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The widespread application of radio frequency identification (RFID) tags in supply chains is said to cause enormous data volume problems that could render RFID event‐driven supply chains unmanageable. An unbiased and quantitative understanding of the characteristics and extent of these data volume problems is necessary to identify and remove adoption barriers. This paper aims to address the issues.

The paper presents a simulation study based on a real‐world scenario that reveals quantitative characteristics of the data volumes problem in an RFID‐enabled supply chain and discusses its implications.

The results suggest that data volumes will be much lower than currently assumed by practitioners. Thus, this work can be seen as a first basis for eliminating unjustified adoption concerns regarding data volumes complexity. However, it finds that the data volume problems bear still significant challenges for researchers and developers of RFID infrastructures with real‐time decision‐making applications.

The simulation study is based on a single product case study of a retail supply chain in Europe. Since a simulation is always a simplification of the real world, the results need to be interpreted carefully in different contexts. The nature and extent of the problem might vary across different products, industries and geographic regions.

Researchers, end‐users and solution providers might use our paper as a guideline how to approach and quantify the data volume problem in their particular case. Moreover, the result data can be used to benchmark and optimize RFID applications.

This paper is one of the first scholarly works that analyze RFID data volume problems in supply chains with a quantitative methodology.

The purpose of this paper is to contribute and enrich the scientific debate about the phenomenon called the Internet of Things (IoT) from a managerial perspective. Through the lenses of management and innovation literature, the authors investigate the main facts that characterize the IoT and developed a conceptual framework to interpret its evolution. The framework has then been applied to the case of a three-dimensional (3D) printing technology used for additive manufacturing.

A theoretical analysis of the phenomenon of the IoT and its main elements has been performed to construct a conceptual framework in a managerial fashion able to describe the evolutionary impacts of the phenomenon on the manufacturing industry.

Through consequential steps, namely radical, modular, architectural and incremental innovation, and by adopting and integrating the Henderson and Clark model, the authors explain the cornerstones of the evolutionary impact of the IoT on the manufacturing industry. Finally, the authors apply the framework to the case of additive manufacturing and 3D printing.

The framework’s practical value is related to its employability in interpreting and possibly forecasting the evolution of manufacturing industries thanks to the advent of the IoT, allowing managers to capture value arising from technological changes.

This study offers a clear and simple model to interpret the impacts of the IoT. Such a goal has been obtained by systematizing the disconnected research on the topic and arranging such contributions into solid paradigms of the managerial literature.