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Similaire à UoH MBA Masterclass 21st November 2014
Similaire à UoH MBA Masterclass 21st November 2014 (20)
UoH MBA Masterclass 21st November 2014
- 1. Darwin’s Medicine Using evolutionary theory to predict the future of the pharmaceutical and medical technology industries Professor Brian D Smith
- 3. How does evolutionary theory help us? What does it predict? What’s the practical implication?
- 4. See “Complex Adaptive Systems: An Introduction to Computational Models of Social Life” by John H Miller and Scott E Page. Princeton University Press, 2007.
- 5. A population of replicators (e.g. Genes) Variation of replicators within the population Variation in traits of the interactors (e.g. Organisms) Selection of organisms by the environment Amplification of successful phenotypes Emergence of new species better fitted to the environment The mechanism of biological evolution
- 6. A population of replicators (e.g. Practices or “organisational routines”) Variation of practices within the population Variation in traits of the interactors (e.g. Firms’ strategies, structures and capabilities) Selection of firms by the environment Amplification of successful strategies, structures and capabilities Emergence of new business models better fitted to the environment The mechanism of industry evolution
- 7. Social technology environment of regulation, economics, politics, healthcare systems etc Physical technology environment of basic and applied physical and natural sciences Business models of firms’ strategies, structures and capabilities
- 13. A population of replicators (e.g. Practices or “organisational routines”) Variation of practices within the population Variation in traits of the interactors (e.g. Firms’ strategies, structures and capabilities) Selection of firms by the environment Amplification of successful strategies, structures and capabilities Emergence of new business models better fitted to the environment The mechanism of industry evolution
- 14. Six Great Shifts Demographics Healthcare inflation Expectations of healthcare Disease patterns Global wealth Global segments International trade Multinational corporations Capital markets Transaction costs Organisational capabilities Business risk Bioinformatics Enabling technologies Systems biology Systems medicine Platform technologies Connectivity Data analysis Artificial intelligence R&D technologies Supply chain management S&M methodologies Management approaches The value shift The global shift The holobiont shift The systeomic shift The information shift The trimorphic shift
- 15. The Value Shift Understand multi- dimensional, customer- perceived value and create context- specific value. Understand only clinical value as defined by healthcare professionals and value- creation only in terms of products Value as clinical, economic and other outcomes, as defined by some combination of healthcare professionals, payers and patients or their proxies. Value as improved clinical outcome, as defined by healthcare professionals Demographics: Shaping both demand and supply Healthcare inflation: Complex, inexorable and structural Expectations of healthcare: Rising, widening and politically embedded Disease patterns: Driven by age and lifestyle
- 16. The Global Shift Understand market heterogeneity, and deliver broad value to global segments Deliver narrow value to clinical categories Demand is global and heterogeneous along multiple dimensions of clinical requirements, payer preferences and patient needs Demand in developed economies and heterogeneity is limited and based mostly on differing clinical requirements Global wealth: Bigger, broader and more polarised Global segments: Alike, different and transnational International trade: Different advantages, new directions Multinational corporations: Powerful, hungry and truly global
- 17. The Holobiont Shift Polycentric networks that aggregate capabilities and manage risk Unicentric structures with fixed capabilities that concentrate risk Polycentric networks with fluid, ill-defined boundaries and scope Organisations with predominant centres and well-defined, stable boundaries and scopes Capital markets: Fluid, global and active Transaction costs: Clearer and shifting Organisational capabilities: Wider, deeper and more dynamic Business risk: Wider, larger and less predictable
- 18. The Systeomic Shift Translate systems knowledge into an improvement in returns or a reduction in risk Employ a reductionist, hierarchical, population based understanding of disease or injury Healthcare is proactive, personalised and participatory Healthcare is reactive, population- based and hierarchical Bioinformatics Expanding in volume, type and application Enabling technologies Shifting to a molecular level Systems biology From stamp collecting to information science Systems medicine: From Osler to the 4Ps
- 19. The Information Shift Adopt and adapt information technology to improve returns and reduce risks Small scale, fragmented, unidirectional and deductive information use Information use is large- scale, integrated, pervasive and inductive Information use is small- scale, fragmented, unidirectional and deductive Platform technologies Processing power, memory, batteries, sensor technology Connectivity: Pervasive, mobile, wearable Data analysis Bigger, more integrated and influential Artificial intelligence Towards the medical machine age
- 20. The Trimorphic Shift Focus strongly on NPD, SCM or CRM Spread resources across the value chain Organisations that are strongly focused on either customer intimacy, operational excellence or product excellence Organisations that are relatively similar in how they distribute effort across their value chain R&D technologies More advance, specialised and expensive S&M approaches: More granular and less complementary Supply chains: Global, polarised and protected Management approaches: More rigorous and less compatible
- 21. Six Great Shifts Demographics Healthcare inflation Expectations of healthcare Disease patterns Global wealth Global segments International trade Multinational corporations Capital markets Transaction costs Organisational capabilities Business risk Bioinformatics Enabling technologies Systems biology Systems medicine Platform technologies Connectivity Data analysis Artificial intelligence R&D technologies Supply chain management S&M methodologies Management approaches The value shift The global shift The holobiont shift The systeomic shift The information shift The trimorphic shift
- 23. Monster imitator Genius Genius Genius Monster imitator Disease managers Trust managers Lifestyle managers Health concierge Value pickers
- 26. 26 Base Pairs (Shared and Variants) Proteins (Active and Regulatory) Phenotypic Traits (e.g. Brain Development) Phenotypic Capacity (e.g. Speech) Genes (Coding and Non-Coding) Capabilities (Operational and Dynamic) Business Model Traits (e.g. Strategies, Processes, Structures) Organisational Capacity (e.g. MA Excellence) Organisational Routines (Explicit and Tacit) Microfoundations (Behaviours, relationships, skills, knowledge)
- 27. Organisational Routines: Leadership engagement in strategy - Market definition – Integrative Conflict – Customer Intimacy Operational Capabilities e.g.: Dynamic Capabilities e.g.: Contextual Segmentation Capabilities Market Aligned Business Intelligence Extended Value Proposition Design Capabilities Value Added Service Development Business Model Traits e.g.: Bicongruent Strategy Formation Processes - Synthetic Market Insight Processes Connected Marketing and Market Access Structures and Processes - Market Aligned Structures Organisational Capability to Create Strong Market Access Strategy Microfoundations: Goal orientation – Demarcation – Technical Competence – Knowledge Management
Notes de l'éditeur
- Good evening. Thank you for inviting me to speak this evening and for that introduction, which reminded me of my advanced years, which in a way is appropriate for this evening, because I’m going to start by talking about death. I recently became an orphan, by which I mean I lost the last of my parents. Both my mother and father died aged 84 but they had very different lives and deaths. My Dad lived a generally healthy life despite having suffering various infectious diseases, industrial injuries and the unwelcome attention of the Wermacht and the Luftwaffe. He lived independently and in reasonable health until he died quiet suddenly of a stroke. My mam died a few weeks ago but her life was marred by physical and mental illness for decades. Eventually, Parkinson’s Disease meant she couldn’t walk, talk or even swallow properly. She died slowly and unpleasantly.
- So how do we get that? What three wishes could a genie give us for a long, healthy life? I’d start by wishing for a strong, productive economy, so we can pay for healthcare. Then I’d ask for an efficient, effective healthcare system. But my third wish is for a successful life sciences industry, one that invents and makes the drugs and devices we need at a price we can afford. Now Genie’s are in short supply in Hertfordshire, so rather than rely on them, we’re working on that problem. In particular, we’re looking at how we can understand and accelerate the evolution of the industries business models. That’s the work of the work my sub-group within Professor Halliday’s Marketing Insight Research Unit here in the business school. And at this point I’d like to thank Sue and all of my colleagues here in the business school for giving me an academic home and making me so welcome.
- I’ve only time to touch on our work tonight so I’m going to focus on these three questions. I’ll talk for about 40 minutes and then I hope we can discuss these ideas.
- The starting point for our work is that the life sciences industry consists of lots of different agents that interact and adapt using simple rules. In other words, it is is a complex adaptive system and the bad news is you can’t simply predict such systems. They don’t behave in a linear way. However, the good news is that we have a theory for understanding such systems. That theory -which according to Dan Dennett is the best idea anyone has ever had – is evolutionary theory. Now I know many of you associate evolution with biology. That’s because most biological systems, like the rain forest or the bugs in your gut – are complex adaptive systems. But evolution is a way of thinking about complex adaptive systems whether they are biological or economic. It is just that, to quote J Stanley Metcalfe, the biologists got there first.
- Now I’m going to guess that it’s been a year or two since some of you were are school, so let me remind you, briefly, how evolution works. It begins with a population of things that can replicate themselves. In biology, these are genes. For various reasons, variation occurs within the population of replicators. This leads to variation in the traits of the things carrying the replicators, which we call interactors because it is these rather than the genes that interact with the outside world. In biology, the interactors are organisms. You and I are interactors. That variation in the traits of the interactors – a big brain, the power of speech for example - is then selected for or against by the environment. That selection leads to the preferential replication or amplification of those traits that fit the environment and extinction of those that don’t. Gradually, we see the emergence of a new population of interactors that is better fitted to the environment and these carry the new genes. And of course we call a population of interactors that share the same genes a species. Those key steps of variation, selection and replication or amplification are characteristic of evolution and indeed they are what separate evolution in this sense from any other kind of change or development.
- Now consider an economic system, like an industry, instead of a biological system. Again we have a population of replicators, but this time they are organisational routines; small sub-processes and ways of doing things like making new discoveries and finding customers. You have thousands of genes and your company has thousands of organisational routines. Again, we see variation in these replicators that leads to variation in the traits of the interactors. But in this case the interactors are organisations, like companies. And the traits of companies are things like structures, strategies, capabilities and so on. Again, those traits are selected for or against by the environment, although we tend to call it the market. Again we see the amplification of successful traits and the gradual emergence of a population with organisational routines that are different from the original population. The only difference is that whilst we call a group of animals that share the same genes a species, when a group of companies share the same routines, and therefore strategies, structures and so on, we call them a business model. And that, with apologies to my specialist colleagues in the room, is the basic idea behind evolutionary theory in the context of an industry such as life sciences. I’d like to move on then to how these ideas apply to the evolution of the pharmaceutical and medical technology sectors but before I do there are a couple of other ideas to which I need to introduce you.
- The first is that idea that an industry evolves, or we might say co-evolves, with its environment. In fact, evolutionary economists talk about two parts of the environment . The physical technology environment, by which we mean all the things we traditionally think of as science and technology, and the social technology, by which we mean laws, demographics and social systems.
- The second is the idea that the environment is itself complex and there are therefore multiple ways to adapt to it. This leads to speciation as business models adapt to different parts of the environment.
- These two ideas come together in the device of the adaptive landscape or fitness landscape. This is simply a three-dimensional graph in which the horizontal axes measure the variation in the social and technological environments and the vertical the fitness of the business model.
- Adaptive landscapes provide an interesting way of demonstrating the history of the pharmaceutical industry If we go back to 1870, the period historians call the beginning of the second industrial revolution, and look at the pharmaceutical industry there is really only one business model – the apothecary. This model was local, small scale, low tech and perfectly adapted to the social and technological environments of the day. Indeed, this model seems to have lasted about 600 years. But this period in history saw great changes in both the social and technological environments. Socially, we saw what Weber called the age of disenchantment, as we stopped believing in magic, and a first period of globalisation. We also saw early social security systems, for example in Bismarck’s Germany. Technologically, we saw advances in organic chemistry, germ theory and other scientific fields. We also saw advances in communication, such as steam trains and ships, telegrams in telephones. The old apothecary model didn’t fit that environment and those changes trigged a surge of industry evolution.
- If we move forward in time, we see that the apothecary model had more or less died out, absorbed into retail. The pharma industry now has two models – research based, such as Bayer, Hoechst and Roche – and OTC, such as Parke Davis and Beechams. Importantly, these two models had different routines, strategies and capabilities. They were both well adapted to the new environment but to different parts of the new environment. Move forward in time and environment changes again. Technologically, we have what is called the therapeutic revolution as first antibiotics, then beta blockers, ACE inhibitors and other new classes of drugs are discovered. Socially, we see the advent of new social healthcare systems, such as our NHS. But we also see the introduction of regulation after the thalidomide disaster and changes in patent laws to encourage generics.
- This leads to the further speciation of the research-based part of the industry. This is the industry I knew when I started as a research chemist at the end of the 1970s. Again, each business model is beautifully adapted to different parts of the social and technological environments. Hopefully, I’ve now given enough illustration of the theory and its application to move onto the main part of my session this evening, which tries to use evolutionary theory to predict the future and make suggestions for not only pharma but medical technology and life sciences more generally.
- As I said at the start of this lecture, what we’re really about is advancing knowledge both for its own sake and to advance practice. One component of that is working out where the industry is going so that firms can get in front of it. And that is a contentious point. Not everybody thinks that evolution is a predictive science, although there is some evidence from biology that there is. But I’m with Richard Dawkins on this one. I think evolution can guide predictions but that we shouldn’t expect them to be as straightforward as they are in some areas of physical science for example. I think that we can make some predictions by trying to identify the emerging properties of the system and working out what selection pressures those properties imply. For example, if I identified that an emergent property of our climate was for hotter, drier summers and more droughts, I might make broad predictions about the how our flora and fauna would change. And that’s what my work involves. Using primary and secondary sources, I am looking at the massive changes in the industry’s social and physical technology environments. I’m looking at how these things overlap and interact. And I’m looking to see what properties of the systems are emerging that will shape the industry.
- What I observe is the emergence of six properties of the life science system, six great shifts in the environment, that selecting for and against business model traits and so directing the evolution of this important sector. Let me tell you a little more about these emerging shifts in the environment before I move on to what that might say about the future of the industry of the industry
- Perhaps most obvious and most emerged in the value shift. A large number of factors, from demographics and its economic implications to our politically expressed expectations for healthcare, are combining to fundamentally shift the way that value is defined in the life sciences market and by whom it is defined. When I joined the industry, value was clinical outcome defined by doctors. Increasingly, it is health-economic outcome defined only partly by doctors and with the involvement of patients and payers like governments and insurance companies. I hope it’s obvious that this shift selects against the business models where the value is only clinical and in favour of business models that create better bang for bucks, which is unlikely to be by products alone.
- The second and equally obvious shift is the global shift. This is driven by emerging markets, the healthcare they demand as well as broader, more general trends in global trade. When I joined the industry, the world was the west and needs were fairly homogeneous. Today, the market is made of global segments in which customer needs vary more within countries than between them. This shift selects against a business model that invents, makes and sells as if the world was still only about a quarter of its population. It selects in favour of companies that can understand and address global segments.
- The third shift is perhaps less obvious but equally important. It is driven by capital markets and their attitudes to risk and return but it is facilitated by technology that influences transaction costs and organisational capabilities. The industry has historically been dominated by firms that were vertically integrated – inventing, making and selling their own products. What we see today is a shift towards networks of organisations as the competing entity. These remind me of holobionts in biology, organisms made up of several different organisms. Coral reefs and lichen are holobionts. And since a fair bit of your body mass is not you but the bugs that live on you, you too are a holobiont of sorts. Again, I see this as a selection pressure in favour of firms that can create and manage their own holobionts and against firms that try to keep the capabilities and the risk within their own boundaries. The value shift, the global shift and the holobionts shift arise mostly from the industry’s social environment but there are also at least three shifts and selection pressures arising from the technological environment.
- A very strong theme that emerges from my research interviews is that the science that underpins the industry is shifting from a natural science to an information science. It’s hard to overstate the impact this is having on the healthcare system and therefore the industry. We’re shifting from a 19th century, Oslerian model of healthcare to what Lee Hood call the 4Ps approach to managing health. This shift will select against firms that continue to treat illnesses and organs and select in favour of organisations that take a systems view of healthcare.
- Closely related to the systeomic shift is the shift in how we gather and use information more generally. Enabled by the convergence of a set of technologies, the information shift is having pervasive effects all along the value chain and big data, wearable technology and the internet of things are only its most obvious manifestations. Our industry has always used information of course, but mostly in a small scale, unidirectional, fragmented and deductive way. The information shift will select against that trait and select for firms that can gather and use data in a large scale, pervasive and integrated way.
- Finally and less obviously, we’re seeing the development of technologies in R&D, supply chain and sales and marketing. Importantly, these technologies, such as the use of synthetic biology, RFID enabled supply chains or behavioural data tend to conflict with each other and polarise the value creation processes of NPD, SCM and CRM. In the past, pharma and medtech companies were comparable in their investments and capabilities but today we see a polarisation into firms that are either research hyper-intensive, operationally super-efficient or seamlessly integrated into their customers’ value chain. The point, as Porter understood in the 1980s, is that this environment will select against generalist companies and select for ever more specialised companies, just as we see in the specialised variety of biological species.
- Clearly, the 24 phenomena in red on this slide are only the most salient points that emerge from my research. I could expand on them but I would rather stress that they combine and interact, in the manner of a complex adaptive system, to create the six great shifts I discuss here. It is the selection pressures that those shifts create that are shaping the industry and that’s what I’d like to cover next.
- The starting point is that these shifts will act together to fragment the industry environment. For the past few decades, most healthcare value has been defined and bought by western governments but that is changing. Instead, governments will be able to afford a decreasing relative share of what is technologically possible. The slack will be taken up by patients, rich or poor, paying for themselves. Similarly, most healthcare value has been created in the NPD part of the value chain but this appears incapable of creating the health economic value we now need. Instead, value creation will differentiate into innovative technology, creating superb but very specialised products, operational excellence, making incredibly cheap but basic products and by customer intimacy, reducing costs by integrating with the providers’ value chain. These changes will create a new adaptive landscape made up of many sub-habitats, each defined by the who defines value and how value is created. And just like a Savannah or a rain forest, the traits needed to survive in each sub-habitat will be very different. You need different capabilities to survive in the tree top canopy, in the undergrowth and in the river. And just as the sloth, the jaguar and the piranha have adapted to different habitats, we’ll see the emergence of different species of life science company, which of course we will call different business models. So the next step in predicting those business models is to predict the fitness landscape to which they will adapt. My work suggests something like this.
- Much more complicated than today’s and implying both extinction of old business models and the emergence of new ones. For example, we should see the emergence of operationally excellent companies making generic drugs and devices and selling them very cheaply to governments and the poor. We see the early signs of this of course with generic drugs and devices but the descendents of those companies will make their ancestors look small, inefficient and costly. These will be the monster imitators, to use Nelson and Winter’s term. These are very likely to be emerging nation companies. The next obvious business model will be the genius company. Compared to today’s research based companies, they will be much more technologically advanced, more specialised and sell extremely expensive products to rich people and rich, pressured governments. Look at Roche, Gilead and Celgene in pharma and some of the emerging neuroscience and implant companies in medtech. A third species will evolve into that space that consumes about 70% of government healthcare spending today, the chronic diseases of Asthma, Diabetes, Dementia and so on. I anticipate companies specialised in those fields will integrate into the providers. We’ve seen this in dialysis and nutrition. Medtronic is beginning this in cardiology and I expect to see it in areas like thrombosis. There will be a great opportunity in consumer markets to manage minor ailments and to maintain health. We’re seeing Walmart and others trying to get into this space now but I expect pharma and medtech and especially diagnostic companies to create holobionts to extract value from this space. Because trusted brands will be important here, I’ve called this model the trust managers. Similarly, I expect some companies to shift to preventative medicine and governments to pay them to do this. Some of the developments in telemedicine point towards this species. For the very richest, I expect there to develop the equivalent of personal banks, health concierge companies that manage your health rather than your wealth. World Clinic is already starting to do that. And finally we can already see firms that develop and retask old technology to fit small niches, like medicines for children or implants for athletes. Companies like Norgine and some branded generics companies are already doing this. The point is that nature abhors any kind of vacuum. If a habitats or sub-habitat exists, we know that something will evolve to occupy that habitat.
- So the future of the life science industry is speciation. Many more business models, each adapted to a different habitat and each requiring different capabilities to compete in that habitat. That, in a nutshell, is my understanding of our future. But Marx said that although philosophers try to understand the world, the point however is to change it. What we need to know, especially if we work in the industry, is how to adapt to this complex, fragmented environment and how to do so faster than our competitor. I’m going to use whatever time I have left to talk about how firms might manage their own evolution.
- Look at the problem this way. Your company is a chimp. Beautifully adapted to the environment of the west African rain forest. But its environment is changing. To thrive in this new environment, it needs to get new capabilities. It needs to learn to speak, to write, to walk upright. Now I would suggest to you that no amount of training is going to enable our friend here to do these things. He hasn’t got the genes to enable him to do those things. Over time, evolution might change the gene pool of chimps, as it did with our common ancestor. But that took millions of years and saw many dead ends. So I might try selective breeding to enhance the gene pool of a population of chimps. And that might get me there a little faster. But I suspect it would still take many generations. And it’s quite likely that some other population of chimps might get there faster, just as homo sapiens overtook other hominid species. So how might make evolution faster and more directed?
- Today’s technology might give us another answer. If we knew the molecular biology well enough, we could genetically engineer chimps, implanting the right genes and switches to express the right proteins that lead to the right physical and mental traits and capabilities. I think this is a really strong metaphor for how we might manage life science companies to out-evolve their rivals. If we know the environment we want to operate in, we can work out the capabilities and traits we will need. And if we know the traits, we might be able to work out the organisational routines needed to enable those traits. And just as DNA has base pair sequences, routines have microfoundations of habits and behaviours, which we can engineer if we understand them enough.
- This is the area that I am researching both here and at my other university, SDA Bocconi in Milan. This slide shows some of our current thinking for one capability – market access – and I hope it serves to illustrate the general principle . It is self evidently complex and difficult and I don’t pretend for a moment we understand it all. But you know what, I don’t think we need to. Even with our limited understanding of human genetics, we’ve been able to make great progress in managing some congenital conditions. And if it were not for ethical considerations, we could do a lot more. And I think we’re moving in that direction. I think even today I can draw lessons from evolutionary economics that can help pharma, medtech and other life science companies adapt to their rapidly changing world. Indeed, that is what I spend some of my time doing.
- And I think this is important work. If we get it right, then this little girl will have a much longer, happier lives than her grandmother did.