Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

I am a chemical engineer specializing in quality engineering and automotive engineering. I hold a master’s degree in production engineering, with a focus on reliability engineering and product lifetime estimation in the automotive sector, including engines, transmissions, and complete vehicles.

Q2. Which elements of Design for Reliability (DFR) methodologies most accelerated durability targets in powertrain development for heavy vehicles, and what integration factors drove those gains?

The use of field data is essential for making accurate predictions. Accelerated testing tailored to the various applications and markets in which the system will be used by customers in the future is also necessary. All of this must be validated and quantified in order to be translated into engineering specifications for the product, the process, and suppliers.

Q3. What gaps in QMS implementations under ISO 9001:2015 led to recurring non-conformances in high-volume manufacturing, and which maturity deficits were primarily responsible?

The lack of a thorough and well-conducted root cause analysis, as well as the effective implementation of the necessary measures to permanently resolve the problem.

Q4. Which continuous improvement systems will best mitigate scaling risks in mixed ICE/BEV lines, and what cross-functional governance gaps could still derail throughput targets?

Ensure a lean production flow tailored to the different characteristics of ICE and BEV products, such as Design Thinking and Design for Reliability during the design phase, as well as the implementation of kaizens to eliminate the 7 wastes of lean manufacturing on the shop floor (genba), thereby fully aligning planning (theoretical) with operations (practical reality). Attention must be paid to the additional and specific occupational safety factors for BEV products.

Q5. In blending RAM modeling with field data for heavy-duty axles, which hybrid prediction techniques most compressed warranty costs, and what calibration factors separated them from baseline simulations?

The use of field or customer censored data, AI, remote real-time monitoring of field data, and bench-test simulations to streamline the calibration process and predict its accuracy.

Q6. What supplier audit sequencing strategies minimized line stoppages from non-conformances, and which risk-tiering flaws led to persistent upstream defects?

Prioritizing audits based on the risk level of the supply chain, establishing relevant requirements, and monitoring effective metrics (KPIs). A poorly conducted FMEA prevents effective preventive quality actions, as well as root cause analysis and the implementation of poorly executed corrective actions.

Q7. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?

I would ask them to provide a breakdown of the technical (quality-related) and financial (impact-related) risks, along with the plan they have established to mitigate them.