Standardization and global information help with instantaneous projectionsI recently discussed with them the need for better KPI measurements in the automotive world. A big KPI in the automotive world is the bookings of the future years, and we measure that not in annual revenue but in the lifetime of any given vehicle. Let's say, on average, a vehicle is in the market for five years, so we add up the five years of revenue that is generated with that vehicle. Every region and every account has targets where they have to say, "I'm going to source amount x in the upcoming year." This LOP calculation is not easily done because IHS also has limitations, so right now, it only projects out till 2031 or 2032. For instance, if we launch a program in 2028 that starts in 2030, only two years of data can be seen, and I cannot calculate the life of program revenue. We discussed it with them. They are looking into AI to make longer projections possible and to also improve the quality of the data that we have in the system to calculate this LOP based on historical data or IHS information. They had people who trained our staff, and some people, even though located in the US, were working 24/7 to accommodate our needs in Asia or Europe. These same people also had to implement the system and not just train, which I thought was a bit too much for people to handle. I told them that it would be beneficial to have someone in Asia or Europe to do those types of training during the time when those regions are working. They can have a hotline for simple questions. Sometimes, simple questions disrupt complex tasks. Having dedicated resources for basic inquiries could alleviate this issue. Additionally, it could be beneficial to have training videos within the system to handle routine questions instead of constantly bugging the people who are supposed to do way more complex things than that.
