Business Models

These models and simulations have been tagged “Business”.

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 Harvested fishery with endogenous investment and ship deployment policy. Ch 9 p345-360 John Morecroft (2007) Strategic Modelling and Business Dynamics

Harvested fishery with endogenous investment and ship deployment policy. Ch 9 p345-360 John Morecroft (2007) Strategic Modelling and Business Dynamics

 Original (more DYNAMO-like) version is here:  http://insightmaker.com/insight/14464        The Simple Retail Sector model from Section 1.7 of  DYNAMO User's Manual  by Alexander L Pugh III, which is adapted from one from  Industrial Dynamics  by Jay Forrester.     http://www.amazon.com/DYNAMO-Manua
Original (more DYNAMO-like) version is here: http://insightmaker.com/insight/14464


The Simple Retail Sector model from Section 1.7 of DYNAMO User's Manual by Alexander L Pugh III, which is adapted from one from Industrial Dynamics by Jay Forrester.

http://www.amazon.com/DYNAMO-Manual-Edition-System-Dynamics/dp/0262660296 (I bought the 5th edition without realising there was a later one, hopefully it's still the same model in there.)
ABM approach to Bass Model of diffusion with a detractor state.    Still a work in progress.
ABM approach to Bass Model of diffusion with a detractor state.

Still a work in progress.
The Simple Retail Sector model from Section 1.7 of  DYNAMO User's Manual  by Alexander L Pugh III, which is adapted from one from  Industrial Dynamics  by Jay Forrester.     http://www.amazon.com/DYNAMO-Manual-Edition-System-Dynamics/dp/0262660296  (I bought the 5th edition without realising there w
The Simple Retail Sector model from Section 1.7 of DYNAMO User's Manual by Alexander L Pugh III, which is adapted from one from Industrial Dynamics by Jay Forrester.

http://www.amazon.com/DYNAMO-Manual-Edition-System-Dynamics/dp/0262660296 (I bought the 5th edition without realising there was a later one, hopefully it's still the same model in there.)

A tweaked version with slightly more explicit stocks is here: http://insightmaker.com/insight/14467
 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 su
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.
 Simple Bass diffusion modified from Sterman Business Dynamics Ch9. Compare with the SI infectious disease model Insight 584, to which we added churn and duplicated it to model a 2 sided marketplace..  In this 2-sided market model -- take etsy for example --  the adoption rate of end users is also a

Simple Bass diffusion modified from Sterman Business Dynamics Ch9. Compare with the SI infectious disease model Insight 584, to which we added churn and duplicated it to model a 2 sided marketplace..

In this 2-sided market model -- take etsy for example --  the adoption rate of end users is also a factor of how many vendors the company has on-boarded.  You can control the impact on end user adoption via the Choice Impact Factor.

Conversely,  the rate at which vendors will be on-boarded is a factor of the perceived opportunity they have to sell their wares.  You can control the rate at which suppliers are on-boarded via the Opportunity Impact Factor.

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Causal loop diagram illustrating one of the contributing factors to employee hiring.
Causal loop diagram illustrating one of the contributing factors to employee hiring.
this is economy as it is in reality.
this is economy as it is in reality.
Process of petrol from a petrol pump being used to fuel vehicles
Process of petrol from a petrol pump being used to fuel vehicles
 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 su
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.
The Simple Retail Sector model from Section 1.7 of  DYNAMO User's Manual  by Alexander L Pugh III, which is adapted from one from  Industrial Dynamics  by Jay Forrester.     http://www.amazon.com/DYNAMO-Manual-Edition-System-Dynamics/dp/0262660296  (I bought the 5th edition without realising there w
The Simple Retail Sector model from Section 1.7 of DYNAMO User's Manual by Alexander L Pugh III, which is adapted from one from Industrial Dynamics by Jay Forrester.

http://www.amazon.com/DYNAMO-Manual-Edition-System-Dynamics/dp/0262660296 (I bought the 5th edition without realising there was a later one, hopefully it's still the same model in there.)

A tweaked version with slightly more explicit stocks is here: http://insightmaker.com/insight/14467
This causal loop diagram is the first step in looking at the relationship between business analysis performance and organizational performance. Over time it will be extended by IIBA R&I to form a simulation.    © International Institute of Business Analysis
This causal loop diagram is the first step in looking at the relationship between business analysis performance and organizational performance. Over time it will be extended by IIBA R&I to form a simulation.

© International Institute of Business Analysis
 Original (more DYNAMO-like) version is here:  http://insightmaker.com/insight/14464        The Simple Retail Sector model from Section 1.7 of  DYNAMO User's Manual  by Alexander L Pugh III, which is adapted from one from  Industrial Dynamics  by Jay Forrester.     http://www.amazon.com/DYNAMO-Manua
Original (more DYNAMO-like) version is here: http://insightmaker.com/insight/14464


The Simple Retail Sector model from Section 1.7 of DYNAMO User's Manual by Alexander L Pugh III, which is adapted from one from Industrial Dynamics by Jay Forrester.

http://www.amazon.com/DYNAMO-Manual-Edition-System-Dynamics/dp/0262660296 (I bought the 5th edition without realising there was a later one, hopefully it's still the same model in there.)
This causal loop diagram is the first step in looking at the relationship between business analysis performance and organizational performance. Over time it will be extended by IIBA R&I to form a simulation.    © International Institute of Business Analysis
This causal loop diagram is the first step in looking at the relationship between business analysis performance and organizational performance. Over time it will be extended by IIBA R&I to form a simulation.

© International Institute of Business Analysis
 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 su
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.
This causal loop diagram is the first step in looking at the relationship between business analysis performance and organizational performance. Over time it will be extended by IIBA R&I to form a simulation.    © International Institute of Business Analysis
This causal loop diagram is the first step in looking at the relationship between business analysis performance and organizational performance. Over time it will be extended by IIBA R&I to form a simulation.

© International Institute of Business Analysis