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The Lean Scale-Up: Innovation & Entrepreneurship for New Ventures

Traditionally, organization design (OD) is an area of expertise focused on the roles and formal structures of organizations. The main goal of OD would be to design the organization in such a way that it makes it possible for the company to reach its vision and thus facilitates the growth.

But the world is changing, and the digital era calls for a fresh view on how to design organizations. Organizations are now operating in a globalized, dynamic world and see their workforce change from homogeneous to diverse and educated. Innovation is almost always focused on information, is knowledge-based, complex and customized – which shortens the time to market and increases first mover advantages. All this, calls for more organic, innovative and learning organizations that are lead by strategic leaders (Greiner, 2004).

As a result, to facilitate the growth of a company, organization design needs process view. In stead of formalizing the structures and architecture, a company that is in the process from start-up to scale-up needs to formalize its processes. Roca wrote a pretty good article about that a while ago: from the sandbox to the hive (Model for Organization Design, Roca).

Many popular tools are based on this process view on organization design. Think about The Lean Start-Up (Ries), Agile, Business Model Generation (Osterwalder) and Customer Development (Steve Blank). What all these tools have in common is that they suggest an easy to use model that is usually cyclic, includes iteration, is non-lineair and are focused on a way of thinking.

In my quest to place these tools into perspective, I generated a new integrative model: The Lean Scale-Up – how to get from start-up to scale-up. Below you’ll find an overview of the model. You can download the full infographic.

An abstract from the infographic in more detail:
Detail

The Making Of…

Of course, the idea originated when I was reading somebody else’s perspective on entrepreneurship – and more specifically on the Lean Startup. It was the Nordstrom Innovation Lab who firstly created the following image on the relation between Design Thinking, Lean and Agile. Simply beautiful. It helped me, and many other to understand the link between the three in more detail.
Nordstrom Innovation Lab

The problem of that model is that it ‘stops’ at the lean startup, while scaling-up is one of the most intriguing aspects of Organization Design. During the start-up phase, organizations are very informal (both processes as structures), but in the process of scaling up, companies need to formalize their processes. Dyer and Furr (Harvard) took it one step further. In their model “End-to-End Innovation“, they proposed a cyclic view on innovation that included design thinking, lean thinking, agile, but also business model design and scale-up. It also related Open Innovation to the cycles, but I think they are wrong by placing OI only in the front end of innovation.

Distinguishing between the Lean Start-up and Business Model Design isn’t as easy as it looks, but I liked their suggestion that Business Model Design is something that usually comes into place when innovations have been created. A Business Model is only a topic when growing. But of course, as Furr says:

Naturally, innovation is a messy process and you may find that you start somewhere else on the figure (e.g., you already have a solution or business model innovation in mind), but the figure helps us remember that even in these cases, each element has to be addressed before you try to scale the business—or you are in grave danger of failure.

Deciding to put the Lean Start-Up process before the creation of a business model was a tough decision. But also Alexander Osterwalder has suggested that the lean start-up focuses on testing, while business models focus on designing the product. He also includes a step in between: the customer development process, developed by Steve Blank in his work The Four Step to Epiphany. He says:

We already now know how to do this kind of designing and testing for business models: by combining the Business Model Canvas with the Customer Development process. Steve Blank has impressively demonstrated this in his work.

We can achieve the same for Value Propositions by combining the VP Canvas with the Lean Startup process. This will help us more systematically work towards achieving what the startup movement calls a product-market fit or problem solution fit. In other words, building/offering stuff that customers really want.

So, in practise, these two are more often combined with each other then followed by each other.

Osterwalder

The research cycle was partially based on ResearchMap.info and Barringers work on New Ventures.

The last cycle, whick takes up 95% of the actual time that businesses are alive, is based on Greiner’s work on revolutions and evolutions as organizations grow. We have wrote on that before, for example in this post.

And last but not least, we have included the funding process of businesses in the scheme. Not very detailed, but you’ll get an idea.

IMG_4152_Dit_is_hem_120_100.jpg This article was written by Jan Spruijt. Jan Spruijt is a senior lecturer and entrepreneur in Innovation Sciences. Connect with Jan to stay in touch:

7 Billion Universities: How Simulation Games could disrupt Education

Over a week ago, I gave a TED talk about the role of simulation games in higher education and how they could disrupt the education system as we know it. Below, you’ll find the video, the slides I used and the full transcript of the text. Are you interested in helping me to make this game ready to enroll in a couple of years?

7 Billion Universities | Jan Spruijt | TEDxVenlo

Innovation Ecosystems

The Innovation Ecosystem

The Innovation Ecosystem is one of the most under-researched topics. One the one hand because policy researchers usually tend to focus more on polls, elections and international collaboration and business researchers usually tend to focus more on organizations and interorganizational collaborations. However, publisher Edward Elgar has repeatedly published interesting works on innovation policy, innovation systems and the like. An ecosystem of innovation could be described as, quoting Wikipedia, the flow of technology and information among people, enterprises and institutions [which] is key to an innovative process. It contains the interaction between actors who are needed in order to turn an idea into a process, product or service on the market. The Innovation Ecosystem is extremely important to the economy and welfare of a country or region. It is one of the main drivers of GDP. Over the past decades more research has been done on the dynamics behind these ecosystems and its subsystems. Below you’ll find a schematic overview of the innovation ecosystem. It will take you to the download side of Innovative Dutch, where you can download it in full resolution.

Download full-resolution infographic
CLICK TO DOWNLOAD

System Dynamics within the Ecosystem

The Innovation Ecosystem could be defined as a dynamical system. Dynamical systems are a theory first mentioned by Jay Forrester in the 50s and applied to a wide range of disciplines such as demography, ecology, evolution, economy and sociology. It suggests that systems contain complex feedback loops, causal links, flows, stocks, delays among the agents. Because these agents influence each other with complex logic, mostly non-linear, it is very hard to predict how the system will behave. Usually the basic feedback loops consist of positive loops, that will keep enhancing itself without limitation. However, the system also holds several negative loops, that will discontinue the positive processes. Systems usually continue switching between positive flows and negative flows, making fluctuations very common. For instance, in climate, is it common to have fluctuations per the hour, per the day, per the season, per the year and over long era’s. The same holds for the economy or for rabbit populations. Complex dynamical systems can be mathematically programmed. The following example shows how a system with only two actors can even achieve chaos within a few cycles when there are small anomalies in its initial circumstances. This is called the chaos theory: imagine the long-term effect a small change can have. For instance, the effect of a local forest fire on the weather world-wide. Or losing a coin on the local economy. Innovation ecosystems work the same. There are many agents, that are influences by a wide range of actors. Imagine the effect of low-level corruption on a national ecosystem or the effect of a successful start-up on the world-wide ecosystem.

Innovation subsystems

There are many different subsystems of innovation, for instance:

  • National Innovation Systems: ‘The network of institutions in the public and private sectors whose activities and interactions initiate, import, modify and diffuse new technologies’ (Freeman, 1987). Another word that is used on a regular basis for NIS is “Institutional Environment” – which describes the institutionalization of innovation policy in governments, research institutes, advisory boards and educational institutes.
  • Regional Innovation Systems: ‘The regional innovation system can be thought of as the institutional infrastructure supporting innovation within the productive system of a region.’ (Asheim & Gertler, 2005). While NIS focuses more on the institutional environment of innovation, RIS usually focuses more on soft factors, such as network characteristics, trust, identity, cosmopolitism, quality of life and culture. These factors are often the first things if we think about successful RIS such as Grenoble, Silicon Valley, Helsinki or Brainport.
  • Sectoral Innovation Systems: during the early zero’s more attention has come to sectoral innovation systems. In contrary to NIS and RIS, SIS focus on globally active sectors that function independently of the institutional environment. For instance, the Dutch government now prolongs the Top Sector Policy, focusing on different global sectors. NIS and RIS are now mainly supportive to SIS in the Netherlands. The Top Sectors defined are Agri-Food, Chemicals, Creative Industry, Energy, High-Tech, Logistics, Life Sciences & Health, Agriculture and Water. Another institute, the EIT, is also focusing on these sectors (Climate, Digital, Health, Raw Materials and Energy).
  • Education Systems: these are the ecosystems that surround educational institutes, such as universities. This group is often referred to as the economics of education. An well-performing education system usually increases expenses because of increased income, increases in return on investments because of higher (company) incomes and increases in productivity. It enables academic inflation.
  • Macro-economical Systems: this system refers to basic economics: output and income (GDP, GRP), unemployment and inflation and deflation.
  • Start-up Systems: a startup ecosystem is a small-scale system that enables startups to arise. It involves aspects such as ideas, inventions, research, education, startups, entrepreneurs, angel investors, seed investors, mentors, advisors and events and is supported by universities, incubators, accelerators, facilitators, investors, coworking spaces and venture capitalists.
  • Innovation Management Systems: these refer to a cyclical view of turning ideas into innovation; I’ve wrote a post about that earlier.
  • Cluster or Science Park Systems: In 2000 Porter already wrote: ‘Geographic, cultural, and institutional proximity provides companies with special access, closer relationships, better information, powerful incentives, and other advantages that are difficult to tap from a distance. […] Competitive advantage lies increasingly in local things – knowledge, relationships, and motivation – that distant rivals cannot replicate.’ (Porter, 2000). Clusters usually go the four phases: emergence, growth, maturity and renewal. The reason why clusters seem to work well is proximity. Cooke et al. (2011) suggest 7 types of proximity, 1) Geographic proximity – referring to the physical distance between actors, 2) cognitive proximity – referring to the closeness in ways of thinking between the actors, 3) communicative proximity – referring to the closeness professional language between the actors, 4) organizational proximity – referring to the arrangements that organizations make to coordinate interactions and collaborate with each other, 5) functional proximity – referring to closeness in expertise in different industries/clusters, 6) cultural proximity – referring to closeness of cultural habits and virtues and 7) social proximity – referring to the intensity of trust-based social relations, such as friendship.

Crises

The above-mentioned (sub)systems of innovation are in fact ‘positive loops’; meaning that they will positively influence each other in an endless loop. As explained earlier, dynamic, chaotic systems, are almost always also containing negative loops, that break the positive flow. These negative loops can turn around the whole mechanism and cause crises, for instance the economic crisis. In the innovation system there are four main negative loops that create discontinuity:

  • Labour market depletion: innovation not only creates new firms which in turn increase employment, innovation also creates more automated, efficient processes that in turn lead to less employment: labour market depletion. Take a look at the book stores for instance: digital innovation has caused the traditional book stores to adjust their business to the online world, closing down book stores and reducing the amount of employees.
  • Other new (disruptive) technologies: from an industry perspective, other new technologies can cause the whole sector to be superfluous. This term is identified as disruptive innovation. Think about how the mobile phone radically made landlines superfluous.
  • Imitation: rising profits within a sector also attracts new companies to the sector that will try to copy the products – at lower costs and without the initial investment. Especially sectors with low entrance barriers are receptive to this, such as software, app development and low-tech products.
  • Policy failures: a various number of reasons can cause policy to fail. The most common ones are bureaucracy, corruption and short-term thinking.

Innovation Policy

The innovation policy regarding RIS and NIS involves many different aspects. One way or another, the institutional environment tries to (positively) influence the main industrial innovation system. A few of the soft factor that policy usually to focus on are:

  • Smart infrastructure: this characteristic is about all kind of infrastructures that the region has to offer. This includes hard infrastructures, soft infrastructures and technological infrastructures.
  • Quality of life: according to Sternberg and Arndt (2001) the quality of life is created by: labour quality, housing amenities, and leisure amenities. All of these factors attract highly qualified people to the region, but moreover, they also make people stay in the region.
  • Cosmopolitanism: this aspect refers to any form of feeling that is evoked by the region. The characteristics of this factor are for example attractiveness for highly educated personnel, a world-wide reputation, a good atmosphere, a shared purpose, and highly motivated people (Whitley, 2002).
  • Talented human capital: Micheals et al. (2001) describe that attracting talent, educating talent, and keeping talent is of high importance to the region. They focus on managerial talent, but they explain that technological, engineering and business talent also must be part of a regional strategy to win the war for talent.
  • Creative cultural environment: a well-developed entrepreneurial climate is attracting and exploiting personal talent and is reinforcing the strong culture of the community. Hofstede, more than 25 years ago, received worldwide praise for constructing four – although years later a fifth one was added – dimensions to characterize cultures of different nations: power distance, uncertainty avoidance, individualism, and femininity (Hofstede, 1980).
  • Trust: there is considerable evidence that a trusting relationship creates greater knowledge sharing. In a trust-based relationship, people are more willing to share useful knowledge. Trust promotes social and emotional ties on the one hand and promotes professional collaboration on the other hand, both facilitators of knowledge sharing (Chowdhury, 2005; Tsai & Ghoshal, 1998; Mayer, Davis, & Schoorman, 1995).
  • Identity: scientists claim that knowledge is more effectively generated, combined and transferred by individuals who identify with a larger collective goal. The individual members then share a sense of purpose with the collective. Ultimately, this will lead to lower network costs, and more trust and commitment (Kogut & Zander, 2003; Dyer & Nobeoka, 2000; Orr, 1990)
  • Diversity: this characteristic of knowledge refers to the extent to which a variety of knowledge, know-how, and expertise is available in a network. New opportunities and resources will be discovered more quickly with access to diverse knowledge and knowledge diversity therefore directly stimulates creativity and innovativeness of the actor in the network. (Galunic & Rodan, 2004; Galunic & Rodan, 2002; Rodan, 2002).

Triple Helix

Over the last decade we’ve heard a lot about the triple helix. More recently also the quadruple and quintuple helix have been introduced. Moreover, also Open Innovation and Co-creation have been growing over the years. What they have in common is that these theories try to integrate the different actors in the traditional dynamical view of ecosystems with each other. In that case, it won’t be a ‘flow’ and it will therefore reduce the time delays within the flow. Simply said: deep integration between goverments and industry could result in quicker innovation. As does deep integration between education and industry; or different industries with each other, et cetera. The triple helix is a modern, 3D, view on system dynamics in the innovation ecosystem.

Games: simulation of complex dynamical systems

Games are a very common way to let actors in the network know how complex dynamics works. These games let you play with a few of the ‘agents’ in the ecosystem to experiment with the effects to better understand long-term behavior of ecosystems. Innovative Dutch creates these kinds of strategic simulation games for governments, companies and higher education; they created this infographic for their newest game; please take a look at their website.

A 5-Dimensional Model for Managing Innovation through Organizational Change

I’m in the lucky position to run into quite a few business owners, corporate directors and leaders on a daily occasion. And when talking to them about innovation – and their ambitions – it almost always comes down to one simple question: “How can we implement innovation in our organization?”. A question which seems easy to ask, but needs a complicated answer.

In the consulting projects that follow, a range of interviews usually indicate the complexity of the question. Leaders on strategic positions indicate they require business model innovation, marketing personnel indicates they need consumer innovation, tactic level manager indicate they need product innovation, business analysts indicate they need process optimization. Everybody more or less indicates they need a culture change. Stakeholders indicate they would like to see the organization collaborate more. And the truth is: they are all important for organizational change.

With years of experience, and lots of projects to test it on, I’ve created a 5-dimensional model for managing innovation through organizational change: a model that will help answering the question that everybody asks: “How can we implement innovation in our organization?”.

The 5-Dimension model of Innovation through Organizational Change looks as followed:

 

MultiloopModelofInnovation_Education

 

Click to download a high-resolution version. We also have created specialized versions for i.e. education, healthcare and industry.

 

So, how did I create this framework? Let’s explain step by step:

Change
First of all, I started by finding a perfect tool for change. The most important factor of change is: the implementation. Because change will only be change when it will be embedded in the daily routine of the organization. A model that is widely used is the PDCA-cycle of Deming: focusing on process change and quality. This will be the basis for our model.

Organizational Change
However, I wasn’t looking for a change as such, but for a model for organizational change. And in the field of innovation, these organizations are so-called ‘learning organizations’: they are open to continuous improvement and change. The best model for that use is the OADI-model, an adaption to the PDCA accredited to MIT Sloan professor Kofman. OADI stands for:

  • Observe
  • Assess
  • Design
  • Implement

This qualitative-research-based and design-oriented approach works well for innovative organizations. Moreover, I combined the model with the learning loop, creating a layered model of redesigning organizations.

Innovation
There are a wide number of different definitions of innovation. Earlier, I have elaborated on the Ten Types of Innovation model. However, in practice, not all Ten Types are of the same importance to organizational change. In fact, I suggest only 5 different types are to be innovated when starting with a organizational change project. In chronological order.

  1. Innovation of the why (the mission, vision and goals. This one is not in the Ten Types model).
  2. Innovation the business and profit model
  3. Innovation of the (primary) processes
  4. Innovation of the product and product systems
  5. Innovation of the customer experience

These 5 dimensions are chronologically ordered. So, it’s best to start with the first. Moreover, they are also ordered in length: they all follow their own OADI-cycle. Innovation of the why takes much longer than innovation of the customer experience.

However, to start changing these 5 dimensions, innovation needs to be integrated in the preconditions. There are three types of preconditions in innovation:

  • Innovation of the network
  • Innovation of the structure

The first one is often referred to as Open Innovation. The second one, innovation of the structure, is relatively under-exposed in the Ten Types model. In the world of Sociotechnical Organization Design, authors often refer to for aspect of ‘structure’:

  • Innovation of the (formal) structure
  • Innovation of the culture
  • Innovation of the (informal and communication) systems
  • Innovation of the people

These factors can be divided into ‘slow factors’ and ‘fast factors’. For instance: structure, systems and the skills of people are refered to as ‘fast factors’ and the culture, attitude, shared values and stakeholder management are seen as ‘slow factors’ (Camp Matrix).
So, in order to go for innovation through organizational change: both the preconditions and the 5 dimensions need to be taken into account. But there is more.

Managing innovation

Up to now I didn’t talk about ‘managing’ this innovation process. This requires a set of special skills. The model out there is the OECD-model for a creative attitude towards innovation, which includes challenging assumptions, wondering, questioning, exploring, investigating, sticking with difficulty, daring to be different, collaborating, sharing, reflecting, crafting, making connections and using intuition. Not a usual set of skills for a change manager, but definitely the best one for managing innovation through organizational change.

 

IMG_4152_Dit_is_hem_120_100.jpg   This article was written by Jan Spruijt. Jan Spruijt is a senior lecturer and entrepreneur in Innovation Sciences. Connect with Jan to stay in touch: