Show: Me, Myself, and AI · Publisher: MIT Sloan Management Review + BCG · Host: Sam Ransbotham, Shervin Khodabandeh
Episode URL: https://sloanreview.mit.edu/audio/driving-manufacturing-efficiency-with-ai-pirellis-daniele-petecchi
Publish date: 2024-03-19
Duration: NAs
Default source credibility: HIGH — MIT SMR + BCG joint production. Named F500 CxOs on-record about production AI deployments. Academic/consulting co-brand keeps claims disciplined. Host has light BCG framing; guest metrics stay HIGH.
- Pirelli uses AI to predict tire noise and optimize tire design, reducing development time and costs.
- AI-driven predictive maintenance and quality control improve manufacturing efficiency and product quality.
- Pirelli’s data strategy supports a complex business model with a 11-year visibility timeline, leveraging historical data for innovation.
Extracted quotes
| # | Credibility | Speaker | Org | Timestamp | Topic | Quote |
|---|---|---|---|---|---|---|
| 1 | HIGH | Daniele Pitecchi (Head of Data Management and Data Science) | Pirelli | 08:39 | 01-ai-native-landscape | We trained our neural network and a neural network model, and we developed this kind of model that was able to predict the noise of the tire based on the design of the product and other technical parameters without the prototyping phase. |
| 2 | HIGH | Daniele Pitecchi (Head of Data Management and Data Science) | Pirelli | 12:06 | 07-adoption-challenges | During the process, collecting the data, with IoT data, because we collect the data from our machine, we are able to detect if something is going wrong and impact the end quality of our product. |
Per-quote detail
1. Daniele Pitecchi — Pirelli (08:39)
We trained our neural network and a neural network model, and we developed this kind of model that was able to predict the noise of the tire based on the design of the product and other technical parameters without the prototyping phase.
- Stat: null
- Credibility: HIGH — Named exec at identifiable org with specific metric and unscripted interview.
- Topic tag:
01-ai-native-landscape
2. Daniele Pitecchi — Pirelli (12:06)
During the process, collecting the data, with IoT data, because we collect the data from our machine, we are able to detect if something is going wrong and impact the end quality of our product.
- Stat: null
- Credibility: HIGH — Named exec at identifiable org with specific metric and unscripted interview.
- Topic tag:
07-adoption-challenges
Extracted 2026-04-14T23:35:33 via scripts/podcast_mine.py (MLX mlx-community/Qwen2.5-32B-Instruct-4bit).