Business Models

These models and simulations have been tagged “Business”.

Related tagsTechnology

Insight diagram
According to:

​System Dynamics Modeling
of Gartner’s Hype Cycle
KAIST
Industrial & Systems Engineering
Ahn Hyunsoup

Gartner Hype Cycle
Insight diagram
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.
Clone of Impact of variable price on revenue & profit
Insight diagram
Clone of Inventarios
Insight diagram

The issue is how to select and keep optometry students. The main theme is balance out expectations with reality especially when many students use optometry as a proxy for later entry into a medical course. The second problem is that the current entry demographic does not meet the needs in terms of culture and expected practice location

Clone of optometry student recruitment, training, retention and expectations
Insight diagram
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.
Clone of Multi-echelon Inventory Optimization
Insight diagram
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


Clone of Bottom-up Sales Forecasting
Insight diagram
This causal loop diagram is the first step in looking at the relationship between business analysis performance and organizational performance.
Organizational Systems Performance Model - Core Variables
Insight diagram
Fragmenteret it landskab
Insight diagram
Building a simple model to understand how to run a profitably fishing business
FishBanks_Karan_DA
Insight diagram
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.
Clone of Multi-echelon Inventory Optimization
Insight diagram
Areas with unmet needs
Opportunity areas for antrimicrobial resistance
Insight diagram
Rich Picture with Simulation
Clone of Rich Picture Cafe + Simulation
Insight diagram

Rich picture version of Causal loop diagram based on Jack  Homer's paper Worker burnout: a dynamic model with implications  for prevention and control System Dynamics Review 1985 1(1)42-62 See IM-333 for the Simulation model and IM-2178 for a related Causal Loop Diagram of Project Turnover

 

Clone of Burnout Dynamics CLD rich pic
Insight diagram

model

Clone of model
Insight diagram
Leverage Action Implementation
Insight diagram
Clone of Clone of Grocery Store System - Stock & Flow Diagram/SD Model
Insight diagram
Clone of Grocery Store System - Stock & Flow Diagram/SD Model
Insight diagram

model

Clone of model
Insight diagram

Rich picture version of Causal loop diagram based on Jack  Homer's paper Worker burnout: a dynamic model with implications  for prevention and control System Dynamics Review 1985 1(1)42-62 See IM-333 for the Simulation model and IM-2178 for a related Causal Loop Diagram of Project Turnover

 

Clone of Burnout Dynamics CLD rich pic
Insight diagram
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.
Clone of Impact of variable price on revenue & profit
Insight diagram
DRAFT

a small model of a "generic" company.
Clone of small model company draft
Insight diagram
How the Lean Startup method, developed by Eric Reis, works as a business system.
Clone of Lean Startup Business System
Insight diagram
​Purpose
Enables the different components in the 5 capability model in a visual manner for Enterprise and Business Architecture stakeholders.  


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.  



Clone of To Be Business and Technology Architecture
Insight diagram
This simulation mimics the flow of projects through an organization. The organization consists of teams that idependently or collaboratively work on projects. Many of the projects have a mulit-team dependency.

If you want to understand more in depth what this simulation is all about, read this blog post: https://stefan-willuda.medium.com/super-powerful-how-full-kitting-will-speed-up-your-cross-team-projects-1598d55fa9d7
Clone of [Published] Full Kitting in Dependent Team Delivery