用统计机器学习支持以专家为中心的新产品引入过程

IF 7.4 3区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Business & Information Systems Engineering Pub Date : 2021-07-02 DOI:10.52825/bis.v1i.57

Shima Zahmatkesh, Alessio Bernardo, E. Falzone, Edgardo Di Nicola Carena, Emanuele Della Valle

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引用次数: 0

摘要

销售具有短期或季节性生命周期产品的行业必须定期推出新产品。由于趋势、季节性或其他外部和不可预测现象(例如COVID-19大流行)等许多因素的波动，预测新产品引入(NPI)的需求可能具有挑战性。传统上，NPI是一个以专家为中心的过程。本文提出了一项使用统计机器学习(即梯度增强和XGBoost)自动预测NPI需求的研究。我们展示了如何克服传统数据准备的缺点，这些缺点是手工过程的基础。此外，我们还说明了交叉验证技术在超参数调整和模型验证中的作用。最后，我们提供了经验证据，证明统计机器学习可以比专家更好地预测NPI需求。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Supporting an Expert-centric Process of New Product Introduction With Statistical Machine Learning

Industries that sell products with short-term or seasonal life cycles must regularly introduce new products. Forecasting the demand for New Product Introduction (NPI) can be challenging due to the fluctuations of many factors such as trend, seasonality, or other external and unpredictable phenomena (e.g., COVID-19 pandemic). Traditionally, NPI is an expertcentric process. This paper presents a study on automating the forecast of NPI demands using statistical Machine Learning (namely, Gradient Boosting and XGBoost). We show how to overcome shortcomings of the traditional data preparation that underpins the manual process. Moreover, we illustrate the role of cross-validation techniques for the hyper-parameter tuning and the validation of the models. Finally, we provide empirical evidence that statistical Machine Learning can forecast NPI demand better than experts.

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来源期刊

Business & Information Systems Engineering Computer Science-Information Systems

CiteScore

13.60

自引率

7.60%

发文量

审稿时长

3 months

期刊介绍： Business & Information Systems Engineering (BISE) is a double-blind peer-reviewed journal with a primary focus on the design and utilization of information systems for social welfare. The journal aims to contribute to the understanding and advancement of information systems in ways that benefit societal well-being.