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SOLVING THE DATA-DRIVEN NEWSVENDOR WITH ATTENTION TO TIME

2023 | European Conference On Information Systems | Citations: 0
Authors: Feddersen, Leif; Cleophas, Catherine

Abstract: Inventory management systems support firms in planning for an uncertain future ... Expand

Abstract: Inventory management systems support firms in planning for an uncertain future by using demand forecasts and optimization models to make restocking decisions. Recent work on the "data-driven" newsvendor found that incorporating machine learning (ML) can improve the success of inventory management by accounting for demand-driving information. However, ML methods are infamously hard to interpret, which may hinder their acceptance. To ameliorate this, we show how to apply an interpretable attention-based architecture, the Temporal Fusion Transformer (TFT), to the data-driven newsvendor problem. Our approach replicates and extends the original TFT time series forecasting method to the inventory management domain. We evaluate our method on two real-world retail datasets, each covering 260 perishable food items, and provide domain-specific benchmarks. The computational study illustrates TFT's interpretable predictions and their comparatively high accuracy. Our work aims to lay the groundwork for further design science research on transparency in human-AI collaboration in this domain. Collapse

Semantic filters: lasso regression taxonomy

Topics: human AI collaboration human computer interaction Python decision making accounting

Methods: time series analysis longitudinal research artificial neural network feature engineering machine learning

Theories: learning theory
In Search of Inspiration: External Mobility and the Emergence of Technology Intrapreneurs
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2022 | Organization Science | Citations: 0
Authors: Ng, Weiyi; Sherman, Eliot L.

Abstract: Recent scholarship has established several ways in which external hiring—versus ... Expand

Abstract: Recent scholarship has established several ways in which external hiring—versus filling a role with a comparable internal candidate—is detrimental to firms. Yet, organizational learning theory suggests that external hires benefit firms: by importing knowledge that is unavailable or obscured to insiders and applying it toward experimentation and risky recombination. Accordingly and consistent with studies of learning by hiring and innovation, we predict that external hires are at greater risk of intrapreneurship than internal hires. We test this prediction via a study of product managers in large technology companies. We use machine learning to operationalize intrapreneurship by comparing product manager job descriptions with the founding statements of venture-backed technology entrepreneurs. Our research design employs coarsened exact matching to balance pretreatment covariates between product managers who arrived at their roles internally versus externally. The results of our analysis indicate that externally hired product managers are substantially more intrapreneurial than observably equivalent internal hires. However, we also find that intrapreneurial product managers have a higher turnover rate, an effect that is primarily driven by external hires. This suggests that hiring for intrapreneurship may be a difficult strategy to sustain. Funding: The authors acknowledge financial support from London Business School, the National University of Singapore [Grant WBS R-313-000-128-133], and a dissertation grant from the Ewing Kauffman Foundation, Kansas City, Missouri. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.1530. Collapse

Semantic filters: lasso regression taxonomy

Topics: intrapreneurship LinkedIn IT career entrepreneurship online labor market

Methods: machine learning lasso regression nonparametric test logrank test experimental group

Theories: organizational learning theory

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SOLVING THE DATA-DRIVEN NEWSVENDOR WITH ATTENTION TO TIME

In Search of Inspiration: External Mobility and the Emergence of Technology Intrapreneurs