Business Models

These models and simulations have been tagged “Business”.

Related tagsTechnology

 
 This insights explores the organizational factors influencing strategy implementation and the interrelationship among some of the factors.
  • This insights explores the organizational factors influencing strategy implementation and the interrelationship among some of the factors.
 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.
Team growth depending on hiring rate, referral rate, departure rate
Team growth depending on hiring rate, referral rate, departure rate
The effect in time of cost reducction on profits
The effect in time of cost reducction on profits
this is economy as it is in reality.
this is economy as it is in reality.
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.
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
 Bottom-Up Sales Forecasting for Startups     The purpose of this simulation is to demonstrate the implications of forecasting sales without consideration for how much it cost you to acquire a lead and how much you have available to spend. A common mistake in sales forecasting is to define your # of
Bottom-Up Sales Forecasting for Startups

The purpose of this simulation is to demonstrate the implications of forecasting sales without consideration for how much it cost you to acquire a lead and how much you have available to spend. A common mistake in sales forecasting is to define your # of expected sales leads based on your total market size and your assumption regarding the % of that market you can reach. 

This model demonstrates the forecasting impact to defining the # of expect leads based on how much it cost you to acquire a lead and how much you have available to spend. 

Important Variables:
1. [UseLAC?] (set to 1 to use the lead acquisition cost to define your reachable market; use 0 to set the reachable market to equal the total available market size)
2. LAC (should equal what it cost you to acquire a lead)
3. SalesMarketingBudget : how much you have available to spend on customer acquisition

Other Variables:
4. Price : Avg spending amount per new customer
5. Total Available Market : Total available market size
6. Conversion Rate : the % of your target market that will become a lead


This model is based off Meadows economic capital with reinforcing growth loop constrained by a renewable resource model.
This model is based off Meadows economic capital with reinforcing growth loop constrained by a renewable resource model.
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
This causal loop diagram is the first step in looking at the relationship between business analysis performance and organizational performance.
This causal loop diagram is the first step in looking at the relationship between business analysis performance and organizational performance.