Augmented Reality in Heavy (Maritime) Industry

12

October

2017

4.91/5 (11)

CapGemini (2012) finds that measuring firms along their digital intensity and transformation management intensity yields four categories of firms and industries. These are “digital beginners”, who score low on both digital intensity and transformation management intensity, “digital conservatives” who score high on the latter but low on digital intensity, “digital fashionistas” who score high on digital intensity but low on transformation intensity, and “digirati”, who score high on both. The crux of the report is that digirati perform better, and how firms can become digirati. Unsurprisingly, high-tech and retail industries generally fall into that category, while for example manufacturing is a beginner.

 

In general, heavy industry firms have been slow to adopt digitization. They operate in asset-heavy industries, and either believe that they are safe from disruption, that digitization will not have a large impact (if any) on their industry, and are even culturally reluctant to transform to a new way of way. Of course, they are wrong. Frontrunners like GE have already demonstrated how integrating various digital technologies in industry, characterized as “Industry 4.0”, can add tremendous value for customers.

 

One other, smaller company that is now looking to take their first steps towards using cutting-edge technologies to beat the competition is Dutch maritime service firm AEGIR-Marine. AEGIR’s core business if performing various after-market maintenance on ship propulsion systems and stern tube seals. It doesn’t sound like the sexiest industry, but the company is now experimenting with one of the flashiest new digital technologies: Augmented Reality (AR).

 

In particular, AEGIR is looking to use AR in two novel ways. The first is for training purposes. As this video demonstrates, AR makes it possible to look inside ship propulsion systems, while they are running. This gives insight into the functionality of such a system that was previously hidden. Secondly, AR goggles can assist service engineers during every step of the way of a repair job. Further applications await, and will surely add yet more value.

 

AEGIR’s case demonstrates that even the most unlikely candidates for digitization can often find new ways to augment their business, even if the gains initially seem small. After all, in today’s dynamic business environment, every little bit of value counts.

 

Capgemini Consulting. (2012). The Digital Advantage: How Digital Leaders Outperform their Peers in Every Industry. MIT Sloan Management Review, 1–24. Retrieved from http://www.capgemini.com/resource-file-access/resource/pdf/The_Digital_Advantage__How_Digital_Leaders_Outperform_their_Peers_in_Every_Industry.pdf

Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business and Information Systems Engineering, 6(4), 239–242. http://doi.org/10.1007/s12599-014-0334-4

Maritime Technology (2017).  AEGIR Marine opened its new Propulsion Workshop, R&D Center and AEGIR Academy. https://maritimetechnology.nl/en/aegir-marine-opened-its-new-propulsion-workshop-rd-center-and-aegir-academy/

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Dear User, You May Be Depressed

13

September

2017

4.68/5 (28)

On August 23rd 2017, Google announced that it would direct users in the US who searched for terms related to clinical depression to the PHQ-9 questionnaire. The PHQ-9 is a clinically validated survey that tests for depression. The goal of this experiment is to encourage sufferers of depression to seek help and treatment.
Google’s mechanism is a fairly simple implementation of the idea that sufferers of mental health issues can be detected and targeted based on their online behavior. A 2017 report by the Australian claimed that Instagram and Facebook are tracking users’ mental health status, but didn’t allow advertisers to target users based on this.
Various researchers have proved this to be possible. Striking examples are De Choudhury et al. (2013), who used twitter data to predict postnatal depression prior to birth, or Katikalpudi et al. (2012) who use browsing meta-data to predict depression, without even looking at browsing content. In my own research, I was able to predict clinical depression with 22% chance-corrected accuracy (64% overall accuracy) based solely on Facebook Likes.
There are two sides to this coin. On the one hand, most would find it immoral to target people struggling with mental disorders for commercial gain. On the other, 300 million people worldwide suffer from depression alone. Only 24% of college students ever receive help for a depression, and targeting these people to encourage them to seek help can create tremendous social value.
My question to readers brings the topic back home: How would you feel about having your online behavior tracked, so that if you ever unknowingly start experiencing symptoms of depression, a system can warn you to go and seek help?

De Choudhury, M., Counts, S., & Horvitz, E. (2013). Predicting postpartum changes in emotion and behavior via social media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems – CHI ’13 (p. 3267). http://doi.org/10.1145/2470654.2466447

Katikalapudi, R., Chellappan, S., Montgomery, F., Wunsch, D., & Lutzen, K. (2012). Associating internet usage with depressive behavior among college students. IEEE Technology and Society Magazine, 31(4), 73–80. http://doi.org/10.1109/MTS.2012.2225462

The Australian Reporters. (2017). Facebook-targets-insecure-young-people-to-sell-ads.

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