Business Models

These models and simulations have been tagged “Business”.

Related tagsTechnology

We want to know the estimated projected Earnings before tax of a 5 year project. For this we have modeled Sales Growth, Cost of goods sold and Admin. expenses applying sensitivity testing by using probability distributions.
We want to know the estimated projected Earnings before tax of a 5 year project. For this we have modeled Sales Growth, Cost of goods sold and Admin. expenses applying sensitivity testing by using probability distributions.
 System dynamics and geometric compromise programming: assessing the 
impact of entrepreneurship wage subsidies on macroeconomic objectives (Article)   
You can access my published article via this link:  http://doi.org/10.1111/itor.13601
System dynamics and geometric compromise programming: assessing the impact of entrepreneurship wage subsidies on macroeconomic objectives (Article)
You can access my published article via this link: http://doi.org/10.1111/itor.13601
 Whether you are a
start-up business or a business that has been standing for quite a while, your
reputation as a business is critical for the functioning of it. Not just your
reputation but the image of your business or brand is also very important; the
people or the customers must be able to visua

Whether you are a start-up business or a business that has been standing for quite a while, your reputation as a business is critical for the functioning of it. Not just your reputation but the image of your business or brand is also very important; the people or the customers must be able to visualize your business when they think of it and it is your duty as the owner to build this image, a positive and radiant image that will stand out from the rest and remain in their minds long after their encounter with it. So the big question is, how can one go about doing this? At first it may sound daunting and seem like such a big feat, I mean think about it, the responsibility of building a powerful image in the minds of many different types of people sounds scary. But really, if you look at it more prospectively you will learn that this is not quite true and is also easier than it sounds. The key is to pay attention to little things rather than only the bigger picture.

By following some of these the problem can be resolved. And there will be growth in the business development.

 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.
 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

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

How the Lean Startup method, developed by Eric Reis, works as a business system.
How the Lean Startup method, developed by Eric Reis, works as a business system.
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.
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.
 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.
How the Lean Startup method, developed by Eric Reis, works as a business system.
How the Lean Startup method, developed by Eric Reis, works as a business system.