This simulation allows you to compare different approaches to influence flow, the Flow Times and the throughput of a work process. The simulation is described in the blog post " Starting late - The Superior Scheduling Approach  - How, despite being identical, one company delivers almost 10 times the
This simulation allows you to compare different approaches to influence flow, the Flow Times and the throughput of a work process. The simulation is described in the blog post "Starting late - The Superior Scheduling Approach - How, despite being identical, one company delivers almost 10 times the value of its competitor using flow-oriented project initiation."

By adjusting the slider 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), 
  • with Kanban Token and Replenishment Token based on the Tameflow approach (a form of drum-buffer-rope) 
  • with Drum Buffer Rope** scheduling method. 
* Well know in (agile) Kanban
** Known in the physical world of factory production

The simulation and the comparison between the different scheduling approaches can be seen here -> https://youtu.be/xXvdVkxeMMQ

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

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" feature delivery process on portfolio level. 

From left to right you find the following ten process steps. 
  1. Ideas
  2. Selected ideas (waiting)
  3. Initiate and pitch
  4. Waiting for preparation
  5. Prepare
  6. Waiting for delivery
  7. Deliver
  8. Waiting for closure
  9. Close and communicate
  10. Closed
5 last month
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.  We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale websi
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

This simulation makes the negative effects of starting work too soon visible. You can play around with the parameters.    Find the full story behind this simulation  here .      If you have questions or feedback get in touch via  @swilluda
This simulation makes the negative effects of starting work too soon visible. You can play around with the parameters.

Find the full story behind this simulation here

If you have questions or feedback get in touch via @swilluda
The complex
systems model ‘Engagement vs Police Expenditure for Justice Reinvestment in
Bourke, NSW’ evaluates the effectiveness of allocating government funding to
either community engagement activities or law enforcement. In this model, it is
possible for the user to designate resources from a sca
The complex systems model ‘Engagement vs Police Expenditure for Justice Reinvestment in Bourke, NSW’ evaluates the effectiveness of allocating government funding to either community engagement activities or law enforcement. In this model, it is possible for the user to designate resources from a scale of 20-100 and to also modify the crime rate for both adults and youth. Below, there are detailed notes that describe the reasoning and assumptions that justify the logic applied to this model. Similar notes can be found when stocks, flows and variables is clicked under the field ‘notes’.

Portions

Government statistics from the Australian Bureau of Statistics (ABS) show that Bourke Shire Regional Council has approximately 3000 residents, made up of 65-63% adults and 35-37% youths.

Crime Rate

Police variable is in the denominator to create a hyperbolic trend. The aim was to achieve a lower crime rate if police expenditure was increased, thus also a higher crime rate if police expenditure was decreased. The figure in the numerator can be changed with the ‘maximum crime rate’ variable which represents the asymptotic crime rate percentage. Where police = 100 the selected crime rate is maximised.

Avoiding Gaol

Originally the formula incorporated the police as a variable, where the total amount of convicted crimes was subtracted from the total amount of crimes committed. However, the constant flow of crimes from repeat offender/a created an unrealistic fluctuation in the simulation. I settled for a constant avoidance rate of 25%. This assumes that an adult or youth committing a crime for the first time is just as likely to avoid conviction as a repeat offender.

Conviction

​It is difficult to predict in a mathematical model how many adults or youths are convicted of crimes they commit. I determined a reasonable guess of maximum 75% conviction rate when Police = 100. In this formula, decreasing police spending equates into decreased conviction rate, which is considered a realistic representation.

Released

​It is assumed that the average sentence for a youth is approximately 6 months detention. For an adult, it will be assumed that the average sentence is 12 months gaol. The discrepancy is due to a few basic considerations that include 1. Adults are more often involved in serious crime which carries a longer sentence 2. youths are convicted with shorter sentences for the same crime, in the hopes that they will have a higher probability of full rehabilitation. 

Engagement

​Rate of adult/youth engagement was estimated to be a linear relation. The maximum rate of engagement, assuming expenditure = 100, is set to 80%. This rate of engagement is a reasonable guess with consideration that there will also exist adults who refused to engage in the community and end up in crime, and adults or youth that refuse to engage in the community or crime. 

Boredom

Engagement Expenditure variable is in the denominator to create a hyperbolic trend. The aim was to achieve a lower boredom rate with a higher engagement expenditure, and thus a higher boredom rate with a lower engagement expenditure. The figure in the numerator of 25 represents the asymptotic boredom rate percentage, where if engagement expenditure = 100 the adult/youth boredom rate is maximised at 25%. 

This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.  We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale websi
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

  Details:   <!--[if !supportLists]-->-         
<!--[endif]-->This
model shows the effect of ‘reinvestment program ‘or the expenditure on policing
and community development affects the cycles of petty-crime and youth
detention, and domestic violence and jail.  More details:   <!--[if

Details:

<!--[if !supportLists]-->-          <!--[endif]-->This model shows the effect of ‘reinvestment program ‘or the expenditure on policing and community development affects the cycles of petty-crime and youth detention, and domestic violence and jail.

More details:

<!--[if !supportLists]-->-          <!--[endif]--> Bourke is a town of 3000 people in the North West of New South Wales, about 750Km from Sydney. See the map: https://goo.gl/maps/VgNqgMNzJ7H2. It’s nowhere and there’s not much to do there if you’re young. So, a lot of kids get into mischief, and a lot of adult’s drink. Sometimes they’re violent.

 

<!--[if !supportLists]-->-          <!--[endif]-->http://www.justreinvest.org.au/justice-reinvestment-in-bourke/

Assumption:

<!--[if !supportLists]-->·       <!--[endif]-->Bourke Funding consist of Law enforcement funding and Community Development funding only

<!--[if !supportLists]-->o   <!--[endif]-->Bourke budget only has $400,000

<!--[if !supportLists]-->·       <!--[endif]-->Juvenile detention stay last for 6 months

<!--[if !supportLists]-->·       <!--[endif]-->There is only 2 options as a Youth, commit petty crime or engage in Youth development programs

<!--[if !supportLists]-->·       <!--[endif]-->1 unit of Police, Juvenile and Educational program HR and Equipment is = 0.25

<!--[if !supportLists]-->o   <!--[endif]-->1 unit increase results in an 0.25 effectiveness increase

<!--[if !supportLists]-->·       <!--[endif]-->Sport clubs, educational programs and social programs are comprised into Youth Development Program as 1 stock.

<!--[if !supportLists]-->·       <!--[endif]-->Juvenile support relies on encouraging youth who are in detention centers to join youth development programs, if not they will reoffend.

Stocks:

<!--[if !supportLists]-->o   <!--[endif]-->Home

<!--[if !supportLists]-->o   <!--[endif]-->Youth Development program

<!--[if !supportLists]-->o   <!--[endif]-->Discharged

<!--[if !supportLists]-->o   <!--[endif]-->Juvenile detention center

<!--[if !supportLists]-->o   <!--[endif]-->Petty Crime

Variable:

<!--[if !supportLists]-->·       <!--[endif]-->Reinvestment Allocation – ranges from 0 – 1 , law enforcement investment allocation is 1 – reinvestment allocation. Slide the slider through 0 to 1 to change the reinvestment allocation by 10% l

<!--[if !supportLists]-->·       <!--[endif]-->Bourke funding budget is fixed to make it seem more realistic (imagine employing a whole army of teachers or police, it wouldn’t make sense)

<!--[if !supportLists]-->·       <!--[endif]-->Youth Population varies , from 1000 to 10,000 for realism along with its time period (4 years). Slider the the slider to increase or decrease the population by 1,000s

Juvenile support effectiveness rate, Youth development program effectiveness rate, conviction rate, Police HR/ equipment, Juvenile Support HR/ equipment, Youth Development program HR/ equipment

Interrelationship and reinforcing loops

<!--[if !supportLists]-->·       <!--[endif]-->The youth population starts as as Neutral (Home) then leans towards alienation and connectedness

<!--[if !supportLists]-->·       <!--[endif]-->Alienation Reinforcing Loop -  Alienation has Conviction rate as a factor as conviction rate increase Alienation increase. This is because as youths get arrested, meaning they’ll have to stay in Detention centers, their friends are more likely to follow on due to them getting ‘bored’.

<!--[if !supportLists]-->·       <!--[endif]-->Connectedness Reinforcing Loop - The opposite exist with Connectedness, as educational program effectiveness increase so as Connectedness. This follows onto the same assumption that youth will always follow peer pressure. The more friends they have in the program, the more likely they will join aswell.

 

Analysis:

<!--[if !supportLists]-->1.       <!--[endif]-->Which loop is the youth in?

<!--[if !supportLists]-->·       <!--[endif]-->Once the allocation slider is used with its minimum or maximum value, the loop at which majority of the youth population is ‘stuck in’ becomes obvious. E.g. Once allocation = 1, the entire youth is stuck between educational program and their home, showing the effectiveness of community development funding. On the other hand, once allocation = 0, the entire youth loops around from doing Petty Crimes, spending their time in Juvenile detention centers, then getting discharged to only commit petty crimes again.

<!--[if !supportLists]-->2.       <!--[endif]-->Alienation vs. Connectedness

<!--[if !supportLists]-->·       <!--[endif]-->Set the allocation slider on 0.8, The massive difference between the youth of population feeling connected with their community and youth being alienated can be seen. The increase in Reinvestment, the increase in connectedness. Try the extremes as well, 100% reinvestment funding results in 0 Alienation rate.

<!--[if !supportLists]-->3.       <!--[endif]--> What is the Youth Engaged in ? Educational Programs or Petty Crime ?

<!--[if !supportLists]-->·       <!--[endif]-->Leaving the slider on 0.8, it can be seen that the there are more youth engaged into educational programs than petty crime. This shows that reinvestment and petty crime has a negative relationship .

<!--[if !supportLists]-->4.       <!--[endif]-->More police = safer ?

<!--[if !supportLists]-->-          <!--[endif]-->Set the slider on 0.1 , it can be seen that Conviction which has police as a factor is positively correlated to Crime. This means that an increase in conviction rate is equivalent to more youth being alienated and committing crime. Therefore, more police less safer.

 Have fun! 

 

 Spring, 2020:       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-mod
Spring, 2020:

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

Without mortality, with time measured in days, with infection rate 1/2, recovery rate 1/3, and initial infectious population I_0=1.27x10-6, we recover 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:
The model is designed to provide a general understanding of the wear and tear on roads or a community's circulation system as a result of vehicle traffic generated by development within and outside of a community. It is not based on realistic assumptions regarding those impacts, it simply attempts t
The model is designed to provide a general understanding of the wear and tear on roads or a community's circulation system as a result of vehicle traffic generated by development within and outside of a community. It is not based on realistic assumptions regarding those impacts, it simply attempts to convey the flow of influence.

The imaginary city has a set area of roads measured in linear yards (width of roads is ignored) and an assumed number of vehicles on those roads set at 30,000 (per day). With those assumptions the wear and tear requiring repair is .02 or 2% Vehicle wear based on the 30,000 per year. There is also a calculated replacement cost of an additional 3% plus through vehicle wear or 5% per year.  An increase in vehicles increases this vehicle wear impact exponentially. The model assumes that there will not be less than 30,000 vehicles.

Expenditures for repair or replacement are set to balance out on an as needed based on 30,000 vehicles. An minimum additional 50 cars from external sources is then assumed. Adding New Homes and/or New Businesses places an even greater burden on the circulation system. 

The model does not consider additional funding. This will be added as a political factor but would need to consider the possibility of decreasing funding for other purposes.

Future additions to the model will include an inflation factor. Unfunded road work will get increasingly more expensive over time. Also a diminished revenue factor. A lack of capacity of the community's roads could likely result in a diminishment of the community's business sector thus reducing sales and property taxes and municipal revenue to expend on the roads. 
           This version 8B of the   CAPABILITY DEMONSTRATION   model. A net Benefit ROI has been added. The Compare results feature allows comparison of alternative intervention portfolios.  Note that the net causal interactions have been effectively captured in a very scoped and/or simplified forma
This version 8B of the CAPABILITY DEMONSTRATION model. A net Benefit ROI has been added. The Compare results feature allows comparison of alternative intervention portfolios.  Note that the net causal interactions have been effectively captured in a very scoped and/or simplified format.  Relative magnitudes and durations of impact remain in need of further data & adjustment (calibration). In the interests of maintaining steady progress and respecting budget & time constraints, significant simplifying assumptions have been made: assumptions that mitigate both completeness & accuracy of the outputs.  This model meets the criteria for a Capability demonstration model, but should not be taken as complete or realistic in terms of specific magnitudes of effect or sufficient build out of causal dynamics.  Rather, the model demonstrates the interplay of a minimum set of causal forces on a net student progress construct -- as informed and extrapolated from the non-causal research literature.
Provided further interest and funding, this  basic capability model may further developed and built out to: higher provenance levels -- coupled with increased factorization, rigorous causal inclusion and improved parameterization.
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
This is a model which attempts to replicate a simple reinforcing loop described by Dennis Sherwood on page 75-87 of his book 'Seeing the forest for the trees - a manager's guide to applying systems thinking.  This is not a realistic model but I just wanted to reproduce it as practice of implementing
This is a model which attempts to replicate a simple reinforcing loop described by Dennis Sherwood on page 75-87 of his book 'Seeing the forest for the trees - a manager's guide to applying systems thinking.

This is not a realistic model but I just wanted to reproduce it as practice of implementing causal loop models.

www.stantonattree.com