KG addresses a growing need for training and education in automotive after-sales arising from the increasing complexity of modern car technology.Â Today, cars are high-tech machines. An average new car has more lines of code than Facebook and more micro-controllers than a...
KG addresses a growing need for training and education in automotive after-sales arising from the increasing complexity of modern car technology.Â Today, cars are high-tech machines. An average new car has more lines of code than Facebook and more micro-controllers than a fighter jet. A lack of technicians who understand complex electronics results in more wrong diagnoses and repeat trips to the mechanic than any other factor.Â Automotive training is still mostly done the same way as decades ago: in training centers, with inefficient methods that have little scalability. Technicians periodically travel to a training center for classes of predetermined content. Many mechanics donâ€™t get trained as often as they should and increasingly fail to properly repair modern cars.Â Finding skilled motor mechanics today is already a serious problem. In the UK, for example, 80% of independent garages say they have trouble finding able technicians, and there will be a need for 25,000 mechanics in the USA by 2020. This problem will intensify as electric vehicles (EVs) are increasingly on the road. Industry estimates predict up to 20 million EVs in circulation by 2020. There is an urgent need for education and training to update technicianâ€™s skills with EV tech. Only 1% of technicians are qualified to repair/maintain EVs.
KG\'s goal is to dramatically improve the quality of after-sales services by breaking down the barriers to delivering technical knowledge in the field. To achieve this goal, KG is striving to become the leading, centralized knowledge platform for professionals across the industry, enabling technicians to get training on demand through Self-Service Training and supporting them on the job through Automated Diagnostics.
The feasibility assessment completed in this project has strongly confirmed the market need for and client interested in these two solutions, thus providing strong support for the continuation of the project. In addition, the assessment also produced important learning for the successful market introduction of these two solutions. For Self-Service Training, a minimal amount of training content and pre-defined use cases needs to be available with the solution upon purchase to facilitate implementation and engage users to develop their own use cases. For Automated Diagnostics, a clearer positioning and more granular differentiation is necessary in order to communicate more effectively the productâ€™s USPs, particularly over clientsâ€™ existing diagnostic solutions.
Demonstrations with Focus Groups and Surveys
Both, Self-Service Training and Automated Diagnostics, received very positive market feedback from automotive after-sales and training professionals across 5 different countries. 61% of survey respondents found that both were complementary and equally valuable while 65% saw an improvement over their current solution in Self-Service Training and 81% in Automated Diagnostics
Cost Analysis of Competing Solutions
Our research showed that most competing solutions were much more expensive than KG Pro 2.0. However, results from our survey indicated that the cost of current training solutions is not a significant limitation for most training professionals.
Cost & time reduction analysis
While Self-Service unlocks important time savings for instructors and training centres by automating the practical training process, the biggest cost saving opportunities for Self-Service Training are in technician-related spending, where cost reductions could total more than â‚¬15.6 million per OEM per country per year. Automated Diagnostic increases techniciansâ€™ productivity by 144% by empowering them to complete more jobs in less time. This creates cost saving opportunities worth more than â‚¬72.2 million annually per car manufacturer per country.
Pricing model validation
Clients generally agreed with our price point, but different client segments had very distinct procurement habits and business model preferences. OEM training departments had a preference for asset-light and purely service-based models, automotive schools were looking to buy for a lifetime and training providers and outsourcing companies would typically price training equipment into their total project price when bidding for multi-year training deals with OEMs.
The project findings will be used to raise awareness for todayâ€™s and future challenges in automotive training and communicate on the benefits of KG Pro 2.0. Likewise, the project results and conclusions will inform our marketing, pricing and go-to-market strategy.
Traditionally, practical automotive repair training involves a significant amount of manual work from the instructor: wires are cut and defect parts are implemented into a test vehicle to prepare a training scenario. To reduce the work of instructors, some training centers have dedicated simulator labs: these are external, often trailer-mounted replicas of specific automotive systems (e.g. engine, breaks, etc.) that enable quicker simulations of defects for training. The downside of these labs is that they are bespoke productions, developed based on a specific brand and model with no option to update. As a result, they become obsolete as technology advances. Also, these simulators are less realistic as they donâ€™t simulate a full vehicle and donâ€™t demonstrate the collateral effects of a defect in one system on the remaining systems of the vehicle.
Self-Service Training is the first solution that makes it possible to encapsulate practical training situations and replicate them on demand wherever needed. This eliminates the need for face-to-face interaction with a trainer and makes training available to an audience that currently has no or very limited access to training: technicians employed by independent garages.
Automated Diagnostics, on the other hand, has the potential to completely change they way knowledge and skills are distributed in the industry. With a smart assistant quickly and efficiently guiding the technician towards the correct solution, fault diagnosis could be democratised and every technicians could be qualified to perform a task that today is restricted to only the most experienced professionals.
More info: http://www.kgprotech.com.