Business Models

These models and simulations have been tagged “Business”.

Related tagsTechnology

Insight diagram
Trial Model
Insight diagram

Rich picture version of Causal loop diagram based on Jack  Homer's paper Worker burnout: a dynamic model with implications  for prevention and control System Dynamics Review 1985 1(1)42-62 See IM-333 for the Simulation model and IM-2178 for a related Causal Loop Diagram of Project Turnover

 

Clone of Burnout Dynamics CLD rich pic
Insight diagram

Rich picture version of Causal loop diagram based on Jack  Homer's paper Worker burnout: a dynamic model with implications  for prevention and control System Dynamics Review 1985 1(1)42-62 See IM-333 for the Simulation model and IM-2178 for a related Causal Loop Diagram of Project Turnover

 

Clone of Burnout Dynamics CLD rich pic
Insight diagram
The effect of time per claim and burnout on pending claims
Hanover Insurance
Insight diagram

Harvested fishery with endogenous investment and ship deployment policy. Ch 9 p345-360 John Morecroft (2007) Strategic Modelling and Business Dynamics. See simpler models at IM-2990 and IM-2991

Fishery Dynamics
Insight diagram
Growth Archetype
Insight diagram

Harvested fishery with endogenous investment and ship deployment policy. Ch 9 p345-360 John Morecroft (2007) Strategic Modelling and Business Dynamics. See simpler models at IM-2990 and IM-2991

Clone of Fishery Dynamics with Ship Deployment Policy
Insight diagram
This models simulates a simple supply chain with one single producer. Two main parameters determine the behaviour: demand (min,max) and the fabrication speed.
Component Shortages (Public)
Insight diagram
Business Models
Insight diagram
​A model of viral growth. Based upon:

http://www.linkedin.com/today/post/article/20121011190820-18876785-how-to-model-viral-growth-simple-loss
Clone of Viral Growth
Insight diagram
Rich Picture with Simulation
Clone of Rich Picture Cafe + Simulation
Insight diagram
EEIS Health App
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.
Clone of Multi-echelon Inventory Optimization
Insight diagram
product life cycle
SD generic behavior S-shaped SIM
Insight diagram
​A model of viral growth. Based upon:


Viral Growth Pengguna Aplikasi
Insight diagram

Based on the model published in Repenning, "Understanding Firefighting in New Product Development," 2001.

Firefghting in New Product Development
Insight diagram
Process of petrol from a petrol pump being used to fuel vehicles
Petrol Station Story/Simulation
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. 


Clone of Multi-echelon Inventory Optimization
Insight diagram
Simple model used to assess the likely outcome of Revenue and Profit due to variability of purchase price, price impact on Units Sold, and Units Sold impact on Unit Cost.
Clone of Impact of variable price on revenue & profit
Insight diagram
Clone of tech model
Insight diagram
Clone of Inventory model with delays
Insight diagram
Simple model used to assess the likely outcome of Revenue and Profit due to variability of purchase price, price impact on Units Sold, and Units Sold impact on Unit Cost.
Clone of Impact of variable price on revenue & profit
Insight diagram
Clone of Inventory model with delays
Insight diagram
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