10.25.23-Digital-Wise-Systems-V2

ARIADNA RODENSTEIN: Well, hello, everyone. This is the faster part of the program. My name is Ariadna Rodenstein. I'm a program manager with MIT Startup Exchange. And welcome again, as Graham said, to the startup lightning talks.

Shortly you will hear from eight startups that are part of Startup Exchange, ten exciting startups. And you will see how they present their AI technologies, very varied, and to be applied to a lot of different industries to help in the road of the acceleration of digital transformation.

So I will give a brief overview of our program, and then we will hear from the startups. MIT Startup Exchange is led by the Office of MIT Corporate Relations, which is also home to the Industrial Liaison Program or ILP. And as you heard earlier today, the two programs are highly integrated. And in the sense that the primary focus of ILP is to bring in large corporations and connect them to all aspects and all resources at the Institute, we at Startup Exchange identify and select and onboard MIT-connected startups to work closely with ILP and bring together the startups and the corporations. And we also help the startups continue to grow as they tackle the biggest challenges in the world today.

So the startups have strong ties to the Institute. And so they could be based on MIT-licensed technology, or be founded by MIT faculty, alumni, or staff. And they can also be vetted by some of our MIT partners, including The Engine, MIT.nano, CSAIL, and others. And we have about 1,000 startups in the program, in the network currently.

They join us at pilot stage or beyond. And they cover all sectors and all geographies. And we also have in the program Stex25, which you might have heard of. It's a program where we can provide customized support to 25 startups during the period of 12 months. And this gives them greater visibility and more connection to industry. We actually have some current and former Stex25 presenting to you today as well.

And so let's talk for a moment about the value of MIT Startup Exchange. So startups have access to a global network of corporations. And ILP members have access to a wide network of vetted MIT-connected startups. And we host about 30 or so events per year. Some are like this, on a hybrid mode, conferences such as this locally, throughout other US cities, and also across the globe.

And also webinars, of course. And so the benefit for the startups on that is that we take them to other markets that they're looking to expand into and have visibility into new ecosystems. And then the benefit for ILP members, represented by so many of you here today and via the live stream, is that we can tailor a program according to your needs.

We also have calls for startups throughout the year. And this is where we post opportunities and they can present their technologies in different formats. And also there's challenges that the ILP members might post. And it's where they can work together where they see a good fit. And the startups can help you fix a problem that you may be facing in your industry.

And we also have curated meetings throughout the year. We work closely with the ILP program directors and bring together the startups and the ILP members for potential for collaboration and partnerships. And I think the main thing to highlight here is that we do this in a very customized approach. We know the startups very well.

We know that your corporations might have different sizes, needs, different ways of working with startups. And so we really target each approach to the best environment that that could be. And this is actually what sets us apart at Startup Exchange. And so when we bring together the startups and the ILP members, the goal, of course, is to have successful partnerships.

These are some examples. And they can take the shape of joint R&D. It could be a pilot or a proof of concept, maybe becoming a customer of the startup, and also all the way to an acquisition. And as I always say, I hope that some of the conversations that happened today will lead to more success cases for everyone.

And before turning it over to the startups, these are some of the upcoming events for November and December. Please check out the ILP and Startup Exchange sites, register, and join us where you can. We have Madrid on November 7th and we have the R&D conference back here at the Marriott on November 15th and 16th. We have a couple of webinars coming up on behavioral economics and digital twins.

And then on December 6th, please join us for the Startup Exchange demo day. We host these on a quarterly basis virtually. And yet we also hosted the first one live in California this past June, and that was an exciting program. So these are the startups you'll hear from today.

And in addition, we have Ikigai Labs at the exhibit for you to engage with them later. So first I'd like to introduce Wise Systems. And this is AI for your smart last mile delivery. Thank you.

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JEMEL DERBALI: So unfortunately for all of you, I am not Layla Shaikley. My name is Jemel Derbali. I am co-founder of Wise Systems and I serve as our COO. We are a company founded out of MIT.

So all of our co-founders are MIT alums. We were affiliated with the Center for Transportation and Logistics, as well as the MIT Media Lab. We did Stex25 and we also got support from the MIT Sloan Trust Center. Wise Systems is a route optimization company. So we focus on helping companies that deliver goods and services in the last mile to plan and execute their routes in ways that are efficient and meet what we all know are increasing customer demands.

Data is at the heart of everything we do, data and machine learning. And for us, data machine learning is all about people. It's about making software more responsive to the needs and lived realities of the users of that software. Go forward here.

So I'll just give a little anecdote. So we often talk about a driver that we did a ride along with. He's a beer delivery driver in South Florida, and we're driving with him. He has his optimized route, and he gets to the afternoon and he suddenly deviates completely from the route, says, you know, I'm not doing this. And we asked him why.

And he said, well, I don't deliver to that guy in the afternoon, in the early afternoon. And we said, why not. And he's like, well, it's right next to a school and I once got robbed by a bunch of teenage girls. And so I just don't get paid enough for that.

So we said, so fair enough. But your route is no good if your driver doesn't follow it. So driver preferences really do matter. And the reality is drivers and dispatchers know a lot about their customers.

They know a lot about what happens on the ground. And it's really important that routes take that data into account. But they're often using software that's relying on very static information.

So what ends up happening is that these drivers and dispatchers are just making the same edits or ignoring the same routes day after day. But the data that they have, the data that you can see, can now be collected. And we see a lot of opportunity in that data, whether you're looking at the preference of a driver or a dispatcher, or the time it takes to complete a specific delivery once you're on-site.

So we can look very deeply at all of the tasks the driver actually has to do when they get on-site. And you need to really think about all of that in making a route. Drivers think about it when they see the route. But their software often doesn't do that.

The other thing we look at is how to factor parking into a plan. So this is Coronado, California, is a kind of small island where there's a lot of foot traffic. So if you're going to do deliveries on Coronado Island, and you are going to go to every single address and park, you are going to spend hours parking. So experienced drivers know this, that even if you have heavy loads, you're going to park in one place and you're going to do three or four stops by foot with a cart.

But if you are a new driver you don't know that. And if you were just following your routes on paper, they're going to tell you to go to every single location. So learning from all of this on the ground data is what allows us to provide better routes to our customers, routes that dispatchers don't need to spend hours editing and that drivers will actually follow. At the heart of our platform is what we call our dynamic optimization engine, which allows us to utilize machine learning throughout the different aspects of the delivery process.

So this delivery process and where we affect it, starts with long-term planning, so strategically planning which routes you need, what your territories are, to daily routing across multiple constraints, to then dispatching and execution of routes, giving drivers tools to help them execute the route while on-site, and also having routes that are adaptable to what's actually happening on the ground, new orders coming in or changes on the ground. Then we also provide tools to help communicate to stakeholders, so customers know when their deliveries are coming and can plan around that.

And then finally we analyze all this data and automatically start to use that data to drive better decision-making into the future. And it works. So we've been able to get these results for our customers. We look at trying to drive perfect deliveries, which we define as being cost-effective, predictable-- so they meet customer demands-- and automated.

These are a few of our customers and partners. We work globally, and we would love to talk to anybody who is interested in transportation or in the transportation industry. We have a booth and a demo set up. And there's a QR code if you want to get in touch with Layla. But thank you so much for the time. Appreciate it.

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