1,720,961 research outputs found
Decision Support through Global Demand Forecasting : Challenges and Directions in Make-To-Order Manufacturing Organisations
No full textDeveloping AI systems for complex real-world settings requires aligning technical development with domain-specific needs. However, a gap often exists between stakeholders and developers; stakeholders may lack technical expertise to express their needs clearly, whereas developers may lack domain knowledge to identify relevant tasks. This thesis aims to bridge that gap by exploring how decision support systems can address complex real-world tasks through tailored technical solutions and evaluation procedures. The work includes a qualitative multiple case study with make-to-order companies to identify and prioritise AI tasks for system development, along with experimental studies that address gaps in intermittent demand forecasting using a novel timing-aware model and evaluation metric. We also conduct a remote sensing ditch detection study for environmental planning. Both cases highlight the need to align models and evaluation procedures with task-specific challenges such as data sparsity, noise, and class imbalance. Our findings show that make-to-order manufacturers prioritise tasks that improve customer understanding, such as demand forecasting and decision risk estimation, as well as production-related tasks like quality inspection and predictive maintenance. Demand forecasting emerged as the most important task, with challenges linked to heterogeneous data stemming from intermittent patterns and numerous unique items. Our experiments show that decomposing demand into timing and magnitude improves forecasting performance, and that timing-aware metrics are essential for fair evaluation on a global scale. The ditch detection case similarly underscores the value of domain-aligned design and evaluation. The thesis contributes empirical insights on industry priorities and technical advances in forecasting and evaluation, emphasising the importance of grounding AI development in real-world conditions
Decision Support through Global Demand Forecasting : Challenges and Directions in Make-To-Order Manufacturing Organisations
No full textDeveloping AI systems for complex real-world settings requires aligning technical development with domain-specific needs. However, a gap often exists between stakeholders and developers; stakeholders may lack technical expertise to express their needs clearly, whereas developers may lack domain knowledge to identify relevant tasks. This thesis aims to bridge that gap by exploring how decision support systems can address complex real-world tasks through tailored technical solutions and evaluation procedures. The work includes a qualitative multiple case study with make-to-order companies to identify and prioritise AI tasks for system development, along with experimental studies that address gaps in intermittent demand forecasting using a novel timing-aware model and evaluation metric. We also conduct a remote sensing ditch detection study for environmental planning. Both cases highlight the need to align models and evaluation procedures with task-specific challenges such as data sparsity, noise, and class imbalance. Our findings show that make-to-order manufacturers prioritise tasks that improve customer understanding, such as demand forecasting and decision risk estimation, as well as production-related tasks like quality inspection and predictive maintenance. Demand forecasting emerged as the most important task, with challenges linked to heterogeneous data stemming from intermittent patterns and numerous unique items. Our experiments show that decomposing demand into timing and magnitude improves forecasting performance, and that timing-aware metrics are essential for fair evaluation on a global scale. The ditch detection case similarly underscores the value of domain-aligned design and evaluation. The thesis contributes empirical insights on industry priorities and technical advances in forecasting and evaluation, emphasising the importance of grounding AI development in real-world conditions
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Explaining rifle shooting factors through multi-sensor body tracking : Using transformers and attention to mine actionable patterns from skeleton graphs
No full textThere is a lack of data-driven training instructions for sports shooters, as instruction has commonly been based on subjective assessments. Many studies have correlated body posture and balance to shooting performance in rifle shooting tasks, but most of them have focused on single aspects of postural control. This thesis has focused on finding relevant rifle shooting factors by examining the entire body over sequences of time. We performed a data collection with 13 human participants who carried out live rifle shooting scenarios while being recorded with multiple biometric sensors, including several body trackers. An experiment was conducted to identify what aspects of rifle shooting could be predicted and explained using these data. We employed a pre-processing pipeline to produce a novel skeleton sequence representation, and used it to train a transformer model. The predictions from this model could be explained on a per sample basis using the attention mechanism, and visualised in an interactive format for humans to interpret. It was possible to separate the different phases of a shooting scenario from body posture with a high classification accuracy (81%). However, no correlation could be shown between shooting performance and body posture from our data. Future work could focus on novel feature engineering, and on examining alternative machine learning approaches. The dataset and pre-processing pipeline, as well as the techniques for generating explainable predictions presented in this thesis has laid the groundwork for future research in the sports shooting domain
Navigating demand forecasting in make-to-order manufacturing : the role of global models and intermittent time-series
No full textDemand forecasting can optimise production and supply chain practices in manufacturing organisations. However, demand forecasting is not widely adopted among make-to-order (MTO) manufacturers with mass customisation offers. Building effective demand forecasting systems is challenging in such organisations due to the numerous unique manufactured articles and sparse demand patterns. This position paper argues that make-to-order manufacturers should employ demand forecasting to a larger extent, and that the forecasting community should address challenges related to the domain. Key challenges include creating models capable of predicting both demand size and timing of intermittent forecasts, as well as a deeper insight into the effects of global deep learning time-series models. We perform a pilot experiment using demand forecasting in a purchasing decision support system to validate the usefulness of demand forecasting for MTO manufacturing organisations with mass customisation offers. A research roadmap is proposed to address the identified challenges
- …
