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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.

Insight diagram
World4 is a predictive model for world population. Population has grown hyper-exponentially in the last millenium, with the doubling time decreasing from 900 years  in 1000 CE to a minimum of ~35 years in 1963 CE. Technology is defined as that which decreases the death rate and/or increases the effective birth rate (i.e. by decreasing infant mortality). Technology grows exponentially, therefore population fits a hyper-exponential (exponent within an exponent). Models for the end of growth are explored using equations that express the ways humans are depleting Earth's biocapacity, the nature of resource depletion, and the relationship between natural resources and human carrying capacity. This simple model, containing just two closed systems, captures the subtle shifts in the population trajectory of the last 50 years. Specifically, hyperexponential growth has given way to subexponential growth. A peak is predicted for the time around 2028.  [Bystroff, C. (2021). Footprints to singularity: A global population model explains late 20th century slow-down and predicts peak within ten years. PloS one, 16(5), e0247214.]
World4.5
Profile photo Christopher Bystroff
22 3 months ago
Insight diagram
Cats can quickly adjust their position in free fall so that they land on all four paws. The simple model for this movement divides the cat into a rear and front section, whose moment of inertia can be changed by extending and retracting the legs. You can find an explanation of this in this video https://youtu.be/SMMgJ6GVMhE 
Falling Cat Problem
Profile photo Easy Physics Werner Maurer
5 days ago
Insight diagram
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
SARS-CoV-19 model
Profile photo Lucia Vega Resto
77
Insight diagram

From Jay Forrester 1971 Book World Dynamics, the earlier, simpler version of Scott Fortmann-Roe's World 3 Limits to Growth Model. adapted from Mark Heffernan's ithink version 

World2 Model of World Dynamics
Profile photo Geoff McDonnell
60 3 months ago
Insight diagram
Simulating Hyperinflation for 3650 days.

If private bond holdings are going down and the government is running a big deficit then the central bank has to monetize bonds equal to the deficit plus the decrease in private bond holdings.  We don't show the details of the central bank buying bonds here, just the net results.

See blog at http://howfiatdies.blogspot.com for more on hyperinflation, including a hyperinflation FAQ.
Hyperinflation Simulation
Profile photo Vincent Cate
14
Insight diagram
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
Profile photo Girikanth A
11