- Why do you think the megatrend you selected is important to your organisations management of process improvement and/or Product development?
Machine learning is revolutionizing the transport industry. Self-driving cars have been headlining the news on a constant basis in recent years. The topic has created an interest so much so that not only traditional car manufacturers but also companies of the new economy like Google, Apple or Tesla are trying to win the race of self-driving cars. 
Car manufacturers like Daimler therefore need machine-learning in their product development of self-driving cars, since data that is collected through driving directly feeds back to the data they use for self-driving cars. More specifically, they use “deep thinking”, a machine learning technology that is based on neurone networks. It simulates the working process of the human brain and copies its architecture of neurones and its connections, which are strengthened or weakened through new “experiences”.
The application of deep thinking can be tested and simulated nowadays because of companies like Google that have enormous amount of data to practice with. In product development of self-driving cars like at Daimler, this availability of data improves the machines capability to classify People or traffic signs on digital pictures.
Since the deep learning process makes the car think like a human, it does not interpret a scenario pixel by pixel but as a whole. It ties to a deeper understanding of a specific situation. Therefore, in product development the car does not have to be “trained” for every detail, but it can even tell the difference of typical road scenes between different cities. Just like humans, cars can then learn from each other and feed it back to the data base used in the development process.
- What is the organizations management doing to address this issue in the short term?
These days several German car manufacturers work on automatic analyses of road scenes, in order to interpret complex traffic scenarios more precisely. Therefore Daimler´s management decided in 2015 to install data collection system in current non-selfdriving cars that allowed them to collect data of unknown sceneries to update the neurone network in the backend over-the-air. For example, the latest E-Klasse is equipped with the so-called Car-to-X-technology and communicates with other cars and the overall neurone infrastructure.
Moreover, Daimler has invested about € 500 million to completely connect their trucks to collect and exchange data. However, Daimler does not merely apply machine learning in their development process but also utilizes this technology in their production facilities to identify and correct manufacturing errors.
- What other steps do you recommend the organizations management to take to address this issue in the short and medium term?
Since the stakes if machine learning goes wrong in automotive are very high, management needs to be concerned about the robustness of data. For instance, a road can have be partly flooded with water and the machine might interpret it as something else (i.e. a cliff) and might then react in a way that crashes the car and severely hurts the passengers inside the car or even on the road.
Moreover, Daimler is not a data company and owning big data is a prerequisite to successfully and sustainably entering the self-driving car market. Therefore, Daimler should consider building partnerships with technology companies that generate large amount of data of streets and traffic. Moreover, the employee hiring principles at Daimler should be matched with the vision for the future, as they bring the expertise about technology and more specifically machine learning
- In the context about this organization, what are the one or two most important open questions related to this issue that you are unsure about that merits comments from your classmates?
How much should Daimler compete or cooperate with other companies like Tesla and Google that are trying to enter the self-driving car market?
One of the reasons why they start collecting data with truck driver is because they care less about their driving data as it overseen by their employer anyway. Would you implement data collecting technologies with consumer already and how would you communicate that given that particularly Europeans are very careful with their data in the context of privacy?
How much demand is there for a self-driving car in the US? Would enough people use it?
 Ematinger, R., 2017. Von der Industrie 4.0 zum Geschäftsmodell 4.0: Chancen der digitalen Transformation. Springer-Verlag.
 Kasparov, G., 2017. Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins. PublicAffairs.
 Fritsch, M., Goecke, H. and Kulpa, A., 2018. Identifikation von empirischen Unternehmenscharakteristika mittels Machine Learning Verfahren: Gemeinsames Projekt von DATAlovers, Institut der deutschen Wirtschaft und IW Consult (No. 35/2018). IW-Report.
 Heinen, N., Heuer, A. & Schautschick, P. Wirtschaftsdienst 2017. Künstliche Intelligenz und der Faktor Arbeit. P. 97: 714.
 Augustine, S. and Augustine, S., 2018. Die Informationsbedürfnisse der Generation Y im Kontext aktueller Nachhaltigkeitsberichterstattung. Die Generation Y und Integrated Reporting: Konsumentenverantwortung durch Nachhaltigkeitsberichterstattung?, pp.163-197.
 “ARTIFICIAL INTELLIGENCE AND LIFE IN 2030.” ARTIFICIAL INTELLIGENCE AND LIFE IN 2030, 2016, www.springerprofessional.de/automatisiertes-fahren/car-to-x/wie-autos-das-denken-lernen/10915232.