Automation is touching every industry; you can’t survive in the 21st century economy without the data and the insights that come from technologies like artificial intelligence and machine learning. The food automation market, for example, is expected to reach $29.4 billion by 2027.
Within the food space is produce and agriculture, and these are sub-spaces that haven’t seen quite as much advancement and adoption. That’s changing now thanks to companies like Fresh4cast, a company that uses AI forecasting to help growers and distributors improve productivity, increase margins and reduce waste. It’s a solution that includes data sets build from historical, as well as trade statistics and weather, and a virtual assistant designed to automate tasks.
At the London Produce Show and Conference, we will be welcoming Fresh4cast’s COO Michele Dall’Olio.
Michele has based his career on the synergy between innovation and fresh produce. Starting with a degree in Agribusiness and a master in Management and Marketing, he explored the complexity of fresh produce data working as Head of Research for a leading Italian consultancy. He then moved to London and started a new journey with Fresh4cast where he is now the COO.
Michele spoke to us about how greater insights can help growers and distributorsDL benefit from increased insights, how that can lead to less food waste, and what he’ll be talking about at the London Produce Show.
Q: Let’s kick this off by giving a little bit of an overview of yourself and about the Fresh4cast and what you do.
A: I’m from Italy, I moved to London five years ago. I have always been working and studying in the fresh produce sector, from high school until now. In my career back in Italy, I was working with a lot of data, I was head of analysis in a lead consultancy there and I basically developed into a more data-oriented person with Fresh4casts. When I moved to London five years ago, I joined as Head of Customer development and now I’m COO, so I’m specifically looking at all the operations, the planning internally, and I’m basically the interface between the customer and our production team.
Q: You said you’ve been in the produce space for a number of years and I’m really fascinated by the idea of applying technologies like artificial intelligence and machine learning to sectors where that kind of technology really hasn’t been applied before. I used to work for a motor company, for example, and that was a space that had been legacy space and the technology was very slow to develop because of the older people that were set in their ways. Do you feel like that was the same thing in the produce space? Was there a lack of innovation for a long time? And is that changing now?
A: We are definitely at a tipping point because, if you think about agriculture in general, and fresh produce is one of the sub sectors of agriculture, it is always lagging a bit behind compared to other sectors, for a variety of reasons. Service-based sectors are always more advanced, when we look at software, for instance. So, we definitely are at a tipping point, because, yes, as a sector, it’s a bit behind, but the benefit is that someone else already explored those paths. If you’re lagging a bit behind, you know what works and what doesn’t; it’s an important factor, especially in AI, because there’s a lot of trial and error, and a lot of errors. There are a lot of very good examples where fresh produce can take inspiration from. So, the data is there, it’s building up and it’s just waiting for a machine learning application or an algorithmic forecaster to untap its potential.
Q: What do you think are some of the reasons why the space was lagging behind before?
A: Well, there are a lot of reasons; it’s a very difficult topic. If you think about innovation in general, not just technological innovation, it’s driven by key factors such as availability of talent, and being able to attract those talents in the sector. Compared to other sectors, of course, agriculture is a lower margin sector, so innovation is there but it’s not always the first priority. And so, people and resources are the main thing that I see at the moment that is actually changing. Until 10 years ago, you didn’t see any fresh produce business having a data scientist in house or a team of people that was analyzing data, or actually hiring companies, such as Fresh4cast, for building a data set, building machine learning forecasters, and so on. Nowadays, there are a lot of requests for this, so the mentality of the top management is changing. That should drive this tipping point off of catching up with other sectors.
Q: It’s funny what you said about being a little bit behind meaning that you get to actually see what works and what doesn’t. I never thought of it that way before. Everybody else does this trial and error and then you come along and go, ‘Okay, well, now we know what works, and we can just apply it.’
A: When we think about the future and present, and we think, ‘now is the present for everyone,’ — but it’s not actually true because, for some people, they’re already in the future. So, we can basically copy or take a lot of inspiration from them.
Q: Talk about the ways that you apply AI and machine learning to the produce sector, and the ways that you use that data.
A: Fresh4cast has the three step approach. First of all, we have the customer as a data asset. As you know, machine learning feeds from data and learns from data, so that’s the very first milestone. Building a data set is easier said than done, because it’s very laborious, and it requires different kinds of skills in the company, but we have different tools over there. So, whenever we have a data set that we can work with, the second bit is that we display it back to the customer using business intelligence tools that we’ve built. So, there is very specific data, for instance data analytics, that helps to understand the seasonality in the fresh produce business, and so on. It’s about understanding what happened in the past in order to understand what is going to happen in the future. And the third point is using algorithmic forecasting, machine learning forecasting, very different tools, in order to extract even more value from that data asset, letting the machine find correlations and try to build models that will predict what’s going to happen in the future, even specific inputs.
Q: So, you get the data and you have to make these forecasts based on that data. And then what do the growers and distributors do with that? How do they put it to use? What are some use cases for them?
A: Well, it depends on the supply chain. So, in order to answer your question, I need to talk about the supply chain approach of Fresh4cast. We work with the whole supply chain; we don’t work only with one aspect. So, we both work with growers, with distributors, with data from retailers, for instance, and so on. And the important bit is that, for each point of the supply chain, the application changes. I’ll give you two key examples: one is at production where, if a grower is going to plant this amount of strawberries, for instance, we give them the weather forecast and other inputs, so they know when to plant them and how much is going to harvest. So, in a nutshell, how many strawberries will be ready next week or in four weeks time and at what quality. On the other side, on the sales side, say there is a distributor that’s supplying, for instance, a big retailer; the distributor needs to foresee and start planning for how much the retailer is going to ask in the next few weeks. So, we are talking about a forecast that tries to predict how much volume will be needed? If there is a big promo in Tesco, for instance, what is going to be the seasonality in the future? The cannibalization between the category and so on.
This is usually something that a human could do, but not at scale. There are a lot of very small tasks that a human could do, but it will take him so long that the data is already old, so it wouldn’t be effective to use that forecast because we already have the actuals. A machine learning application, especially in fresh produce, is something that is automating a lot of very small tasks in a clever way. It’s like a proficient assistant: it gives you an output, and the human, at the end of the day, decides what to do with it and makes decisions using this information.
Q: You’re telling growers when and how much to grow, and you’re telling distributors and retailers how much they’re going to sell, is that right? So, everybody in the supply chain is getting this data to know how much to expect and how much they should expect to sell?
A: Exactly. If you want to be demand driven, you need to have a forecast in all of the key steps of your supply chain that feeds into the other. So, for instance, if you have a product that you will have next week, how much sales will you have next week? These two pieces of information together creates synergy and allows you to plan better, for instance, your warehouse activities, like how many man hours you need to pack the product.
Q: Where do you pull your data from? Like you said, you’re using an existing database. Is any of your data proprietary?
A: We are a software as a service, first of all, so their data is confined inside the customer’s walls. It doesn’t go anywhere and we only use the data for the customer. So, we don’t do data aggregation with other customers or build models across customers. We do every application in isolation because we also work with fierce competitors. So, that’s the way to go. We provide some data such as weather and international trade, but it’s all publicly available data, we don’t have any proprietary data, we just have proprietary models that interpret the data.
Q: It’s interesting that you don’t aggregate that data. Wouldn’t that be a more helpful way to get a broader view of the market?
A: We have a few cases where a few companies put together their data, but we need to have written consent. By default, we always work only with the data from the specific customer. And the reason why is that aggregation is useful for generic market trends. So, companies like Nielsen, they aggregate data across a lot of companies, so they have market trends. On our end, we tend to do the opposite: we specialize and fine tune the forecasting model specifically on that customer’s operations and that customer data. Because even if one company says the same thing as another one, it doesn’t mean that their business structure and supply chain are similar. They could have a very different structure and, therefore, whenever you change something in the structure, the data reflects the operation. So, it would be a different kind of data.
Q: I would think that what one retailer sells would sell the same at another retailer but it sounds like maybe that’s not necessarily the case.
A: We don’t work directly with retailers; our customers always specialize only in fresh produce. Some of our customer data comes from the retailer, so we can forecast that, but our customers are the growers and distributors. The retailers, we can have the data about them, but they usually have their own forecasting system internally. Just to clarify.
Q: I know that you also offer a virtual analyst for your customers and I’m very interested in learning more about that. I saw that it can send email reports, alerts, prepare Excel reports, and PowerPoint presentations. What’s the technology behind that?
A: Saga is our virtual assistant and you already mentioned a lot of the use cases that we use it for. It’s basically a very proficient assistant that automates boring tasks. That means it’s very quick at doing them and it takes out that overhead of admin-based work that all the employees have in their routine job. From sales to production, they always have to work with an Excel file, for instance. With Saga, if a grower sends their estimate to the central planning team, they CC Saga in their email, then Saga is able to see the attachment, incorporate the attachment in our database, display analytics, and come back with an email report, which is very bespoke, depending on the customer. Basically, it’s good at interfacing, especially with email attachment and preparing reports on the fly. So, again, it’s all about automation, at the end of the day.
Q: I’m assuming that the whole point of that is to free employees up to do more complicated tasks rather than, like you said, repetitive boring stuff that takes up a lot of time but it doesn’t require much skill.
A: Exactly. The second point I mentioned before is the business intelligence bit. If you think about how much time you spend on getting the file out of ERP, for instance, elaborating with Excel, remapping, and so on, you will probably spend 80% on transforming and manipulating the data and 20% of your remaining time on actually analyzing the data and making a decision from what you just discovered. With automation, you get rid of all the preparation, so you get rid of all that 80%, but you have ready made analytics, so you can focus your attention on making better decisions for the business. And maybe you have some extra time to have coffee. That’s a very Italian thing to say, I realize.
Q: Have you been able to actually measure improved productivity for your customers? And do you have any numbers you could share with me?
A: Productivity is quite difficult. I could share with you a couple of examples of what happens, but they would be customer specific, so I would avoid that. I can share it with you, though, the improvement of our specialized business intelligence tools that allows the growers or the planner to improve their own accuracy. So, the key part of improving is measuring at the very beginning; you need to measure, understand, and after that you can improve. We have a case study where growers were producing forecasts for their crops and, using our business intelligence tool, they were measuring the accuracy of their own forecast on a daily and weekly basis. They managed to shave 20% of their total errors. So, just looking at their data and having these tools that give you key KPIs, or key performance indicators, on how good your forecast is, where your errors are, and so on, they could shave, without any other inputs, 20% of their errors out of their forecast activity.
Q: How do you measure the reduction in food waste?
A: The reduction in food waste depends, again, on the level of supply chain we are talking about. I’m focusing a lot on the production side but, if you think about your sales side, if you have too much product, and you didn’t know in advance, and you’re not able to sell it in your warehouse, you will have what’s called an overstock. Usually it is not a big problem in other categories but we are in fresh produce, so the shelf life, how long you can keep the product in the fridge, is very, very short. That’s one of the reasons why the founder, Mihai Ciobanu, actually focused on the fresh produce at the very beginning with forecasting, because it’s very, very difficult to forecast. And, on top of that, if you get the forecast wrong, you can lose a lot of money, basically, throwing away a product that should have been sold.
Q: Give me a preview of what you’ll be talking about at the London Produce Show and Conference.
A: The production will be focused on how to leverage your own data assets and extra value from it. Specifically, we will look at how the forecasting activity, and specifically the machine learning tool, is helping both growers and distributors to improve efficiency and reduce waste in their own supply chain. We will have a couple of practical examples of how better forecasting is helping with these two topics.