Hi, I'm Ewan, and I'm a Process Development Engineer working in Home Care at Unilever. Today, I'll be talking you through how we built an active global community for a digital-first approach to innovation and sustainability. Now, whether you know the brands or the company itself, Unilever is one of the world's largest consumer goods companies, with a portfolio of leading purposeful brands, home care brands like Persil or OMO and Comfort, and other brands you'll know, like Ben & Jerry's and Dove. We have an unrivaled presence in future growth markets. As a business, we have a determinedly commercial focus to be sustainable.

Now, this focus as a sustainable business helps drive a real impact to the planet through the 3.4 billion people that are using our products every day across over 190 countries. The reach we have as a sustainable business resulted in over €60 billion in turnover in 2022.

Now, our purpose and ambition as a business really shines through in our clean future strategy. Now, this is the delivery of products that are unmissable superior in terms of product performance, products that provide great value across all price tiers, in all of our brands and products that are sustainable. It's really the combination of all three in every product that we deliver to our consumers who are at the heart of our strategy, that makes this such a progressive strategy. There are over 50 proof points of the real impact and business power that this strategy has enabled.

In home care, it's process development that are the ones that are delivering our clean future strategy to factory scale. Taking the ambition of formulation scientists and marketeers, and scaling up from lab concepts all the way to optimized factory scale production around the world. Typically, we do this in four key steps, the first of which is building an understanding of how the formulation and the process interact. Whether that's the effect of temperature making the product thicker and harder to pump, whether its effects on product clarity, so whether it looks clear or hazy, we really need to build an understanding of these interactions upfront, so they can feed into all the subsequent steps. The next of which is a process route design.

Starting to understand and build an idea of how we want to process this, both at pilot scale, so small scale, and also at large scale, so in our factories around the world. From these two steps, we can then start building scale up rules. This is in pilot plant scale up work. Whether we're making product in 50 kilogram batches, 100 kilogram batches, or 200 kilogram batches, even more. This is where we really start to develop rules that will apply at factory scale, where we're producing in the order of tons per batch. Resulting from these is the main plant trial. Testing our product in live factories around the world.

In each of these steps, we can generate a lot of data, a lot of understanding to build into the next step. We want to be as exploratory as we can to really push the innovation process to innovate products that are conceptually new and our consumers want. Our ambition here is, can we link these data and our ambition to be exploratory into a digital driven innovation pipeline throughout the whole process.

This is what we tried. We wanted to pilot a hypothesis driven approach to design of experiments doe, and we had a new formulation that we needed to launch one of our popular home care brands. We needed to launch this in over eleven factories worldwide, comprising of over 25 to different types, sizes and scale of mixer. Now this is a big challenge and it's essentially a global rollout. How can we optimize this for unmissable superiority product performance to provide great value to our consumers and also to provide a sustainable product?

Because every optimization we do at this step can benefit our 3.4 billion consumers around the world, and essentially our planet as well. We started by forming a hypothesis, so that consisted of which process parameters we thought might influence our product, whether the product quality parameters itself or the process. We trialed a design of experiments approach at pilot scale. Doing this at smaller scale before we scale to a factory, allows us to minimize cost, raw materials, and actually expedite the process as well. From these data, we were able to start modeling some of our quality parameters. An actual example is shown here.

This enabled us to identify the actual critical control points, not just in the process itself, but some of the formulation parameters as well. What we see here is the prediction of our product. Viscosity depends on temperature. Another quality parameter, parameter 2 and materials A and B.

Now parameter 2 and material A don't really have much of an impact on viscosity, but the really interesting part is in the temperature and material B. If we reduce the temperature even further, we remain in the green zone within specification. If we reduce material B, it's the same. There's a potential for reducing energy savings through temperature reduction and the potential for reducing cost through reducing material B. Both of these actually will result in a greenhouse gas reduction.

This was the business benefit that we managed to provide through this approach. Double-digit million euros in material savings and a global energy reduction for sustainability, all for a clean future. Now, this is the work of one engineer or a small team. If this is what we can achieve, why wouldn't we want to make these key skills in every engineer? That's what we set out to do.

We wanted to make design of experiments and modeling key skills for every engineer in home care process development around the world. These engineers have different first languages, live in different countries, in different time zones, and are at different stages in their development. The aim here was to ensure that everybody could benefit from one program. Different teams work on different products, so they develop specializations, maybe they want to use certain features of jump. We created a global community of practice where engineers can get together, share their learnings, and we can upskill together.

The approach we took for this was an approach called the 70:20:10 approach. Ten percent of the time spent on structured training, and this was led by JMP champions, so engineers with a higher proficiency in the use of JMP within our team, working with other engineering teams to upskill. Twenty percent was shared learnings. Regular community of practice sessions with engineers as participants and their mentors, so we can share our learnings, our struggles, challenges, and really how we've progressed with jump, with a name to upskill everybody. The 70% is the most crucial part of this program. That's delivering impact through hands on work in key technologies in high value projects.

These projects are business big bets that we want to launch. Using DOE can help expedite timelines, can help optimize products, and can help us understand the behavior of the product to a level we never had before. If we made DOE optional and modeling optional, nothing would happen. We're trying to change ways of working here, so we have to integrate this as an approach for our high value projects.

We started by trying to cement good, structured data capture, forming a foundation for all of our future modeling work. We wanted, again, to cement a DOE first approach to our exploration. We typically used a custom design in JMP for this, using a response surface. This was complex enough to accurately represent the formulations and the processes, but not too complex that it took a very long time or would be impossible for our engineers around the world to understand.

Building on this, we wanted to build data analysis and modeling skills in our engineers, so they can start developing insights about the formulations and about the processes. Again, this is straightforward and simple. Linear regression using standard least squares. We wanted to ensure that there's no multicollinearity in our models. If we see an effective temperature, we want to be 100% sure that it's temperature and not an artifact of another interaction. The real ambition of this journey is that we can build expert engineers that can create value adding technology insights.

Moving on from custom designs and basic regression onto other functionalities like simulations. Whether we can simulate product specifications, for example, using desirability functions to optimize our products for batch cycle time or cost, and move on to more complex ways of modeling. Away from numerical modeling for viscosity, and starting to consider other factors like foam.

This is what we managed to do. I'm going to talk you through a couple of case studies where we've managed to develop superior products, where we've managed to provide great value products and deliver the savings to our consumers, and where we've managed to deliver sustainable products to market all three at the same time in our products. The first is an example of a product that's one of the most complex products we've ever developed.

It all started with characterization through DOE. Modeling the formulation space helped us really build an in depth understanding of the formulation. From this understanding, we're able to optimize the formulation. This is key, because with our approach to clean future, we want to reshape our formulations, not just for liquids, but powders, gels, capsules, creams and bars. We're having to learn how we can produce the most sustainable formulations possible and deliver these with great performance and great value. This is why characterization is critical.

This product was very complex, and we have to be able to make it a factory. What we did is work with the factory teams to incorporate factory data into our model, and we were able to simulate how the formulation behaves in our factories, building confidence, not just in the formulation team, the process development team, but also factory teams and marketing teams. The results of our modeling enabled us to save multi-millions of euros in capital investment.

The situation we're in now is that we can de risk the formulations in the formulation development phase, before scale, at work, before factory launch, because we can build confidence in the right first timescale up of higher performing than ever products, better value than ever performing products. This product was 100% sustainable. Through DOE and modeling, we're able to produce superior products, great value products and sustainable products.

The second example is extremely similar. This is a product you probably use very frequently. It's very well known. Again, started with characterizing not just the formulation, but also the process as well. Investigating the interactions here led to new understanding. Understanding we did not have before on both the formulation and the process.

With this understanding and with this model, we were able to reduce the time it takes to make every single batch by 26% at factory scale, a 26% batch cycle time reduction. This is not just a time saving, because all that saved time is now time that we can make other products, this product and all of our other brands that we can deliver to consumers. We're unlocking factory make capacity and building on our model here. Beyond batch cycle time, we're able to incorporate cost and other quality parameters to start modeling and profiling multiple parameters at once.

This enabled us to maintain a high performance, reduce cost that we can pass on as savings to the consumer and improve product sustainability through a 21% reduction in our polymer, which typically are non-biodegradable. Again, we're able to maintain performance, provide great value to our consumers, and deliver sustainable products, not just for our consumers, but for our planet as well.

To share a few key learnings from our journey. It was really the focus on high-value projects that helped us to create the real business impact we see here. It enabled us to bring stakeholders on board with the digital first approach to innovation and really helped to start expand this, not just in formulation, but process development, supply chain and other teams. We've moved on from process development, and we're expanding this now.

The second is that frequent presentations. The shared learnings, 20% of our journey helped maintain teams development. It helped maintain the pace of value creation for the business. Having written these presentations and given these presentations, it then became much easier to transfer this understanding to other teams as well, and also stakeholders. It helped stakeholder buy in as well.

The third learning is that one-to-one mentoring was a huge investment of resource at the beginning, but it was by far the most valuable for us. That early stage investment really helped quickly upskill engineers, and having engineers that professional with JMP, work with other engineers helped us maintain our focus on what really mattered in terms of a formulation and process perspective and overarching. All of this is a focus on the journey, and its long term commitment is key if we want to successfully change our ways of working. This is not overnight. It's a long journey, and it has some sacrifices as well.

Your productivity is slightly lower when learning new tools, when starting to work in different ways, your output is lower, but the results really come when that productivity begins to increase because every technical insight you can pull out of your models results in a new space to explore, which then results in more insights. You start here to develop a circular innovation process. That circular innovation process is what we, now are doing, not just in process development, but all across Home Care.

I'm Ewan, and I've just taken you through how we built an active global community for a digital first-approach to innovation and sustainability at Unilever. Thank you very much.