Create an Insight Maker account to start building models. Insight Maker is completely free.


Start Now

Insight Maker runs in your web-browser. No downloads or plugins are needed. Start converting your ideas into your rich pictures, simulation models and Insights now. Features

Simulate

Explore powerful simulation algorithms for System Dynamics and Agent Based Modeling. Use System Dynamics to gain insights into your system and Agent Based Modeling to dig into the details. Types of Modeling

Collaborate

Sharing models has never been this easy. Send a link, embed in a blog, or collaborate with others. It couldn't be simpler. More

Free & Open

Build your models for free. Share them with others for free. Harness the power of Insight Maker for free. Open code mean security and transparency. More


Explore What Others Are Building

Here is a sample of public Insights made by Insight Maker users. This list is auto-generated and updated daily.

 SARS-CoV-19 spread  in different countries - please  adjust variables accordingly        Italy     elderly population (>65): 0.228  estimated undetected cases factor: 4-11  starting population size: 60 000 000  high blood pressure: 0.32 (gbe-bund)  heart disease: 0.04 (statista)  free intensive
SARS-CoV-19 spread in different countries
- please adjust variables accordingly

Italy
  • elderly population (>65): 0.228
  • estimated undetected cases factor: 4-11
  • starting population size: 60 000 000
  • high blood pressure: 0.32 (gbe-bund)
  • heart disease: 0.04 (statista)
  • free intensive care units: 3 100

Germany
  • elderly population (>65): 0.195 (bpb)
  • estimated undetected cases factor: 2-3 (deutschlandfunk)
  • starting population size: 83 000 000
  • high blood pressure: 0.26 (gbe-bund)
  • heart disease: 0.2-0.28 (herzstiftung)
  • free intensive care units: 5 880

France
  • elderly population (>65): 0.183 (statista)
  • estimated undetected cases factor: 3-5
  • starting population size: 67 000 000
  • high blood pressure: 0.3 (fondation-recherche-cardio-vasculaire)
  • heart disease: 0.1-0.2 (oecd)
  • free intensive care units: 3 000

As you wish
  • numbers of encounters/day: 1 = quarantine, 2-3 = practicing social distancing, 4-6 = heavy social life, 7-9 = not caring at all // default 2
  • practicing preventive measures (ie. washing hands regularly, not touching your face etc.): 0.1 (nobody does anything) - 1 (very strictly) // default 0.8
  • government elucidation: 0.1 (very bad) - 1 (highly transparent and educating) // default 0.9
  • Immunity rate (due to lacking data): 0 (you can't get immune) - 1 (once you had it you'll never get it again) // default 0.4

Key
  • Healthy: People are not infected with SARS-CoV-19 but could still get it
  • Infected: People have been infected and developed the disease COVID-19
  • Recovered: People just have recovered from COVID-19 and can't get it again in this stage
  • Dead: People died because of COVID-19
  • Immune: People got immune and can't get the disease again
  • Critical recovery percentage: Chance of survival with no special medical treatment
Clusters of interacting methods for improving health services network design and delivery. Includes Forrester quotes on statistical vs SD methods and the Modeller's dilemma. Simplified version of  IM-14982  combined with  IM-17598  and  IM-9773
Clusters of interacting methods for improving health services network design and delivery. Includes Forrester quotes on statistical vs SD methods and the Modeller's dilemma. Simplified version of IM-14982 combined with IM-17598 and IM-9773
 This model is derived from the paper " Nobody Ever Gets Credit for Fixing Problems that Never Happened: Creating and Sustaining Process Improvement " by Nelson P. Repenning and John D Sterman. See  Insight 752  for a causal loop version of this model.  @ LinkedIn ,  Twitter ,  YouTube

This model is derived from the paper "Nobody Ever Gets Credit for Fixing Problems that Never Happened: Creating and Sustaining Process Improvement" by Nelson P. Repenning and John D Sterman. See Insight 752 for a causal loop version of this model.

@LinkedInTwitterYouTube

This complex system models the dynamics of energy demand and consumption within the small, rural town of Uxbridge, ON. The town of Uxbridge, ON has a total population of approximately 21,500 individuals as of 2021. Within this town, there are an estimated total of 8,310 residential dwellings and 855
This complex system models the dynamics of energy demand and consumption within the small, rural town of Uxbridge, ON. The town of Uxbridge, ON has a total population of approximately 21,500 individuals as of 2021. Within this town, there are an estimated total of 8,310 residential dwellings and 855 businesses, all of which consume various degrees of energy from various sources on the daily. 

The inflow of energy, which is stored in Uxbridge's energy grid of available and generated energy (the stock), comes from various means of fuel sources consisting of nuclear, gas, hydro, wind, solar, and biofuel power plants. The energy these sources generate is utilized as a source of power for the residences and businesses of Uxbridge, ON. 

The outflow of energy from Uxbridge's energy grid provides both the residents and businesses of Uxbridge, ON with the energy that they will consume. The demand and thus, total energy consumed by both divisions is dependent on two main variables, those being, the average number of households and/or businesses, and the average electricity consumption for both. There is also the contribution of energy to residents from their own micro-environments, specifically in the form of wind and solar power, which is utilized as a means to reduce the town's dependence on its energy grid and move toward implementing a more sustainable energy system. In such, one can describe the outflow of energy as that which is provided from Uxbridge's energy grid and consumed by residences and businesses in Uxbridge, ON. 

If the demand outweighs the supply, there will not be enough energy generated and therefore, there will not be enough energy available to meet the needs of the town. In opposition, if the supply outweighs the demand, there will be enough energy generated and therefore, be available to meet the needs of the town. It is important to note trends within the data that display and suggest if there is a greater supply or demand for energy within the town, and how this relationship changes throughout various times of the day. 

Note: The amount of energy available that is provided by the various fuel sources, and the consumption by the residences and business of the town can fluctuate and differ throughout different hours of the day. As some sources' generation of energy, such as solar power, are dependent on the degree of available sunlight, the numbers utilized in this model are based off of daily averages but are subject to change. Therefore, the numbers of this graph should not be considered to be accurate for all hours of the day. 

All data used within this model was obtained from the various sources on the internet. The data used within the model is based off of estimate values. Data pertaining to energy information, residential home numbers, and business numbers for Uxbridge, ON was obtained from the following source(s):  

1. https://www12.statcan.gc.ca/census-recensement/2021/dp-pd/prof/details/page.cfm?Lang=E&SearchText=Uxbridge&DGUIDlist=2021A00053518029&GENDERlist=1,2,3&STATISTIClist=1&HEADERlist=0

2. https://www.uxbridge.ca/en/business-and-development/community-profile.aspx

Data for the amount of energy generated per hour in Ontario, as well as per the various fuel types and the energy they generate per hour in Ontario was obtained from the following source(s): 

3. https://live.gridwatch.ca/home-page.html
The SEIRS(D) model for the purpose of experimenting with the phenomena of viral spread. I use it for COVID-19 simulation.
The SEIRS(D) model for the purpose of experimenting with the phenomena of viral spread. I use it for COVID-19 simulation.
This simulation allows you to compare different approaches to influence flow, the Flow Times and the throughput of a work process.   By adjusting the sliders below you can    observe the work process  without  any work in process limitations ( WIP Limits ),   with process step specific WIP Limits* (
This simulation allows you to compare different approaches to influence flow, the Flow Times and the throughput of a work process.

By adjusting the sliders below you can 
  • observe the work process without any work in process limitations (WIP Limits), 
  • with process step specific WIP Limits* (work state WIP limits), 
  • or you may want to see the impact of the Tameflow approach with Kanban Token and Replenishment Token 
  • or see the impact of the Drum-Buffer-Rope** method. 
* Well know in (agile) Kanban
** Known in the physical world of factory production

The "Tameflow approach" using Kanban Token and Replenishment Token as well as the Drum-Buffer-Rope method take oth the Constraint (the weakest link of the work process) into consideration when pulling in new work items into the delivery "system". 

You can also simulate the effects of PUSH instead of PULL. 

Feel free to play around and recognize the different effects of work scheduling methods. 

If you have questions or feedback get in touch via twitter @swilluda

The work flow itself
Look at the simulation as if you would look on a kanban board

The simulation mimics a "typical" software delivery process. 

From left to right you find the following ten process steps. 
  1. Input Queue (Backlog)
  2. Selected for work (waiting for analysis or work break down)
  3. Analyse, break down and understand
  4. Waiting for development
  5. In development
  6. Waiting for review
  7. In review
  8. Waiting for deployment
  9. In deployment
  10. Done