Marketing

A Comparison of Business Simulations in Innovation, Marketing, Product Management

A Comparison of Business Simulations in Innovation, Marketing, Product Management

Jan Spruijt

Why simulation-based learning is becoming AI-resistant by design

In times of rapid advances in artificial intelligence, a fundamental shift is taking place in higher education. As AI systems become increasingly capable of generating analyses, solving structured problems, and supporting managerial decision-making, the question is no longer whether students can arrive at the right answers. Instead, the question is whether they are engaging in forms of learning that cannot be easily replicated or outsourced.

Simulation-based learning is gaining renewed relevance in this context.

Unlike traditional assignments, simulations introduce a layer of interdependence that is inherently difficult for AI to resolve. Outcomes are not only determined by models or data, but by the decisions of multiple teams acting simultaneously. Each decision influences the environment in which others operate, creating feedback loops that evolve over time.

While AI can optimize within a predefined system, it struggles to anticipate how that system will change when multiple actors interact dynamically. This makes simulations one of the few educational formats where learning remains fundamentally human: shaped by judgment, timing, interaction, and uncertainty.

A closer look at widely used simulations such as Glo-Bus, Capsim, Marketplace Simulations, Cesim and Markstrat reveals that they differ significantly in what they actually model and therefore in what they teach.

Comparative overview of widely used simulations

Simulation Organizational perspective Thematic focus Primary decision domain Role of innovation Learning orientation Level of abstraction Type of complexity
Glo-Bus Product & market positioning Competitive strategy in technology markets Pricing, production, product features, market positioning Implicit (via R&D and product design) Competitive performance and market positioning Medium Financial and market-driven
Capsim Functional business system Integrated firm management Cross-functional decisions (R&D, finance, marketing, operations) Partial General management and business acumen Medium Analytical and data-intensive
Marketplace Simulations Customer & market interface Marketing strategy and customer behavior Product launch, branding, segmentation, pricing Limited Customer insight and market strategy Medium Market dynamics and positioning
Cesim (Global Challenge) Strategy & external environment Global strategy and sustainability trade-offs Global expansion, strategy, sustainability decisions Emerging Strategic decision-making in global contexts Medium–high Strategic and financial
Markstrat Product portfolio management Marketing strategy and product lifecycle management Product portfolio, segmentation, R&D Moderate Market strategy and lifecycle management Medium Analytical and market-based
Innovation Management Game (Innovative Dutch) Innovation system & organizational design Managing exploration vs exploitation and innovation portfolios Portfolio balance, resource allocation, long-term alignment Central and explicit Innovation capability and strategic alignment High Systemic and strategic

Two fundamentally different logics

At first glance, these simulations appear structurally similar. Teams make decisions in iterative rounds, receive feedback, and compete for performance. Yet beneath this shared format lie two fundamentally different logics.

Most simulations, including Glo-Bus, Capsim, Marketplace, Cesim, and Markstrat, model the organization as a firm operating within a competitive market. The task of the participant is to improve performance within that environment. Success is typically measured in terms of profit, market share, or strategic positioning.

In contrast, the Innovation Management Game operates at a different level. Rather than simulating the market itself, it focuses on how organizations design and manage their innovation system over time.

Most simulations model the outcomes of innovation: products, markets, and performance.
The Innovation Management Game models the system that produces those outcomes.

Innovation Management Game session in practice

What becomes visible in practice is that simulation-based learning is not only about making decisions, but about experiencing how those decisions interact over time. Participants are confronted with uncertainty, conflicting priorities, and the need to align short-term actions with long-term strategy.

This is where the difference between market-based simulations and innovation-oriented simulations becomes tangible. Rather than optimizing isolated decisions, participants must develop coherence across their choices and understand how their actions shape the system as a whole.

The Innovation Management Game explicitly focuses on this dynamic, placing participants in a setting where innovation is not a single decision, but an evolving system of trade-offs.

Market-based simulations: optimizing within a given system

Simulations such as Glo-Bus, Capsim, Marketplace, Cesim, and Markstrat are rooted in a paradigm of optimization. The organization is treated as a system that already exists, and the role of the participant is to make better decisions than competitors within that system.

Glo-Bus illustrates this clearly. Although positioned as a technology design simulation, participants primarily configure products and compete on price, features, and market positioning. Innovation is present, but mainly as a function of product development.

Capsim extends this by integrating multiple business functions. Participants must align decisions across departments, interpret financial data, and manage trade-offs between growth and profitability. The complexity increases, but remains analytical in nature.

Marketplace Simulations emphasize customer-driven decision-making, focusing on segmentation, branding, and product launches. Cesim adds layers of international strategy and sustainability, while Markstrat deepens understanding of portfolio management and product life cycles.

Across these simulations, a consistent pattern emerges: complexity arises from the number of variables and the need to interpret data. The challenge is to optimize performance within a relatively well-defined system.

Designing the system: a different level of decision-making

The Innovation Management Game starts from a different premise. Instead of assuming a fixed system, it focuses on how that system is designed and evolves.

Participants are not primarily concerned with pricing or production decisions. Instead, they must address questions such as how resources should be allocated between exploration and exploitation, how balanced the innovation portfolio is, and how short-term results relate to long-term renewal.

These decisions are inherently systemic. Their impact is often indirect, delayed, and dependent on how different elements of the organization interact over time.

As a result, the nature of decision-making changes. There is no single optimal solution. Instead, participants must develop coherence in their choices and understand how those choices shape the organization’s ability to innovate.

Analytical versus systemic complexity

This difference is most clearly visible in the type of complexity each simulation introduces.

Market-based simulations are analytically complex. Participants are presented with large amounts of data and must identify patterns, calculate trade-offs, and make decisions that optimize performance indicators. The challenge is primarily cognitive and quantitative.

The Innovation Management Game introduces systemic complexity. Outcomes depend on interactions between decisions, timing, and alignment. Cause-and-effect relationships are less direct, and success depends on the ability to understand and manage interdependencies.

This distinction is increasingly relevant. While analytical complexity can often be supported, or even partially automated, by AI, systemic complexity remains difficult to externalize.

Abstraction and learning outcomes

Another key difference lies in the level of abstraction.

Most simulations operate at a relatively concrete level, focusing on products, markets, and financial results. This makes them highly effective for developing business acumen and functional knowledge.

The Innovation Management Game operates at a higher level of abstraction. It focuses on capabilities, processes, and strategic alignment. This allows participants to explore how organizations create value over time, rather than how they optimize performance in a given moment.

The learning outcomes therefore differ. Market-based simulations are effective in developing business acumen and functional knowledge. Simulations focused on innovation systems contribute to a broader understanding of strategy, organizational design, and long-term value creation.

Implications for education

These differences are not merely technical. They reflect broader shifts in how organizations are understood.

Traditional simulations align with a view of organizations as entities competing in relatively stable environments. Success depends on efficiency, optimization, and informed decision-making.

More recent approaches reflect a view of organizations as adaptive systems operating under uncertainty. Here, success depends on the ability to innovate, experiment, and continuously rebalance competing priorities.

In this context, simulation-based learning is not only a way to teach business fundamentals, but also a way to engage with the limits of those fundamentals.

Conclusion

Business simulations are often treated as a single category, but a closer analysis reveals that they operate at fundamentally different levels.

Simulations such as Glo-Bus, Capsim, Marketplace, Cesim, and Markstrat provide valuable insights into how organizations perform within markets. They strengthen analytical thinking, financial reasoning, and competitive decision-making.

The Innovation Management Game operates at a different level. It focuses on how innovation systems are designed, balanced, and evolved over time, shifting attention from outcomes to the mechanisms that produce them.

In an AI-driven world, this distinction becomes increasingly important. As analytical tasks become easier to automate, the ability to think in systems, navigate uncertainty, and design for long-term innovation may well become the defining capability for future professionals.

The question for educators is therefore not which simulation is best, but what kind of thinking they want to develop.

The 3 Phases of Responsible Innovation

Jan Spruijt

Over the last few month, the phrase “Responsible Innovation” has been booming on scientific social media. It has emerged from Corporate Social Responsibility as a topic that researches the effect and consequences of innovation on the long term. This could be technological effects, antropological effects or ethical effects.

The fundament of this research topic lies in the Collingridge Dilemma:

The Collingridge dilemma is a methodological quandary in which efforts to control technology development face a double-bind problem: an information problem – impacts cannot be easily predicted until the technology is extensively developed and widely used – and a power problem – control or change is difficult when the technology has become entrenched.

The way to start innovating in order to enhance responsible innovation is three-fold:

1. Value-consciousness in design, research and development: this aspect means that design or R&D should start with a clear answer to the ‘why of innovation’. In other words: does this idea or design provide a solution to one of the grand challenges that we are facing in 5, 10 or 30 years? Values are the key to those answers.

2. Ethical Parallel Research: every step of the innovation management funnel  should be taken with the influence of ethical researchers and if possible, also researches from other parallel industries. This way, the impact that the innovation has on the long term can be easily addressed and tackeled early stage.

3. Constructive Technology Assessment: innovation teams shouldn’t be monodisciplinary, but multidisciplinary. That way, early-stage innovation (ideas) can be assessed and tested upon. Multidisciplinary teams form the basis of Open Innovation.

If you are interested in the material, take into account the following material:

Responsible Innovation: Managing the Responsible Emergence of Science and Innovation in Society

Science and innovation have the power to transform our lives and the world we live in – for better or worse – in ways that often transcend borders and generations: from the innovation of complex financial products that played such an important role in the recent financial crisis to current proposals to intentionally engineer our Earth’s climate. The promise of science and innovation brings with it ethical dilemmas and impacts which are often uncertain and unpredictable: it is often only once these have emerged that we feel able to control them. How do we undertake science and innovation responsibly under such conditions, towards not only socially acceptable, but socially desirable goals and in a way that is democratic, equitable and sustainable? Responsible innovation challenges us all to think about our responsibilities for the future, as scientists, innovators and citizens, and to act upon these.

The Importance of Responsible-Innovation and the Necessity of ‘Innovation-Care’

This study deals with responsibility as part of innovation. By nature, innovation gives birth to development for the organization and can only be at the core of any strategy within an ever-increasingly global economic context. However it also raises new questions stemming mostly from the impossibility to forecast the success of the innovations. More precisely, the questions raised by innovation also concern its consequences on society as a whole. Today, the innovator should understand his responsibility, the consequence of each innovation.

Moreover, common acceptance of the word ‘responsibility’ raises some questions about its use and how it should be understood. What does ‘responsibility’ mean? Who is responsible and for what? Through the notion of ‘care’, we aim at providing an evolution of responsible-innovation. The concept of ‘innovation-care’ is centered on people and more precisely focuses on taking care of them. The purpose of innovation-care is indeed to innovate and keep up with the level of productivity necessary to any organization while taking into account the essential interdependence between the status of the innovator and that of the citizen.

Enhancing Socially Responsible Innovation in Industry

This thesis presents a study that aims to explore to what extent corporate researchers in the field of industrial Life Science & Technology (LST) can consider social and ethical aspects of LST innovation to improve their Research and Development (R&D) practices. Innovators, particularly those working in controversial scientific and technology fields such as industrial LST, are encouraged to adopt socially responsible innovation methods. This requires that researchers, who work in such fields, consider the broader social and ethical context of their R&D activities.

The presented study explores first how corporate researchers can integrate such aspects in their daily work and how this could improve their work. Second it investigates whether such integration leads to a quantitatively assessable improvement of the quality of R&D. The results indicate that integration is possible, and leads to a measurable improvement of the quality of R&D work. In addition, researchers see a number of improvements in their R&D work, e.g. in the quality of communication and cooperation, and how to link their own work to corporate strategies and marketing. This thesis can be useful for innovators who wish to enhance socially responsible innovation practices, as it presents a tool for R&D management that allows for the operationalisation of socially responsible innovation and improved R&D performance.

First annual conference Responsible Innovation

The Innovation Spiral: a closer look on Ernst & Young’s innovation model

The Innovation Spiral: a closer look on Ernst & Young’s innovation model

Jan Spruijt

“Mention the word “innovation” and most people will think of extraordinary inventions created by solitary geniuses,” as mentioned in the first line of Ernst & Young‘s introduction to (one of) their innovation model(s). The article is titled: Innovation for Growth: a spiral approach to business model innovation. A promising introduction: it seems to include (organizational) growth theories, innovation management theory and business model theory. Again, after last year’s successful article on Deloitte’s Fast Growth Track, we’ll take a closer look on this model. Is this model theoretically justified? And if yes – assuming it’s an absolute yes – why does it work and how could it help you?

Business Model Innovation versus Innovation for Growth

First of all, let’s take a closer look at one of their general promises; on the one hand the article promises to innovate your business model. Or, as Henry Chesbrough has written it:

“There was a time, not so long ago, when ‘‘innovation’’ meant that companies needed to invest in extensive internal research laboratories, hire the most brilliant people they could find, and then wait patiently for novel products to emerge. Not anymore. The costs of creating, developing, and then shipping these novel products have risen tremendously (think of the cost of developing a new drug, or building a new semiconductor fabrication facility, or launching a new product into a crowded distribution channel). Worse, shortening product lives mean that even great technologies no longer can be relied upon to earn a satisfactory profit before they become commoditized. Today, innovation must include business models, rather than just technology and R&D.”

Source: Chesbrough (20o7): Business Model Innovation: it’s not just about technology anymore

So, the strategic focus of organizations has made a transition from product or service innovation towards business model innovation. That said, it surely doesn’t mean that service or product innovation is of less relevance: it has just shifted from a strategic level to a more tactical level. I got the opportunity ask (well, actually I’m filming, a colleague is asking the questions) Alexander Osterwalder about the place of innovation in the Business Model theory. This is what he said:

So the business model is not directly linked to innovation per se. Osterwalder:

“What it does is, it gives you a language. It’s very tangible, very visual, that will help you to create better conversations and it will make it easier for you to convince people of innovative possibilities.”

Concluding this part: it’s hard to focus on both Business Model Innovation and “Innovation for Growth”, because they are both executed at completely different levels.

Spiral Approach to Innovation: Innovation Processes

Well, so far the analysis of the title page. Let’s take a closer look at their PDF. I will include it here for your convenience:

[gview file=”http://www.ey.com/Publication/vwLUAssets/Growing_beyond_-_Innovation_report_2012/$FILE/Innovation-Report-2012_DIGI.pdf” height=”500px” width=”100%”]

I’ll directly skip to the folowing passage in the text:

“For the most innovative companies today, innovation isn’t a linear process. Rather, it’s a continuous cycle with ups and downs, inputs from different places, repetitions, failures, and many steps back and forth.”

Our guts feeling says that this statement is right. Indeed, it is. Innovation management is a process and many processes are theoretically seen as cycles.The origin of innovation studies lies within the product life cycle, firstly decribed by Lewitt in 1965 and later elaborated on by Perreault, for instance in 2000. It basically consists of four phases: market introduction, market growth, stability and decline. More focused on innovation, Rogers (1995) created a more specified model, ‘the diffusion of innovation and adopter categories.’

These models are singular, while innovation is repeatable. That can be shown by the following figure:

im3.png

 

The art of innovation, the process of innovation, is often referred to as innovation management. Innovation Management, or New Business Development, aims to enhance the possibility of technical and commercial success of new products and services (Schilling and Hill, 1998, Brown and Eisenhardt, 1997, Robert, 1994 and Clark and Fujimoto, 1991). The article Fast Track Growth for Innovation shows more indepth information into the different steps of the innovation process.
Typically, each process is cyclic, in order to enhance the room for reflection and dynamical growth. Francis Bacon in 1620 wrote about this explaining that every scientific process should consist of hypothesis – experiment – evaluation. In 1982 Deming developed the Plan-Do-Check-Act cycle, which we all have heard of. Cole, in 2002, was the first who explicitly refered to innovation as a cycle: Probe – Test – Evaluate – Learn. Bacon gave his cycle the name ‘inductive approach’ – basically the same as a spiral approach.

The Model Magnified: Is it good or could it be better?

So, the circle as round: yes, innovation should be a spiral approach. Below a look on Ernst & Young’s inductive spiral approach:

spiral.jpg

Wow, that’s something, isn’t it? At least it’s all-inclusive. Let’s take start with the second cycle: “Innovation Process”

  • Innovation Process: Ernst & Young have defined 5 steps: Intuition, Socialization, Ideation, Development and Exploitation. Clearly, it shows similarities with other – more theoretically accepted – models. The first two are quite surprising to me: Intuition and Socialization. The article explains: “Our research reveals a major shift in how leading companies go about innovation today. Intuition is the process of obtaining ideas, from anywhere and everywhere. Socialization happens when the idea is discussed and debated with other people, formally and informally.” I think this is a interesting perspective to look at the first step in innovation. On the one hand, it’s a modern way of looking at things: it’s fast and creates immediate action. It includes social media and people as a source for information and ideas, something that most models don’t include. On the other hand, it kind of simplified. Like (market) research and problem finding isn’t a scientific issue anymore, but more something that we come up by intuition. Perhaps intuition could play a small role, but it defintely isn’t how organizations repeatedly will structure innovation processes for the continuation of their core business. So yes, it’s a contemporary approach, but it’s not comprehensive.
    Even more, the relations between the different steps are quite strange. They all go two ways, except from the last one (and: is it actually the last one?), between exploitation and intuition. A two way arrow is a rather unfortunate way of showing that the process is iterative, meaing things could happen simultaneously in time. It definitely isn’t a two way process: after (unsuccesfull) exploitation, it’s not very logical to go back to the development phase, because the source of the problem needs to be re-identified and a new idea has to be created before redeveloping the product or service.
  • The other circles: to my opinion, the other circles try to include all exogene factors that could play a role in the primary innovation process. They are not cyclic at all and therefore it seems a forced way of including them in the model. It seems like a ‘sales pitch’ telling the clients all factors that could be taken into account during the advisory project. Perfectly plausible, but it should’t all be included in the model, because it doesn’t always make sense. For instance, the inner circle explain the different areas of innovation that could be addressed (processes, products and services and business model). Like explained before, these are three completely different strategic areas. Of course, they have to be addressed simultaneously, the influence each other, which explains their presence in this model. Also the outer circles don’t contribute to the value of the model. They are more seperate wheels (or clouds) around the model containing – very useful! – insights in innovation enablers and possible collaborators (read: possible clients).
  • The boxes: they only seem to offer information that didn’t fit inside the wheels. Please be honest, would you have missed them if they weren’t there?

Summing up, I’m not very enthousiastic by the spiral approach towards business model innovation of Ernst & Young. It’s mostly a marketing instrument. Though a good one: it includes all expertises that Ernst & Young could probably help you with and is therefore a useful instrument for explaining how they could of help (and not how innovative business models could be (re)developed).

A New Spiral Approach towards Innovation

Of course, I will not only analyse the current model, I will also propose a better one. One that takes into account the five steps of the innovative process, but also the recent developments in innovation systems. And I left out all unnecessary information. This is what I get:

cycle1.jpg

Obviously, when ‘walking’ through this innovation process, it’s not necessary to stay at one level and address each step for the same amount of time. It’s more often and iterative process than not, like the following figure shows:

cycle2.jpg

Please, let me know what you think of this analysis. Am I right, or completely wrong?

I would like to end with a quote from Maria Pinelli, Ernst & Youngs Global Vice Chair, which I actually find one of the best quotes I have recently bumped into:

“It is not enough just to be innovative. It is essential to be innovative all the time.”