Strategizing for 2027 and Beyond: Managing Your Portfolio Toward Results

Anthony Schiavo: Hello and welcome to the webinar, Strategizing for 2027 and Beyond. Part 2, managing your portfolio toward results. My name is Anthony Schiavo. I’m a Senior Director and a Principal Analyst here at Lux Research. And I’ll be moderating today’s session. Presenting today is my colleague, Dr. Ari Van Berkel, the Chief Product Officer here at Lux Research. Throughout this webinar, you can type any questions you have into the questions box on your screen. Time permitting, we will answer all the questions that we can. If your question does not get answered, please do not hesitate to e-mail it to [email protected] and we will respond. If at any point you experience technical difficulties, such as a frozen screen, simply refresh your browser and check that your internet connection is strong. Before we start the webinar, a word about who we are. Lux Research helps organizations make more confident decisions about innovation and what matters next. As an independent research and advisory firm, we work with many of the world’s leading companies to identify emerging opportunities, evaluate technologies, and make high impact innovation decisions. Our scientists, engineers, analysts, and industry experts combine original research and methodologies with practical decision frameworks to help organizations separate signal from noise. Every day, we help our clients answer three critical questions. Where should we focus our innovation efforts? Which technologies deserve investment? And which partners can accelerate innovation? In today’s webinar, we’re going to be sharing a lot about that, especially about how to organize and perhaps even optimize some of that innovation work. So with that, let’s jump into the webinar.

Arij van Berkel: All right. Well, thank you, Anthony, for that introduction. Let me start by saying that this is the second part of a two-part webinar. There won’t be a third part, so it’s two parts. And we’re doing this every year. Most of our clients right now are in a planning cycle for their next fiscal year. Not everyone has the same fiscal year, but most have a fiscal year that starts around January. And we thought it’s useful to provide input into that planning cycle. And this year, we really wanted to talk about benchmarking and about how to see what are good metrics for your innovation. Obviously also inspired by Peter Drucker, who mentioned that any business enterprise has two and only these two basic functions, marketing and innovation. At Lux, we support both marketing and innovation through our consumer insights and our technology research. So without further ado, let’s take a look at planning your innovation and managing it towards success. So in part one, we looked at innovation inputs. We looked at how can you benchmark two major inputs. One is the amount of effort you put into your innovation as measured, for example, by your R&D intensity. And secondly, the organization structure, the innovation engine, how comprehensive is it? What elements are in there? How does that compare to your peers? In this part two, we will look a bit more inside the engine. How do you know the engine is running properly? How do you know everything is well aligned and moving smoothly? And for that, it’s good to have this framework in mind. So when we think about innovation, we think about creating change. Innovation is about change at various different levels. So ultimately, innovation should change the impact your organization has on the world in any way that you would like to change your impact. That is your company strategy. So for example, you could reduce the CO2 footprint of your company. That’s changing the impact. But also, you could provide healthier food options to consumers. That’s also part of your impact for some companies, that is. And there are many other ways in which you can define your impact. That impact can only change if you have a change in the markets, in how you approach markets or in which markets you approach. And that can only happen if you have different products or services to offer. And many of those different products or services are enabled by technology, not all of them, but many are. And that technology requires innovation assets to develop. And those innovation assets could be hard assets, labs, facilities, pilot plants, and they could be soft assets like relationships with universities, partnerships in open innovation ecosystems. So within that entire system, we distinguish three layers of innovation, or three levels, as mentioned here, right? You have the program level, which contains all of your innovation efforts and acts in the long term. Your program could be divided into portfolios. For example, a portfolio per market or a portfolio per business unit or a portfolio per type of product. And within the portfolio, you have the actual innovation actions. We call them actions here because one sort of action is a research and development and innovation project, RD&I project. It could also be a partnership. It could be a licensing deal. It could be an investment from your corporate venture capital. It could be a tech scouting activity. So there’s many different innovation actions. That’s why we prefer the term action as opposed to project. But in many cases, an action will just be a project. All right. So all of that is happening. It’s a large activity. It’s actually grown a lot in the past 25 years. It went from managing your corporate R&D to all of these different types of actions at these different levels with different types of targets. So how do you get in control? And we’ll be following my good friend, Dr. Felix Sparks here, who’s a figment of GPT’s imagination, I might say. But he’s a systems engineer, an innovation systems engineer. So we’ll be following him as he tries to get a grip on all of this. And the approach we’ll take here is by looking at it from a control engineer’s perspective. So what if your innovation system is a dynamic system and your job is to create the control room and to turn the knobs in the control room to optimize the way the system is working. Now, obviously I realized that that’s an idealized vision and I know that innovation in practice is maybe a bit less neatly divided into different system components. Please try and forget about that for a moment, and let’s look at where the system view gets us, because the system view will help us define good metrics and good control knobs. Then we’ll get back to the messy real-world experience later on. What does the system look like? Most innovation systems that we’ve seen look like this. There is a pool of ideas all the way to the left, which is filled continuously. For example, through tech scouting. The pool of ideas serves as a pool to select from for things that you are going to put into your innovation process. And the innovation process typically takes the shape of some sort of stage gating funnel where you go from a proof of concept to developing a business case to really developing and prototyping the new solution to validating the solution and eventually launching it. And after launch, your innovation ends up in your product portfolio. And then there’s a feedback loop where you can have stopped or parked ideas that either go back into the idea pool or are archived and eliminated from the system completely. And on top of that, there are knobs and there are metrics, right? And that’s the orange bit here on the slide where you have your innovation management dials, right? You can change the resources, you can change the selection criteria, which determine how the system operates. So let’s take a closer look and I’ll give you a couple of very broad ranges of what you should imagine these kinds of metrics in different parts of the system are. These are by necessity, they’re very broad ranges because you are part of very different industry sectors that have a very different environment and you’re also probably part of very different geographies. which means that there’s a lot of difference between your environment and therefore you should have your own set of metrics eventually. But I think the ranges I’ll show will at least give you some guidance of what you should be thinking of as a reasonable metric here. The slides will be provided to you at the end of this webinar. While you’re more than welcome to take screenshots, the downside is you’ll have me in the picture all the time, but you can also just wait for the slides and that will give you all the numbers that I’ll show here. So if you look at the first part, so here you can see the pool of ideas all the way to the left. And there are two types of metrics for each one of these. So for every one, I will show you two types of metrics. One is activity. That’s the resources you put into it. Are you deployed? Are you doing enough to fill the pool? So that could be patent filings, right? Which could be between 20 and 200 per technology domain that your company covers per year, that is. You could run ideation workshops and you should be running those maybe between four and 40 per business line. It depends a little bit on how big the product portfolio for each business line is. You should probably be running consumer or client surveys, maybe between four and 12 per market segment. So that’s the activity part. Then on the other side, how do you know the activities are effective? How do you know you’re getting enough out of that? Well, in the idea pool, you should have about 5 to 50 patent families for each technology domain. You should have between 5 and 20 business ideas per business line continuously. You should have identified client or consumer trends for each market segment and there should be between 3 and 10. There may be circumstances where it’s a lot more, as you can see, there’s a white line, right? The indicator of where the actual level is. And for the business ideas, I purposely put it much higher than in the green zone, right? There could be reasons why you wanted to have many more. It could also be you have certain design or design ideas and solutions or solution ideas in your pool of ideas to draw from. And again, you can see the gauges here. So you have this and then you start selecting ideas to work on. So what is a good kind of activity to do that? So what is that tab that moves ideas from your idea pool into your innovation funnel? So you should be doing feasibility studies probably, maybe between five and 25 per business line per year. You should be screening your ideas. Maybe the screening could take between two and eight weeks. So don’t go crazy screening for an entire year. Don’t fall into the analysis paralysis trap, but also do take your time. Screening is meaningless if it takes just an afternoon, typically sometimes it can be done. Develop business cases. Look at how many ideas you are typically funding. So are you getting say 10% of ideas out of your idea pool or is it closer to 80%? And a healthy ratio is between 10% and 35%. How many ideas are you stopping somewhere along the funnel, right? And somewhere along the funnel between 40 and 80% of ideas should not make it through typically. And again, that depends a lot on the sector and the kind of innovation you’re trying to accomplish. So then you can look at what should be in your development pipeline. So typically we see between three and eight active portfolios and you can see within the projects, the actions within each stage. You can look at maybe between, if you have between five and 10 in stage one, then it’s probably between three and 10 in stage two and between two and five in stage three. Those are kind of the ratios. Now, that could be for your portfolio or for your program. So it depends a little again on how you’re organized. I would say look at the portfolio level. So if you have four active portfolios with each about 10 active actions in stage one, that means that overall in your entire program, you have about 40. And then finally, launch. Now, this is interesting because some people would argue that this is not part of your innovation system anymore. So launch should not be part of your innovation system. It is where you hand over to the business and you forget about it. You’re busy back at the start of your innovation funnel. I would argue it is part of your innovation system because a lot of things can go wrong in the handover there. So you should really be actively involved in handing over the project and making sure that manufacturing is started, that sales is started, that marketing is involved, et cetera. And so there are some metrics there that you should be, if not accountable, at least responsible for. And those metrics are more suggestions here because those should be related also to the impact targets of your organization. So it could be about cutting emissions, it could be about new jobs created, it could be about cost reduction, growing sales. That’s not uncommon, you might say. It could also be about being able to set a higher price for your product portfolio on average. And I’m sure there are many other impact targets that could be mentioned, but these are some of the more common ones. So those are the numbers. And we’ll look a bit more on some specific comparative numbers in a moment. But what are the knobs? What can you do to change these numbers? What are the knobs that you can be turning or that in my example, Dr. Felix Sparks could be turning? Well, for example, on scouting, on any of these, you can set knobs on the resources, on the people, on the budget, on the time it takes, and you can change selection criteria. For example, on scouting, you could say, well, I want a novelty score between five and 10. So it should be, it could be not too novel, but it should not be completely mature already. And I want a good strategic fit and it should score between six and 10, depending on how you score that obviously. And typically we see in scouting, some companies have one or two people doing scouting. I would say up to six to 10 people is quite normal. And you could see a cadence of about three months. So you need new ideas generated roughly every three months. And then don’t forget, you also need to maintain your idea pool. So you may wonder, okay, there’s something on the scouting tab, right on the flow of new ideas, but then there is also the pool of new ideas. It needs to be maintained. What do I mean by that? You need to monitor the ideas in that pool and you need to make sure that those ideas stay current, that you are still aware of what’s happening there. And that could take about two people. It could be in the pool for about 12 months. You monitor it for a year, then you take a final decision. Do you want to keep it in the pool or do you want to archive it and just forget about it? In selection, again, typical criteria could be the market size, could be the technical feasibility. You might want to dedicate maybe four people continuously to doing market studies, to doing feasibility studies. We sometimes find there’s no people assigned to it permanently. They’re just assigned on a project basis. Some companies have closer to, say, 10 people doing that. And typically you should take about up to two months, right? That’s the two to eight weeks that I showed earlier. Then the development pipeline, of course, is where the magic happens. So this is where you have the bulk of your resources. Even in the launch phase, you should still have about, say, eight people dedicated to handing over to your business, and it should take about six months. They should stay involved for about six months. And then you still have people also dedicated to supporting the product portfolio afterwards. If you want to have a reasonable guideline, you could say that your development pipeline should represent about 80% of your effort and budget. I think that is across the board something that we find, which also means 20% of your effort, budget, and resources are dedicated to things like scouting, selection, organizing, the handover of your innovation to the business, et cetera. So that is a pretty good ratio. If you look at the product portfolio and the support for that, you should imagine that front-end loading needs to be about 10% of the capital expenditure involved in deploying your innovations. So if your innovation involves building a multi-billion dollar facility, then you should dedicate more time and attention to launching the product portfolio and the launch. So those are rough numbers. Those are pretty high level ranges that I showed you, right? So how can you diagnose your own system now, right? And that is really taking four steps. So first is just measuring. Draw your system like I drew it here and look at what’s there. So do you know how many ideas you have in your idea pool? Are they well documented? Can you go to a list of ideas that you could be evaluating? Do you know how that compares to your peers? So you can do that for all of the elements in the system. And then can you find somewhere a constraint? Can you find a bottleneck? Where do you find things are significantly less than your peers in the industry? Where do you find that things take longer or that you lose a lot of ideas or that things are not progressing as well as you would hope they would? And then find a way to solve that. So let’s assume you have your whole system picture already. So you have measured, but now we’re going to benchmark, right? So we asked Felix to benchmark against his peers and you can see that his peers really look like peers in this case. So first, let’s take a look at productivity. And we looked at a number of peer groups. Specifically, we looked at diversified chemical companies and specialty chemicals as a different type. So that’s the chemical industry, light blue here. Then we looked at oil and gas and utilities. So that’s the energy industry, orange here. We separately looked at electrical components and diversified industrials. That’s the green category here. So it’s the kind of the equipment manufacturers if you like. And then we looked at the end of the value chain at food products and personal and household products. So personal care and home care. And you can see the results here. We plotted the production of patent families per year per billion dollars of revenue against the R&D intensity. You can see the lines here showing how many families per million dollars of R&D are being produced. What you can see is that the patents per dollar of revenue are spread, you can see industries clustering around here, but particularly if you look at the number of families per million dollars of R&D, you can see that it’s, there’s a large spread, but it’s between one-tenth and three families per million dollars invested in R&D. That is roughly where all of these companies sit. You can also see that utilities, utilities is a very small sample because a lot of utilities are not that explicitly active in R&D. These are all quite small samples and we’re still adding to them as we speak. So this research is ongoing and there will be more added to it. But you can see that there’s a big spread in utilities. That’s because utilities globally have very different approaches to R&D. You can see oil and gas are all just below the $1 million per R&D, right, between the– and many of them between 0.3 families per million dollar R&D. Since they have a low R&D intensity but still very high revenue, oil and gas companies are still producing quite a lot of patents. So it may also be kind of a saturation effect. More interestingly, if you look at the equipment manufacturers, the electrical components and the diversified industrials, you can see they’re relatively high in patent production. We can look at some other metrics. An interesting one is collaboration. One thing we looked at is collaboration as evidenced by publications. So we looked at academic research for the companies that we investigated. And we looked at whether these publications were done with only affiliations from the company. So for example, would Philips Research publish papers only with Philips Research people? Or is there a collaboration either with academia or with other companies? As you can see, the diversified chemicals are actually most collaborative here. Now, you can have an argument about how significant the differences are here, but what stands out to me really is integrated oil and gas, still publishing a lot on their own. So without any collaboration, particularly diversified industrials. Now, publication is also an IP management activity, because by publishing, you’re creating freedom to operate, preventing anyone else from patenting something that maybe you also don’t want to patent. So I can see how diversified industrials can have an interest in, even though it’s not about a collaboration, it’s still publishing quite a few things on their own. Having said that, you can see the median is always 50%. So at least 50% of the publications are always done together with other institutes or other companies or organizations. We can also look at patent protection. Now here we looked at how big patent families are. So are you just protecting in one geography or are you protecting your IP, your patents in multiple geographies, multiple countries, for example? Unsurprisingly, utilities have a very strong tie to the geography and tend to protect, patent protect things only in their country or geography of interest. As you can see, everyone else, about 50% of patents are applied for in more than one geography. This may be a little bit skewed because sometimes you have some more time to apply for additional geographies and then it will only become apparent a bit later. And then how much of your total innovation spending is spent externally? That’s another interesting question. So what do we mean by that? So first of all, we estimated total innovation spending. Total innovation spending is not the same as R&D expenditure. R&D expenditure is a part of total innovation expenditure, but there are other things happening as well, like investments in open innovation networks, spending on corporate venture capital, sometimes tech scouting may be a separate activity where you fund things that are not part of your R&D expenditure. A good example is innovation challenges, for example. So we looked at the total innovation expenditure. We made an assessment of that. And then within that, we looked how much is used for external funding. And you can see here clearly integrated oil and gas companies, they spent a median of about 10 to 12% on external innovation. Whereas for example, the chemical industry tends to spend a lot less, closer to like 2% of their total innovation spending. And you can see utilities do tend to spend a bit more externally. Again, utilities are very distinct, so don’t take that as to be meaningful for all utilities. This is not true across all utilities because this is the subset of utilities that report R&D expenditures. So there are many utilities that don’t report any R&D expenditure and they will be all the way down to maybe even 80% of their innovation spending being external because they don’t have their own in-house R&D efforts. Looking a little bit closer at external collaboration and plotting it against R&D intensity is interesting because what we can see here is that there’s nothing to see and that’s interesting in itself. You can see that external collaboration really is a strategy decision. So there is no correlation. A lot of these are outside of the bands of their own industry. So they’re very spread, right? There’s a lot of diversity within an industry segment. So an industry segment is not a good predictor for how much external collaboration, how much of your innovation efforts you’re spending outside of your company. And also R&D intensity is not a good predictor. So really this is a choice that you’re making. When you’re comparing yourself to your peers, you should keep that in mind. There is not a sector or a peer average that you should be comparing to. It’s just if you feel that you are very externally oriented and you find that most of your peers are even more so externally oriented, then maybe your self-image should be adjusted or the other way around, of course. Let’s say you’ve drawn up your system. You’ve looked at your own metrics. You’ve looked at your industry peers. You’ve compared it. You think you have diagnosed it. How do you change it? Clearly not as Felix is changing it here because clearly he doesn’t really understand how to use a spanner on a nut properly. But let’s assume you’re all very capable, and I do believe you are all very capable. So what are some good interventions? And we’ll be looking at four cases here just to see what intervening in this system, what turning a knob actually looks like. So here’s one example, and these are all historic examples. So this is Procter and Gamble, who in around the turn of the century launched a specific group within their innovation organization called Connect and Develop. And Connect and Develop was inspired by the CEO, at the time arriving CEO, who found that P&G was not doing enough externally. It was too inwardly oriented in its innovation and so it wasn’t getting enough good ideas, at least he thought it wasn’t. In fact, P&G estimated that for every capable scientist inside, there would be about 200 people outside who were equally capable. So you should, in theory at least, be able to multiply the available pool of ideas by about a factor 200. Now, of course, that will never happen, but what did they get? So the goal was the new Connect and Develop group should make sure that about half the innovations that P&G was working on involved some sort of external partner. So that led to an entire building of innovation assets, participation in open innovation networks, connecting to universities, et cetera. And the result was they went from 15 to 35% of the new products where there were some sort of externally sourced elements. And they did that in about six years. And the R&D productivity, the innovation success rate actually, so the number of ideas that were selected for development that ended up being launched more than doubled. 60% higher R&D productivity. That’s one example. So this is an example of turning the scouting tab and dedicating more resources to technology scouting with a very specific target. Another example is Shell’s GameChanger. So this is again turning the knob for scouting and selection and creating its own budget and system for that. So this is really, Shell launched this in around ’96 already, initially sourcing both internal and external ideas, but then gradually it started to go towards just external ideas. And it really is about finding unconventional ideas and giving those ideas a shot, unconventional as in ideas that would not be created or live within Shell, but that could be given an opportunity to prove their worth outside of Shell. It’s estimated that at the peak, about 40% of the core R&D program was in some way inspired by the game changer. And remember, they started by internal idea sourcing as well. I would say about 20% of the game changer team itself concluded that about 20% of the ideas were fit to be adopted by the Shell organization in their own estimates, which is pretty good. 20%, one in five, that’s not a bad score at all. AstraZeneca was looking at another problem. So they found another bottleneck, which is they had a good pool of ideas and those ideas were flowing quite well into the innovation pipeline. But the innovation pipeline had a very poor conversion ratio. Just 4% of ideas were making it through the innovation pipeline. So that’s an issue, of course, and what was the intervention, really being much more explicit about the selection criteria. And they defined five selection criteria here. The target, a tissue, the safety, the patients, and the commercial potential. And there were clear go/no-go criteria, particularly for admission into the development pipeline, but also at the various gates. And by making those criteria much clearer, you not only select, you’re not only more selective about the ideas, but you’re also being much more clear about the objectives to the people that are working on these ideas. So it has a double effect. You’re taking out ideas that will never make it anyway, but you’re also putting everyone on alert who’s working on the ideas that do make it into the system because they know a lot more about what they need to achieve in order to make it through the next gate. So the amount of projects in the pipeline halved as a result of that and the success rate went from four to 19%. which if you do the math, you can see the success rate became almost five times as large with half the projects. So more projects make it through the pipeline, whereas you could also spend more on each project. So it could also go faster in some cases. Eli Lilly did something similar, but really looked at the first stage gate. where they outsourced a lot of the work on the first stage gates to speed it up and to have a much more critical external eye on it, an external evaluation on it. So their idea was to find candidates that would fail anyway at later gates, finding them early on so they could spend more money on the ones that survived. And to do that, they set up something called Chorus, which was an independent group that really evaluated all of these ideas early on. And that really helped them to reduce the number of things that they were working on in the later stages and therefore have more money available for those. Now there’s also an example of where you can turn the knobs in the wrong way. And this is Lego, where Lego was dealing with the fact that their patents were expiring. So their protection was expiring. And their response was to produce a lot more new releases. So they were creating all sorts of special boxes, special editions, themed products with a lot of special components as well. And so they increased the number of products on sale in the product portfolio by a factor of three, whereas sales themselves only increased by 5%. And meanwhile, they were increasing their complexity in delivering those products, in manufacturing and delivering those products to a large extent. So they were growing their business only a little bit and growing their costs a lot. And so the intervention was to be much more critical about the product release tab, dial it back down, and they dialed down the unique elements, the number of unique Lego bricks they had to produce from about 12,000 to about 7,000, making the company profitable again. So what does it all mean? If you view your innovation system as a system and you look at it from a control engineering perspective, if you look at it as an innovation systems engineer, then this will help you to get a clear picture of where you are and it will enable you to benchmark your effort. There are well-established reasonable ranges and I’ve shown a number of them. And as I said, we will share the deck so you will have those. And then changing your system really is possible where you have targets of interventions. So you can set a target saying half the things I work on need to have some sort of external partner. That is a perfectly reasonable intervention, which you can organize. And we’ve seen many other interventions here as well. So really your agenda, your action-to-do list is building your innovation system model. Identifying what knobs you have at your disposal and there may be knobs missing and you need to discuss that internally. Identify any bottlenecks and design interventions for it and keep on doing that, right? Keep measuring the change. Also trust your analysis. The interventions will be slow to evolve. It took P&G six years to show the results. And six years is a pretty good timing, it’s not too long. So with that, let me thank you. You can see Felix is a lot more relaxed right now, having mapped everything and being in control. So let’s see if there are any questions.

Anthony Schiavo: Yeah, thank you. Thank you, Ari and thank you to our audience. We will now be taking questions from the audience that you may have on the presentation. You can type those in the questions box. If we do not get to your question on the call, we will come back to you and we’ll be in touch after the webinar. Ari, we have a couple questions here. I wanted to start actually with one of my own. We’ve just conducted our Lux Research Innovation Survey and we’ll be doing a webinar about that very shortly. One of the things we found in that survey is that, in the real world, these innovation structures are often quite messy. We found that innovation teams are doing a bit of everything: you have open innovation teams doing R&D, you have R&D teams managing open innovation. Everyone is doing a little bit of CVC, it seems like. And so when you present these structures and talk about modifying them, it’s all well and good when they’re numbers on the spreadsheet or knobs on this image. But how does this work in the real world? Do you have any guidance in that context?

Arij van Berkel: Yeah, I recognize that and there are a lot of reasons why it’s messy in practice because you need a lot of the expertise in many of the different functions. So while you’re doing tech scouting, you may have a couple of people who are dedicated to tech scouting, but they will get a lot of different ideas. And they will want to know if those ideas are worth anything or what kind of questions should they be asking? And so they will involve some of your heads of departments for your R&D group, for example. So those people will say, yeah, I’m also involved in tech scouting because they’re continuously looking at those ideas and helping to evaluate them. So I can see how there’s a lot of interaction in the system that is not shown in the diagrams here. I would say that that still needs to happen. So don’t make that go away, please, because that is really critical to the functioning of your innovation. However, do treat it as a system like this. And that means make sure that you have a dedicated meeting and a dedicated metric about your scouting and a dedicated meeting about managing your innovation funnel and don’t mix the two. So make sure that your decision process is well organized as a system, that people are running around in the system and providing input in every part of it. That’s fine, as long as you separate it as a decision process.

Anthony Schiavo: So we have another question here. And There have actually been a few sort of questions or comments about this point. But you brought up a number of different industries, right? And you mentioned how these industries sort of perform differently in terms of their innovation outputs. And then you gave a couple of examples. AstraZeneca was one in the pharmaceutical industry. I think that’s an industry that people know and recognize as having a lot of innovation. There’s a lot of molecules, a lot of new potential products, a lot of screening. How do you think about bringing some of these practices from other innovation or other industries, I should say, to more technical or more industrial industries? Now you mentioned the industrials industry sort of producing somewhat less different types of innovation outputs. So I guess the big picture question is, can these industries learn from each other? Are differences in output a function of industry or is this something that we can change?

Arij van Berkel: No, I think there are different stages. So what we see is particularly for industrials, so for the equipment manufacturers, we see that the innovation has gradually become increasingly visible and important. So we went from innovation not being mentioned at all in some cases in the 1980s to innovation effort now being on par with capital expenditure. So it’s becoming highly visible and it’s becoming a critical part of business. Peter Drucker said so already in the 1950s, but, okay, it took a couple of years for the rest of the industry to catch up. The thing is, industries like pharmaceuticals are way ahead of that. So they’ve experienced, they’ve gone through that experience where innovation is such a major part of their expenditure that it’s highly scrutinized. And so it forced them to develop these systems sooner than anyone else. But everyone else is now also forced to develop these systems. So if anything, you could learn from their learning curve and their experience in developing these systems and see how it applies to your industry. So I would highly encourage industries to look across each other’s borders and borrow best practices with the appropriate modification, of course.

Anthony Schiavo: All right, well, I know we’ve been going long, so I think that will conclude our webinar for today. The slide presentation and recording from this webinar will be sent to all attendees via e-mail today. After leaving the webinar, you will be prompted to complete a survey on today’s presentation. And we really appreciate any feedback you have. It really helps us inform and improve future webinars. You can also take a moment to check out our upcoming webinars on our website. I mentioned one already on the innovation survey. If you found this interesting, I think you’ll appreciate that one as well. Thank you for joining us and have a great day. Thank you.