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System Dynamics Models

System Dynamics is a powerful approach for modeling change within systems and Insight Maker is perfect for building System Dynamics models.

Agent Based Models

Explore the interactions between dynamic agents. Use Insight Maker to define agent behavior and simulate a population of agents interacting.

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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

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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.

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.

Input Queue (Backlog)
Selected for work (waiting for analysis or work break down)
Analyse, break down and understand
Waiting for development
In development
Waiting for review
In review
Waiting for deployment
In deployment
Done

Kanban Board Simulation - WIP Limit, Tameflow Kanban Token and Drum-Buffer-Rope

Stefan Willuda

La situación modelada expresa el crecimiento de las ventas impulsadas por la motivación y productividad, pero es frenada por el tamaño del nicho de mercado.

Límite de Crecimiento

Marcelo A. Castro Iglesias

626

La situación modelada expresa el crecimiento del deseo de fracaso de Flanders de Homero hacia Ned Flanders, pero es frenada por el sentimiento de culpa y la capacidad de tolerancia al sufrimiento de Flanders

TP - Busqueda del tesoro (Los simpsons)

Gabi Rivarola

2 weeks ago

Spring, 2020: in the midst of on-line courses, due to the pandemic of Covid-19.

With the onset of the Covid-19 coronavirus crisis, we focus on SIRD models, which might realistically model the course of the disease.

We start with an SIR model, such as that featured in the MAA model featured in

https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

Without mortality, with time measured in days, with infection rate 1/2, recovery rate 1/3, and initial infectious population I_0=1.27x10-4, we reproduce their figure

With a death rate of .005 (one two-hundredth of the infected per day), an infectivity rate of 0.5, and a recovery rate of .145 or so (takes about a week to recover), we get some pretty significant losses -- about 3.2% of the total population.

Resources:

http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

Coronavirus: A Simple SIR (Susceptible, Infected, Recovered) with death

Andrew E Long

As initially proposed by Pr. William M White of Cornell University:

http://www.geo.cornell.edu/eas/education/course/descr/EAS302/302_06Lab11.pdf

http://www.eas.cornell.edu/

Global Carbon Cycle

France Caron

573

The Lotka-Volterra equations (also known as the predator-prey equations) model the dynamic interactions between two biological populations: a predator and its prey.

This form of modeling dynamic systems is based on "stocks" (amounts) and "flows" (rates of change). For the predator-prey model we ask what factors impact the flows (the thick arrows) into and out of the populations of sheep and wolves (the stocks)? The dotted arrows can be used to connect and describe the relationships within the model. Additional variables, e.g, sheep fertility, can be added to the model to make relations clearer.

Overall, the dynamics for this kind of modeling centers on the factors that impact the flows. Click on any of the flow-arrows to see how the factors connect by the dotted arrows are used to determine the flows in or out of the stocks (the populations).

Lotka-Volterra Model

Walter M. Stroup

2 weeks ago