Diagram and simulation of disease spread

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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.

Based on Chris Argyris 2010 Book Organizational Traps Oxford University Press, built around Insight 619 on single and double loop learning

Organizational Traps Argyris

Geoff McDonnell

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

The limits to growth structure is based on the basic growth structure. And, as should be obvious, nothing grows forever as growth requires resources. Those required resources become a limits to growth. See also Archetypes.

Video

This model is part of

And? Understanding Relationships & Their Implications.

Limits to Growth

Gene Bellinger

60 7 months ago

Pemodelan untuk penyebaran DENV, salah satu jenis virus demam berdarah dengue (DBD) yang disebarkan oleh nyamuk Aedes Aegipty. DENV tidak dapat menular antar manusia, hanya dapat menular antara manusia dan nyamuk. Orang yang sudah pernah terkena virus DENV akan memiliki kemungkinan kekebalan terhadap varian virus tersebut.

Dengue Fever FIXXXX

Rama Soni Ravictor

6 days ago

This model shows the relationship between Snowshoe Hare and Lynx populations in Northern Ontario. This prey-predator relationship model depicts the dynamics between the prey (Snowshoe Hare) and predator (Lynx).

Snowshoe Hare and Lynx Population Dynamics (Northern Ontario) - Group 10

Alexis

2 weeks ago

Multi-echelon inventory optimization (sounds like a complicated phrase!) looks at the way we are placing the inventory buffers in the supply chain. The traditional practice has been to compute the safety stock looking at the lead times and the standard deviation of the demand at each node of the supply chain. The so called classical formula computes safety stock at each node as Safety Stock = Z value of the service level* standard deviation * square root (Lead time). Does it sound complicated? It is not. It is only saying, if you know how much of the variability is there from your average, keep some 'x' times of that variability so that you are well covered. It is just the maths in arriving at it that looks a bit daunting.

While we all computed safety stock with the above formula and maintained it at each node of the supply chain, the recent theory says, you can do better than that when you see the whole chain holistically.

Let us say your network is plant->stocking point-> Distributor-> Retailer. You can do the above safety stock computation for 95% service level at each of the nodes (classical way of doing it) or compute it holistically. This simulation is to demonstrate how multi-echelon provides better service level & lower inventory. The network has only one stocking point/one distributor/one retailer and the same demand & variability propagates up the supply chain. For a mean demand of 100 and standard deviation of 30 and a lead time of 1, the stock at each node works out to be 149 units (cycle stock + safety stock) for a 95% service level. You can start with 149 units at each level as per the classical formula and see the product shortage. Then, reduce the safety stock at the stocking point and the distributor levels to see the impact on the service level. If it does not get impacted, it means, you can actually manage with lesser inventory than your classical calculations.

That's what your multi-echelon inventory optimization calculations do. They reduce the inventory (compared to classical computations) without impacting your service levels.

Hint: Try with the safety stocks at distributor (SS_Distributor) and stocking point (SS_Stocking Point) as 149 each. Check the number of stock outs in the simulation. Now, increase the safety stock at the upper node (SS_stocking point) slowly upto 160. Correspondingly keep decreasing the safety stock at the distributor (SS_Distributor). You will see that for the same #stock outs, by increasing a little inventory at the upper node, you can reduce more inventory at the lower node.

Multi-echelon Inventory Optimization

Girikanth A