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

2021 ◽  
pp. 187-198
Author(s):  
Shima Zahmatkesh ◽  
Alessio Bernardo ◽  
Emanuele Falzone ◽  
Edgardo Di Nicola Carena ◽  
Emanuele Della Valle
Keyword(s):  
Machine Learning ◽  
New Product ◽  
Parameter Tuning ◽  
Life Cycles ◽  
New Product Introduction ◽  
Gradient Boosting ◽  
Statistical Machine Learning ◽  
Product Introduction ◽  
Better Than

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.

Multi-level ambidexterity in new product introduction at Tata Motors, India

2018 ◽  
Vol 5 (3) ◽  
pp. 211-235 ◽  
Author(s):  
Saurav Snehvrat ◽  
Swarup Dutta
Keyword(s):  
New Product ◽  
New Product Introduction ◽  
Distinct Form ◽  
Product Introduction ◽  
Content Type ◽  
New Product Introductions ◽  
Exploitative Learning ◽  
Multi Level ◽  
Voice Of Customer

Purpose The purpose of this paper is to explore the multi-faceted role of metaroutines in dealing with nested ambidexterity challenges experienced during new product introductions (NPIs) at Tata Motors, an Indian automotive giant. Design/methodology/approach This study utilizes an in-depth multi-level case study highlighting the ambidexterity dynamics across strategic, business unit and functional levels. Findings The authors visualize the NPI system found in the firm, including the interactions between structure, process and governance, as a metaroutine. Based on this visualization, the authors argue that certain ostensive (like voice of customer, commonality) and performative (role of leadership and creative recombination) aspects of the metaroutine aid exploratory and exploitative learning across levels at Tata Motors. Further, the authors argue that the role of embedded NPI metaroutine aspects in promoting multi-level ambidexterity offers a distinct form when compared with other academically established forms of structural, contextual and temporal ambidexterity. Practical implications This study focuses on the aspects of the NPI metaroutine that enable ambidexterity within the studied firm. The authors argue that apart from the structural, temporal and contextual mechanisms, managers also need to focus on the nuances of NPI metaroutines and their potential to promote ambidexterity across levels. Originality/value The authors visualize the interactions between the process, structure and governance mechanisms, related to NPI, as a metaroutine. The authors argue that metaroutine enabled approaches to ambidexterity offer a distinct form when compared with other academically established forms of structural, contextual and temporal ambidexterity. Also, metaroutine enabled ambidexterity explains a possible way through which multi-level ambidexterity can be promoted and managed within organizations.

Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions

2020 ◽  
Author(s):  
Chao Shen ◽  
Ye Hu ◽  
Zhe Wang ◽  
Xujun Zhang ◽  
Haiyang Zhong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Biological Activities ◽  
Gradient Boosting ◽  
Linear Regression Method ◽  
Scoring Functions ◽  
Binding Affinities ◽  
Training Set ◽  
Sequence Similarities ◽  
Better Than

Abstract How to accurately estimate protein–ligand binding affinity remains a key challenge in computer-aided drug design (CADD). In many cases, it has been shown that the binding affinities predicted by classical scoring functions (SFs) cannot correlate well with experimentally measured biological activities. In the past few years, machine learning (ML)-based SFs have gradually emerged as potential alternatives and outperformed classical SFs in a series of studies. In this study, to better recognize the potential of classical SFs, we have conducted a comparative assessment of 25 commonly used SFs. Accordingly, the scoring power was systematically estimated by using the state-of-the-art ML methods that replaced the original multiple linear regression method to refit individual energy terms. The results show that the newly-developed ML-based SFs consistently performed better than classical ones. In particular, gradient boosting decision tree (GBDT) and random forest (RF) achieved the best predictions in most cases. The newly-developed ML-based SFs were also tested on another benchmark modified from PDBbind v2007, and the impacts of structural and sequence similarities were evaluated. The results indicated that the superiority of the ML-based SFs could be fully guaranteed when sufficient similar targets were contained in the training set. Moreover, the effect of the combinations of features from multiple SFs was explored, and the results indicated that combining NNscore2.0 with one to four other classical SFs could yield the best scoring power. However, it was not applicable to derive a generic target-specific SF or SF combination.

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