Simulace.info - User contributions [en]

Profit in store vs e-shop

2023-01-25T23:01:18Z

Ploo00:

== Problem definition ==
An unnamed company that sells carpets has its own store in Prague. During COVID-19 the company reopened an e-shop, so it currently has two mutually supporting sales channels. Both types of stores have their advantages and disadvantages. At the same time, there are various factors that affect the profit. Examples of these factors are the following: customer satisfaction and needs (carpet quality, order processing speed, price, etc.), expenses (advertising, rent, employees, etc.), the possibility of expansion, etc. To ensure customer satisfaction the company should make some expenses.

The goal of this simulation is to find out what parameters can increase profit the most (individually for each type of store), to find a balance between expenses to satisfy the customers in order to achieve the profit, and in the end to compare these parameters.

== Method ==
The Vensim program is used as a modeling tool, which makes it possible to display and simulate the dynamics of the entire system.

== Variables ==

''' Profit '''

the amount of money a business makes after all of its expenses have been paid.
= revenue - cost
Units: CZK

''' Cost '''

the amount of money that is required to acquire a product or to raise customers' satisfaction.
= fixed cost e-shop + fixed cost store + variable cost e-shop + variable cost store
Units: CZK

''' Revenue '''

the amount of money a business earns from its sales.
= revenue e-shop + revenue store
Units: CZK

''' Fixed cost e-shop '''

costs that do not change with the level of sales for an e-commerce business.
= salary * employments e-shop + 1000
Units: CZK

''' Fixed cost store '''

costs that do not change with the level of sales for a physical store.
= salary * employments store + building rent
Units: CZK

''' Variable cost e-shop '''

costs that vary with the level of sales for an e-shop.
= 5000 + advertising budget + gifts + (money paid for discount/2)
Units: CZK

''' Variable cost store '''

costs that vary with the level of sales for a physical store
= 10000 + gifts + (money paid for discount/2)
Units: CZK

''' Advertising budget '''

the amount of money allocated by a business for promoting its products. The company is advertising through Google Ads and pays for each click.
= RANDOM UNIFORM(20, 1000 , 50 )*15
Units: CZK

''' Building rent '''

the cost of renting a physical location for a business.
= 80000 + parking
Units: CZK

''' Salary '''

the amount of money that is paid to an employee for their work.
= 25000 * opening hours
Units: CZK

''' Employments store '''

the number of employees working in a physical store.
= 4

''' Employments e-shop '''

he number of employees working in an e-shop.
= 1

''' Opening hours '''

the time when a business is open. If there is a holiday the store is open and employees will get a +25% to their salary.
= IF THEN ELSE(holiday=1, 1.25 , 1 )

''' Holiday '''

= RANDOM UNIFORM(0, 1, 1)

''' Parking '''

the availability of parking for customers at a physical location.
= 5000
Units: CZK

''' Revenue e-shop '''

the amount of money earned from an e-shop. The company's revenue always increases by 20% spent on advertisements.
= price * quantity sold e-shop * customer satisfaction e-shop + (advertising budget*0.2)
Units: CZK

''' Revenue store '''

the amount of money earned from a physical store.
= price * quantity sold store * customer satisfaction store
Units: CZK

''' Quantity sold e-shop '''

the number of products sold by e-shop.
= RANDOM UNIFORM(500, 1000 , 800)
Units: m^3

''' Quantity sold store '''

the number of products sold by a physical store.
= RANDOM UNIFORM(300, 700, 500 )
Units: m^3

''' Price '''

the amount of money that is charged for a carpet
= RANDOM UNIFORM(150, 650 , 300)+delivery
Units: CZK

''' Delivery '''

the delivery cost depends on the order weight. If the order cost is more, than 5000 CZK then the delivery is free.
= IF THEN ELSE(order cost>5000, 0 , IF THEN ELSE(order weight<5, 159 , IF THEN ELSE(order weight>5 :AND: order weight<15, 289 , IF THEN ELSE(order weight>15 :AND: order weight<30, 399 , IF THEN ELSE(order weight>30 :AND: order weight<50, 599 , IF THEN ELSE(order weight>50 :AND: order weight<75, 799 , IF THEN ELSE(order weight>75 :AND: order weight<100, 999 , IF THEN ELSE(order weight>100 :AND: order weight<150, 1389 , IF THEN ELSE(order weight>150 :AND: order weight<300, 1649 , IF THEN ELSE(order weight>300 :AND: order weight<400, 1749 , 1979 ) ) ) ) ) ) ) ) ) )
Units: CZK

''' Order weight '''

the weight of an order placed by a customer.
= RANDOM UNIFORM(1, 500, 1)
Units: kg

''' Order cost '''

the cost of processing and fulfilling an order placed by a customer.
= RANDOM NORMAL(200, 100000 , 5000 , 2000, 1000)
Units: CZK

''' Gifts '''

items that are given to customers as part of a promotion. If the order cost is bigger than 2000, customers will get a gift, which cost 500 CZK.
= IF THEN ELSE(order cost>2000, 500, 0 )
Units: CZK

''' Discount '''

a reduction in the regular price of a product. The current discount is 10 %
= RANDOM UNIFORM(0.6, 0.9 , 0.7)
Units: %

''' Money paid for discount '''

the money which the company spent to give the customer a discount
= order cost*discount
Units: CZK

''' Customer satisfaction '''

the degree to which customers are happy with a business's products.
= customer satisfaction grow

''' Customer satisfaction grow '''

the increase in customer satisfaction over time.
= customer satisfaction e-shop + customer satisfaction store

''' Customer satisfaction e-shop '''

the degree to which customers are happy with e-shop's products.

If the store gives gifts, it will increase revenue by 2%.
If the store gives a discount of 30%, it will increase revenue by 20%.
If the store gives a discount of 10-20%, it will increase revenue by 10%.

If the store has gifts and a 10-20% discount, the revenue will increase by 12%. If the store has gifts and a 30% discount, the revenue will increase by 22%.

= IF THEN ELSE( (IF THEN ELSE(gifts=500, 1.02 , 1 ) + IF THEN ELSE(discount<0.7, 1 , IF THEN ELSE(discount>0.7 :AND: discount<0.8, 1.2 , 1.1 ) ) )= 1.12 , 1.12 , 1.22)
Units: %

''' Customer satisfaction store '''

the degree to which customers are happy with a physical store's products.

If the store has an available parking spot, it will increase revenue by 2%.
If the store gives gifts, it will increase revenue by 2%.
If the store gives a discount of 30%, it will increase revenue by 20%.
If the store gives a discount of 10-20%, it will increase revenue by 10%.
If the store works on holidays, it will increase revenue by 5%.

If the store has gifts, parking, working on holidays, and a 10-20% discount, the revenue will increase by 19%. If the store has gifts, parking, working on holidays, and a 30% discount, the revenue will increase by 29%.

= IF THEN ELSE( (IF THEN ELSE(parking=5000, 1.02 , 1 ) + IF THEN ELSE(opening hours=1.25, 1.05 , 1 ) + IF THEN ELSE(gifts=500, 1.02 , 1 ) + IF THEN ELSE(discount<0.7, 1 , IF THEN ELSE(discount>0.7 :AND: discount<0.8, 1.2 , 1.1 ) ) )= 1.19 , 1.19 , 1.29)
Units: %

== Model ==

The model represents a company and its revenue and costs separated into different variables to represent different sales channels (e-shop and store). As can be seen in the model fixed costs for the e-shop and store do not differ that much. Variable cost does not differ that much either, except for the advertisement budget, which influences e-shop variable cost a lot. On the other hand, revenue is influenced by more variables. Besides typical variables such as the price of the product and sold quantity, it is also influenced by customers' satisfaction. Customers' satisfaction influences the revenue for both e-shop and store.

[[File:model_sfd.png|Stock and flow diagram]]

== Results ==

According to the graphs below we can see, that e-shop revenue is much higher than the store's revenue. E-shop revenue is influenced by higher variable costs as the company is spending money on advertising, which makes clients buy more through e-shop.

[[File:Revenue.png|Company's revenue]] [[File:Cost.png|Comapny's costs]]

---

In the graphs below we can see the revenue of the e-shop and store apart in more detail. On the graphs can be seen how e-shop revenue is influenced by advertisement.

[[File:Revenue_e-shop.png|Revenue e-shop]] [[File:Revenue store.png|Revenue store]]

---

Even though e-shop revenue is much higher, we can see that customers should be more satisfied with the store as there are more 'perks' in the store.

[[File:Customer_satisfaction_eshop.png|Customer satisfaction eshop tree]] [[File:Customer_satisfaction_store.png|Customer satisfaction store]]

== Conclusion ==
The final model is not ideal. It is hard to find all variables, that influence company's profit, especially if we are taking into account customers' satisfaction. The simulation does not count some important variables such as the costs that the company needs to spend to buy new carpets, for example. According to the model we can say, that the profit is better at e-shop, even though customer satisfaction is higher at the store. The reason for that is probably advertisement which is increasing the sales on e-shop.

== Code ==

[[File:SFD.mdl|SFD model]]

Profit in store vs e-shop

2023-01-25T23:00:01Z

Ploo00:

== Problem definition ==
An unnamed company that sells carpets has its own store in Prague. During COVID-19 the company reopened an e-shop, so it currently has two mutually supporting sales channels. Both types of stores have their advantages and disadvantages. At the same time, there are various factors that affect the profit. Examples of these factors are the following: customer satisfaction and needs (carpet quality, order processing speed, price, etc.), expenses (advertising, rent, employees, etc.), the possibility of expansion, etc. To ensure customer satisfaction the company should make some expenses.

The goal of this simulation is to find out what parameters can increase profit the most (individually for each type of store), to find a balance between expenses to satisfy the customers in order to achieve the profit, and in the end to compare these parameters.

== Method ==
The Vensim program is used as a modeling tool, which makes it possible to display and simulate the dynamics of the entire system.

== Variables ==

''' Profit '''

the amount of money a business makes after all of its expenses have been paid.
= revenue - cost
Units: CZK

''' Cost '''

the amount of money that is required to acquire a product or to raise customers' satisfaction.
= fixed cost e-shop + fixed cost store + variable cost e-shop + variable cost store
Units: CZK

''' Revenue '''

the amount of money a business earns from its sales.
= revenue e-shop + revenue store
Units: CZK

''' Fixed cost e-shop '''

costs that do not change with the level of sales for an e-commerce business.
= salary * employments e-shop + 1000
Units: CZK

''' Fixed cost store '''

costs that do not change with the level of sales for a physical store.
= salary * employments store + building rent
Units: CZK

''' Variable cost e-shop '''

costs that vary with the level of sales for an e-shop.
= 5000 + advertising budget + gifts + (money paid for discount/2)
Units: CZK

''' Variable cost store '''

costs that vary with the level of sales for a physical store
= 10000 + gifts + (money paid for discount/2)
Units: CZK

''' Advertising budget '''

the amount of money allocated by a business for promoting its products. The company is advertising through Google Ads and pays for each click.
= RANDOM UNIFORM(20, 1000 , 50 )*15
Units: CZK

''' Building rent '''

the cost of renting a physical location for a business.
= 80000 + parking
Units: CZK

''' Salary '''

the amount of money that is paid to an employee for their work.
= 25000 * opening hours
Units: CZK

''' Employments store '''

the number of employees working in a physical store.
= 4

''' Employments e-shop '''

he number of employees working in an e-shop.
= 1

''' Opening hours '''

the time when a business is open. If there is a holiday the store is open and employees will get a +25% to their salary.
= IF THEN ELSE(holiday=1, 1.25 , 1 )

''' Holiday '''

= RANDOM UNIFORM(0, 1, 1)

''' Parking '''

the availability of parking for customers at a physical location.
= 5000
Units: CZK

''' Revenue e-shop '''

the amount of money earned from an e-shop. The company's revenue always increases by 20% spent on advertisements.
= price * quantity sold e-shop * customer satisfaction e-shop + (advertising budget*0.2)
Units: CZK

''' Revenue store '''

the amount of money earned from a physical store.
= price * quantity sold store * customer satisfaction store
Units: CZK

''' Quantity sold e-shop '''

the number of products sold by e-shop.
= RANDOM UNIFORM(500, 1000 , 800)
Units: m^3

''' Quantity sold store '''

the number of products sold by a physical store.
= RANDOM UNIFORM(300, 700, 500 )
Units: m^3

''' Price '''

the amount of money that is charged for a carpet
= RANDOM UNIFORM(150, 650 , 300)+delivery
Units: CZK

''' Delivery '''

the delivery cost depends on the order weight. If the order cost is more, than 5000 CZK then the delivery is free.
= IF THEN ELSE(order cost>5000, 0 , IF THEN ELSE(order weight<5, 159 , IF THEN ELSE(order weight>5 :AND: order weight<15, 289 , IF THEN ELSE(order weight>15 :AND: order weight<30, 399 , IF THEN ELSE(order weight>30 :AND: order weight<50, 599 , IF THEN ELSE(order weight>50 :AND: order weight<75, 799 , IF THEN ELSE(order weight>75 :AND: order weight<100, 999 , IF THEN ELSE(order weight>100 :AND: order weight<150, 1389 , IF THEN ELSE(order weight>150 :AND: order weight<300, 1649 , IF THEN ELSE(order weight>300 :AND: order weight<400, 1749 , 1979 ) ) ) ) ) ) ) ) ) )
Units: CZK

''' Order weight '''

the weight of an order placed by a customer.
= RANDOM UNIFORM(1, 500, 1)
Units: kg

''' Order cost '''

the cost of processing and fulfilling an order placed by a customer.
= RANDOM NORMAL(200, 100000 , 5000 , 2000, 1000)
Units: CZK

''' Gifts '''

items that are given to customers as part of a promotion. If the order cost is bigger than 2000, customers will get a gift, which cost 500 CZK.
= IF THEN ELSE(order cost>2000, 500, 0 )
Units: CZK

''' Discount '''

a reduction in the regular price of a product. The current discount is 10 %
= RANDOM UNIFORM(0.6, 0.9 , 0.7)
Units: %

''' Money paid for discount '''

the money which the company spent to give the customer a discount
= order cost*discount
Units: CZK

''' Customer satisfaction '''

the degree to which customers are happy with a business's products.
= customer satisfaction grow

''' Customer satisfaction grow '''

the increase in customer satisfaction over time.
= customer satisfaction e-shop + customer satisfaction store

''' Customer satisfaction e-shop '''

the degree to which customers are happy with e-shop's products.

If the store gives gifts, it will increase revenue by 2%.
If the store gives a discount of 30%, it will increase revenue by 20%.
If the store gives a discount of 10-20%, it will increase revenue by 10%.

If the store has gifts and a 10-20% discount, the revenue will increase by 12%. If the store has gifts and a 30% discount, the revenue will increase by 22%.

= IF THEN ELSE( (IF THEN ELSE(gifts=500, 1.02 , 1 ) + IF THEN ELSE(discount<0.7, 1 , IF THEN ELSE(discount>0.7 :AND: discount<0.8, 1.2 , 1.1 ) ) )= 1.12 , 1.12 , 1.22)
Units: %

''' Customer satisfaction store '''

the degree to which customers are happy with a physical store's products.

If the store has an available parking spot, it will increase revenue by 2%.
If the store gives gifts, it will increase revenue by 2%.
If the store gives a discount of 30%, it will increase revenue by 20%.
If the store gives a discount of 10-20%, it will increase revenue by 10%.
If the store works on holidays, it will increase revenue by 5%.

If the store has gifts, parking, working on holidays, and a 10-20% discount, the revenue will increase by 19%. If the store has gifts, parking, working on holidays, and a 30% discount, the revenue will increase by 29%.

= IF THEN ELSE( (IF THEN ELSE(parking=5000, 1.02 , 1 ) + IF THEN ELSE(opening hours=1.25, 1.05 , 1 ) + IF THEN ELSE(gifts=500, 1.02 , 1 ) + IF THEN ELSE(discount<0.7, 1 , IF THEN ELSE(discount>0.7 :AND: discount<0.8, 1.2 , 1.1 ) ) )= 1.19 , 1.19 , 1.29)
Units: %

== Model ==

The model represents a company and its revenue and costs separated into different variables to represent different sales channels (e-shop and store). As can be seen in the model fixed costs for the e-shop and store do not differ that much. Variable cost does not differ that much either, except for the advertisement budget, which influences e-shop variable cost a lot. On the other hand, revenue is influenced by more variables. Besides typical variables such as the price of the product and sold quantity, it is also influenced by customers' satisfaction. Customers' satisfaction influences the revenue for both e-shop and store.

[[File:model_sfd.png|Stock and flow diagram]]

== Results ==

According to the graphs below we can see, that e-shop revenue is much higher than the store's revenue. E-shop revenue is influenced by higher variable costs as the company is spending money on advertising, which makes clients buy more through e-shop.

[[File:Revenue.png|Company's revenue]] [[File:Cost.png|Comapny's costs]]

---

In the graphs below we can see the revenue of the e-shop and store apart in more detail. On the graphs can be seen how e-shop revenue is influenced by more variables.

[[File:Revenue_e-shop.png|Revenue e-shop]] [[File:Revenue store.png|Revenue store]]

---

Even though e-shop revenue is much higher, we can see that customers should be more satisfied with the store as there are more 'perks' in the store.

[[File:Customer_satisfaction_eshop.png|Customer satisfaction eshop tree]] [[File:Customer_satisfaction_store.png|Customer satisfaction store]]

== Conclusion ==
The final model is not ideal. It is hard to find all variables, that influence company's profit, especially if we are taking into account customers' satisfaction. The simulation does not count some important variables such as the costs that the company needs to spend to buy new carpets, for example. According to the model we can say, that the profit is better at e-shop, even though customer satisfaction is higher at the store. The reason for that is probably advertisement which is increasing the sales on e-shop.

== Code ==

[[File:SFD.mdl|SFD model]]

File:Customer satisfaction store.png

2023-01-25T22:56:32Z

Ploo00:

File:Customer satisfaction eshop.png

2023-01-25T22:56:19Z

Ploo00:

File:Revenue store.png

2023-01-25T22:51:29Z

Ploo00:

File:Revenue e-shop.png

2023-01-25T22:51:11Z

Ploo00:

File:SFD.mdl

2023-01-25T22:49:44Z

Ploo00:

Profit in store vs e-shop

2023-01-25T22:48:58Z

Ploo00:

== Problem definition ==
An unnamed company that sells carpets has its own store in Prague. During COVID-19 the company reopened an e-shop, so it currently has two mutually supporting sales channels. Both types of stores have their advantages and disadvantages. At the same time, there are various factors that affect the profit. Examples of these factors are the following: customer satisfaction and needs (carpet quality, order processing speed, price, etc.), expenses (advertising, rent, employees, etc.), the possibility of expansion, etc. To ensure customer satisfaction the company should make some expenses.

The goal of this simulation is to find out what parameters can increase profit the most (individually for each type of store), to find a balance between expenses to satisfy the customers in order to achieve the profit, and in the end to compare these parameters.

== Method ==
The Vensim program is used as a modeling tool, which makes it possible to display and simulate the dynamics of the entire system.

== Variables ==

''' Profit '''

the amount of money a business makes after all of its expenses have been paid.
= revenue - cost
Units: CZK

''' Cost '''

the amount of money that is required to acquire a product or to raise customers' satisfaction.
= fixed cost e-shop + fixed cost store + variable cost e-shop + variable cost store
Units: CZK

''' Revenue '''

the amount of money a business earns from its sales.
= revenue e-shop + revenue store
Units: CZK

''' Fixed cost e-shop '''

costs that do not change with the level of sales for an e-commerce business.
= salary * employments e-shop + 1000
Units: CZK

''' Fixed cost store '''

costs that do not change with the level of sales for a physical store.
= salary * employments store + building rent
Units: CZK

''' Variable cost e-shop '''

costs that vary with the level of sales for an e-shop.
= 5000 + advertising budget + gifts + (money paid for discount/2)
Units: CZK

''' Variable cost store '''

costs that vary with the level of sales for a physical store
= 10000 + gifts + (money paid for discount/2)
Units: CZK

''' Advertising budget '''

the amount of money allocated by a business for promoting its products. The company is advertising through Google Ads and pays for each click.
= RANDOM UNIFORM(20, 1000 , 50 )*15
Units: CZK

''' Building rent '''

the cost of renting a physical location for a business.
= 80000 + parking
Units: CZK

''' Salary '''

the amount of money that is paid to an employee for their work.
= 25000 * opening hours
Units: CZK

''' Employments store '''

the number of employees working in a physical store.
= 4

''' Employments e-shop '''

he number of employees working in an e-shop.
= 1

''' Opening hours '''

the time when a business is open. If there is a holiday the store is open and employees will get a +25% to their salary.
= IF THEN ELSE(holiday=1, 1.25 , 1 )

''' Holiday '''

= RANDOM UNIFORM(0, 1, 1)

''' Parking '''

the availability of parking for customers at a physical location.
= 5000
Units: CZK

''' Revenue e-shop '''

the amount of money earned from an e-shop. The company's revenue always increases by 20% spent on advertisements.
= price * quantity sold e-shop * customer satisfaction e-shop + (advertising budget*0.2)
Units: CZK

''' Revenue store '''

the amount of money earned from a physical store.
= price * quantity sold store * customer satisfaction store
Units: CZK

''' Quantity sold e-shop '''

the number of products sold by e-shop.
= RANDOM UNIFORM(500, 1000 , 800)
Units: m^3

''' Quantity sold store '''

the number of products sold by a physical store.
= RANDOM UNIFORM(300, 700, 500 )
Units: m^3

''' Price '''

the amount of money that is charged for a carpet
= RANDOM UNIFORM(150, 650 , 300)+delivery
Units: CZK

''' Delivery '''

the delivery cost depends on the order weight. If the order cost is more, than 5000 CZK then the delivery is free.
= IF THEN ELSE(order cost>5000, 0 , IF THEN ELSE(order weight<5, 159 , IF THEN ELSE(order weight>5 :AND: order weight<15, 289 , IF THEN ELSE(order weight>15 :AND: order weight<30, 399 , IF THEN ELSE(order weight>30 :AND: order weight<50, 599 , IF THEN ELSE(order weight>50 :AND: order weight<75, 799 , IF THEN ELSE(order weight>75 :AND: order weight<100, 999 , IF THEN ELSE(order weight>100 :AND: order weight<150, 1389 , IF THEN ELSE(order weight>150 :AND: order weight<300, 1649 , IF THEN ELSE(order weight>300 :AND: order weight<400, 1749 , 1979 ) ) ) ) ) ) ) ) ) )
Units: CZK

''' Order weight '''

the weight of an order placed by a customer.
= RANDOM UNIFORM(1, 500, 1)
Units: kg

''' Order cost '''

the cost of processing and fulfilling an order placed by a customer.
= RANDOM NORMAL(200, 100000 , 5000 , 2000, 1000)
Units: CZK

''' Gifts '''

items that are given to customers as part of a promotion. If the order cost is bigger than 2000, customers will get a gift, which cost 500 CZK.
= IF THEN ELSE(order cost>2000, 500, 0 )
Units: CZK

''' Discount '''

a reduction in the regular price of a product. The current discount is 10 %
= RANDOM UNIFORM(0.6, 0.9 , 0.7)
Units: %

''' Money paid for discount '''

the money which the company spent to give the customer a discount
= order cost*discount
Units: CZK

''' Customer satisfaction '''

the degree to which customers are happy with a business's products.
= customer satisfaction grow

''' Customer satisfaction grow '''

the increase in customer satisfaction over time.
= customer satisfaction e-shop + customer satisfaction store

''' Customer satisfaction e-shop '''

the degree to which customers are happy with e-shop's products.

If the store gives gifts, it will increase revenue by 2%.
If the store gives a discount of 30%, it will increase revenue by 20%.
If the store gives a discount of 10-20%, it will increase revenue by 10%.

If the store has gifts and a 10-20% discount, the revenue will increase by 12%. If the store has gifts and a 30% discount, the revenue will increase by 22%.

= IF THEN ELSE( (IF THEN ELSE(gifts=500, 1.02 , 1 ) + IF THEN ELSE(discount<0.7, 1 , IF THEN ELSE(discount>0.7 :AND: discount<0.8, 1.2 , 1.1 ) ) )= 1.12 , 1.12 , 1.22)
Units: %

''' Customer satisfaction store '''

the degree to which customers are happy with a physical store's products.

If the store has an available parking spot, it will increase revenue by 2%.
If the store gives gifts, it will increase revenue by 2%.
If the store gives a discount of 30%, it will increase revenue by 20%.
If the store gives a discount of 10-20%, it will increase revenue by 10%.
If the store works on holidays, it will increase revenue by 5%.

If the store has gifts, parking, working on holidays, and a 10-20% discount, the revenue will increase by 19%. If the store has gifts, parking, working on holidays, and a 30% discount, the revenue will increase by 29%.

= IF THEN ELSE( (IF THEN ELSE(parking=5000, 1.02 , 1 ) + IF THEN ELSE(opening hours=1.25, 1.05 , 1 ) + IF THEN ELSE(gifts=500, 1.02 , 1 ) + IF THEN ELSE(discount<0.7, 1 , IF THEN ELSE(discount>0.7 :AND: discount<0.8, 1.2 , 1.1 ) ) )= 1.19 , 1.19 , 1.29)
Units: %

== Model ==

The model represents a company and its revenue and costs separated into different variables to represent different sales channels (e-shop and store). As can be seen in the model fixed costs for the e-shop and store do not differ that much. Variable cost does not differ that much either, except for the advertisement budget, which influences e-shop variable cost a lot. On the other hand, revenue is influenced by more variables. Besides typical variables such as the price of the product and sold quantity, it is also influenced by customers' satisfaction. Customers' satisfaction influences the revenue for both e-shop and store.

[[File:model_sfd.png|Stock and flow diagram]]

== Results ==

According to the graphs below we can see, that e-shop revenue is much higher than the store's revenue. E-shop revenue is influenced by higher variable costs as the company is spending money on advertising, which makes clients buy more through e-shop.

[[File:Revenue.png|Company's revenue]] [[File:Cost.png|Comapny's costs]]

== Conclusion ==
The final model is not ideal. It is hard to find all variables, that influence company's profit, especially if we are taking into account customers' satisfaction. The simulation does not count some important variables such as the costs that the company needs to spend to buy new carpets, for example.

File:Cost.png

2023-01-25T22:44:53Z

Ploo00:

File:Revenue.png

2023-01-25T22:43:56Z

Ploo00:

File:Model sfd.png

2023-01-25T22:32:17Z

Ploo00:

Profit in store vs e-shop

2023-01-25T22:03:08Z

Ploo00:

== Problem definition ==
An unnamed company that sells carpets has its own store in Prague. During COVID-19 the company reopened an e-shop, so it currently has two mutually supporting sales channels. Both types of stores have their advantages and disadvantages. At the same time, there are various factors that affect the profit. Examples of these factors are the following: customer satisfaction and needs (carpet quality, order processing speed, price, etc.), expenses (advertising, rent, employees, etc.), the possibility of expansion, etc. To ensure customer satisfaction the company should make some expenses.

The goal of this simulation is to find out what parameters can increase profit the most (individually for each type of store), to find a balance between expenses to satisfy the customers in order to achieve the profit, and in the end to compare these parameters.

== Method ==
The Vensim program is used as a modeling tool, which makes it possible to display and simulate the dynamics of the entire system.

== Variables ==

''' Profit '''

the amount of money a business makes after all of its expenses have been paid.
= revenue - cost
Units: CZK

''' Cost '''

the amount of money that is required to acquire a product or to raise customers' satisfaction.
= fixed cost e-shop + fixed cost store + variable cost e-shop + variable cost store
Units: CZK

''' Revenue '''

the amount of money a business earns from its sales.
= revenue e-shop + revenue store
Units: CZK

''' Fixed cost e-shop '''

costs that do not change with the level of sales for an e-commerce business.
= salary * employments e-shop + 1000
Units: CZK

''' Fixed cost store '''

costs that do not change with the level of sales for a physical store.
= salary * employments store + building rent
Units: CZK

''' Variable cost e-shop '''

costs that vary with the level of sales for an e-shop.
= 5000 + advertising budget + gifts + (money paid for discount/2)
Units: CZK

''' Variable cost store '''

costs that vary with the level of sales for a physical store
= 10000 + gifts + (money paid for discount/2)
Units: CZK

''' Advertising budget '''

the amount of money allocated by a business for promoting its products. The company is advertising through Google Ads and pays for each click.
= RANDOM UNIFORM(20, 1000 , 50 )*25
Units: CZK

''' Building rent '''

the cost of renting a physical location for a business.
= 80000 + parking
Units: CZK

''' Salary '''

the amount of money that is paid to an employee for their work.
= 25000 * opening hours
Units: CZK

''' Employments store '''

the number of employees working in a physical store.
= 4

''' Employments e-shop '''

he number of employees working in an e-shop.
= 1

''' Opening hours '''

the time when a business is open. If there is a holiday the store is open and employees will get a +25% to their salary.
= IF THEN ELSE(holiday=1, 1.25 , 1 )

''' Holiday '''

= RANDOM UNIFORM(0, 1, 1)

''' Parking '''

the availability of parking for customers at a physical location.
= 5000
Units: CZK

''' Revenue e-shop '''

the amount of money earned from an e-shop.
= price * quantity sold e-shop * (1+(advertising budget-0.5)) * customer satisfaction e-shop
Units: CZK

''' Revenue store '''

the amount of money earned from a physical store.
= price * quantity sold store * (1+(advertising budget-0.5)) * customer satisfaction store
Units: CZK

''' Quantity sold e-shop '''

the number of products sold by e-shop.
= RANDOM UNIFORM(500, 1000 , 800)
Units: m^3

''' Quantity sold store '''

the number of products sold by a physical store.
= RANDOM UNIFORM(300, 700, 500 )
Units: m^3

''' Price '''

the amount of money that is charged for a carpet
= RANDOM UNIFORM(150, 650 , 300)+delivery
Units: CZK

''' Delivery '''

the delivery cost depends on the order weight. If the order cost is more, than 5000 CZK then the delivery is free.
= IF THEN ELSE(order cost>5000, 0 , IF THEN ELSE(order weight<5, 159 , IF THEN ELSE(order weight>5 :AND: order weight<15, 289 , IF THEN ELSE(order weight>15 :AND: order weight<30, 399 , IF THEN ELSE(order weight>30 :AND: order weight<50, 599 , IF THEN ELSE(order weight>50 :AND: order weight<75, 799 , IF THEN ELSE(order weight>75 :AND: order weight<100, 999 , IF THEN ELSE(order weight>100 :AND: order weight<150, 1389 , IF THEN ELSE(order weight>150 :AND: order weight<300, 1649 , IF THEN ELSE(order weight>300 :AND: order weight<400, 1749 , 1979 ) ) ) ) ) ) ) ) ) )
Units: CZK

''' Order weight '''

the weight of an order placed by a customer.
= RANDOM UNIFORM(1, 500, 1)
Units: kg

''' Order cost '''

the cost of processing and fulfilling an order placed by a customer.
= RANDOM NORMAL(200, 100000 , 5000 , 2000, 1000)
Units: CZK

''' Gifts '''

items that are given to customers as part of a promotion. If the order cost is bigger than 2000, customers will get a gift, which cost 500 CZK.
= IF THEN ELSE(order cost>2000, 500, 0 )
Units: CZK

''' Discount '''

a reduction in the regular price of a product. The current discount is 10 %
= RANDOM UNIFORM(0.6, 0.9 , 0.7)
Units: %

''' Money paid for discount '''

the money which the company spent to give the customer a discount
= order cost*discount
Units: CZK

''' Customer satisfaction '''

the degree to which customers are happy with a business's products.
= customer satisfaction grow

''' Customer satisfaction grow '''

the increase in customer satisfaction over time.
= customer satisfaction e-shop + customer satisfaction store

''' Customer satisfaction e-shop '''

the degree to which customers are happy with e-shop's products.

If the store gives gifts, it will increase revenue by 2%.
If the store gives a discount of 30%, it will increase revenue by 20%.
If the store gives a discount of 10-20%, it will increase revenue by 10%.

If the store has gifts and a 10-20% discount, the revenue will increase by 12%. If the store has gifts and a 30% discount, the revenue will increase by 22%.

= IF THEN ELSE( (IF THEN ELSE(gifts=500, 1.02 , 1 ) + IF THEN ELSE(discount<0.7, 1 , IF THEN ELSE(discount>0.7 :AND: discount<0.8, 1.2 , 1.1 ) ) )= 1.12 , 1.12 , 1.22)
Units: %

''' Customer satisfaction store '''

the degree to which customers are happy with a physical store's products.

If the store has an available parking spot, it will increase revenue by 2%.
If the store gives gifts, it will increase revenue by 2%.
If the store gives a discount of 30%, it will increase revenue by 20%.
If the store gives a discount of 10-20%, it will increase revenue by 10%.
If the store works on holidays, it will increase revenue by 5%.

If the store has gifts, parking, working on holidays, and a 10-20% discount, the revenue will increase by 12%. If the store has gifts and a 30% discount, the revenue will increase by 24%. If the store has gifts, parking, working on holidays, and a 30% discount, the revenue will increase by 34%.

= IF THEN ELSE( (IF THEN ELSE(parking=5000, 1.02 , 1 ) + IF THEN ELSE(opening hours=1.05, 1.1 , 1 ) + IF THEN ELSE(gifts=500, 1.02 , 1 ) + IF THEN ELSE(discount<0.7, 1 , IF THEN ELSE(discount>0.7 :AND: discount<0.8, 1.2 , 1.1 ) ) )= 1.24 , 1.24 , 1.34)
Units: %

== Model ==

== Results ==

== Conclusion ==

Profit in store vs e-shop

2023-01-25T19:45:19Z

Ploo00:

Profit in store vs e-shop

2023-01-25T19:24:25Z

Ploo00: Created page with "== Problem definition == An unnamed company that sells carpets has its own store in Prague. During COVID-19 the company reopened an e-shop, so it currently has two mutually su..."

WS 2022/2023

2023-01-25T18:51:16Z

Ploo00:

Semestral papers from winter term 2022/2023. Please, put here links to the pages with your paper. First you need to have your [[Assignments WS 2022/2023|assignment approved]]

==Simulations==
--[[User:Julian Bleyer|Julian Bleyer]] ([[User talk:Xkrep33|talk]]) 0:44, 18 January 2023(CET) Aircraft Evacuation Simulation: [[Airplane_Evacuation]]

--[[User:Baumareb|Baumareb]] ([[User talk:Baumareb|talk]]) 13:55, 18 January 2023 (CET) Cartel simulation with leniency program by Rebecca Baumann (baur00): [[cartel_simulation]]

--[[User:Kock06|Kock06]] ([[User talk:Kock06|talk]]) 14:03, 22 January(GTM+8) Receivables prediction by Monte Carlo simulation: [[Receivables]]

--[[User:Ruzv01|Ruzv01]] ([[User talk:Ruzv01|talk]]) 10:33, 22 January(CET) Household electricity consumption: [[Household electricity consumption]]

--[[User:Haki00|Haki00]] ([[User talk:Haki00|talk]]) 21:38, 22 January(CET) Artsakh blockade: [[Artsakh blockade]]

--[[User:Edema|Edema]] ([[User talk:Haki00|talk]]) 22:00, 22 January(CET) Mortgage Assessment: [[Mortgage Assessment]]

--[[User:BortnikSvitlana|BortnikSvitlana]] ([[User talk:BortnikSvitlana|talk]]) 22:36, 22 January(CET) Traffic Simulation at an Intersection: [[Traffic Simulation at an Intersection]]

--[[User:Ceta|Ceta]] ([[User talk:Ceta|talk]]) 22:48, 22 January 2023 (CET) Pumped hydroelectric energy storage (PHES)System Simulation [[Pump_storage]]

--[[User:Botd00|Botd00]] ([[User talk:Botd00|talk]]) 22:46, 22 January(CET) Twitter Simulation: [[Twitter simulation]]

-- [[User:Miln02|Miln02]] ([[User talk:Miln02|talk]]) 23:05, 22 January 2023 (CET) Saving for an apartment [[Savingforanapartment]]

-- [[User:Abizah1|Abizah1]] ([[User talk:Abizah1|talk]]) 23:55, 22 January 2023 (CET) https://www.simulace.info/index.php/Boxing_athlete

-- [[User:Pierreatekwana1|Pierreatekwana1]] ([[User talk:Pierreatekwana1|talk]]) 00:24, 23 January 2023 (CET) Crop yield simulation: [[Cropyield]]

-- [[User:Kane02|Kane02]] ([[User talk:Kane02|talk]]) 18:52, 23 January 2023 (CET) Car Park Solution for a New Cinema [[Car Park Solution for a New Cinema]]

-- [[User:Kadt02|Kadt02]] ([[User talk:Kadt02|talk]]) 19:43, 23 January 2023 (CET) Comparing searching stategies [[Finding strategies comparison]]

-- [[User:Ploo00|Ploo00]] ([[User talk:Ploo00|talk]]) 19:50, 25 January 2023 (CET) Profit in store vs e-shop [[Profit in store vs e-shop]]

Drifting Goals/cs

2023-01-08T20:27:40Z

Ploo00:

{{DISPLAYTITLE:Eroze cílů}}

== Introduction ==
These days most organizations put emphasis on bigger goals. The goals on the one hand should be inspiring and motivating. On the other hand, they suppose to be doable. When the organization is not capable of meeting its goals, they are trying to come up with some solution. Sometimes the reason for not reaching the goal may be more simple than we expect or/and even unexpected. Organizations often have set more than one goal and sometimes one of the goals gets all the attention whereas the other goal remains under the pressure. Organizations react to this by reducing the goal to shorten the gap, but over time they unintentionally drift further from what they are really trying to achieve. The concept which will help us to understand this problem is the "Drifting Goals" archetype.

== Drifting Goals ==
Drifting Goals is a system archetype in systems thinking, that defines the behavior that occurs when there is a gap between the current state and the desired state. Setting a goal defines the desired state; the current state is where you are today. The gap is the distance between where you are and where you want to be. <ref name="BT1">KISHAU, Rogers, 2022. Drifting Goals & What To Do When You’re Not Where You Want To Be - BigThinking.io. BigThinking.io. Online. 5 September 2022. [Accessed 8 January 2023]. Retrieved from: https://bigthinking.io/drifting-goals-goal-setting/ </ref>

Drifting goals is a concept that refers to situations where short-term decisions result in a retrogression of long-term goals. It starts with an initial gap between desired performance and the actual performance. If an organization has a gap between a goal and actual results, it will attempt to take some actions to close the gap. There are two main reactions to shorten the gap. One can either increase his efforts (corrective actions) or reduce his goal. <ref name="Kessels"># KESSELS, Sjoerd, 2019. Short term thinking: Drifting Goals. Adaptive Organization. Online. 18 January 2019. [Accessed 8 January 2023]. Retrieved from: https://www.adaptive-organizations.com/short-term-thinking-drifting-goals/ </ref>

It may take some time for corrective actions to take effect. If the gap continually increases or persists due to the delay of corrective actions, the pressure to lower the goals will increase. While corrective actions take time to have an effect, the effect of lowering the goal is immediate. Since such a situation is not a one-time occurrence, the organization can continue to slowly reduce the goal over time. The goal reduction will remain unnoticed, as it happens gradually over time and will lead to a general standard decrease. <ref name="Kessels" />

So, to sum up there are two reactions to the increasing or persisting gap: <ref name="SysThink">ANDERSON, Virginia, 2015. Introducing the Systems Archetypes: Drifting Goals. The Systems Thinker. Online. 12 November 2015. [Accessed 8 January 2023]. Retrieved from: https://thesystemsthinker.com/introducing-the-systems-archetypes-drifting-goals/ </ref> <ref name="BT1" />

'''Corrective action'''
* Increasing action reduces the gap by taking additional steps to move the organization’s current state closer to the desired state.
* Requires time, effort, funds, and attention.
* Can pose several problems:
** there is no guarantee that it will work,
** its effects may be seen with delay, so it can lead to impatience.

'''Reduce the goal'''

* Move the goal closer to the organization’s current state to reduce the gap immediately.
* Can be dangerous as the gap will disappear, but so does the pressure to take corrective action to improve the actual state.
* Further can lead to a decrease in the pressure to act in the long-term and once an intolerable decrease can become a new standard.

<blockquote>
Changing the goal is not always wrong. Sometimes it is necessary in cases when the goal turned out to be anyhow inappropriate or the current state requires more flexibility. However, it can lead to a long-term standard decrease, so it is important to distinguish the difference between legitimate adjustment of the goal and the Drifting goal. <ref name="SysThink" />
</blockquote>

=== Causal loop diagram <ref name="SysThink" />===
The causal loop diagram of the Drifting Goals archetype consists of two balancing loops (see Pic.1). As was stated before, the situation starts when there is a gap between desired performance and actual performance. The goal is being compared to a current state. If the gap persists, corrective actions are taken in order to improve the current state. This forms a basic balancing loop (B1). A delay between corrective action and the actual state represents the fact, that the results may take some time to become evident.

A second balancing loop (B2) is driven by pressure to get closer to the goal by lowering the goal. As the gap increases the pressure to lower the goal increases too.

The main difference between the 2 loops is that lowering the goals takes less time to get to a goal, whereas corrective actions usually take time for an organization to get to the goal.

[[File:Drifting_goals_template.jpg|x{20}px|frameless|center|caption]]
Picture 1. Drifting Goals Template <ref name="SysThink" />

=== Behavior over time <ref name="Braun">BRAUN, William, 2002. The System Archetypes. Online. 2002. Retrieved from: https://www.studocu.com/ph/document/university-of-negros-occidental-recoletos/accounting-1/sys-archetypes/9771310 </ref> ===

Drifting goals has a long-term effect on goal setting, as every time when an organization cannot meet the goal, it will set the goal lower and lower to ensure they meet the goal. In Picture 2, we can see, that over time the gap is still the same, as the organization is not trying to improve the state and find the problem. But they are just adjusting the goal so it will be closer to an actual state. Meanwhile, the actual state is also decreasing.

[[File:Behavior_over_time.png|{500}px|frameless|center|caption]]
Picture 2. Behavior over time <ref name="Braun" />

== Boiled frog <ref name="BT2"> KIM, Daniel H., 1994. Systems archetypes. Online. Cambridge, Mass: Pegasus Communications. Toolbox reprint series. ISBN 978-1-883823-00-9. Retrieved from: https://thesystemsthinker.com/wp-content/uploads/2016/03/Systems-Archetypes-I-TRSA01_pk.pdf </ref> ==
To make it clearer, let's look at the example of the "boiled frog" syndrome. The nature of this syndrome is the following. If you put a frog in a pot of boiling water, it will immediately jump out and escape. But if you put it in a pot of nicely warm water and raise the temperature slowly, the frog will swim and enjoy the lukewarm water. As the temperature rises, it becomes more lethargic until it eventually dies. In this case, the frog became a victim of the drifting goals scenario.

[[File:boiled_frog_syndrome.png|x{300}px|center|caption]]
Picture 3. Boiled frog syndrome <ref> J. VAN SANTEN, 2019. The Boiling Frog. Tapas.io. Online. 8 October 2019. [Accessed 8 January 2023]. Retrieved from: https://tapas.io/episode/1539438 </ref>

In this case, the goal is the desired body temperature. If the gap between the desired temperature and the temperature of the water is too large, the frog immediately jumps out of the water. But if the temperature rises slowly, the difference between its ideal temperature and the current temperature increases slowly too. This closes the gap between desired and actual temperatures, eliminating the need to take corrective action before it is too late.

The reason drifting targets are compared to "boiled frogs" is that goals such as the frog's ideal temperature tend to drift slowly and usually go unnoticed. Like a frog that doesn't recognize a gradual rise in temperature, organizations often stay unalarmed about performance degradation if it occurs over a long period of time.

== Other examples ==
This concept can be used in different aspects of our lives. Let's see more real-life examples.

=== Government budget <ref name="BT2" /> ===
Imagine there is a gap between the previously acceptable deficit level and the actual (see Pic.4). The gap can be closed in two ways: by reducing government expenses (B3) or by increasing tax revenues (B4). However, the current situation leads to increased spending, and taxation showed mixed results. This situation will result in an even higher deficit. The raising deficit will lead to a more significant gap between the current deficit and the maximum acceptable one. As a result, the gap will create pressure on raising the deficit target and eventually resulting in a higher maximum acceptable deficit (B5).

In this case, B3 and B4 are “corrective actions”, whereas B5 is a “pressure to lower the goal”.

[[File:budget_deficit.png|x{20}px|frameless|center|caption]]
Picture 4. Budget deficit <ref name="BT2" />

=== Company’s change <ref name="SysThink" /> ===
Imagine a company dedicated to producing quality products. However, to improve efficiency, the company is launching a number of cost-cutting initiatives. They increase line speed and change cooking methods.

Over the next five years, sales of the products will begin to decrease. This led managers to believe that there had been a change in consumer eating habits. After another two years, sales continue to decrease. The company decided to make consumer research. The results show that the taste of their products has changed for the worse.

Further analysis showed that the gap between the quality standard and the actual product quality first appeared over 10 years ago. This gap should have been a signal to management that the company's processes, equipment, or ingredients needed attention and possible investment. However, the company was distracted by its cost control campaign and the quality standard was lowered in favor of other changes. As a result, the quality very slowly fell below the level of consumer tolerance.

== How to deal with Drifting Goals ==
So after all the question is how can we react to Drifting Goals? How can we deal with the gap? And how to prevent goals from drifting?

=== "Soaring" Goals <ref name="SysThink" /> ===
Drifting goals does not always lead to decreasing levels of performance. This concept can be reversed in the case when the company’s goals and standards continually improve. In this case, every time when the company meets its goal, they are increasing the goal even higher. This will start the gap again, which will lead the company to action to bring higher performance.

=== What to do with a gap? <ref name="BT1" /> ===
What to do when there is a gap, should we focus on the gap or focus on setting the goal more achievable? The answer is the gap. It is easy to set a goal, but it is much harder to reach the goal. It is better to learn some lessons by trying to reach the goal than just to adjust it so it will be easier to get to.

=== How to prevent goals from drifting? <ref name="Kessels"/> ===
The drifting goals model is a simple model of reality. In real life, there are so many factors, which make it harder to understand what causes the gap. But it can be used to identify potential issues.

After all that has been said we need to remember that:

*Goals are often related, so before jumping to some conclusion, we need to study the relationship between goals.
*Lowering the goals can be done only when the relationship with other goals is understood.
*It is not the best way to base future goals on previous performance. This will lead to drifting goals.

== An example to practice ==
Try to create a diagram based on the company's change example. You can use the Vensim program.

== References ==
<references />

Drifting Goals

2023-01-08T20:27:22Z

Ploo00:

== Introduction ==
These days most organizations put emphasis on bigger goals. The goals on the one hand should be inspiring and motivating. On the other hand, they suppose to be doable. When the organization is not capable of meeting its goals, they are trying to come up with some solution. Sometimes the reason for not reaching the goal may be more simple than we expect or/and even unexpected. Organizations often have set more than one goal and sometimes one of the goals gets all the attention whereas the other goal remains under the pressure. Organizations react to this by reducing the goal to shorten the gap, but over time they unintentionally drift further from what they are really trying to achieve. The concept which will help us to understand this problem is the "Drifting Goals" archetype.

== Drifting Goals ==
Drifting Goals is a system archetype in systems thinking, that defines the behavior that occurs when there is a gap between the current state and the desired state. Setting a goal defines the desired state; the current state is where you are today. The gap is the distance between where you are and where you want to be. <ref name="BT1">KISHAU, Rogers, 2022. Drifting Goals & What To Do When You’re Not Where You Want To Be - BigThinking.io. BigThinking.io. Online. 5 September 2022. [Accessed 8 January 2023]. Retrieved from: https://bigthinking.io/drifting-goals-goal-setting/ </ref>

Drifting goals is a concept that refers to situations where short-term decisions result in a retrogression of long-term goals. It starts with an initial gap between desired performance and the actual performance. If an organization has a gap between a goal and actual results, it will attempt to take some actions to close the gap. There are two main reactions to shorten the gap. One can either increase his efforts (corrective actions) or reduce his goal. <ref name="Kessels"># KESSELS, Sjoerd, 2019. Short term thinking: Drifting Goals. Adaptive Organization. Online. 18 January 2019. [Accessed 8 January 2023]. Retrieved from: https://www.adaptive-organizations.com/short-term-thinking-drifting-goals/ </ref>

It may take some time for corrective actions to take effect. If the gap continually increases or persists due to the delay of corrective actions, the pressure to lower the goals will increase. While corrective actions take time to have an effect, the effect of lowering the goal is immediate. Since such a situation is not a one-time occurrence, the organization can continue to slowly reduce the goal over time. The goal reduction will remain unnoticed, as it happens gradually over time and will lead to a general standard decrease. <ref name="Kessels" />

So, to sum up there are two reactions to the increasing or persisting gap: <ref name="SysThink">ANDERSON, Virginia, 2015. Introducing the Systems Archetypes: Drifting Goals. The Systems Thinker. Online. 12 November 2015. [Accessed 8 January 2023]. Retrieved from: https://thesystemsthinker.com/introducing-the-systems-archetypes-drifting-goals/ </ref> <ref name="BT1" />

'''Corrective action'''
* Increasing action reduces the gap by taking additional steps to move the organization’s current state closer to the desired state.
* Requires time, effort, funds, and attention.
* Can pose several problems:
** there is no guarantee that it will work,
** its effects may be seen with delay, so it can lead to impatience.

'''Reduce the goal'''

* Move the goal closer to the organization’s current state to reduce the gap immediately.
* Can be dangerous as the gap will disappear, but so does the pressure to take corrective action to improve the actual state.
* Further can lead to a decrease in the pressure to act in the long-term and once an intolerable decrease can become a new standard.

<blockquote>
Changing the goal is not always wrong. Sometimes it is necessary in cases when the goal turned out to be anyhow inappropriate or the current state requires more flexibility. However, it can lead to a long-term standard decrease, so it is important to distinguish the difference between legitimate adjustment of the goal and the Drifting goal. <ref name="SysThink" />
</blockquote>

=== Causal loop diagram <ref name="SysThink" />===
The causal loop diagram of the Drifting Goals archetype consists of two balancing loops (see Pic.1). As was stated before, the situation starts when there is a gap between desired performance and actual performance. The goal is being compared to a current state. If the gap persists, corrective actions are taken in order to improve the current state. This forms a basic balancing loop (B1). A delay between corrective action and the actual state represents the fact, that the results may take some time to become evident.

A second balancing loop (B2) is driven by pressure to get closer to the goal by lowering the goal. As the gap increases the pressure to lower the goal increases too.

The main difference between the 2 loops is that lowering the goals takes less time to get to a goal, whereas corrective actions usually take time for an organization to get to the goal.

[[File:Drifting_goals_template.jpg|x{20}px|frameless|center|caption]]
Picture 1. Drifting Goals Template <ref name="SysThink" />

=== Behavior over time <ref name="Braun">BRAUN, William, 2002. The System Archetypes. Online. 2002. Retrieved from: https://www.studocu.com/ph/document/university-of-negros-occidental-recoletos/accounting-1/sys-archetypes/9771310 </ref> ===

Drifting goals has a long-term effect on goal setting, as every time when an organization cannot meet the goal, it will set the goal lower and lower to ensure they meet the goal. In Picture 2, we can see, that over time the gap is still the same, as the organization is not trying to improve the state and find the problem. But they are just adjusting the goal so it will be closer to an actual state. Meanwhile, the actual state is also decreasing.

[[File:Behavior_over_time.png|{500}px|frameless|center|caption]]
Picture 2. Behavior over time <ref name="Braun" />

== Boiled frog <ref name="BT2"> KIM, Daniel H., 1994. Systems archetypes. Online. Cambridge, Mass: Pegasus Communications. Toolbox reprint series. ISBN 978-1-883823-00-9. Retrieved from: https://thesystemsthinker.com/wp-content/uploads/2016/03/Systems-Archetypes-I-TRSA01_pk.pdf </ref> ==
To make it clearer, let's look at the example of the "boiled frog" syndrome. The nature of this syndrome is the following. If you put a frog in a pot of boiling water, it will immediately jump out and escape. But if you put it in a pot of nicely warm water and raise the temperature slowly, the frog will swim and enjoy the lukewarm water. As the temperature rises, it becomes more lethargic until it eventually dies. In this case, the frog became a victim of the drifting goals scenario.

[[File:boiled_frog_syndrome.png|x{300}px|center|caption]]
Picture 3. Boiled frog syndrome <ref> J. VAN SANTEN, 2019. The Boiling Frog. Tapas.io. Online. 8 October 2019. [Accessed 8 January 2023]. Retrieved from: https://tapas.io/episode/1539438 </ref>

In this case, the goal is the desired body temperature. If the gap between the desired temperature and the temperature of the water is too large, the frog immediately jumps out of the water. But if the temperature rises slowly, the difference between its ideal temperature and the current temperature increases slowly too. This closes the gap between desired and actual temperatures, eliminating the need to take corrective action before it is too late.

The reason drifting targets are compared to "boiled frogs" is that goals such as the frog's ideal temperature tend to drift slowly and usually go unnoticed. Like a frog that doesn't recognize a gradual rise in temperature, organizations often stay unalarmed about performance degradation if it occurs over a long period of time.

== Other examples ==
This concept can be used in different aspects of our lives. Let's see more real-life examples.

=== Government budget <ref name="BT2" /> ===
Imagine there is a gap between the previously acceptable deficit level and the actual (see Pic.4). The gap can be closed in two ways: by reducing government expenses (B3) or by increasing tax revenues (B4). However, the current situation leads to increased spending, and taxation showed mixed results. This situation will result in an even higher deficit. The raising deficit will lead to a more significant gap between the current deficit and the maximum acceptable one. As a result, the gap will create pressure on raising the deficit target and eventually resulting in a higher maximum acceptable deficit (B5).

In this case, B3 and B4 are “corrective actions”, whereas B5 is a “pressure to lower the goal”.

[[File:budget_deficit.png|x{20}px|frameless|center|caption]]
Picture 4. Budget deficit <ref name="BT2" />

=== Company’s change <ref name="SysThink" /> ===
Imagine a company dedicated to producing quality products. However, to improve efficiency, the company is launching a number of cost-cutting initiatives. They increase line speed and change cooking methods.

Over the next five years, sales of the products will begin to decrease. This led managers to believe that there had been a change in consumer eating habits. After another two years, sales continue to decrease. The company decided to make consumer research. The results show that the taste of their products has changed for the worse.

Further analysis showed that the gap between the quality standard and the actual product quality first appeared over 10 years ago. This gap should have been a signal to management that the company's processes, equipment, or ingredients needed attention and possible investment. However, the company was distracted by its cost control campaign and the quality standard was lowered in favor of other changes. As a result, the quality very slowly fell below the level of consumer tolerance.

== How to deal with Drifting Goals ==
So after all the question is how can we react to Drifting Goals? How can we deal with the gap? And how to prevent goals from drifting?

=== "Soaring" Goals <ref name="SysThink" /> ===
Drifting goals does not always lead to decreasing levels of performance. This concept can be reversed in the case when the company’s goals and standards continually improve. In this case, every time when the company meets its goal, they are increasing the goal even higher. This will start the gap again, which will lead the company to action to bring higher performance.

=== What to do with a gap? <ref name="BT1" /> ===
What to do when there is a gap, should we focus on the gap or focus on setting the goal more achievable? The answer is the gap. It is easy to set a goal, but it is much harder to reach the goal. It is better to learn some lessons by trying to reach the goal than just to adjust it so it will be easier to get to.

=== How to prevent goals from drifting? <ref name="Kessels"/> ===
The drifting goals model is a simple model of reality. In real life, there are so many factors, which make it harder to understand what causes the gap. But it can be used to identify potential issues.

After all that has been said we need to remember that:

*Goals are often related, so before jumping to some conclusion, we need to study the relationship between goals.
*Lowering the goals can be done only when the relationship with other goals is understood.
*It is not the best way to base future goals on previous performance. This will lead to drifting goals.

== An example to practice ==
Try to create a diagram based on the company's change example. You can use the Vensim program.

== References ==
<references />

Drifting Goals/cs

2023-01-08T20:21:51Z

Ploo00:

{{DISPLAYTITLE:Eroze cílů}}

== Introduction ==
These days most organizations put emphasis on bigger goals. The goals on the one hand should be inspiring and motivating. On the other hand, they suppose to be doable. When the organization is not capable of meeting its goals, they are trying to come up with some solution. Sometimes the reason for not reaching the goal may be more simple than we expect or/and even unexpected. Organizations often have set more than one goal and sometimes one of the goals gets all the attention whereas the other goal remains under the pressure. Organizations react to this by reducing the goal to shorten the gap, but over time they unintentionally drift further from what they are really trying to achieve. The concept which will help us to understand this problem is the "Drifting Goals" archetype.

== Drifting Goals ==
Drifting Goals is a system archetype in systems thinking, that defines the behavior that occurs when there is a gap between the current state and the desired state. Setting a goal defines the desired state; the current state is where you are today. The gap is the distance between where you are and where you want to be. <ref name="BT1">KISHAU, Rogers, 2022. Drifting Goals & What To Do When You’re Not Where You Want To Be - BigThinking.io. BigThinking.io. Online. 5 September 2022. [Accessed 8 January 2023]. Retrieved from: https://bigthinking.io/drifting-goals-goal-setting/ </ref>

Drifting goals is a concept that refers to situations where short-term decisions result in a retrogression of long-term goals. It starts with an initial gap between desired performance and the actual performance. If an organization has a gap between a goal and actual results, it will attempt to take some actions to close the gap. There are two main reactions to shorten the gap. One can either increase his efforts (corrective actions) or reduce his goal. <ref name="Kessels"># KESSELS, Sjoerd, 2019. Short term thinking: Drifting Goals. Adaptive Organization. Online. 18 January 2019. [Accessed 8 January 2023]. Retrieved from: https://www.adaptive-organizations.com/short-term-thinking-drifting-goals/ </ref>

It may take some time for corrective actions to take effect. If the gap continually increases or persists due to the delay of corrective actions, the pressure to lower the goals will increase. While corrective actions take time to have an effect, the effect of lowering the goal is immediate. Since such a situation is not a one-time occurrence, the organization can continue to slowly reduce the goal over time. The goal reduction will remain unnoticed, as it happens gradually over time and will lead to a general standard decrease. <ref name="Kessels" />

So, to sum up there are two reactions to the increasing or persisting gap: <ref name="SysThink">ANDERSON, Virginia, 2015. Introducing the Systems Archetypes: Drifting Goals. The Systems Thinker. Online. 12 November 2015. [Accessed 8 January 2023]. Retrieved from: https://thesystemsthinker.com/introducing-the-systems-archetypes-drifting-goals/ </ref> <ref name="BT1" />

'''Corrective action'''
* Increasing action reduces the gap by taking additional steps to move the organization’s current state closer to the desired state.
* Requires time, effort, funds, and attention.
* Can pose several problems:
** there is no guarantee that it will work,
** its effects may be seen with delay, so it can lead to impatience.

'''Reduce the goal'''

* Move the goal closer to the organization’s current state to reduce the gap immediately.
* Can be dangerous as the gap will disappear, but so does the pressure to take corrective action to improve the actual state.
* Further can lead to a decrease in the pressure to act in the long-term and once an intolerable decrease can become a new standard.

<blockquote>
Changing the goal is not always wrong. Sometimes it is necessary in cases when the goal turned out to be anyhow inappropriate or the current state requires more flexibility. However, it can lead to a long-term standard decrease, so it is important to distinguish the difference between legitimate adjustment of the goal and the Drifting goal. <ref name="SysThink" />
</blockquote>

=== Causal loop diagram <ref name="SysThink" />===
The causal loop diagram of the Drifting Goals archetype consists of two balancing loops (see Pic. 1). As was stated before, the situation starts when there is a gap between desired performance and actual performance. The goal is being compared to a current state. If the gap persists, corrective actions are taken in order to improve the current state. This forms a basic balancing loop (B1). A delay between corrective action and the actual state represents the fact, that the results may take some time to become evident.

A second balancing loop (B2) is driven by pressure to get closer to the goal by lowering the goal. As the gap increases the pressure to lower the goal increases too.

The main difference between the 2 loops is that lowering the goals takes less time to get to a goal, whereas corrective actions usually take time for an organization to get to the goal.

[[File:Drifting_goals_template.jpg|x{20}px|frameless|center|caption]]
Picture 1. Drifting Goals Template <ref name="SysThink" />

=== Behavior over time <ref name="Braun">BRAUN, William, 2002. The System Archetypes. Online. 2002. Retrieved from: https://www.studocu.com/ph/document/university-of-negros-occidental-recoletos/accounting-1/sys-archetypes/9771310 </ref> ===

Drifting goals has a long-term effect on goal setting, as every time when an organization cannot meet the goal, it will set the goal lower and lower to ensure they meet the goal. In Picture 2, we can see, that over time the gap is still the same, as the organization is not trying to improve the state and find the problem. But they are just adjusting the goal so it will be closer to an actual state. Meanwhile, the actual state is also decreasing.

[[File:Behavior_over_time.png|{500}px|frameless|center|caption]]
Picture 2. Behavior over time <ref name="Braun" />

== Boiled frog <ref name="BT2"> KIM, Daniel H., 1994. Systems archetypes. Online. Cambridge, Mass: Pegasus Communications. Toolbox reprint series. ISBN 978-1-883823-00-9. Retrieved from: https://thesystemsthinker.com/wp-content/uploads/2016/03/Systems-Archetypes-I-TRSA01_pk.pdf </ref> ==
To make it clearer, let's look at the example of the "boiled frog" syndrome. The nature of this syndrome is the following. If you put a frog in a pot of boiling water, it will immediately jump out and escape. But if you put it in a pot of nicely warm water and raise the temperature slowly, the frog will swim and enjoy the lukewarm water. As the temperature rises, it becomes more lethargic until it eventually dies. In this case, the frog became a victim of the drifting goals scenario.

[[File:boiled_frog_syndrome.png|x{300}px|center|caption]]
Picture 3. Boiled frog syndrome <ref> J. VAN SANTEN, 2019. The Boiling Frog. Tapas.io. Online. 8 October 2019. [Accessed 8 January 2023]. Retrieved from: https://tapas.io/episode/1539438 </ref>

In this case, the goal is the desired body temperature. If the gap between the desired temperature and the temperature of the water is too large, the frog immediately jumps out of the water. But if the temperature rises slowly, the difference between its ideal temperature and the current temperature increases slowly too. This closes the gap between desired and actual temperatures, eliminating the need to take corrective action before it is too late.

The reason drifting targets are compared to "boiled frogs" is that goals such as the frog's ideal temperature tend to drift slowly and usually go unnoticed. Like a frog that doesn't recognize a gradual rise in temperature, organizations often stay unalarmed about performance degradation if it occurs over a long period of time.

== Other examples ==
This concept can be used in different aspects of our lives. Let's see more real-life examples.

=== Government budget <ref name="BT2" /> ===
Imagine there is a gap between the previously acceptable deficit level and the actual (see Pic. 3). The gap can be closed in two ways: by reducing government expenses (B3) or by increasing tax revenues (B4). However, the current situation leads to increased spending, and taxation showed mixed results. This situation will result in an even higher deficit. The raising deficit will lead to a more significant gap between the current deficit and the maximum acceptable one. As a result, the gap will create pressure on raising the deficit target and eventually resulting in a higher maximum acceptable deficit (B5).

In this case, B3 and B4 are “corrective actions”, whereas B5 is a “pressure to lower the goal”.

[[File:budget_deficit.png|x{20}px|frameless|center|caption]]
Picture 4. Budget deficit <ref name="BT2" />

=== Company’s change <ref name="SysThink" /> ===
Imagine a company dedicated to producing quality products. However, to improve efficiency, the company is launching a number of cost-cutting initiatives. They increase line speed and change cooking methods.

Over the next five years, sales of the products will begin to decrease. This led managers to believe that there had been a change in consumer eating habits. After another two years, sales continue to decrease. The company decided to make consumer research. The results show that the taste of their products has changed for the worse.

Further analysis showed that the gap between the quality standard and the actual product quality first appeared over 10 years ago. This gap should have been a signal to management that the company's processes, equipment, or ingredients needed attention and possible investment. However, the company was distracted by its cost control campaign and the quality standard was lowered in favor of other changes. As a result, the quality very slowly fell below the level of consumer tolerance.

== How to deal with Drifting Goals ==
So after all the question is how can we react to Drifting Goals? How can we deal with the gap? And how to prevent goals from drifting?

=== "Soaring" Goals <ref name="SysThink" /> ===
Drifting goals does not always lead to decreasing levels of performance. This concept can be reversed in the case when the company’s goals and standards continually improve. In this case, every time when the company meets its goal, they are increasing the goal even higher. This will start the gap again, which will lead the company to action to bring higher performance.

=== What to do with a gap? <ref name="BT1" /> ===
What to do when there is a gap, should we focus on the gap or focus on setting the goal more achievable? The answer is the gap. It is easy to set a goal, but it is much harder to reach the goal. It is better to learn some lessons by trying to reach the goal than just to adjust it so it will be easier to get to.

=== How to prevent goals from drifting? <ref name="Kessels"/> ===
The drifting goals model is a simple model of reality. In real life, there are so many factors, which make it harder to understand what causes the gap. But it can be used to identify potential issues.

After all that has been said we need to remember that:

*Goals are often related, so before jumping to some conclusion, we need to study the relationship between goals.
*Lowering the goals can be done only when the relationship with other goals is understood.
*It is not the best way to base future goals on previous performance. This will lead to drifting goals.

== An example to practice ==
Try to create a diagram based on the company's change example. You can use the Vensim program.

== References ==
<references />

Drifting Goals

2023-01-08T20:20:34Z

Ploo00: Created page with "== Introduction == These days most organizations put emphasis on bigger goals. The goals on the one hand should be inspiring and motivating. On the other hand, they suppose to..."

== Introduction ==
These days most organizations put emphasis on bigger goals. The goals on the one hand should be inspiring and motivating. On the other hand, they suppose to be doable. When the organization is not capable of meeting its goals, they are trying to come up with some solution. Sometimes the reason for not reaching the goal may be more simple than we expect or/and even unexpected. Organizations often have set more than one goal and sometimes one of the goals gets all the attention whereas the other goal remains under the pressure. Organizations react to this by reducing the goal to shorten the gap, but over time they unintentionally drift further from what they are really trying to achieve. The concept which will help us to understand this problem is the "Drifting Goals" archetype.

== Drifting Goals ==
Drifting Goals is a system archetype in systems thinking, that defines the behavior that occurs when there is a gap between the current state and the desired state. Setting a goal defines the desired state; the current state is where you are today. The gap is the distance between where you are and where you want to be. <ref name="BT1">KISHAU, Rogers, 2022. Drifting Goals & What To Do When You’re Not Where You Want To Be - BigThinking.io. BigThinking.io. Online. 5 September 2022. [Accessed 8 January 2023]. Retrieved from: https://bigthinking.io/drifting-goals-goal-setting/ </ref>

Drifting goals is a concept that refers to situations where short-term decisions result in a retrogression of long-term goals. It starts with an initial gap between desired performance and the actual performance. If an organization has a gap between a goal and actual results, it will attempt to take some actions to close the gap. There are two main reactions to shorten the gap. One can either increase his efforts (corrective actions) or reduce his goal. <ref name="Kessels"># KESSELS, Sjoerd, 2019. Short term thinking: Drifting Goals. Adaptive Organization. Online. 18 January 2019. [Accessed 8 January 2023]. Retrieved from: https://www.adaptive-organizations.com/short-term-thinking-drifting-goals/ </ref>

It may take some time for corrective actions to take effect. If the gap continually increases or persists due to the delay of corrective actions, the pressure to lower the goals will increase. While corrective actions take time to have an effect, the effect of lowering the goal is immediate. Since such a situation is not a one-time occurrence, the organization can continue to slowly reduce the goal over time. The goal reduction will remain unnoticed, as it happens gradually over time and will lead to a general standard decrease. <ref name="Kessels" />

So, to sum up there are two reactions to the increasing or persisting gap: <ref name="SysThink">ANDERSON, Virginia, 2015. Introducing the Systems Archetypes: Drifting Goals. The Systems Thinker. Online. 12 November 2015. [Accessed 8 January 2023]. Retrieved from: https://thesystemsthinker.com/introducing-the-systems-archetypes-drifting-goals/ </ref> <ref name="BT1" />

'''Corrective action'''
* Increasing action reduces the gap by taking additional steps to move the organization’s current state closer to the desired state.
* Requires time, effort, funds, and attention.
* Can pose several problems:
** there is no guarantee that it will work,
** its effects may be seen with delay, so it can lead to impatience.

'''Reduce the goal'''

* Move the goal closer to the organization’s current state to reduce the gap immediately.
* Can be dangerous as the gap will disappear, but so does the pressure to take corrective action to improve the actual state.
* Further can lead to a decrease in the pressure to act in the long-term and once an intolerable decrease can become a new standard.

<blockquote>
Changing the goal is not always wrong. Sometimes it is necessary in cases when the goal turned out to be anyhow inappropriate or the current state requires more flexibility. However, it can lead to a long-term standard decrease, so it is important to distinguish the difference between legitimate adjustment of the goal and the Drifting goal. <ref name="SysThink" />
</blockquote>

=== Causal loop diagram <ref name="SysThink" />===
The causal loop diagram of the Drifting Goals archetype consists of two balancing loops (see Pic. 1). As was stated before, the situation starts when there is a gap between desired performance and actual performance. The goal is being compared to a current state. If the gap persists, corrective actions are taken in order to improve the current state. This forms a basic balancing loop (B1). A delay between corrective action and the actual state represents the fact, that the results may take some time to become evident.

A second balancing loop (B2) is driven by pressure to get closer to the goal by lowering the goal. As the gap increases the pressure to lower the goal increases too.

The main difference between the 2 loops is that lowering the goals takes less time to get to a goal, whereas corrective actions usually take time for an organization to get to the goal.

[[File:Drifting_goals_template.jpg|x{20}px|frameless|center|caption]]
Picture 1. Drifting Goals Template <ref name="SysThink" />

=== Behavior over time <ref name="Braun">BRAUN, William, 2002. The System Archetypes. Online. 2002. Retrieved from: https://www.studocu.com/ph/document/university-of-negros-occidental-recoletos/accounting-1/sys-archetypes/9771310 </ref> ===

Drifting goals has a long-term effect on goal setting, as every time when an organization cannot meet the goal, it will set the goal lower and lower to ensure they meet the goal. In Picture 2, we can see, that over time the gap is still the same, as the organization is not trying to improve the state and find the problem. But they are just adjusting the goal so it will be closer to an actual state. Meanwhile, the actual state is also decreasing.

[[File:Behavior_over_time.png|{500}px|frameless|center|caption]]
Picture 2. Behavior over time <ref name="Braun" />

== Boiled frog <ref name="BT2"> KIM, Daniel H., 1994. Systems archetypes. Online. Cambridge, Mass: Pegasus Communications. Toolbox reprint series. ISBN 978-1-883823-00-9. Retrieved from: https://thesystemsthinker.com/wp-content/uploads/2016/03/Systems-Archetypes-I-TRSA01_pk.pdf </ref> ==
To make it clearer, let's look at the example of the "boiled frog" syndrome. The nature of this syndrome is the following. If you put a frog in a pot of boiling water, it will immediately jump out and escape. But if you put it in a pot of nicely warm water and raise the temperature slowly, the frog will swim and enjoy the lukewarm water. As the temperature rises, it becomes more lethargic until it eventually dies. In this case, the frog became a victim of the drifting goals scenario.

[[File:boiled_frog_syndrome.png|x{300}px|center|caption]]
Picture 3. Boiled frog syndrome <ref> J. VAN SANTEN, 2019. The Boiling Frog. Tapas.io. Online. 8 October 2019. [Accessed 8 January 2023]. Retrieved from: https://tapas.io/episode/1539438 </ref>

In this case, the goal is the desired body temperature. If the gap between the desired temperature and the temperature of the water is too large, the frog immediately jumps out of the water. But if the temperature rises slowly, the difference between its ideal temperature and the current temperature increases slowly too. This closes the gap between desired and actual temperatures, eliminating the need to take corrective action before it is too late.

The reason drifting targets are compared to "boiled frogs" is that goals such as the frog's ideal temperature tend to drift slowly and usually go unnoticed. Like a frog that doesn't recognize a gradual rise in temperature, organizations often stay unalarmed about performance degradation if it occurs over a long period of time.

== Other examples ==
This concept can be used in different aspects of our lives. Let's see more real-life examples.

=== Government budget <ref name="BT2" /> ===
Imagine there is a gap between the previously acceptable deficit level and the actual (see Pic. 3). The gap can be closed in two ways: by reducing government expenses (B3) or by increasing tax revenues (B4). However, the current situation leads to increased spending, and taxation showed mixed results. This situation will result in an even higher deficit. The raising deficit will lead to a more significant gap between the current deficit and the maximum acceptable one. As a result, the gap will create pressure on raising the deficit target and eventually resulting in a higher maximum acceptable deficit (B5).

In this case, B3 and B4 are “corrective actions”, whereas B5 is a “pressure to lower the goal”.

[[File:budget_deficit.png|x{20}px|frameless|center|caption]]
Picture 4. Budget deficit <ref name="BT2" />

=== Company’s change <ref name="SysThink" /> ===
Imagine a company dedicated to producing quality products. However, to improve efficiency, the company is launching a number of cost-cutting initiatives. They increase line speed and change cooking methods.

Over the next five years, sales of the products will begin to decrease. This led managers to believe that there had been a change in consumer eating habits. After another two years, sales continue to decrease. The company decided to make consumer research. The results show that the taste of their products has changed for the worse.

Further analysis showed that the gap between the quality standard and the actual product quality first appeared over 10 years ago. This gap should have been a signal to management that the company's processes, equipment, or ingredients needed attention and possible investment. However, the company was distracted by its cost control campaign and the quality standard was lowered in favor of other changes. As a result, the quality very slowly fell below the level of consumer tolerance.

== How to deal with Drifting Goals ==
So after all the question is how can we react to Drifting Goals? How can we deal with the gap? And how to prevent goals from drifting?

=== "Soaring" Goals <ref name="SysThink" /> ===
Drifting goals does not always lead to decreasing levels of performance. This concept can be reversed in the case when the company’s goals and standards continually improve. In this case, every time when the company meets its goal, they are increasing the goal even higher. This will start the gap again, which will lead the company to action to bring higher performance.

=== What to do with a gap? <ref name="BT1" /> ===
What to do when there is a gap, should we focus on the gap or focus on setting the goal more achievable? The answer is the gap. It is easy to set a goal, but it is much harder to reach the goal. It is better to learn some lessons by trying to reach the goal than just to adjust it so it will be easier to get to.

=== How to prevent goals from drifting? <ref name="Kessels"/> ===
The drifting goals model is a simple model of reality. In real life, there are so many factors, which make it harder to understand what causes the gap. But it can be used to identify potential issues.

After all that has been said we need to remember that:

*Goals are often related, so before jumping to some conclusion, we need to study the relationship between goals.
*Lowering the goals can be done only when the relationship with other goals is understood.
*It is not the best way to base future goals on previous performance. This will lead to drifting goals.

== An example to practice ==
Try to create a diagram based on the company's change example. You can use the Vensim program.

== References ==
<references />

Drifting Goals/cs

2023-01-08T20:16:54Z

Ploo00:

== Introduction ==
These days most organizations put emphasis on bigger goals. The goals on the one hand should be inspiring and motivating. On the other hand, they suppose to be doable. When the organization is not capable of meeting its goals, they are trying to come up with some solution. Sometimes the reason for not reaching the goal may be more simple than we expect or/and even unexpected. Organizations often have set more than one goal and sometimes one of the goals gets all the attention whereas the other goal remains under the pressure. Organizations react to this by reducing the goal to shorten the gap, but over time they unintentionally drift further from what they are really trying to achieve. The concept which will help us to understand this problem is the "Drifting Goals" archetype.

== Drifting Goals ==
Drifting Goals is a system archetype in systems thinking, that defines the behavior that occurs when there is a gap between the current state and the desired state. Setting a goal defines the desired state; the current state is where you are today. The gap is the distance between where you are and where you want to be. <ref name="BT1">KISHAU, Rogers, 2022. Drifting Goals & What To Do When You’re Not Where You Want To Be - BigThinking.io. BigThinking.io. Online. 5 September 2022. [Accessed 8 January 2023]. Retrieved from: https://bigthinking.io/drifting-goals-goal-setting/ </ref>

Drifting goals is a concept that refers to situations where short-term decisions result in a retrogression of long-term goals. It starts with an initial gap between desired performance and the actual performance. If an organization has a gap between a goal and actual results, it will attempt to take some actions to close the gap. There are two main reactions to shorten the gap. One can either increase his efforts (corrective actions) or reduce his goal. <ref name="Kessels"># KESSELS, Sjoerd, 2019. Short term thinking: Drifting Goals. Adaptive Organization. Online. 18 January 2019. [Accessed 8 January 2023]. Retrieved from: https://www.adaptive-organizations.com/short-term-thinking-drifting-goals/ </ref>

It may take some time for corrective actions to take effect. If the gap continually increases or persists due to the delay of corrective actions, the pressure to lower the goals will increase. While corrective actions take time to have an effect, the effect of lowering the goal is immediate. Since such a situation is not a one-time occurrence, the organization can continue to slowly reduce the goal over time. The goal reduction will remain unnoticed, as it happens gradually over time and will lead to a general standard decrease. <ref name="Kessels" />

So, to sum up there are two reactions to the increasing or persisting gap: <ref name="SysThink">ANDERSON, Virginia, 2015. Introducing the Systems Archetypes: Drifting Goals. The Systems Thinker. Online. 12 November 2015. [Accessed 8 January 2023]. Retrieved from: https://thesystemsthinker.com/introducing-the-systems-archetypes-drifting-goals/ </ref> <ref name="BT1" />

'''Corrective action'''
* Increasing action reduces the gap by taking additional steps to move the organization’s current state closer to the desired state.
* Requires time, effort, funds, and attention.
* Can pose several problems:
** there is no guarantee that it will work,
** its effects may be seen with delay, so it can lead to impatience.

'''Reduce the goal'''

* Move the goal closer to the organization’s current state to reduce the gap immediately.
* Can be dangerous as the gap will disappear, but so does the pressure to take corrective action to improve the actual state.
* Further can lead to a decrease in the pressure to act in the long-term and once an intolerable decrease can become a new standard.

<blockquote>
Changing the goal is not always wrong. Sometimes it is necessary in cases when the goal turned out to be anyhow inappropriate or the current state requires more flexibility. However, it can lead to a long-term standard decrease, so it is important to distinguish the difference between legitimate adjustment of the goal and the Drifting goal. <ref name="SysThink" />
</blockquote>

=== Causal loop diagram <ref name="SysThink" />===
The causal loop diagram of the Drifting Goals archetype consists of two balancing loops (see Pic. 1). As was stated before, the situation starts when there is a gap between desired performance and actual performance. The goal is being compared to a current state. If the gap persists, corrective actions are taken in order to improve the current state. This forms a basic balancing loop (B1). A delay between corrective action and the actual state represents the fact, that the results may take some time to become evident.

A second balancing loop (B2) is driven by pressure to get closer to the goal by lowering the goal. As the gap increases the pressure to lower the goal increases too.

The main difference between the 2 loops is that lowering the goals takes less time to get to a goal, whereas corrective actions usually take time for an organization to get to the goal.

[[File:Drifting_goals_template.jpg|x{20}px|frameless|center|caption]]
Picture 1. Drifting Goals Template <ref name="SysThink" />

=== Behavior over time <ref name="Braun">BRAUN, William, 2002. The System Archetypes. Online. 2002. Retrieved from: https://www.studocu.com/ph/document/university-of-negros-occidental-recoletos/accounting-1/sys-archetypes/9771310 </ref> ===

Drifting goals has a long-term effect on goal setting, as every time when an organization cannot meet the goal, it will set the goal lower and lower to ensure they meet the goal. In Picture 2, we can see, that over time the gap is still the same, as the organization is not trying to improve the state and find the problem. But they are just adjusting the goal so it will be closer to an actual state. Meanwhile, the actual state is also decreasing.

[[File:Behavior_over_time.png|{500}px|frameless|center|caption]]
Picture 2. Behavior over time <ref name="Braun" />

== Boiled frog <ref name="BT2"> KIM, Daniel H., 1994. Systems archetypes. Online. Cambridge, Mass: Pegasus Communications. Toolbox reprint series. ISBN 978-1-883823-00-9. Retrieved from: https://thesystemsthinker.com/wp-content/uploads/2016/03/Systems-Archetypes-I-TRSA01_pk.pdf </ref> ==
To make it clearer, let's look at the example of the "boiled frog" syndrome. The nature of this syndrome is the following. If you put a frog in a pot of boiling water, it will immediately jump out and escape. But if you put it in a pot of nicely warm water and raise the temperature slowly, the frog will swim and enjoy the lukewarm water. As the temperature rises, it becomes more lethargic until it eventually dies. In this case, the frog became a victim of the drifting goals scenario.

[[File:boiled_frog_syndrome.png|x{300}px|center|caption]]
Picture 3. Boiled frog syndrome <ref> J. VAN SANTEN, 2019. The Boiling Frog. Tapas.io. Online. 8 October 2019. [Accessed 8 January 2023]. Retrieved from: https://tapas.io/episode/1539438 </ref>

In this case, the goal is the desired body temperature. If the gap between the desired temperature and the temperature of the water is too large, the frog immediately jumps out of the water. But if the temperature rises slowly, the difference between its ideal temperature and the current temperature increases slowly too. This closes the gap between desired and actual temperatures, eliminating the need to take corrective action before it is too late.

The reason drifting targets are compared to "boiled frogs" is that goals such as the frog's ideal temperature tend to drift slowly and usually go unnoticed. Like a frog that doesn't recognize a gradual rise in temperature, organizations often stay unalarmed about performance degradation if it occurs over a long period of time.

== Other examples ==
This concept can be used in different aspects of our lives. Let's see more real-life examples.

=== Government budget <ref name="BT2" /> ===
Imagine there is a gap between the previously acceptable deficit level and the actual (see Pic. 3). The gap can be closed in two ways: by reducing government expenses (B3) or by increasing tax revenues (B4). However, the current situation leads to increased spending, and taxation showed mixed results. This situation will result in an even higher deficit. The raising deficit will lead to a more significant gap between the current deficit and the maximum acceptable one. As a result, the gap will create pressure on raising the deficit target and eventually resulting in a higher maximum acceptable deficit (B5).

In this case, B3 and B4 are “corrective actions”, whereas B5 is a “pressure to lower the goal”.

[[File:budget_deficit.png|x{20}px|frameless|center|caption]]
Picture 4. Budget deficit <ref name="BT2" />

=== Company’s change <ref name="SysThink" /> ===
Imagine a company dedicated to producing quality products. However, to improve efficiency, the company is launching a number of cost-cutting initiatives. They increase line speed and change cooking methods.

Over the next five years, sales of the products will begin to decrease. This led managers to believe that there had been a change in consumer eating habits. After another two years, sales continue to decrease. The company decided to make consumer research. The results show that the taste of their products has changed for the worse.

Further analysis showed that the gap between the quality standard and the actual product quality first appeared over 10 years ago. This gap should have been a signal to management that the company's processes, equipment, or ingredients needed attention and possible investment. However, the company was distracted by its cost control campaign and the quality standard was lowered in favor of other changes. As a result, the quality very slowly fell below the level of consumer tolerance.

== How to deal with Drifting Goals ==
So after all the question is how can we react to Drifting Goals? How can we deal with the gap? And how to prevent goals from drifting?

=== "Soaring" Goals <ref name="SysThink" /> ===
Drifting goals does not always lead to decreasing levels of performance. This concept can be reversed in the case when the company’s goals and standards continually improve. In this case, every time when the company meets its goal, they are increasing the goal even higher. This will start the gap again, which will lead the company to action to bring higher performance.

=== What to do with a gap? <ref name="BT1" /> ===
What to do when there is a gap, should we focus on the gap or focus on setting the goal more achievable? The answer is the gap. It is easy to set a goal, but it is much harder to reach the goal. It is better to learn some lessons by trying to reach the goal than just to adjust it so it will be easier to get to.

=== How to prevent goals from drifting? <ref name="Kessels"/> ===
The drifting goals model is a simple model of reality. In real life, there are so many factors, which make it harder to understand what causes the gap. But it can be used to identify potential issues.

After all that has been said we need to remember that:

*Goals are often related, so before jumping to some conclusion, we need to study the relationship between goals.
*Lowering the goals can be done only when the relationship with other goals is understood.
*It is not the best way to base future goals on previous performance. This will lead to drifting goals.

== An example to practice ==
Try to create a diagram based on the company's change example. You can use the Vensim program.

== References ==
<references />

Drifting Goals/cs

2023-01-08T19:58:03Z

Ploo00:

== Introduction ==
These days most organizations put emphasis on bigger goals. The goals on the one hand should be inspiring and motivating. On the other hand, they suppose to be doable. When the organization is not capable of meeting its goals, they are trying to come up with some solution. Sometimes the reason for not reaching the goal may be more simple than we expect or/and even unexpected. Organizations often have set more than one goal and sometimes one of the goals gets all the attention whereas the other goal remains under the pressure. Organizations react to this by reducing the goal to shorten the gap, but over time they unintentionally drift further from what they are really trying to achieve. The concept which will help us to understand this problem is the "Drifting Goals" archetype.

== Drifting Goals ==
Drifting Goals is a system archetype in systems thinking, that defines the behavior that occurs when there is a gap between the current state and the desired state. Setting a goal defines the desired state; the current state is where you are today. The gap is the distance between where you are and where you want to be. <ref name="BT1">KISHAU, Rogers, 2022. Drifting Goals & What To Do When You’re Not Where You Want To Be - BigThinking.io. BigThinking.io. Online. 5 September 2022. [Accessed 8 January 2023]. Retrieved from: https://bigthinking.io/drifting-goals-goal-setting/ </ref>

Drifting goals is a concept that refers to situations where short-term decisions result in a retrogression of long-term goals. It starts with an initial gap between desired performance and the actual performance. If an organization has a gap between a goal and actual results, it will attempt to take some actions to close the gap. There are two main reactions to shorten the gap. One can either increase his efforts (corrective actions) or reduce his goal. <ref name="Kessels"># KESSELS, Sjoerd, 2019. Short term thinking: Drifting Goals. Adaptive Organization. Online. 18 January 2019. [Accessed 8 January 2023]. Retrieved from: https://www.adaptive-organizations.com/short-term-thinking-drifting-goals/ </ref>

It may take some time for corrective actions to take effect. If the gap continually increases or persists due to the delay of corrective actions, the pressure to lower the goals will increase. While corrective actions take time to have an effect, the effect of lowering the goal is immediate. Since such a situation is not a one-time occurrence, the organization can continue to slowly reduce the goal over time. The goal reduction will remain unnoticed, as it happens gradually over time and will lead to a general standard decrease. <ref name="Kessels" />

So, to sum up there are two reactions to the increasing or persisting gap: <ref name="SysThink">ANDERSON, Virginia, 2015. Introducing the Systems Archetypes: Drifting Goals. The Systems Thinker. Online. 12 November 2015. [Accessed 8 January 2023]. Retrieved from: https://thesystemsthinker.com/introducing-the-systems-archetypes-drifting-goals/ </ref> <ref name="BT1" />

'''Corrective action'''
* Increasing action reduces the gap by taking additional steps to move the organization’s current state closer to the desired state.
* Requires time, effort, funds, and attention.
* Can pose several problems:
** there is no guarantee that it will work,
** its effects may be seen with delay, so it can lead to impatience.

'''Reduce the goal'''

* Move the goal closer to the organization’s current state to reduce the gap immediately.
* Can be dangerous as the gap will disappear, but so does the pressure to take corrective action to improve the actual state.
* Further can lead to a decrease in the pressure to act in the long-term and once an intolerable decrease can become a new standard.

<blockquote>
Changing the goal is not always wrong. Sometimes it is necessary in cases when the goal turned out to be anyhow inappropriate or the current state requires more flexibility. However, it can lead to a long-term standard decrease, so it is important to distinguish the difference between legitimate adjustment of the goal and the Drifting goal. <ref name="SysThink" />
</blockquote>

=== Causal loop diagram <ref name="SysThink" />===
The causal loop diagram of the Drifting Goals archetype consists of two balancing loops (see Pic. 1). As was stated before, the situation starts when there is a gap between desired performance and actual performance. The goal is being compared to a current state. If the gap persists, corrective actions are taken in order to improve the current state. This forms a basic balancing loop (B1). A delay between corrective action and the actual state represents the fact, that the results may take some time to become evident.

A second balancing loop (B2) is driven by pressure to get closer to the goal by lowering the goal. As the gap increases the pressure to lower the goal increases too.

The main difference between the 2 loops is that lowering the goals takes less time to get to a goal, whereas corrective actions usually take time for an organization to get to the goal.

[[File:Drifting_goals_template.jpg|x{20}px|frameless|center|caption]]
Picture 1. Drifting Goals Template <ref name="SysThink" />

=== Behavior over time <ref name="Braun">BRAUN, William, 2002. The System Archetypes. Online. 2002. Retrieved from: https://www.studocu.com/ph/document/university-of-negros-occidental-recoletos/accounting-1/sys-archetypes/9771310 </ref> ===

Drifting goals has a long-term effect on goal setting, as every time when an organization cannot meet the goal, it will set the goal lower and lower to ensure they meet the goal. In Picture 2, we can see, that over time the gap is still the same, as the organization is not trying to improve the state and find the problem. But they are just adjusting the goal so it will be closer to an actual state. Meanwhile, the actual state is also decreasing.

[[File:Behavior_over_time.png|{500}px|frameless|center|caption]]
Picture 2. Behavior over time <ref name="Braun" />

== Boiled frog <ref name="BT2"> KIM, Daniel H., 1994. Systems archetypes. Cambridge, Mass: Pegasus Communications. Toolbox reprint series. ISBN 978-1-883823-00-9.</ref> ==
To make it clearer, let's look at the example of the "boiled frog" syndrome. The nature of this syndrome is the following. If you put a frog in a pot of boiling water, it will immediately jump out and escape. But if you put it in a pot of nicely warm water and raise the temperature slowly, the frog will swim and enjoy the lukewarm water. As the temperature rises, it becomes more lethargic until it eventually dies. In this case, the frog became a victim of the drifting goals scenario.

[[File:boiled_frog_syndrome.png|x{300}px|center|caption]]
Picture 3. Boiled frog syndrome <ref name="BT2"/>

In this case, the goal is the desired body temperature. If the gap between the desired temperature and the temperature of the water is too large, the frog immediately jumps out of the water. But if the temperature rises slowly, the difference between its ideal temperature and the current temperature increases slowly too. This closes the gap between desired and actual temperatures, eliminating the need to take corrective action before it is too late.

The reason drifting targets are compared to "boiled frogs" is that goals such as the frog's ideal temperature tend to drift slowly and usually go unnoticed. Like a frog that doesn't recognize a gradual rise in temperature, organizations often stay unalarmed about performance degradation if it occurs over a long period of time.

== Other examples ==
This concept can be used in different aspects of our lives. Let's see more real-life examples.

=== Government budget <ref name="BT2" /> ===
Imagine there is a gap between the previously acceptable deficit level and the actual (see Pic. 3). The gap can be closed in two ways: by reducing government expenses (B3) or by increasing tax revenues (B4). However, the current situation leads to increased spending, and taxation showed mixed results. This situation will result in an even higher deficit. The raising deficit will lead to a more significant gap between the current deficit and the maximum acceptable one. As a result, the gap will create pressure on raising the deficit target and eventually resulting in a higher maximum acceptable deficit (B5).

In this case, B3 and B4 are “corrective actions”, whereas B5 is a “pressure to lower the goal”.

[[File:budget_deficit.png|x{20}px|frameless|center|caption]]
Picture 4. Budget deficit <ref name="BT2" />

=== Company’s change <ref name="SysThink" /> ===
Imagine a company dedicated to producing quality products. However, to improve efficiency, the company is launching a number of cost-cutting initiatives. They increase line speed and change cooking methods.

Over the next five years, sales of the products will begin to decrease. This led managers to believe that there had been a change in consumer eating habits. After another two years, sales continue to decrease. The company decided to make consumer research. The results show that the taste of their products has changed for the worse.

Further analysis showed that the gap between the quality standard and the actual product quality first appeared over 10 years ago. This gap should have been a signal to management that the company's processes, equipment, or ingredients needed attention and possible investment. However, the company was distracted by its cost control campaign and the quality standard was lowered in favor of other changes. As a result, the quality very slowly fell below the level of consumer tolerance.

== How to deal with Drifting Goals ==
So after all the question is how can we react to Drifting Goals? How can we deal with the gap? And how to prevent goals from drifting?

=== "Soaring" Goals <ref name="SysThink" /> ===
Drifting goals does not always lead to decreasing levels of performance. This concept can be reversed in the case when the company’s goals and standards continually improve. In this case, every time when the company meets its goal, they are increasing the goal even higher. This will start the gap again, which will lead the company to action to bring higher performance.

=== What to do with a gap? <ref name="BT1" /> ===
What to do when there is a gap, should we focus on the gap or focus on setting the goal more achievable? The answer is the gap. It is easy to set a goal, but it is much harder to reach the goal. It is better to learn some lessons by trying to reach the goal than just to adjust it so it will be easier to get to.

=== How to prevent goals from drifting? <ref name="Kessels"/> ===
The drifting goals model is a simple model of reality. In real life, there are so many factors, which make it harder to understand what causes the gap. But it can be used to identify potential issues.

After all that has been said we need to remember that:

*Goals are often related, so before jumping to some conclusion, we need to study the relationship between goals.
*Lowering the goals can be done only when the relationship with other goals is understood.
*It is not the best way to base future goals on previous performance. This will lead to drifting goals.

== An example to practice ==
Try to create a diagram based on the company's change example. You can use the Vensim program.

== References ==
<references />

Drifting Goals/cs

2023-01-08T19:24:32Z

Ploo00:

== Introduction ==
These days most organizations put emphasis on bigger goals. The goals on the one hand should be inspiring and motivating. On the other hand, they suppose to be doable. When the organization is not capable of meeting its goals, they are trying to come up with some solution. Sometimes the reason for not reaching the goal may be more simple than we expect or/and even unexpected. Organizations often have set more than one goal and sometimes one of the goals gets all the attention whereas the other goal remains under the pressure. Organizations react to this by reducing the goal to shorten the gap, but over time they unintentionally drift further from what they are really trying to achieve. The concept which will help us to understand this problem is the "Drifting Goals" archetype.

== Drifting Goals ==
Drifting Goals is a system archetype in systems thinking, that defines the behavior that occurs when there is a gap between the current state and the desired state. Setting a goal defines the desired state; the current state is where you are today. The gap is the distance between where you are and where you want to be.

Drifting goals is a concept that refers to situations where short-term decisions result in a retrogression of long-term goals. It starts with an initial gap between desired performance and the actual performance. If an organization has a gap between a goal and actual results, it will attempt to take some actions to close the gap. There are two main reactions to shorten the gap. One can either increase his efforts (corrective actions) or reduce his goal.

It may take some time for corrective actions to take effect. If the gap continually increases or persists due to the delay of corrective actions, the pressure to lower the goals will increase. While corrective actions take time to have an effect, the effect of lowering the goal is immediate. Since such a situation is not a one-time occurrence, the organization can continue to slowly reduce the goal over time. The goal reduction will remain unnoticed, as it happens gradually over time and will lead to a general standard decrease.

So, to sum up there are two reactions to the increasing or persisting gap:

'''Corrective action'''
* Increasing action reduces the gap by taking additional steps to move the organization’s current state closer to the desired state.
* Requires time, effort, funds, and attention.
* Can pose several problems:
** there is no guarantee that it will work,
** its effects may be seen with delay, so it can lead to impatience.

'''Reduce the goal'''

* Move the goal closer to the organization’s current state to reduce the gap immediately.
* Can be dangerous as the gap will disappear, but so does the pressure to take corrective action to improve the actual state.
* Further can lead to a decrease in the pressure to act in the long-term and once an intolerable decrease can become a new standard.

<pre>
Changing the goal is not always wrong. Sometimes it is necessary in cases when the goal turned out to be anyhow inappropriate or the current state requires more flexibility. However, it can lead to a long-term standard decrease, so it is important to distinguish the difference between legitimate adjustment of the goal and the Drifting goal.
</pre>

=== Causal loop diagram ===
The causal loop diagram of the Drifting Goals archetype consists of two balancing loops (see Pic. 1). As was stated before, the situation starts when there is a gap between desired performance and actual performance. The goal is being compared to a current state. If the gap persists, corrective actions are taken in order to improve the current state. This forms a basic balancing loop (B1). A delay between corrective action and the actual state represents the fact, that the results may take some time to become evident.

A second balancing loop (B2) is driven by pressure to get closer to the goal by lowering the goal. As the gap increases the pressure to lower the goal increases too.

The main difference between the 2 loops is that lowering the goals takes less time to get to a goal, whereas corrective actions usually take time for an organization to get to the goal.

[[File:Drifting_goals_template.jpg|x{20}px|frameless|center|caption]]
Picture 1. Drifting Goals Template

=== Behavior over time ===

Drifting goals has a long-term effect on goal setting, as every time when an organization cannot meet the goal, it will set the goal lower and lower to ensure they meet the goal. In Picture 2, we can see, that over time the gap is still the same, as the organization is not trying to improve the state and find the problem. But they are just adjusting the goal so it will be closer to an actual state. Meanwhile, the actual state is also decreasing.

[[File:Behavior_over_time.png|{500}px|frameless|center|caption]]
Picture 2. Behavior over time

== Boiled frog ==
To make it clearer, let's look at the example of the "boiled frog" syndrome. The nature of this syndrome is the following. If you put a frog in a pot of boiling water, it will immediately jump out and escape. But if you put it in a pot of nicely warm water and raise the temperature slowly, the frog will swim and enjoy the lukewarm water. As the temperature rises, it becomes more lethargic until it eventually dies. In this case, the frog became a victim of the drifting goals scenario.

[[File:boiled_frog_syndrome.png|x{300}px|center|caption]]
Picture 3. Boiled frog syndrome

In this case, the goal is the desired body temperature. If the gap between the desired temperature and the temperature of the water is too large, the frog immediately jumps out of the water. But if the temperature rises slowly, the difference between its ideal temperature and the current temperature increases slowly too. This closes the gap between desired and actual temperatures, eliminating the need to take corrective action before it is too late.

The reason drifting targets are compared to "boiled frogs" is that goals such as the frog's ideal temperature tend to drift slowly and usually go unnoticed. Like a frog that doesn't recognize a gradual rise in temperature, organizations often stay unalarmed about performance degradation if it occurs over a long period of time.

== Other examples ==
This concept can be used in different aspects of our lives. Let's see more real-life examples.

=== Government budget ===
Imagine there is a gap between the previously acceptable deficit level and the actual (see Pic. 3). The gap can be closed in two ways: by reducing government expenses (B3) or by increasing tax revenues (B4). However, the current situation leads to increased spending, and taxation showed mixed results. This situation will result in an even higher deficit. The raising deficit will lead to a more significant gap between the current deficit and the maximum acceptable one. As a result, the gap will create pressure on raising the deficit target and eventually resulting in a higher maximum acceptable deficit (B5).

In this case, B3 and B4 are “corrective actions”, whereas B5 is a “pressure to lower the goal”.

[[File:budget_deficit.png|x{20}px|frameless|center|caption]]
Picture 4. Budget deficit

=== Company’s change ===
Imagine a company dedicated to producing quality products. However, to improve efficiency, the company is launching a number of cost-cutting initiatives. They increase line speed and change cooking methods.

Over the next five years, sales of the products will begin to decrease. This led managers to believe that there had been a change in consumer eating habits. After another two years, sales continue to decrease. The company decided to make consumer research. The results show that the taste of their products has changed for the worse.

Further analysis showed that the gap between the quality standard and the actual product quality first appeared over 10 years ago. This gap should have been a signal to management that the company's processes, equipment, or ingredients needed attention and possible investment. However, the company was distracted by its cost control campaign and the quality standard was lowered in favor of other changes. As a result, the quality very slowly fell below the level of consumer tolerance.

== "Soaring" Goals ==
Drifting goals does not always lead to decreasing levels of performance. This concept can be reversed in the case when the company’s goals and standards continually improve. In this case, every time when the company meets its goal, they are increasing the goal even higher. This will start the gap again, which will lead the company to action to bring higher performance.

== What to do with a gap? ==
What to do when there is a gap, should we focus on the gap or focus on setting the goal more achievable? The answer is the gap. It is easy to set a goal, but it is much harder to reach the goal. It is better to learn some lessons by trying to reach the goal than just to adjust it so it will be easier to get to.

== Hot to prevent goals from drifting? ==
The drifting goals model is a simple model of reality. In real life, there are so many factors, which make it harder to understand what causes the gap. But it can be used to identify potential issues.

After all that has been said we need to remember that:

*Goals are often related, so before jumping to some conclusion, we need to study the relationship between goals.
*Lowering the goals can be done only when the relationship with other goals is understood.
*It is not the best way to base future goals on previous performance. This will lead to drifting goals.

== An example to practice ==
Try to create a diagram based on the company's change example. You can use the Vensim program.

== References ==

# ANDERSON, Virginia, 2015. Introducing the Systems Archetypes: Drifting Goals. The Systems Thinker. Online. 12 November 2015. [Accessed 8 January 2023]. Retrieved from: . https://thesystemsthinker.com/introducing-the-systems-archetypes-drifting-goals/
# BRAUN, William, 2002. The System Archetypes. Online. 2002. Retrieved from: https://www.studocu.com/ph/document/university-of-negros-occidental-recoletos/accounting-1/sys-archetypes/9771310
# KESSELS, Sjoerd, 2019. Short term thinking: Drifting Goals. Adaptive Organization. Online. 18 January 2019. [Accessed 8 January 2023]. Retrieved from: https://www.adaptive-organizations.com/short-term-thinking-drifting-goals/
# KIM, Daniel H., 1994. Systems archetypes. Cambridge, Mass: Pegasus Communications. Toolbox reprint series. ISBN 978-1-883823-00-9.
# KISHAU, Rogers, 2022. Drifting Goals & What To Do When You’re Not Where You Want To Be - BigThinking.io. BigThinking.io. Online. 5 September 2022. [Accessed 8 January 2023]. Retrieved from: https://bigthinking.io/drifting-goals-goal-setting/

File:Behavior over time.png

2023-01-08T19:05:49Z

Ploo00:

Drifting Goals/cs

2023-01-07T17:40:18Z

File:Boiled frog syndrome.png

2023-01-07T17:37:08Z

Ploo00:

File:Budget deficit.png

2023-01-07T15:22:30Z

Ploo00:

File:Drifting goals template.jpg

2023-01-07T14:41:18Z

Ploo00:

Course materials/cs

2023-01-07T14:22:45Z

Ploo00:

{{DISPLAYTITLE:Učební texty}}

Jak psát učební texty s největší nadějí na úspěch si lze přečíst v článku [[How to write a term paper/cs|Jak psát učební texty]]. '''POZOR!''' Přesvědčte se, že opravdu máte úlohu zadánu jako učební text a nikoliv heslo do Wikipedie. Wikičlánek se odevzdává vždy, výhradně a pouze přímo do Wikipedie!

==Učební texty online==
*[[Monte Carlo method/cs|Metoda Monte Carlo]]
**[[Pseudorandom number generators/cs|Generátory pseudonáhodných čísel]]
**[[Probability distributions/cs|Pravděpodobnostní rozdělení]]
**[[Variance reduction/cs|Redukce rozptylu]]
**[[Interpretation of MC simulation results (Stochastic methods)/cs|Zpracování a interpretace výsledků produkovaných Monte Carlo simulací (statistické metody)]]
**[[Monte Carlo method application in simulations/cs|Užití metody Monte Carlo v simulacích]]
*[[Statistics101: Probability distributions/cs|Opáčko ze statistiky: Pravděpodobnostní rozdělení]]
**[[Normal distribution/cs|Normální rozdělení]]
**[[Poisson distribution|Poissonovo rozdělení]]
**[[Exponential distribution|Exponenciální rozdělení]]
**[[Uniform distribution/cs|Rovnoměrné rozdělení]]
**[[Log-normal distribution/cs|Logaritmicko-normální rozdělení]]
*[[Discrete event simulation/cs|Diskrétní simulace]]
**[[Queueing theory/cs|Teorie front]]
**[[Quality management/cs|Řízení kvality]]
*[[Game theory/cs|Teorie her]]
**[[Decision strategies/cs|Rozhodovací strategie]]
**[[Nash equilibrium/cs|Nashova rovnováha]]
***[[Mixed strategy/cs|Smíšená strategie]]
**[[One-shot games/cs|Jednorázové hry]]
***[[Normal form/cs|Normální forma]]
***[[Prisoner's dilemma/cs|Vězňovo dilema]]
***[[The Chicken Game/cs|Hra kuře]]
***[[The Battle of Sexes/cs|Bitva pohlaví]]
***[[Coordination game/sk|Koordinačná hra]]
**[[Repeated games/cs|Opakované hry]]
***[[Meta-strategies/cs|Meta-strategie]]
**[[Multistage Games/cs|Vícekolové hry]]
***[[Extensive form/cs|Rozšířená forma]]
**[[Multiplayer games/cs|Hry více hráčů]]
***[[Auctions/cs|Aukce]]
****[[Vickrey auction/cs|Vickreyova aukce]]
***[[N-player prisoner's dilemma/cs|Vězňovo dilema více hráčů]]
**[[Cooperative games/cs|Kooperativní hry]]
*[[Multi-agent systems/cs|Multiagentní systémy]]
**[[Agents/cs|Agenti]]
***[[Agent reasoning/cs|Uvažování agenta]]
****[[Markov decision process/cs|Markovův rozhodovací proces]]
****[[Fuzzy logic/cs|Fuzzy logika]]
**[[Agent environments/cs|Agentní prostředí]]
**[[Agent negotiation/cs|Agentní vyjednávání]]
***[[Rubinstein bargaining model/cs|Rubinsteinův model vyjednávání]]
*[[System Dynamics/cs|Systémová dynamika]]
**[[Causal Loop Diagram/cs|Diagram kauzálních smyček]]
**[[Stock and Flow Diagram/cs|Diagram toků (SFD)]]
**[[Leverage points/cs|Body působení]]
**[[System Archetypes/cs|Systémové archetypy]]
***[[Tragedy of the commons/cs|Tragédie obecní pastviny]]
***[[Growth and Underinvestment/cs|Růst a nedostatečné investice]]
***[[Limits to Growth/cs|Meze růstu]]
***[[Succes to the Successful/cs|Úspěch úspěšným]]
***[[Drifting Goals/cs|Eroze cílů]]
*[[Serious Gaming/cs|Serious Gaming]]

==Povinná literatura==
* Šalamon, T. (2011). [http://www.designofagentbasedmodels.info ''Design of Agent-Based Models : Developing Computer Simulations for a Better Understanding of Social Processes'']. Řepín, Czech Republic: Bruckner Publishing

==Doporučená literatura==
* Dlouhý, M. Fábry, J. Kuncová, M. (2003). ''Simulace pro ekonomy''. Praha, Czech Republic: Oeconomica
* Colman, A. M. (1982). ''Game theory and experimental games : the study of strategic interaction''. Oxford, UK: Pergamon
* Sterman, J. (2000). ''Business dynamics : systems thinking and modeling for a complex world''. Boston, Ma.: Irwin/McGraw-Hill
* Mildeová, S. Vojtko, V. (2003). ''Systémová dynamika''. Praha, Czech Republic: Oeconomica
* Wooldridge, M. J. (2002). ''An introduction to multiagent systems''. Chichester, UK: John Wiley & Sons
* Polak, B. (2007) ''Game Theory'' In: Yale University: Open Yale Courses, [http://oyc.yale.edu/economics/econ-159 http://oyc.yale.edu/]

Assignments WS 2022/2023

2022-12-17T18:50:55Z

Ploo00:

{{Ambox
| text = <div>
Please, put here your assignments. Do not forget to sign them. You can use <nowiki>~~~~</nowiki> (four tildas) for an automatic signature. Use Show preview in order to check the result before your final sumbition.
</div>
}}

{{Ambox
| text = <div>
Please, strive to formulate your assignment carefully. We expect an adequate effort to formulate the assignment as it is your semestral paper. Do not forget that your main goal is a research paper. It means your simulation model must generate the results that are specific, measurable and verifiable. Think twice how you will develop your model, which entities you will use, draw a model diagram, consider what you will measure. No sooner than when you have a good idea about the model, submit your assignment. And of course, read [[How to deal with the simulation assignment|How to deal with the simulation assignment]].
</div>
}}

{{Ambox
| text = <div>
Topics on gambling, cards, etc. are not welcome.
</div>
}}

{{Ambox
| type = content
| text = <div>
In order to avoid possible confusion, please, check if you have added '''approved''' in bold somewhere in our comment under your submission. If there is no '''approved''', it means the assignment was not approved yet.
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== Effect of leniency programs on cartel rates by [[User:Baumareb|Baumareb]] ([[User talk:Baumareb|talk]]) 11:18, 7 December 2022 (CET) ==

''' Simulation '''

The leniency program of the European Commission offers the companies involved in a cartel either complete or partial immunity from fines if they self-report and hand over evidence. It was introduced in 1996, following the surge in amnesty applications in the wake of the 1993 revision of the Corporate Leniency Program of the US Department of Justice’s Antitrust Division. Reports from various implemented leniency programs showed that such programs led to numerous applications. However, despite the clear increase in leniency applications, the question poses itself as to whether the programs were also successful in a sense that the actual cartel rate in those countries declined.
The simulation will be based on a study of Harrington and Chang from 2015, in which they concluded the following:

• The actual cartel rate decreases in case that the leniency program does not affect the non-leniency enforcement

• But: if the non-leniency enforcement is affected because resources are shifted to the prosecution of leniency application cases, there might be two possibilities, the cartel rate might increase.

This simulation focuses on the latter case. Assuming endogenized non-leniency enforcement, the introduction of a leniency program might have a differential impact on different industries. If a leniency program is introduced, the cartels that are about to collapse will seek to self-report. This in turn shifts resources from exposing active cartels to prosecuting cartels that are already collapsing. This creates more work for the authorities, who, instead of focusing on active cartels may now focus on dying cartels. This crowding-out effect coming about with the introduction of a leniency program shall be simulated in this project.

''' Goal '''

The simulation will have the following objectives:

* Illustrate the change in cartel rates and the change in the average life expectancy of a cartel triggered by the introduction of a leniency program in case of endogenized non-leniency enforcement for industries with unstable cartels (e.g. industries with a high number of competitors, or demand with more price elasticity) and for industries with stable cartels (e.g. industries with less competitors and demand with less price elasticity).
* Illustrate how many resources may be shifted from non-leniency enforcement to prosecuting leniency application cases without it having an undesired effect on the actual cartel rate.

''' Practical relevance '''

The simulation may be used by law enforcement officials to evaluate whether a leniency program leads to the desired effect (i.e. the decrease in the cartel rate) or not. Also, it can help for deciding whether the non-leniency enforcement needs to be strengthened to prevent the crowding-out effect.

''' Method '''

The described scenario is a multi-agent simulation in which the agents are pursuing a utility-based approach. Thus, the simulation will be done with NetLogo.
The following features will be included into the simulation:

- For both industries with stable and industries with unstable cartels:

* Number of active cartels (dying after reaching avg. life expectancy)
* Number of competitors
* Average life expectancy of a cartel
* “Birth” of new cartels

- For leniency/non-leniency enforcement:
* Resources and their assignment to either leniency or non-leniency enforcement
* Capacity of taking down an active cartel
* Capacity of taking down a cartel based on leniency applications

The simulation will be based on the 2015 research from Harrington and Chang as well as on publicly accessible data from the European Commission regarding antitrust cases from 1964 until today.

''' Sources '''
* Harrington Jr, J. E., & Chang, M. H. (2015). When can we expect a corporate leniency program to result in fewer cartels?. The Journal of Law and Economics, 58(2), 417-449.
* Ordóñez‐De‐Haro, J. M., Borrell, J. R., & Jiménez, J. L. (2018). The European commission's fight against cartels (1962–2014): A retrospective and forensic analysis. JCMS: Journal of Common Market Studies, 56(5), 1087-1107.

[[User:Baumareb|Baumareb]] ([[User talk:Baumareb|talk]]) 11:18, 7 December 2022 (CET) Rebecca Baumann (baur00)

: This isn't an easy topic. Be careful about available data. '''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:46, 15 December 2022 (CET)

== The prediction of divorce rate in Czech Republic for the following 50 years ==

''' The goal of the simulation '''

Divorce in the Czech republic must always contain at least one hearing in front of the court. Legally, there are many more parties involved, such as a notary, who must verify the signatures on all the important documents and many times, divorce lawyers are also necessary. To be able to satisfy the needs of the public, all the involved parties must have an idea about how many married couples are likely to get divorced in the years to come. This simulation will help prepare the courts, notaries and lawyers by making a prediction on the amount of divorces in the next 50 years. This will also help law students choose the field of law that they will specialize in by answering the question whether divorce lawyers will be necessary in the future or not.

''' Method '''

Vensim will be used for this simulation. The used data will come from the Czech Statistical Office and possibly other sources (Refer to [1] and [2]), such as published studies on the most common reasons for divorce. When possible, the data about each reason of divorce will be also found and the simulation model will contain this data.

== Edit: additional details ==

''' What all parameters will the simulation work with and how?'''

1. Number of marriages – the more marriages, the more divorces

a/ Number of people in the age 25 to 34 (i.e., the most common age to get married) – the more there is of these people, the more marriages there will be

b/ Number of divorced people in the age 40 to 49 (i.e., the most common age to get re-married after a divorce) – the more there is of these people, the more marriages there will be, however not as much as the number above

2. Micro causes of divorces = Top 10 causes of divorce as researched by the Czech Statistical Office, published yearly – the more common are these causes (alcoholism, infidelity etc), the more divorces there will be

a/ Ill-considered marriage

b/ Alcoholism

c/ Infidelity

d/ Lack of interest in the family (incl. abandon. of living together)

e/ Ill-treatment, criminal conviction

f/ Different characters, views and interests

g/ Health reasons

h/ Sexual discord

i/ Other causes

j/ Cause not given

3. Number of people in the age 40 to 49 – the more there is of these people, the more divorces there will be (it is the most common age to get divorced)

4. Macro causes of divorces

a/ Economic independence of women = the more economically independent women are, the more likely they are to divorce in case of an unhappy marriage – this will be evaluated through a comparison of data of average income of men vs. women

b/ Being religious – divorce is far less common for religious people.

''' What data source will be used for deriving the equations?'''

Based on my current research of data sources, the Czech Statistical Office has the all the data necessary for this paper.

[1] Scott, S. B., Rhoades, G. K., Stanley, S. M., Allen, E. S., & Markman, H. J. (2013). Reasons for Divorce and Recollections of Premarital Intervention: Implications for Improving Relationship Education. Couple & family psychology, 2(2), 131–145. https://doi.org/10.1037/a0032025

[2] Hawkins, Alan & Willoughby, Brian & Doherty, William. (2012). Reasons for Divorce and Openness to Marital Reconciliation. Journal of Divorce & Remarriage. 53. 453-463. 10.1080/10502556.2012.682898.

: Sounds interesting, but I miss more detail about the simulation. What all parameters will the simulation work with and how? What data source will be used for deriving the equations? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 11:03, 15 December 2022 (CET)
:: ''' Approved'''. Just make sure that the equtions, reasons for divorce and their impact on divorce rate are properly quantified.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 07:23, 17 December 2022 (CET)

==Crop Yield Forecasting==

''' Simulation '''

Crop growth and development simulations and yield forecasting will be performed using variables such as crop type, planting date, soil type, soil texture, and climate data (temperature, rainfall, etc.).

'''Problem definition'''

Arable land is increasingly limited, while the world's population has steadily been increasing over the years. In order to meet rapidly rising demand, production must be increased while natural resources must be protected. New agricultural research is needed to provide information on how to achieve sustainable agriculture in the face of global climate variability. Predicting crop yield under different conditions, such as different irrigation regimes, planting dates, and crop management practices, has become critical for farmers and other stakeholders who use these predictions to make more informed decisions about how to allocate resources, such as labor, equipment, and inputs, to maximize yield and productivity.

'''Method'''

Crop yield simulation tools include AquaCrop, DSSAT, and CropSyst. These tools use mathematical models to simulate crop growth and development based on input data like weather, soil type, and management practices. These tools use this data to estimate the crop's potential yield, as well as other important factors like water use and crop evapotranspiration. For this assignment I will be using AquaCrop which is a crop water productivity model developed by the United Nations Food and Agriculture Organization (FAO). It is used to simulate crop growth and yield under various environmental and management conditions. AquaCrop simulates crop growth and development, and estimates yield based on soil conditions, climate, irrigation, and management practices. The application gives access to various FAO databases with all the necessary data needed to perform a comprehensive simulation of the crop yield.

'''Citations'''

* Y. Lu, C. Wei, M. F. McCabe, and J. Sheffield, “Multi-variable assimilation into a modified AquaCrop model for improved maize simulation without management or crop phenology information,” Agricultural Water Management, vol. 266, p. 107576, May 2022, doi: 10.1016/j.agwat.2022.107576.
* P. N. Kephe, K. K. Ayisi, and B. M. Petja, “Challenges and opportunities in crop simulation modelling under seasonal and projected climate change scenarios for crop production in South Africa,” Agriculture & Food Security, vol. 10, no. 1, p. 10, Apr. 2021, doi: 10.1186/s40066-020-00283-5.
* N. T. Olivera, O. B. Manrique, Y. G. Masjuan, and A. M. G. Alega, “Evaluation of AquaCrop model in crop dry bean growth simulation,” Revista Ciencias Técnicas Agropecuarias, vol. 25, no. 3, pp. 23–30, Accessed: Dec. 10, 2022. [Online]. Available: https://www.redalyc.org/journal/932/93246970003/html/
* N. Pirmoradian, Z. Saadati, M. Rezaei, and M. R. Khaledian, “Simulating water productivity of paddy rice under irrigation regimes using AquaCrop model in humid and semiarid regions of Iran,” Appl Water Sci, vol. 10, no. 7, p. 161, Jun. 2020, doi: 10.1007/s13201-020-01249-5.

[[User:Pierreatekwana|Pierreatekwana]] ([[User talk:Pierreatekwana|talk]]) 15:06, 15 December 2022 (CET)

: Topic souds interesting, but the proposed simulation tool has to be one of the ones we have used in our class ( as specified in How to deal with the simulation assignment:
One of your key course requirements is a submission of simulation. You choose your topic yourself, the same as a method and a tool that you will use. It could be any of the development environments we have used (Excel, Simprocess, Netlogo, or Vensim).) [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 07:05, 17 December 2022 (CET)

== Electricity Spot Market Simulation by [[User:Ceta|Ceta]] ([[User talk:Ceta|talk]]) 01:13, 16 December 2022 (CET) ==

'''Simulation'''

I’ve been working as a professional in the Turkish Electricity Market (EXIST) for more than 10 years. I had the chance to practice in business some fundamental methods such as Stochastic Dual Dynamic Programming (SDDP) for Hydro-Thermal dispatch optimization. The spot exchange of power or the power market itself has many examples of simulation. And the simulation of the uncertainties both in the short term and the long term are very critical in terms of new investments, portfolio management, and resource optimization.

'''Method'''

Both Excel and Netlogo simulations can be derived

'''• Inputs
'''

- Portfolios

(Thermals: Coal-Fired, Natural Gas-Fired, Other Thermals; Renewables: Hydro, Wind, Solar PV, Other Renewables)

- Resource Prices (Eur/mwh)

- Installed Capacity (in MW)

'''• Variables'''

- Power Demand (MWh)

- Calendar Day

'''• Uncertain Events
'''
- Maintenance (availability)

- Climate Factors (Wind, Hydro Sources, Temperature effect on Load-Demand)

- Natural Gas Shortage (Due to Climate Factors and/or Supply)

'''• Outputs
'''
- Marginal Cost

- Market Clearing Prices (MCP)

•''' Goals
'''
- Objective Function Y= Min (MCP)

- Effects of Natural Gas Shortages

- Effects of Climate Factors

- Effects of Maintenance

- Effects of Calendar

- Sensitivity Analysis of commissioning a Nuclear Power Plant in the market

'''Citations'''

- Sensfuß, Frank; Ragwitz, Mario; Genoese, Massimo; Möst, Dominik (2007) : Agent-based simulation of electricity markets: a literature review, Working Paper Sustainability and Innovation, No. S5/2007, Fraunhofer-Institut für System- und Innovationsforschung ISI, Karlsruhe,

- https://nbn-resolving.de/urn:nbn:de:0011-n-661574

- https://www.econstor.eu/bitstream/10419/28520/1/570113083.pdf

- https://www.tudelft.nl/evenementen/2021/powerweb/electricity-market-simulation-game ==

''' Data '''

- EXIST Transparency Portal https://seffaflik.epias.com.tr/transparency/

- Electricity Transmission Operator https://www.teias.gov.tr/en-US

== Profit in store vs e-shop ==

''' Method:''' System Dynamics

'''Software:''' Vensim

'''Simulation'''

An unnamed company that sells carpets has its own store in Prague. During COVID-19 the company reopened an e-shop, so it currently has two mutually supporting sales channels. Both types of stores have their advantages and disadvantages. At the same time, there are various factors that affect the profit. Examples of these factors are the following: customer satisfaction and needs (carpet quality, order processing speed, price, etc.), expenses (advertising, rent, employees, etc.), the possibility of expansion, etc. To ensure customer satisfaction the company should make some expenses.

'''Model parameters'''

*Expenses
**fixed
**variable
*Revenues
**customer satisfaction -> influence amount of expenses
***Product quality,
***Speed of orders/purchases processing,
***Opening hours, working on weekends and holidays,
***The possibility of picking up the order in the store/speed of delivery
***Increasing customer satisfaction using sales and giving gifts for the order
***Store availability
***Parking
***Complaints fees
***Services: floor coverings including consultations and estimates, whipstitch of carpet
**price
**a number of sales, etc.

''' The goal of the simulation '''

The goal of this simulation is to find out what parameters can increase profit the most (individually for each type of store), to find a balance between expenses to satisfy the customers in order to achieve the profit, and in the end to compare these parameters.

'''Data'''

Real data provided by the owners of the store

[[User:Ploo00|Ploo00]] ([[User talk:Ploo00|talk]]) 01:41, 16 December 2022 (CET)
:Please elaborate in more detail as we have discussed in class [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 07:17, 17 December 2022 (CET)
::I changed the assignment a little bit. Can you please look at it? [[User:Ploo00|Ploo00]] ([[User talk:Ploo00|talk]]) 19:50, 17 December 2022 (CET)

==Comparison of strategies for finding a lost person in the forest==

''' Author:''' Tomáš Kadaně (kadt02)

'''Type:''' Multi-agent

'''Software:''' NetLogo

'''Description:'''

The simulation will focus on comparing the times needed to find a lost person in a forest (area with trees).
The metric to compare the strategies will be the number of ticks needed to find the wanted person. Both the person being searched for and the searcher will be in a random location at the beginning of the simulation.
Within the simulation, I will take several measurements for each strategy and number of searchers (1 to 5), so that the number is statistically significant and use, for example, the means to compare which strategy is the most appropriate.

The model will be able to simulate several search strategies

*one step forward and then turn of random degree (-45 to 45 degrees), so random walk
*walk straight until it hits the edge of the forest or tree, then turn and continue walking straight
*first walk to the nearest corner of the forest and then a some kind of serpentine search
*possibly other strategies

'''Goals:'''

Finding the most appropriate strategy for finding a person in the forest depending on the number of people searching.

'''Agents:'''

*Searchers (e.g. police officers)
*Lost person

'''Parameters:'''

*Number of searchers
*Type of strategy
*Ticks needed to find person

'''Possible extensions:'''

*Searchers with certain pace of walking
*Finding the person won’t mean be at same location but seeing it for some distance (again certain ability of the searcher to see for certain distance)
*Cooperation of finders (formations, place distribution)
*Lost person will be moving when being looked for

[[User:Kadt02|Kadt02]] ([[User talk:Kadt02|talk]]) 16:01, 17 December 2022 (CET)

==Saving for an apartment==

Author: miln02

'''Problem definition'''

Jon has finally graduated to be an engineer and has found his first job. As he is living with his parents and doesn’t own his apartment, he made the decision to start saving so he can buy an apartment in the next 15 years. He already has some money that he has saved so far just sitting in his bank account, so he will use that as an initial investment, and after that he will invest a fixed amount every year.
He now must make a very important decision. Where should he invest his money? After doing some research, he focused on choosing between four different options:

1. Deposit money in the bank.

2. Purchase government bonds.

3. Invest in one of the world indices.

4. Invest all the money in one stock.

'''Goal'''

Create simulation that can be used as a support when making investment decision.

'''Method'''

For helping Jon to make a decision, I will use Monte Carlo simulation and Excel as an environment. The historical yield and volatility data will be used to calculate the average behaviour of all 4 options, and we will simulate possible results after 15 years. Since it can’t be expected that the market will be stable for all 15 years, economic crises will be generated.

'''Model parameters'''

* Investments:

-Initial one-time investment

-Fixed annual investment

* Market data:

-Deposits (Rate)

-Government bonds (Yield, volatility)

-Index (Yield, volatility)

-Stock (Yield, volatility)

* Economic crises probability

'''Sources'''

* https://finance.yahoo.com/

* http://www.worldgovernmentbonds.com/

* Bank website for deposit rates

[[User:Miln02|Miln02]] ([[User talk:Miln02|talk]]) 16:17, 17 December 2022 (CET)

<nowiki>~~~~</nowiki>

Assignments WS 2022/2023

2022-12-16T00:41:26Z

Ploo00:

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Please, put here your assignments. Do not forget to sign them. You can use <nowiki>~~~~</nowiki> (four tildas) for an automatic signature. Use Show preview in order to check the result before your final sumbition.
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Please, strive to formulate your assignment carefully. We expect an adequate effort to formulate the assignment as it is your semestral paper. Do not forget that your main goal is a research paper. It means your simulation model must generate the results that are specific, measurable and verifiable. Think twice how you will develop your model, which entities you will use, draw a model diagram, consider what you will measure. No sooner than when you have a good idea about the model, submit your assignment. And of course, read [[How to deal with the simulation assignment|How to deal with the simulation assignment]].
</div>
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Topics on gambling, cards, etc. are not welcome.
</div>
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{{Ambox
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In order to avoid possible confusion, please, check if you have added '''approved''' in bold somewhere in our comment under your submission. If there is no '''approved''', it means the assignment was not approved yet.
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== Effect of leniency programs on cartel rates by [[User:Baumareb|Baumareb]] ([[User talk:Baumareb|talk]]) 11:18, 7 December 2022 (CET) ==

''' Simulation '''

The leniency program of the European Commission offers the companies involved in a cartel either complete or partial immunity from fines if they self-report and hand over evidence. It was introduced in 1996, following the surge in amnesty applications in the wake of the 1993 revision of the Corporate Leniency Program of the US Department of Justice’s Antitrust Division. Reports from various implemented leniency programs showed that such programs led to numerous applications. However, despite the clear increase in leniency applications, the question poses itself as to whether the programs were also successful in a sense that the actual cartel rate in those countries declined.
The simulation will be based on a study of Harrington and Chang from 2015, in which they concluded the following:

• The actual cartel rate decreases in case that the leniency program does not affect the non-leniency enforcement

• But: if the non-leniency enforcement is affected because resources are shifted to the prosecution of leniency application cases, there might be two possibilities, the cartel rate might increase.

This simulation focuses on the latter case. Assuming endogenized non-leniency enforcement, the introduction of a leniency program might have a differential impact on different industries. If a leniency program is introduced, the cartels that are about to collapse will seek to self-report. This in turn shifts resources from exposing active cartels to prosecuting cartels that are already collapsing. This creates more work for the authorities, who, instead of focusing on active cartels may now focus on dying cartels. This crowding-out effect coming about with the introduction of a leniency program shall be simulated in this project.

''' Goal '''

The simulation will have the following objectives:

* Illustrate the change in cartel rates and the change in the average life expectancy of a cartel triggered by the introduction of a leniency program in case of endogenized non-leniency enforcement for industries with unstable cartels (e.g. industries with a high number of competitors, or demand with more price elasticity) and for industries with stable cartels (e.g. industries with less competitors and demand with less price elasticity).
* Illustrate how many resources may be shifted from non-leniency enforcement to prosecuting leniency application cases without it having an undesired effect on the actual cartel rate.

''' Practical relevance '''

The simulation may be used by law enforcement officials to evaluate whether a leniency program leads to the desired effect (i.e. the decrease in the cartel rate) or not. Also, it can help for deciding whether the non-leniency enforcement needs to be strengthened to prevent the crowding-out effect.

''' Method '''

The described scenario is a multi-agent simulation in which the agents are pursuing a utility-based approach. Thus, the simulation will be done with NetLogo.
The following features will be included into the simulation:

- For both industries with stable and industries with unstable cartels:

* Number of active cartels (dying after reaching avg. life expectancy)
* Number of competitors
* Average life expectancy of a cartel
* “Birth” of new cartels

- For leniency/non-leniency enforcement:
* Resources and their assignment to either leniency or non-leniency enforcement
* Capacity of taking down an active cartel
* Capacity of taking down a cartel based on leniency applications

The simulation will be based on the 2015 research from Harrington and Chang as well as on publicly accessible data from the European Commission regarding antitrust cases from 1964 until today.

''' Sources '''
* Harrington Jr, J. E., & Chang, M. H. (2015). When can we expect a corporate leniency program to result in fewer cartels?. The Journal of Law and Economics, 58(2), 417-449.
* Ordóñez‐De‐Haro, J. M., Borrell, J. R., & Jiménez, J. L. (2018). The European commission's fight against cartels (1962–2014): A retrospective and forensic analysis. JCMS: Journal of Common Market Studies, 56(5), 1087-1107.

[[User:Baumareb|Baumareb]] ([[User talk:Baumareb|talk]]) 11:18, 7 December 2022 (CET) Rebecca Baumann (baur00)

: This isn't an easy topic. Be careful about available data. '''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:46, 15 December 2022 (CET)

== The prediction of divorce rate in Czech Republic for the following 50 years ==

''' The goal of the simulation '''

Divorce in the Czech republic must always contain at least one hearing in front of the court. Legally, there are many more parties involved, such as a notary, who must verify the signatures on all the important documents and many times, divorce lawyers are also necessary. To be able to satisfy the needs of the public, all the involved parties must have an idea about how many married couples are likely to get divorced in the years to come. This simulation will help prepare the courts, notaries and lawyers by making a prediction on the amount of divorces in the next 50 years. This will also help law students choose the field of law that they will specialize in by answering the question whether divorce lawyers will be necessary in the future or not.

''' Method '''

Vensim will be used for this simulation. The used data will come from the Czech Statistical Office and possibly other sources (Refer to [1] and [2]), such as published studies on the most common reasons for divorce. When possible, the data about each reason of divorce will be also found and the simulation model will contain this data.

== Edit: additional details ==

''' What all parameters will the simulation work with and how?'''

1. Number of marriages – the more marriages, the more divorces

a/ Number of people in the age 25 to 34 (i.e., the most common age to get married) – the more there is of these people, the more marriages there will be

b/ Number of divorced people in the age 40 to 49 (i.e., the most common age to get re-married after a divorce) – the more there is of these people, the more marriages there will be, however not as much as the number above

2. Micro causes of divorces = Top 10 causes of divorce as researched by the Czech Statistical Office, published yearly – the more common are these causes (alcoholism, infidelity etc), the more divorces there will be

a/ Ill-considered marriage

b/ Alcoholism

c/ Infidelity

d/ Lack of interest in the family (incl. abandon. of living together)

e/ Ill-treatment, criminal conviction

f/ Different characters, views and interests

g/ Health reasons

h/ Sexual discord

i/ Other causes

j/ Cause not given

3. Number of people in the age 40 to 49 – the more there is of these people, the more divorces there will be (it is the most common age to get divorced)

4. Macro causes of divorces

a/ Economic independence of women = the more economically independent women are, the more likely they are to divorce in case of an unhappy marriage – this will be evaluated through a comparison of data of average income of men vs. women

b/ Being religious – divorce is far less common for religious people.

''' What data source will be used for deriving the equations?'''

Based on my current research of data sources, the Czech Statistical Office has the all the data necessary for this paper.

[1] Scott, S. B., Rhoades, G. K., Stanley, S. M., Allen, E. S., & Markman, H. J. (2013). Reasons for Divorce and Recollections of Premarital Intervention: Implications for Improving Relationship Education. Couple & family psychology, 2(2), 131–145. https://doi.org/10.1037/a0032025

[2] Hawkins, Alan & Willoughby, Brian & Doherty, William. (2012). Reasons for Divorce and Openness to Marital Reconciliation. Journal of Divorce & Remarriage. 53. 453-463. 10.1080/10502556.2012.682898.

: Sounds interesting, but I miss more detail about the simulation. What all parameters will the simulation work with and how? What data source will be used for deriving the equations? [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 11:03, 15 December 2022 (CET)

==Crop Yield Forecasting==

''' Simulation '''

Crop growth and development simulations and yield forecasting will be performed using variables such as crop type, planting date, soil type, soil texture, and climate data (temperature, rainfall, etc.).

'''Problem definition'''

Arable land is increasingly limited, while the world's population has steadily been increasing over the years. In order to meet rapidly rising demand, production must be increased while natural resources must be protected. New agricultural research is needed to provide information on how to achieve sustainable agriculture in the face of global climate variability. Predicting crop yield under different conditions, such as different irrigation regimes, planting dates, and crop management practices, has become critical for farmers and other stakeholders who use these predictions to make more informed decisions about how to allocate resources, such as labor, equipment, and inputs, to maximize yield and productivity.

'''Method'''

Crop yield simulation tools include AquaCrop, DSSAT, and CropSyst. These tools use mathematical models to simulate crop growth and development based on input data like weather, soil type, and management practices. These tools use this data to estimate the crop's potential yield, as well as other important factors like water use and crop evapotranspiration. For this assignment I will be using AquaCrop which is a crop water productivity model developed by the United Nations Food and Agriculture Organization (FAO). It is used to simulate crop growth and yield under various environmental and management conditions. AquaCrop simulates crop growth and development, and estimates yield based on soil conditions, climate, irrigation, and management practices. The application gives access to various FAO databases with all the necessary data needed to perform a comprehensive simulation of the crop yield.

'''Citations'''

* Y. Lu, C. Wei, M. F. McCabe, and J. Sheffield, “Multi-variable assimilation into a modified AquaCrop model for improved maize simulation without management or crop phenology information,” Agricultural Water Management, vol. 266, p. 107576, May 2022, doi: 10.1016/j.agwat.2022.107576.
* P. N. Kephe, K. K. Ayisi, and B. M. Petja, “Challenges and opportunities in crop simulation modelling under seasonal and projected climate change scenarios for crop production in South Africa,” Agriculture & Food Security, vol. 10, no. 1, p. 10, Apr. 2021, doi: 10.1186/s40066-020-00283-5.
* N. T. Olivera, O. B. Manrique, Y. G. Masjuan, and A. M. G. Alega, “Evaluation of AquaCrop model in crop dry bean growth simulation,” Revista Ciencias Técnicas Agropecuarias, vol. 25, no. 3, pp. 23–30, Accessed: Dec. 10, 2022. [Online]. Available: https://www.redalyc.org/journal/932/93246970003/html/
* N. Pirmoradian, Z. Saadati, M. Rezaei, and M. R. Khaledian, “Simulating water productivity of paddy rice under irrigation regimes using AquaCrop model in humid and semiarid regions of Iran,” Appl Water Sci, vol. 10, no. 7, p. 161, Jun. 2020, doi: 10.1007/s13201-020-01249-5.

[[User:Pierreatekwana|Pierreatekwana]] ([[User talk:Pierreatekwana|talk]]) 15:06, 15 December 2022 (CET)

== Electricity Spot Market Simulation by [[User:Ceta|Ceta]] ([[User talk:Ceta|talk]]) 01:13, 16 December 2022 (CET) ==

'''Simulation'''

I’ve been working as a professional in the Turkish Electricity Market (EXIST) for more than 10 years. I had the chance to practice in business some fundamental methods such as Stochastic Dual Dynamic Programming (SDDP) for Hydro-Thermal dispatch optimization. The spot exchange of power or the power market itself has many examples of simulation. And the simulation of the uncertainties both in the short term and the long term are very critical in terms of new investments, portfolio management, and resource optimization.

'''Method'''

Both Excel and Netlogo simulations can be derived

'''• Inputs
'''

- Portfolios

(Thermals: Coal-Fired, Natural Gas-Fired, Other Thermals; Renewables: Hydro, Wind, Solar PV, Other Renewables)

- Resource Prices (Eur/mwh)

- Installed Capacity (in MW)

'''• Variables'''

- Power Demand (MWh)

- Calendar Day

'''• Uncertain Events
'''
- Maintenance (availability)

- Climate Factors (Wind, Hydro Sources, Temperature effect on Load-Demand)

- Natural Gas Shortage (Due to Climate Factors and/or Supply)

'''• Outputs
'''
- Marginal Cost

- Market Clearing Prices (MCP)

•''' Goals
'''
- Objective Function Y= Min (MCP)

- Effects of Natural Gas Shortages

- Effects of Climate Factors

- Effects of Maintenance

- Effects of Calendar

- Sensitivity Analysis of commissioning a Nuclear Power Plant in the market

'''Citations'''

- Sensfuß, Frank; Ragwitz, Mario; Genoese, Massimo; Möst, Dominik (2007) : Agent-based simulation of electricity markets: a literature review, Working Paper Sustainability and Innovation, No. S5/2007, Fraunhofer-Institut für System- und Innovationsforschung ISI, Karlsruhe,

- https://nbn-resolving.de/urn:nbn:de:0011-n-661574

- https://www.econstor.eu/bitstream/10419/28520/1/570113083.pdf

- https://www.tudelft.nl/evenementen/2021/powerweb/electricity-market-simulation-game ==

''' Data '''

- EXIST Transparency Portal https://seffaflik.epias.com.tr/transparency/

- Electricity Transmission Operator https://www.teias.gov.tr/en-US

== Profit in store vs e-shop ==

''' Method:''' System Dynamics

'''Software:''' Vensim

'''Simulation'''

An unnamed company that sells carpets has its own store in Prague. During COVID-19 the company reopened an e-shop, so it currently has two mutually supporting sales channels. Both types of stores have their advantages and disadvantages. At the same time, there are various factors that affect the profit. Examples of these factors are the following: customer satisfaction and needs (carpet quality, order processing speed, price, etc.), expenses (advertising, rent, employees, etc.), the possibility of expansion, etc.

'''Model parameters'''

*Expenses
**fixed
**variable
*Revenues
**customer satisfaction
**price
**a number of sales, etc.

''' The goal of the simulation '''

The goal of this simulation is to find out how the company's profits will develop in the next 5 years, individually in the store and e-shop. Find out what parameters can increase profits the most, individually for each type of store, and compare these parameters.

'''Data'''

Real data provided by the owners of the store

[[User:Ploo00|Ploo00]] ([[User talk:Ploo00|talk]]) 01:41, 16 December 2022 (CET)

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