Business Models

These models and simulations have been tagged “Business”.

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 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.
 
 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.
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.
 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


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

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

 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.)
Process of petrol from a petrol pump being used to fuel vehicles
Process of petrol from a petrol pump being used to fuel vehicles
​The Problem:  What is the true cost of escalating too many Tier 1/Level 1 tickets to Level 2/3 engineers?    Things to measure: How does this impact:1. (MONEY) Cost per incident - what does this cost the business? 2. (TIME) Service Level - how does this impact desired service levels/SLAs? 3. (PEOPL
​The Problem: 
What is the true cost of escalating too many Tier 1/Level 1 tickets to Level 2/3 engineers?

Things to measure: How does this impact:1. (MONEY) Cost per incident - what does this cost the business? 2. (TIME) Service Level - how does this impact desired service levels/SLAs? 3. (PEOPLE) Agent utilization - how does this impact backlog? are we overworking engineers? Does this contribute to staff burnout?

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
 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

Závislosti v přípravě obchodního reportingu datových a masových služeb
Závislosti v přípravě obchodního reportingu datových a masových služeb
​The Problem:  What is the true cost of escalation?    Things to measure: How does this impact:1. (MONEY) Cost per incident - what does this cost the business? 2. (TIME) Service Level - how does this impact desired service levels/SLAs? 3. (PEOPLE) Agent utilization - how does this impact backlog? ar
​The Problem: 
What is the true cost of escalation?

Things to measure: How does this impact:1. (MONEY) Cost per incident - what does this cost the business? 2. (TIME) Service Level - how does this impact desired service levels/SLAs? 3. (PEOPLE) Agent utilization - how does this impact backlog? are we overworking engineers? Does this contribute to staff burnout?

 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


 Morecroft, John (2015) Strategic Modelling and Business Dynamics, Ch 1 p13 -14

Morecroft, John (2015) Strategic Modelling and Business Dynamics, Ch 1 p13 -14

 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.

Copyright 2016 Whatifics Pte Ltd

 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.