, Machine Learning, and Artificial Intelligence as the promising research directions in the field of digital supply chain resilience.
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    Value Driver Trees for KPI-Based Decision Analytics: Process Performance in the Order-to-Delivery Process
    (2024-01-03) Morgret, Linda; Feldmann, Carsten; Matthies, Benjamin
    The order-to-delivery process is one of the most complex logistics processes. Knowing how to successfully satisfy customers through this process is a critical competitive factor for companies. However, there are no suitable methods for value-based decision-making in this process. One goal of this research is to systematically derive a value driver tree based on axiomatic design. Value driver trees are conceptual models that mathematically or logically explain the cause-and-effect relationships between value drivers and their key performance indicators. A systematic literature review and expert interviews in the German manufacturing industry were conducted to provide practitioners with a validated model. In addition, statistical certainty about the relationships between the drivers of the tree is required. A correlation analysis based on real-world case study data confirmed monotonic relationships between selected metrics extending decision analytics research.
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