4 results
Now showing1 - 4 of 4
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Supply Chain Analytics: Investigating Literature-Practice Perspectives and Research OpportunitiesSupported by ever-increasing amounts of data and maturing technologies, big data analytics offers viable, promising improvements for various fields and applications. Supply chain analytics (SCA), the application of big data analytics to supply chain management, can enhance and innovate supply chain processes and services in most companies. To reap such benefits, supply chain managers must overcome various obstacles, including the identification of appropriate methods, data, and application cases. The degree to which the potential value of SCA actually is being harnessed by practitioners remains uncertain. The study aims to synthesize scientific and practical perspectives regarding the SCA dimensions: goal and motivation, method, data, and application area. For this purpose the research applies a multi-vocal literature review (MLR) and a survey approach. The study reviews over 1481 publications and consults 278 respondents to reveal six different goals and seven motivations for SCA. Moreover, descriptive, predictive, and prescriptive analytics and many different data types enabling SCA within different application areas are examined. The cross-analysis between scientific and practical perspectives identifies several gaps, such as lack of specific data usage, low practical SCA maturity, or undersaturated research areas that show future paths of academic research.Publicationtype: Journal Article63 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Supply chain analytics : application areas and industrial adoption(Bundesvereinigung Logistik (BVL) e.V., 2021); ; ; ; Publicationtype: Conference Paper70 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Automatic identification system (AIS) data based ship-supply forecastingPurpose: The bulk cargo shipping industry is characterized by high cost pressure. Chartering vessels at low prices is important to increase the margin of transporting cargo. This paper proposes a three-step, AI-based methodology to support this by forecasting the number of available ships in a region at a certain time. Methodology: Resulting from discussions with experts, this work proposes a threestep process to forecast ship numbers. It implements, compares and evaluates different AI approaches for each step based on sample AIS data: Markov decision process, extreme gradient boosting, artificial neural network and support vector machine. Findings: Forecasting ship numbers is done in three steps: Predicting the (1) next unknown destination, (2) estimated time of arrival and (3) anchor time for each ship. The proposed prediction approach utilizes Markov decision processes for step (1) and extreme gradient boosting for step (2) and (3). Originality: The paper proposes a novel method to forecast the number of ships in a certain region. It predicts the anchor time of each ship with an MAE of 5 days and therefore gives a good estimation, i.e. the results of this method can support ship operators in their decision-making.Publicationtype: Conference Paper1143 487 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A Typology of operations research methods in humanitarian logisticsPublicationtype: Conference Paper62