Home / Blog / The economics of the Digital Transformation – V

The economics of the Digital Transformation – V


Another of my chicken scratches to show how data are being created in a manufacturing process. A first set is created through design activities (Computer Aided Design) resulting a model that can be subjected to simulation. Once the model is sound it is used to synchronise suppliers and the various components are manufactured and assembled into the final products, generating more data in the process. These data form the Digital Twin, actually they create instances of Digital Twins, one associated to each product instance. The products are then moved on the delivery chain and sold giving origine to usage and operation. These latter are source of many more data that are being shadowed in the Digital Twin. As shown in the upper part of the graphic, the number of data increases and expands in the operation. These data are made accessible to the manufacturing and design to fine tune the operation, to design better products and augment features as well as to generate services.

Leveraging on Data

The Digital Transformation accelerates the generation of data and companies are learning to capitalise on those data, sometimes clashing with fuzzy regulation and unclear definition of ownership.

Take the example of Tesla. Tesla have been designed through computer modelling and this process generates a digital model of the car that is used for simulation and for interaction with component suppliers. The model is then turned into software for the manufacturing of the car, to create parts and assemble them into the finished product. All the manufacturing phases are recorded and are added to the model of the car. At this point there are many instances of that model, each one mirroring a specific car (there might be difference, in colour, optional features, even in components that might be provided by different suppliers and of course in the robots and workers that manufactured a specific car. Once the car is sold sensors embedded in the car keep track of its operation, where it goes (Tesla is adding data on 1 million miles of road driven every 10 hours!, on November 2018 they reached the thresholds of 1 billion miles recorded), when it is recharged, how it is used… All this information is harvested by Tesla for proactive maintenance (to detect possible malfunction) as well as to detect issues in the manufacturing that might become apparent only at a later stage. As an example, a few cars after some thousands miles on the road showed an abnormal vibration detected by the sensors on the left suspension.

Through data analytics AI software was able to pinpoint the problem in an asymmetry in the fastening of bolts on the two sides of the car assembly line, with one robot placing a higher pressure than the other. At that point the software inserted a notice in those cars digital twin (the car data sheet) so that once one of them was taken to a repair shop the notice will be read and recalibration executed. All of this without the owner ever becoming aware of the problem (the vibration was so tiny that went undetected by the driver).

Also, all data harvested from driving, including the data generated by the digital cameras used by the automatic driving assistant (autopilot) are continuously analysed to improve the autopilot using machine learning. In other words all Tesla cars are getting better every single day by leveraging on the experience of each and all of them.

Data are so fundamental for Tesla and its business that the company has been defined “The Data Company”.

Although operating in a tiny vertical sector, the one of private transportation, Tesla is gathering a massive amount of data that can generate plenty of metadata, like:

  • Where are people driving, when they are driving, how they drive, with whom they drive (this information is easily obtained by sensing the smartphone on the car, via Bluetooth). This information places Tesla in an ideal position to evaluate risks and therefore to offer competitive car insurance;
  • What are the road conditions and how these condition change over time, whether there are potholes requiring maintenance and so on;
  • What traffic is on a particular road at a particular time, detecting traffic anomalies versus the usual pattern. Get information on other cars driving around (their number plate is easily captured by the Tesla camera and it would be straightforward to map, given sufficient time, the whereabouts of many other people in a given area);
  • Who is walking around and who is getting in and out of shops and homes. A bit of data correlation would make it easy to find out about other people habits in a specific area;
  • The car suspension can provide data on the “weight on the car” meaning that over time it is possible to gauge various parameters about the driver, the passengers and even the groceries being transported;
  • ….

Notice that what listed above is not stating that Tesla is actually gathering these data, just that it would not be a technical problem to harvest them. As a matter of fact Elon Musk has stated several times the advantage given to Tesla by the use of Big Data and AI Data Analytics and once even voiced the idea of using these data for selling services. That resulted in a backlash by several Tesla owners concerned of the violation of privacy on the one hand and on the exploitation of their data to make money on the other.

Clearly Tesla is the perfect example of a company born out of the Digital Transformation of a classic business. It is not just the shift to electrical power engines, it is the use of data that is making Tesla worth more than Exxon in the stock market. Its turnaround is way, way, lower than Exxon, yet the stock market expectation  for a bright future is all on Tesla side.

Tesla is just an example of the power of leveraging data for a manufacturer:

  • fine tuning of manufacturing based on operation feedback
  • continuous improvement of products through data analytics from the all products sold looking at the way they are used
  • development of services flanking the product
  • offer new services in other market segments that are in principle completely outside of the company business (like providing info to municipalities on proactive road maintenance).

Tesla has been used as an example. According to a new McKinsey report the monetisation of big data harvested from cars can lead to a 750 billion $ market in 2030.

Today there are many companies that are leveraging on the result of the digital transformation of their business and exploit data to increase revenues, decrease cost and strengthen their market position.

About Roberto Saracco

Roberto Saracco fell in love with technology and its implications long time ago. His background is in math and computer science. Until April 2017 he led the EIT Digital Italian Node and then was head of the Industrial Doctoral School of EIT Digital up to September 2018. Previously, up to December 2011 he was the Director of the Telecom Italia Future Centre in Venice, looking at the interplay of technology evolution, economics and society. At the turn of the century he led a World Bank-Infodev project to stimulate entrepreneurship in Latin America. He is a senior member of IEEE where he leads the Industry Advisory Board within the Future Directions Committee and co-chairs the Digital Reality Initiative. He teaches a Master course on Technology Forecasting and Market impact at the University of Trento. He has published over 100 papers in journals and magazines and 14 books.