Driving Manufacturing Efficiency With AI: Pirelli’s Daniele Petecchi

**Sam Ransbotham** (0:02)
When you think about digital twins and generative AI, you probably don't think about the tires on your car. But on today's episode, learn how firms are using AI to develop this key component.

**Daniele Petecchi** (0:14)
I'm Daniele Petecchi from Pirelli, and you are listening to me, myself, and AI.

**Sam Ransbotham** (0:20)
Welcome to Me, Myself, and AI, a podcast on artificial intelligence and business. Each episode, we introduce you to someone innovating with AI.
I'm Sam Ransbotham, Professor of Analytics at Boston College. I'm also the AI and Business Strategy Guest Editor at MIT Sloan Management Review.

**Shervin Khodabandeh** (0:39)
I'm Shervin Khodabandeh, senior partner with BCG and one of the leaders of our AI business. Together, MIT SMR and BCG have been researching and publishing on AI since 2017, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate.
Hi, everyone. Today, Sam and I are very pleased to be talking with Daniele Petecchi, head of data management and data science at Pirelli. Daniele, welcome to the show.
And let's get started. So for those of us who are not familiar with Pirelli, I happen to have four of your wonderful tires on my car, actually. Eight of them on my two cars. But for those of us who might not be familiar, can you describe the company and then your role at Pirelli?

**Daniele Petecchi** (1:35)
For sure. Pirelli is a manufacturer focused on the tire, as you said, and in particular, tire for car, motor and bike. Our target is to be on the cutting edge of the R&D and technical point in the automotive industry. Pirelli is an Italian company, is an old company, is more than 150 years old.
So during that time, Pirelli changed. And now we are leader in the prestige and premium segment, the value, but also we are known also for the unique supplier of the Formula One. If you see on Netflix, Drive to Survive, that is the series that is related Formula One.

**Sam Ransbotham** (2:23)
So now we know what kind of car Shervin drives. He's the only driver in the luxury. Is this your Lamborghini?

**Shervin Khodabandeh** (2:29)
You see, I'm a high value customer.

**Daniele Petecchi** (2:31)
Yeah, high value.

**Sam Ransbotham** (2:32)
Good, good, good.

**Shervin Khodabandeh** (2:33)
Maybe you tell us a bit about your role at Pirelli?

**Daniele Petecchi** (2:36)
Yes, I am responsible of what we call data management and AI. But at the end, I deal with all is related to data.
From business intelligence, reporting, dashboarding, to data engineer, data science, AI, deep learning, gene AI. We work with all the data. We work with data from our plant. We work with data of R&D. And we work for business with the AI, also on commercial supply chain and so on.

**Shervin Khodabandeh** (3:07)
Maybe give us some ideas of how data and AI is influencing all of the elements that you talked about, the whole value chain from maybe production and R&D to maybe supply chain. Can you give us some examples?

**Daniele Petecchi** (3:23)
Oh, yes, yes. This business model is a simple business model, but is for sure powerful. Our target is to work with our car maker. So this is our idea. We work with the original equipment and then we can support our replacement market. Moreover, we do this with the benefit in terms of time frame of our business. Car maker calls to develop the tire for a new car three years in advance of the launch of a new car model. Then the new car model is launched.
After four years, we have the first replacement wave.
After four years again, we have the second wave of the replacement. At the end, this provides us a visibility in this time frame of 11 years. So when we start with the new business, we predict the next 11 years due to the fact that we launch the new car, the car maker launches a new car, and then we work on the replacement.
So this is powerful for our business model, but it is also a challenge for the use of data because we have to work over these 11 years to guarantee the supply of the product, the quality of the product, considering that we are working on the prestige and premium market and the value of Ferrari, Lamborghini, BMW. I don't want to say all the brands, but they are the top brands. Based on this, let's consider that every year we produce more than 60 or 70 millions of tires. We produce this tire over 18 plants around the world. We provide our tire of almost 60 markets around the world. And to manage this complexity, because each year we produce 2, 3,000 of different SKUs. The tire is pretty different for each model, because there are the rims, there are the inches, there are the winter and summer tires, and so on.