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This paper provides evidence based quantification of both “actual” disruption of industries as well as a measure of disruption “hype”. The data cover a seven-year period from 2012 to 2018 across 12 industries. The authors’ complemented the research with a survey of 2000 business executives. Whereas there has been some measures of disruption in the past, no research to the authors’ knowledge has been conducted that measure both actual disruption and disruption hype.

The current fascination with disruption hides an awkward truth, we assume it is happening, but do we really know for sure? Disruption is rarely defined and almost never measured. Equally, the influence of the hype around disruption is hard to gauge. The authors do not know to what extent hype is driving management action. This is worrisome as the disruption “noise level” can lead to unhealthy collective thinking and bad business decision-making. Some rigour is required. To craft winning strategies, executives should take a more evidence-based approach for managing disruption.

The authors’ failed to find evidence of any correlation between the hype around an industry disruption and actual disruption within that industry. So the important conclusion for executives is “do not believe the hype”. We found some surprising differences by industry between actual disruption and the hype by industry.

Disruption is one of the most talked about subject in the field of strategy, yet there is little quantification. With this research, the authors’ aim is to advance the fact-based understanding of disruption. Disruption hype is never measured but has a strong influence on executives. The authors have quantified hype using online, search, social media and survey sources. Much more is needed to be able to measure hype more accurately.

The authors’ recommend a set of practical guidelines for executives to support fact-based strategy formulation: analysis of actual disruption, scenario planning and strategic responses.

The “noise” around industry disruption is so high that it is assumed to happen. Much of what is written is quasi-fake news. The authors need to rebalance the debate with fact-based analysis.

To authors’ knowledge, there has never been any fact-based analysis of both actual and hype disruption levels.

One major problem preventing further application and benefits from additive manufacturing (AM) nowadays is that AM build parts always end up with poor geometrical quality. To help improving geometrical quality for AM, this study aims to propose geometrical deviation identification and prediction method for AM, which could be used for identifying the factors, forms and values of geometrical deviation of AM parts.

This paper applied the skin model-based modal decomposition approach to describe the geometrical deviations of AM and decompose them into different defect modes. On that basis, the approach to propose and extend defect modes was developed. Identification and prediction of the geometrical deviations were then carried out with this method. Finally, a case study with cylinders manufactured by fused deposition modeling was introduced. Two coordinate measuring machine (CMM) machines with different measure methods were used to verify the effectiveness of the methods and modes proposed.

The case study results with two different CMM machines are very close, which shows that the method and modes proposed by this paper are very effective. Also, the results indicate that the main geometrical defects are caused by the shrinkage and machine inaccuracy-induced errors which have not been studied enough.

This work could be used for identifying and predicting the forms and values of AM geometrical deviation, which could help realize the improvement of AM part geometrical quality in design phase more purposefully.