Alessandra Huydts
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Huydts
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Alessandra
Email
alessandra.huydts@unisg.ch
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+41 71 224 7405

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  • Publication
    Type: journal article
    Journal: Controlling : Zeitschrift für erfolgsorientierte Unternehmenssteuerung
    Volume: 34
    Issue: 2
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/109357
  • Publication
    Navigating Uncertainty: Human vs. Algorithmic Forecasting Approaches in Times of Crisis
    ( 2024-03-01)
    Alessandra Huydts
    ;
    Klaus Möller
    Forecasting plays a pivotal role in effective operational management, providing critical insights for decisiForecasting plays a pivotal role in effective operational management, providing critical insights for decision-makers. This study investigates the impact a crisis has on short-term forecasting performance, comparing human and algorithmic approaches. Drawing on data from a Swiss automotive distributor, the research distinguishes between four crisis types, where we focus on external sudden and external smoldering crises. Employing a mixed-method sequential explanatory research design, we find that algorithmic forecasts outperform human forecasts. Furthermore, variations in forecasting accuracy are observed across the different crisis situations. We find that independent of the specific business situation, overall, algorithmic forecasts outperform human forecasts. This study contributes valuable insights into the effectiveness of different forecasting methods in diverse crises, enhancing decision-making knowledge and resilience in times of uncertainty.on-makers. This study investigates the impact a crisis has on short-term forecasting performance, comparing human and algorithmic approaches. Drawing on data from a Swiss automotive distributor, the research distinguishes between four crisis types, where we focus on external sudden and external smoldering crises. Employing a mixed-method sequential explanatory research design, we find that algorithmic forecasts outperform human forecasts. Furthermore, variations in forecasting accuracy are observed across the different crisis situations. We find that independent of the specific business situation, overall, algorithmic forecasts outperform human forecasts. This study contributes valuable insights into the effectiveness of different forecasting methods in diverse crises, enhancing decision-making knowledge and resilience in times of uncertainty.
    Type: conference paper
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/119593
  • Publication
    Navigating Uncertainty: Human vs. Algorithmic Forecasting Approaches in Times of Crisis
    ( 2024-07-08)
    Alessandra Huydts
    ;
    Klaus Möller
    Forecasting plays a pivotal role in effective operational management, providing critical insights for decision-makers. This paper endeavors to discern the comparative performance of human and algorithmic forecasting, especially within crises, to test the resilience and adaptability of these methodologies. Drawing on data from a Swiss automotive distributor, the research distinguishes between four crisis types, where we focus on external sudden and external smoldering crises. Employing a mixed-method research design, we find that independent of the specific crisis situation, overall, algorithmic forecasts outperform human forecasts. However, for both human and algorithmic forecasts, variations in forecasting accuracy are observed, with smoldering and sudden crises exhibiting less accurate forecasts than non-crisis situations. This study contributes valuable insights into the effectiveness of different forecasting methods in diverse crises, enhancing decision-making knowledge and resilience in times of uncertainty. Furthermore, we hope that by showing the superiority of algorithmic forecasts and delineating their applicability, we can relieve decision-makers of the inherent distrust in algorithms.
    Type: working paper
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/120766
  • Publication
    Applying CRISP-DM in the Finance Function: A Cash Forecasting Case Study
    ( 2024-07-08)
    Alessandra Huydts
    ;
    Klaus Möller
    ;
    Michael Weiser
    Business analytics promises faster and more accurate cash forecasting results, but implementation is not trivial as the underlying business drivers need to be modeled. Thus, in this article, we elaborate on the established CRISP-DM process model for the implementation of business analytics use cases and apply it to the management accounting and finance context. We show how CRISP-DM can be applied to build a machine learning-based cash forecasting model based on a real-world case study at a large European car retailer. This will help managers improve and structure their business analytics initiatives.
    Type: working paper
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/120765