Business Dynamics Modelling

“That just made matters worse!”

“There is no silver bullet”

“I didn’t expect that to happen!”

“We’ve spent all this money and all we’ve done is move the problem somewhere else, our customers are still not happy.”

“I have only so much resource. Where do I deploy it to have the most bang for buck?”


Sound familiar?


Organisations exist in response to dynamic market and/or social needs. They are a complex interaction of processes, people and systems. They are dynamic. Feedback loops, where effects affect causes abound. You can’t look at things as a static, linear set of cause and effects. This leads to poor decisions, unintended consequences and wasted resources.

Using refined facilitation techniques and sophisticated modelling tools, Enzyme can help bring understanding and insight to support your decision making. Applying the principles of System Thinking, we build simple, yet immensely powerful simulation models.

  • A major teaching hospital saved 1000s of bed days and reduced length of stay whilst improving patient care outcomes through the modelling of rehab patient flow through the hospital and out into the wider health care network. Click Here
  • A leading Australian Medical School used a financial model to assist in strategy development.
  • A Federal government department saved $8m over 5 years in improved information management practices, having built a model to assess the economic and productivity value of digitisation to manage new and stored records. Click Here
  • A major telco built an Integrated business model which linked customer loss and retention drivers and price sensitivity data with the financial results to assist in making resource allocation decisions in the $M100’s. Click Here
  • A Fortune 100 organisation used a Workforce planning model to improve the planning around skill requirements to support the doubling of a line of business over 5 years.
  • A major financial institution modelled the transition of a multi-billion dollar loan book from old to new products in order to minimise customer loss and maximise gross margin.
  • A major bank in the UK built a dynamic model of an end to end marketing process to identify the optimum response to address an impending customer satisfaction disaster as a result of a poorly designed campaign and, in fact achieve significant business success. Click Here
  • Several organisations have used our models to optimise Customer Retention strategies. Click Here
  • A University Library used multiple models over time to dramatically improve levels of service and genuine customer satisfaction levels.


IM model


When to use it

  • Looking to discover the right mix of initiatives to optimise the outcome for the business
  • Understand the complex interplay of multiple factors
  • Build consensus and momentum around decisions based on shared discovery/understanding/insight
  • Challenge mental models and assumptions
  • Avoid the limitations of static cause and effect thinking
  • Simulate policy and strategy options
  • Integrate the impact of soft and hard variables on performance
  • Explore the dynamic interaction of the key drivers of your business and learn how to optimise performance


In other words, when you need to make important resource allocation decisions, optimising bang for buck and you need to make them quickly in a way that the management team can understand and support.


In common with all our methods, these are constructed remarkably quickly, with a focus on insight and impact over 3 decimal place accuracy. There are a few core principles we apply in our modelling work:

  1.  Use the language and disciplines of Systems Thinking to understand the end to end process in a simple, intuitive and easy to communicate fashion. This ensures the modellers and business users understand each other and the final product is both useful and credible. Unlike many complex excel type models, this approach avoids the mysterious “black box” syndrome and the risks of “the missing billions”.
  2. Use expert facilitation techniques to engage the users and internal “experts” in the discovery and development work. This ensures the model remains relevant and on target, as well as creating a shared view across all the stakeholder groups in the end to end process.
  3. Always strive to minimise the complexity. Our techniques and modelling tools can and have produced extremely sophisticated and detailed models. However, experience has shown that most value is delivered when we focus on the insight and develop detail only where it is most needed. We are mindful of the guidance from statistician George Cox when he said “all models are wrong, but some are useful”. This has served our clients well and has delivered many transformative insights which have led to dramatic improvements in performance and efficiency.
  4. We start with a high level, simple model and, in conjunction with the business users, progressively add the detail as it proves to be of value. That way our models remain useful, flexible and easy to maintain and modify.
  5. Work to transfer the capability to continue this work within the client organisation.


Syd Uni CLD