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

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. 

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. 

© International Institute of Business Analysis

What is the true cost of escalation?

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 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. 

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 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. 

Enter volumes based on transactions from all your applications based on the business process layer.  Actual transaction volumes recorded for your expense or revenue stream.  Example; how many applications are sponsored by engineering that allow CREATE and UPDATE of a supplier or customer.  Enter the number of transactions in the engineering variable.  The engineering group has no authority to create either a supplier or customer.  All organizations are allowed to submit a certain set of inputs for a super user group to review and then those super users create and update the master record.  

Food for thought, the difference between the records created by the super user group and the volume of created party management records is the opportunity to scale and protect the reputation of your client.  

Now lets do the same for every update to a party management record.  All changes to a supplier or customer could effect pricing and tax or duty fees and each must use the formal change management process with many changes needing to have an authorized representative from the supplier or customer.  Digital records are in need of key control designs.  These are monitored and reported to the SEC.  

BUSINESS ARCHITECTURE 

5 Capability Model
The 5 capability model has many stock and flow children which each organization will need to model based on their current state.  

• Aligns to APQC Process Framework
• Aligns to Principles in ISO 9001, 26000 and 27001 

ENTERPRISE ARCHITECTURE 
Aligns Zachman Framework Enterprise and Business Architecture with Executive and Leaders from a business management level across any organization.  

A method in which to align and benchmark any organization or agency, with the system(s) logic required from Architects in Row 3, to enable Row 4 engineers who need to supply physics. 


Semantic
Getting terms to align to the generic objects can be a trying task, unless you simply list the stakeholders "semantic" term below the stakeholder in the presentation layer by order shown in the business process management section above the capability management group.