Reviewing of Applied Research with an Industry 4.0 Perspective

Abstract

This note provides a perspective on questions to be used in reviewing applied research using analytics and statistical analysis. The paper emphasizes applied research related to Industry 4.0. It expands on a paper providing similar guidelines with a general focus on information quality (Kenett and Shmueli, 2016a). The note is methodological. It covers aspects of study design, algorithmic and inferential methods in frequentism analysis, Bayesian methods in Bayesian analysis, selective inference aspects, severe testing properties and presentation of findings. Information quality is based on good responses to questions about a specific report such as what is the goal of the analysis, is the data resolution adequate for the stated or implicit goal, how is data from different sources integrated, has a generalization claim been made, on what basis?

In reviewing studies done in Industry 4.0 topics, one finds data collected actively or passively, models developed with empirical methods, first principles or as hybrid models. Industry, as opposed to science, is less concerned with reproducibility of results, but it should. The industrial cycle provides short term opportunities to try out new products of new process set ups and, based on the results, determine follow up actions. Deriving misleading conclusions can be however very costly and time consuming.

Keywords: Industry 4.0. Advanced Manufacturing, Data Analytics, Information Quality, Frequentism, Bayesian Analysis, Severe Testing, Data Analysis, Reviewing Guidelines

JEL Classification: C02, L00, L15, L6