Campbellsville AnyLogistix GFA Polarbear GBI Questions Discussion
Description
Download attached documents for additional instructions.
Start with the Polarbear-GBI consolidation exercise and read through this first.
Use the Polarbear-GBI Answer sheet to submit your answers.
The two Excel file provide are called for as you go through the exercise.
Note: This product only works on PC, no MAC. If you have a MAC, find a friend with a PC that you can borrow.
Unformatted Attachment Preview
Polarbear-GBI answer sheet: Please put your answers in red
a. Place a screen shot of you GFA clearly showing the name of your scenario (must have
your name or initials in it, and the map of Germany here. No screen shot-No credit for
any of the following!
1. What are the optimal coordinates of the DC?
2. What is the maximum distance from the optimal DC location to a customer?
3. What is the minimum distance from the optimal DC location to a customer?
4. What are the total costs of the SC?
(Note: to compute the sum of costs or flows in GFA Results, just slightly drag the heading of
the column Period in table Product flows in the space over the table)
5. Compare the data in statistics Flowsand TableDemand. Do we satisfy all customer
demands from the optimal DC location? If Yes, why? If no, why?
6. What are the total costs of the SC?
7. Compare the results with one and two DCs in terms of costs and responsiveness.
8. What other costs were not considered in selecting the optimal facility location in the GFA?
b. place a screen shot here clearly showing your new NO results with your name or initials in the
name of the scenario. No screen shot-No credit for any of the following!
9. What is the most profitable SC design?
10. Is demand for all customers satisfied? Why or Why not?
11. What is the total revenue of the most profitable SC?
12. What is total profit of the most profitable SC?
13. Compare the data in statistics Production Flows and Table Demand. Does the
production quantity correspond to the total demand? Explain.
14. Compare the optimal SC design as computed in the NO and the initial SC design (factory and
DC in Germany) in terms of profit.
15. What other costs should be considered when redesigning the SC according to NO results?
16. What other factors, apart from costs, should be considered when re-designing the SC
according to the results of the NO?
Exercises in Supply Chain Optimization
and Simulation using anyLogistix
Prof. Dr. Dmitry Ivanov
Berlin School of Economics and Law
Professor of Supply Chain and Operations Management
Modified by Dr. Ed Lindoo, Campbellsville University, 2020.
To be cited as: Ivanov D. (2019). Exercises in Supply Chain Optimization and Simulation using anyLogistix,
nd
Berlin School of Economics and Law, 2 , updated edition
© Prof. Dr. Dmitry Ivanov, 2019. All rights reserved.
1. Introduction
Supply chain network design and operational planning decisions can have a drastic impact on the
profitability and success of a company. Whether to have one warehouse or two, close a factory or rent a
new one, or to choose one network path over another are all consequential decisions a supply chain (SC)
manager must make. However, these decisions must be the result of more than experience or intuition, and,
as a result, research in SC management (SCM) is geared towards providing the data, tools, and models
necessary for supporting SC managers analytical decisions. One of these decision-supporting tools is
anyLogistix, a software which facilitates Greenfield Analysis, Network Optimization, and Simulation.
anyLogistix has become more and more popular with the provision of the free PLE version, and because it is
an easy-to-use software, includes simulation and optimization, and covers all standard teaching topics
(center-of-gravity, efficient vs responsive SC design, SC design through network optimization, inventory
control simulation with safety stock computations, sourcing (single vs. multiple) and shipment (LTL vs FTL)
policy simulation, and milk-run optimization).
The ALX exercise book addresses the application of quantitative analysis methods and software to decisionmaking in global supply chains and operations. Understanding of optimization and simulation methods in
SCM is the core of the course. Technical skills for using simulation and optimization software in praxis can
be acquired with the help of anyLogistix software. This case study is designed to stimulate and enhance
conceptual and analytical decision-making skills in actual operating situations. The case method requires
you to prepare a decision based on careful evaluation of case facts and numbers to the extent possible. As
with all business situations, there may be insufficient facts, ambiguous goals, and dynamic environments.
This case seeks to convey the following skills:
Analytical Skills: Students will possess the analytical and critical thinking skills to evaluate issues faced in
business and professional careers.
Technical Skills: Students will possess the necessary technological skills to analyze problems, develop
solutions, and convey information using optimization and simulation software.
Along these lines, throughout the course we will examine two scenarios:
Building a new SC from scratch -a case study of the Polarbear Bicycle company, which
must create and optimize its SC in order to maintain profitability and keep its competitive
edge in an increasingly global market where sales prices are driven down while costs re
main stable and seeks to analyze the performance of their existing SC and optimize its distribution
network, while considering the risks and ripple effect.
Using the models available in anyLogistix, we will conduct analyses to (1) determine an optimal location
using Greenfield Analysis (GFA) for a new warehouse, given the location of their current customers and those
customers relative demands, (2) compare alternative network designs using Network Optimization (NO).
2. Case study
2.1 Description of Case Study
We consider a company called Polarbear Bicycle. Polarbear Bicycle was founded as an e-commerce start-up
selling bicycles, however they were just purchased by the company you work for as an analyst Global
Bikes (GBI). With this new purchase, the board of directors of GBI is asking a number of questions that you
as an analyst for GBI need to answer. Polarbears portfolio includes four different types of bicycles: x-cross,
urban, all terrain, and tour bicycles. You have been assigned the task to find the best location for one or two
Customer
Cologne
Cologne
Cologne
Cologne
Bremen
Bremen
Bremen
Bremen
Frankfurt am Main
Frankfurt am Main
Frankfurt am Main
Frankfurt am Main
Stuttgart
Stuttgart
Stuttgart
Stuttgart
Bicycle Type
x-cross
urban
all terrain
tour
x-cross
urban
all terrain
tour
x-cross
urban
all terrain
tour
x-cross
urban
all terrain
tour
Costs
Factory Nuremberg: fixed (other) costs, per day
Factory Poland: fixed (other) costs, per day
DC Germany: fixed (other) costs, per day
DC Germany: carrying costs (per bicycle)
DC Czech Republic: fixed (other) costs, per day
DC Czech Republic: carrying costs
DC Germany: processing costs (inbound and outbound shipping
per pcs)
DC Czech Republic: processing costs (inbound and outbound
shipping per pcs)
Factory Nuremberg: production costs (per bicycle)
Factory Poland: production (per bicycle)
All bicycles: product purchasing costs
Transportation costs; Paths: from factory -to DCs
Transportation costs; Paths: from DCs -to customers
Unit revenue
Table 1
Demand per day
2
50
15
10
7
30
20
20
6
5
4
5
15
15
1
40
Value in USD
15,000
5,000
15,000
3.00
5,000
2.00
2.00
1.00
250
150
30
0.01 * product(pcs) * distance
0.01 * product(pcs) * distance
499
new distribution centers (DC). First, you estimate customer demand based on Table 1 above. Polarbear
distributes their bicycles to four locations throughout Germany: Cologne, Bremen, Frankfurt am Main, and
Stuttgart. Table 1 shows customer demand, which is equal to 245 bicycles per day.
GBI now needs you to analyze supply and distribution network alternatives and to develop a best-case
scenario for Polarbear-GBI Bicycle. You are charged with conducting a GFA to determine the possible
location of a new DC or DCs in Germany, as well as a network optimization to compare several options for
network paths.
2.2 Greenfield Analysis (GFA) for Facility Location Planning: Selecting the Best
Warehouse Location for Polarbear-GBI Bicycle
Now we conduct a GFA for the outbound network of Polarbear-GBI Bicycle considering the four customers
located in Cologne, Bremen, Frankfurt am Main, and Stuttgart. The aim of this GFA is to determine the
optimal location of one (or two) new DCs in Germany subject to total minimum transportation costs. Note:
for the purposes of this analysis we are not considering current GBI customers or DCs within Europe.
Polarbear-GBI makes and sells very unique bicycles that currently are not a good fit within the GBI
network, therefore we consider a completely separate distribution network.
Creating an ALX model.
Step 1. Open Anylogistix. Click on New Scenario, click OK. Next click on import scenario then select the
file you downloaded, PB GFA Level 2 with Solutions.xlsx. Change the scenario name to your name
Note: You may receive a warning about old data file. You should be able to say OK and just ignore it.
Performing experiments. Data from Table 1 has already been entered for Customers, Demand, and
Products.
Step 2. Go to GFA Experiment and run it for Number of sites = 1 and the period of two months.
Select custom periods and make sure the default dates 11/1/17 12/31/17 are set.
Step 3. Analyze the results using statistics Flows and New Sites:
Note: Use the Polarbear-GBI case study answer sheet to submit ALL of your answers.
1. What are the optimal coordinates of the DC?
2. What is the maximum distance from the optimal DC location to a customer?
3. What is the minimum distance from the optimal DC location to a customer?
4. What are the total costs of the SC? (Note: to compute the sum of costs or flows in GFA Results, just
slightly drag the heading of the column Period in table Product flows in the space over the
table.
5. Compare the data in statistics Flowsand TableDemand. Do we satisfy all customer demands
from the optimal DC location? If Yes, why? If no, why?
Step 4. Go to GFA Experiment and run it for Number of sites = 2.
Step 5. Analyze the results using statistics Flows and New Sites:
6. What are the total costs of the SC?
7. Compare the results with one and two DCs in terms of costs and responsiveness.
8. What other costs were not considered in selecting the optimal facility location in the GFA?
2.3 Network Optimization (NO) for Facility Location Planning: Comparing Po
larbears Supply Chain Design Alternatives
After selling the bicycles from the newly established DC(s) according to the GFA results, Polarbear-GBI
decided to produce their own bicycles. Their production facility has now been established in Nuremberg and
250 bikes are produced each day. Recently, they have received an offer from a Polish production factory to
rent a DC in the Czech Republic at a reasonable price. The same company also wants to offer them rental of
a factory in Warsaw, Poland, even though they already have one factory in Germany. Polarbear-GBI must
now decide which SC design is more profitable:
Option 1: DC in Germany and Factory in Germany
Option 2: DC in Germany and Factory in Poland
Option 3: DC in Czech Republic and Factory in Poland
Option 4: DC in Czech Republic and Factory in Germany
In Fig. 1, the different possibilities for the path networks are shown. The dotted lines show possible
alternatives and the solid lines the existing structure of Polarbears SC.
Figure
1.
Network
optimization
alternatives
The aim of the
NO
is
to
determine which network design is optimal based on Polarbears selected KPIs, e.g., profit.
Therefore, the factory in Warsaw, Poland, the DC in the Czech Republic, and the DC in Steimelhagen were
added as inputs to the model along with the Nuremburg factory. To enable the models calculation, the
reality of the case must be simplified: all demand is assumed to be deterministic without any uncertain
fluctuations. To define the two-stage NO problem (transport between factories and DCs and between DCs
and customers) from a mathematical perspective, several parameters must be input as data. These are
shown in Table 2.
The costs of the rent for the factory in Poland and the DC in Czech Republic are included in othercosts.
For transport, it is always assumed that each truckload fits 80 bicycles, and trucks travel at a speed of 80
km/h.
Creating an ALX model
Step 0. Probably best to close and re-open ALX at this point. Now create an new scenario as you did in
Step 1 above and import the file PB NO Level 2 Solution.xlsx. Rename it so that it has your name or
initials as the scenario name: Note: Data from Table 2 has been entered for you.
Note: You may receive a warning about old data file. You should be able to say OK and just ignore it.
Performing experiments Step 1. Go to NO Experiment and run it with the Demand variation type 95100%.
3
NOTE! In order to run the NO experiment, make sure the units in experiment settings is set from m to
pcs to align it with product data.
Step 2. Analyze the results using statistics Optimization Results, Flow Details, Production Flows,
Demand, and Overall Stats:
b. place a screen shot here clearly showing your new NO results with your name or initials in the
scenario name.
9. What is the most profitable SC design?
10. Is demand for all customers satisfied? Why or Why not?
11. What is the total revenue of the most profitable SC?
12. What is total profit of the most profitable SC?
13. Compare the data in statistics Production Flows and Table Demand. Does the production
quantity correspond to the total demand? Explain.
14. Compare the optimal SC design as computed in the NO and the initial SC design (factory and DC in
Germany) in terms of profit.
15. What other costs should be considered when redesigning the SC according to NO results?
16. What other factors, apart from costs, should be considered when re-designing the SC according to
the results of the NO?
description Copy of scenario PB GFA Level 1.Description of the original scenario:
type
GFA
name
PB GFA Level 2 with Solutions
creationDate
2018-06-19
Name
Type
Cologne Customer
Bremen Customer
Stuttgart Customer
Frankfurt am
Customer
Main
Location Inclusion Type
Icon
Cologne location
Include
Bremen location
Include
Stuttgart location
Include
Frankfurt am
Include
Main location
4
4
4
4
Name
Type
Location
Inclusion Type
Icon
Customer Product Demand Type
Time Period
Cologne bicycle “all terrain”
PeriodicDemand[period::1.0;quantity::15.0]
(All periods)
Cologne bicycle “tour”
PeriodicDemand[period::1.0;quantity::10.0]
(All periods)
Cologne bicycle “urban”
PeriodicDemand[period::1.0;quantity::50.0]
(All periods)
Cologne bicycle “x-cross”
PeriodicDemand[period::1.0;quantity::2.0]
(All periods)
Bremen bicycle “all terrain”
PeriodicDemand[period::1.0;quantity::20.0]
(All periods)
Bremen bicycle “tour”
PeriodicDemand[period::1.0;quantity::20.0]
(All periods)
Bremen bicycle “urban”
PeriodicDemand[period::1.0;quantity::30.0]
(All periods)
Bremen bicycle “x-cross”
PeriodicDemand[period::1.0;quantity::7.0]
(All periods)
Frankfurt am
bicycle
Main “all terrain”
PeriodicDemand[period::1.0;quantity::4.0]
(All periods)
Frankfurt am
bicycle
Main “tour”
PeriodicDemand[period::1.0;quantity::5.0]
(All periods)
Frankfurt am
bicycle
Main “urban”
PeriodicDemand[period::1.0;quantity::5.0]
(All periods)
Frankfurt am
bicycle
Main “x-cross”
PeriodicDemand[period::1.0;quantity::6.0]
(All periods)
Stuttgart bicycle “all terrain”
PeriodicDemand[period::1.0;quantity::1.0]
(All periods)
Stuttgart bicycle “tour”
PeriodicDemand[period::1.0;quantity::40.0]
(All periods)
Stuttgart bicycle “urban”
PeriodicDemand[period::1.0;quantity::15.0]
(All periods)
Stuttgart bicycle “x-cross”
PeriodicDemand[period::1.0;quantity::15.0]
(All periods)
id
date
quantity
Name
DescriptionCustomers Sites
Suppliers Groups
Name
Locations
Code
Name
City
Region
Cologne location
Bremen location
Stuttgart location
Frankfurt am Main location
Country
Address
Latitude
50,8337
53,05442
48,77791
50,07829
Longitude Autofill Coordinates
6,855469 FALSE
8,85498 FALSE
9,294434 FALSE
8,635254 FALSE
Autofill Coordinates
Name
Product
Amount from
Amount to Unit to
Name
Periods
Name
Start
End
Demand Coefficient
Basic period2017-01-012017-12-31
1
Name
Products
Name
Unit
bicycle “all terrain”
pcs
bicycle “tour”
pcs
bicycle “urban”
pcs
bicycle “x-cross”
pcs
Delivery Destination
Product
Source
Time PeriodInclusion Type
Name
Type
Location
Products
Inclusion Type
Icon
Currency
Volume
Distance
Time
USD
m³
km
day
maxDist
20
minimizeSitesNumber
FALSE
destinations(All customers)
nSitesConstr
2
distanceUnitkm
productUnitpcs
sourcingPriority
FALSE
toSiteTranspCoeff 0,9
statsDistanceStep 100
latLonOffset
1
newSiteIcon
2
name
type
GFA
scenario PB GFA Level 2 with Solutions
statisticsSettings
GFA_FLOWS::true;d;f
GFA_NEW_SITES::true;d;f
GFA_DISTANCE_BY_DEMAND::true;d;f
GFA_DEMAND_BY_DISTANCE::true;d;f
GFA_TOTAL_DEMAND_BY_DISTANCE::true;d;f
Units settings
Currency::USD
Volume::m³Time::day Distance::km
timeType Custom period
startPeriod
endPeriod
startDate 2017-11-01T00:00
stopDate 2017-12-31T00:00
dashboardData
Page name Chart type Accumulative
Stats namesLayout dataDetalizationFilters
Chart name
dashboardData
Flows
CUSTOM_TABLE
TRUE GFA_FLOWS0,0,36,8
Flows
dashboardData
New sites CUSTOM_TABLE
TRUE GFA_NEW_SITES
0,0,36,8
New sites
dashboardData
Distance byCUSTOM_TABLE
demand
TRUE GFA_DISTANCE_BY_DEMAND
0,0,36,8
Distance by demand
dashboardData
Demand byCUSTOM_TABLE
distance
TRUE GFA_DEMAND_BY_DISTANCE
0,0,18,8
Demand by distance
dashboardData
Demand byCUSTOM_TABLE
distance
TRUE GFA_TOTAL_DEMAND_BY_DISTANCE
0,0,18,8
Total demand by distance
preProcessor
postProcessor
Total demand by distance
minPopulation 50000
destinations(All customers)
ignoreNotRealRoutes
FALSE
nSitesConstr
1
distanceUnitkm
productUnitm³
sourcingPriority
FALSE
toSiteTranspCoeff 0,5
statsDistanceStep 100
latLonOffset
100
newSiteIcon
2
name
type
GFAWithRoads
scenario PB GFA Level 2 with Solutions
statisticsSettings
GFA_FLOWS::true;d;f
GFA_NEW_SITES::true;d;f
GFA_DISTANCE_BY_DEMAND::true;d;f
GFA_DEMAND_BY_DISTANCE::true;d;f
GFA_TOTAL_DEMAND_BY_DISTANCE::true;d;f
Units settings
Currency::USD
Volume::m³Time::day Distance::km
timeType All periods
startPeriod
endPeriod
startDate 2018-01-01T00:00
stopDate 2018-12-31T00:00
dashboardData
Page name Chart type Accumulative
Stats namesLayout dataDetalizationFilters
Chart name
dashboardData
Product Flows
CUSTOM_TABLE
TRUE GFA_FLOWS0,0,36,8
Product Flows
dashboardData
New Site Locations
CUSTOM_TABLE
TRUE GFA_NEW_SITES
0,0,36,8
New Site Locations
dashboardData
Distance Coverage
CUSTOM_TABLE
by Demand
TRUE GFA_DISTANCE_BY_DEMAND
0,0,36,8
Distance Coverage by Demand
dashboardData
Demand Coverage
CUSTOM_TABLE
by Distance
TRUE GFA_DEMAND_BY_DISTANCE
0,0,18,8
Demand Coverage by Distance
dashboardData
Demand Coverage
CUSTOM_TABLE
by Distance
TRUE GFA_TOTAL_DEMAND_BY_DISTANCE
0,0,18,8
Total Demand Coverage by Dista
preProcessor
postProcessor
Total Demand Coverage by Distance
customType
name
type
Custom
scenario PB GFA Level 2 with Solutions
statisticsSettings
GFA_FLOWS::true;d;f
GFA_NEW_SITES::true;d;f
GFA_DISTANCE_BY_DEMAND::true;d;f
GFA_DEMAND_BY_DISTANCE::true;d;f
GFA_TOTAL_DEMAND_BY_DISTANCE::true;d;f
LINEAR_COSTS::true;d;f
Flows Details::true;d;f
Sites Initial::true;d;f
Units settings
Currency::USD
Volume::m³Time::day Distance::km
timeType All periods
startPeriod
endPeriod
startDate 2019-01-01T00:00
stopDate 2019-12-31T00:00
dashboardData
Page name Chart type Accumulative
Stats namesLayout dataDetalizationFilters
Chart name
dashboardData
Log
preProcessor
postProcessor
Sites Fix::true;d;f
Storage by Product::true;d;f
Production Production
cost::true;d;f
Multiple
flows::true;d;f
Flows
Working
Constraints::true;d;f
Sites::true;d;f
Multiple Storages
Demand::true;d;f
Constraints::true;d;f
VEHICLES_FLOWS::true;d;f
Chart name
Named Expressions::true;d;f
Objective Members::true;d;f
Overall Stats::true;d;f
Flows Amount::true;d;f
DAILY_VEHICLES_SHIPPED::true;d,Type,Object,Vehicle
DAILY_VEHICLES_USAGE::true;d,Type,Object,Vehicle
TRAVELLED_DISTANCE::true;d,Type,Object,Vehicle
DAILY_PRODUCTS_SHIPPED_INTERNAL::true
AVAILABLE_INVENTORY_AMOUN
type;f
type;f type
CURRENT_BACKLOG_PRODUCTS::true;d,Type,Object,Product,Period;f
MAX_CAPACITY_VOLUME::true;d,Type,Object;f
ON_HAND_INVENTORY_VOLUME::true;d,Type,Object,Product,Period;f
FACILITY_CO2_STATS::true;d,Type,Object,Period;f
STORING_CO2_PER_M3_STATS::true;d,Type,Object,Product,Period;f
MAX_CAPACITY_INTERNAL2::true;d,Type,Object,Period;f
FACILITY_COSTS::true;d,Type,Object,Period;f
CARRYING_COSTS::true;d,Type,Object,Produ
TRANSPORTATION_COSTS::true;d
OTHER_COSTS::true;d,Type,Object;f
REVENUE::true;d,Type,Object,Product;f
PRODUCTS_LOST::true;d,Type,Object,Product;f
ORDERS_LOST::true;d,Type,Object,Product;f
DAILY_INCOMING_REPLENISHMENT_PRODUCTS::true;d,Type,Object,Product,P
DAILY_INCOMING_REPLENISHMENT_ORDERS::true;d,Type,Object,P
DAILY_PRODUCTS_SHIPPED::true;d,Type,Object,Produc
PRODUCT_FLOWS_TABLE::true;d,Object;f
INVENTORY_PURCHASES::true;d,
INITIAL_COSTS::true;d,Type,Object;f
DAILY_ITEMS_RECEIVED::true;d,Type,Object,Product,Period;f
DAILY_ORDERS_RECEIVED::true;d,Type,Object,Product,Period;f
INTERESTS_STATS::true;d,Type,Object;f
DAILY_OUTGOING_REPLENISHMENT_PRODUCTS::true;d,Type,Object,Product,P
DAILY_OUTGOING_REPLENISHMENT_ORDERS::true;d,Type,Object,P
DAILY_ORDERS_SHIPPED::true;d,Type,Object,Product,V
LOADING_TIME_VEHICLE::true;d,Type,Objec
UNLOADING_TIME_VEHICLE::tru
GATES_BUSY::true;d,Type,Object,Staff
GATES_IDLE::true;d,Type,Object,Staff
CURRENT_BACKLOG_ORDERS::true;d,Type,Object,Product,Period;f
CLOSURE_COSTS::true;d,Type,Object;f
type;f ORDERED_PRODUCTS_SENT::true;d,Type,Object,Product,Period;f
type;f PRODUCTS_BULLWHIP_EFFECT::true;d,Type,Object,Product;f
INPUT_PROCESSING_COSTS::true;d,Type,Object,Produc
PROCESSING_CO2_INPUT_STATS::true;d,Typ
PROCESSING_CO2_OUTPUT_STA
CLOSURE_CO2::true;d,Type,Object;f
INITIAL_CO2::true;d,Type,Object;f
OTHER_CO2::true;d,Type,Object;f
TRANSPORTATION_CO2::true;d,Type,Object,Vehicle
TOTAL_COSTS::true;d,Type,Object;f
OUTPUT_PROCESSING_COSTS::true;d,Type,Object,Product,Period;f
Shipments schedule::false;d,Object;f
PRODUCTION_COSTS::true;d,Type,Object,Pr
type,Destination;f
PRODUCED::true;d,Type,Object,P
PRODUCTION_REQUESTS::true;d,Type,Object,Product,Period;f
PRODUCTION_LINE_BUSY_TIME::true;d,Type,Object,Product,Period;f
PRODUCTION_LINE_IDLE_TIME::true;d,Type,Object,Product,Period;f
PRODUCED_ORDERS::true;d,Type,Object,Product,Period;f
PRODUCTION_REQUEST_ORDERS::true;d,Type,Object,Product,Period;f
PRODUCTION_CO2::true;d,Type,Object,Product,Period;f
STAFF_BUSY_TIME::true;d,Type,Object;f
STAFF_IDLE_TIME::true;d,Type,Object;f
Utilized Volume (Yogurt Factory):
Delayed Batches
DC Rating
(Yogurt
(Online
ZONE_LOAD::true;d,Type,Object,Zone;f
Factory)::true;d,Type,Object;f
Shop)::true;d,Type,Object;f
BUSY_STAFF::true;d,Type,Object,Staff
ZONE_CAPACITY::true;d,Type,Object,Zone;f
STAFF_TOTAL::true;d,Type,Object,Staff
CUSTOMER_REVENUE::true;d,Type,Object,Product,Perio
type;f CUSTOMER_DELAYED_ORDERS::true;d,Type
CUSTOMER_IN_TIME_ORDERS::t
type;f
CUSTOMER_ORDERS_TOTAL::true;d,Type,Object,Product,Period;f
CUSTOMER_DELAYED_PRODUCTS::true;d,Type,Object,Product,Source,Period;f
CUSTOMER_IN_TIME_PRODUCTS::true;d,Type,Object,Product,Source,Period;f
CUSTOMER_PRODUCTS_TOTAL::true;d,Type,Object,Product,Period;f
ORDERS::true;d,Type,Object,Product,Period;f
ORDERED_PRODUCTS::true;d,Type,Object,Product,Period;f
DROPPED_ORDERS::true;d,Type,Object,Product,Source,
DROPPED_ORDERED_PRODUCTS::true;d,Typ
LEAD_TIME::true;d,Type,Object,P
SUCCESSFUL_ORDERS_SIZE::true;d,Type,Object,Product,Source,Period;f
SUCCESSFUL_ORDERS::true;d,Type,Object,Product,Source,Period;f
UNSUCCESSFUL_ORDERS_SIZE::true;d,Type,Object,Product,Source,Period;f
UNSUCCESSFUL_ORDERS::true;d,Type,Object,Product,Source,Period;f
Negative Feedback
Positive (Online
Feedback
No Feedback
Shop)::true;d,Type,Object;f
(Online
Demand
(Online
Shop)::true;d,Type,Object;f
Placed
Shop)::true;d,Type,Object;f
PRODUCT_VOLUMES::true;d,Pro
(Dropped Late Orders) by Cu
PRODUCT_COSTS::true;d,Product;f
PRODUCT_PRICES::true;d,Product;f
VEHICLE_VOLUMES::true;d,Vehicle
CASH::true;d,Period,Account;f
INTERESTS::true;d,Period,Account;f
ACCOUNT_PAYABLE::true;d,Period,Account;f
type;f
LOAN::true;d,Period,Account;f
ACCOUNT_RECEIVABLE::true;d,Period,Accou
EVENTS_TABLE::true;d,Object;f
TOTAL_INTERESTS_PAID::true;d,Period,Account;f
TOTAL_CO2::true;d,Object;f
EBITDA::true;d,Object;f
FACILITY_CO2::true;d,Object,Period;f
CARRYING_CO2::true;d,Object,Product,Period;f
INVENTORY_MINUS_BACKLOG_AMOUNT::true;d,Object,Product;f
INPUT_PROCESSING_CO2::true;d,Object,Product,Period
OUTPUT_PROCESSING_CO2::true;d,Object,P
GENERAL_ORDERS_SERVICE_LEV
GENERAL_PRODUCTS_SERVICE_LEVEL_ALPHA_TYPE::true;d,Object,Product,Source,Period;f
GENERAL_MONEY_SERVICE_LEVEL_BETA_TYPE::true;d,Object,Product,Source,Period;f
OPPORTUNITY_COSTS::true;d,Object,Product;f
GENERAL_COST_PER_ORDER::true;d,Object;f
GENERAL_COST_PER_PRODUCT::true;d,Object;f
GENERAL_ORDERS_SERVICE_LEVEL_BY_ELT::true;d,Object,Product,
GENERAL_PRODUCTS_SERVICE_LEVEL_BY_ELT::true;d,O
AVERAGE_ON_HAND_INVENTORY_DAYS::tru
AVERAGE_ON_HAND_INVENTOR
ELT_SERVICE_LEVEL_BY_REVENUE::true;d,Object,Product,Source,Period;f
AVAILABLE_INVENTORY_VOLUME_INTEGRAL::true;d,Object,Product,Period;f
MEAN_LEAD_TIME::true;d,Object,Product,Source;f
PRODUCTION_UTILIZATION::true;d,Object,Product,Period;f
TRANSPORT_UTILIZATION::true;d,Type,Object,Vehicle
VEHICLES_USAGE::true;d,Object,Vehicle
MAX_VEHICLES_USAGE::true;d,Object,Vehicle
Max lead time::true;d,Object,Product,Source
GATES_UTILIZATION::true;d,Obje
type;f
type;f
type;f
AVAILABLE_INVENTORY_CUSTOM::true;d,Object,Product,Period;f
AVAILABLE_INVENTORY_INTEGRAL_CUSTOM::true;d,Object,Product,Period;f
PRODUCED_CUSTOM::true;d,Type,Object,Product,Period;f
CASH_MINUS_LOAN::true;d,Period,Account;f
CASH_MINUS_INTERESTS::true;d,Period,Account;f
CASH_MINUS_LOAN_INTERESTS::true;d,Period,Account;f
LOAN_PLUS_INTERESTS::true;d,Period,Account;f
STAFF_UTILIZATION_DC_WITH_STAFF::true;
STAFF_UTILIZATION_EXTENDED_
SPACE_UTILIZATION::true;d,Object,Zone;f
PROFIT_AND_LOSS_STATEMENT::true;d,Object;f
SAFETY_STOCK_INVENTORY_BASED::true;d,Type,Object,Product,Period;f
DC
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a. Place a screen shot of you GFA clearly showing the name of your scenario (must have
your name or initials in it, and the map of Germany here. No screen shot-No credit for
any of the following!
1. What are the optimal coordinates of the DC?
2. What is the maximum distance from the optimal DC location to a customer?
3. What is the minimum distance from the optimal DC location to a customer?
4. What are the total costs of the SC?
(Note: to compute the sum of costs or flows in GFA Results, just slightly drag the heading of
the column Period in table Product flows in the space over the table)
5. Compare the data in statistics Flowsand TableDemand. Do we satisfy all customer
demands from the optimal DC location? If Yes, why? If no, why?
6. What are the total costs of the SC?
7. Compare the results with one and two DCs in terms of costs and responsiveness.
8. What other costs were not considered in selecting the optimal facility location in the GFA?
b. place a screen shot here clearly showing your new NO results with your name or initials in the
name of the scenario. No screen shot-No credit for any of the following!
9. What is the most profitable SC design?
10. Is demand for all customers satisfied? Why or Why not?
11. What is the total revenue of the most profitable SC?
12. What is total profit of the most profitable SC?
13. Compare the data in statistics Production Flows and Table Demand. Does the
production quantity correspond to the total demand? Explain.
14. Compare the optimal SC design as computed in the NO and the initial SC design (factory and
DC in Germany) in terms of profit.
15. What other costs should be considered when redesigning the SC according to NO results?
16. What other factors, apart from costs, should be considered when re-designing the SC
according to the results of the NO?
Exercises in Supply Chain Optimization
and Simulation using anyLogistix
Prof. Dr. Dmitry Ivanov
Berlin School of Economics and Law
Professor of Supply Chain and Operations Management
Modified by Dr. Ed Lindoo, Campbellsville University, 2020.
To be cited as: Ivanov D. (2019). Exercises in Supply Chain Optimization and Simulation using anyLogistix,
nd
Berlin School of Economics and Law, 2 , updated edition
© Prof. Dr. Dmitry Ivanov, 2019. All rights reserved.
1. Introduction
Supply chain network design and operational planning decisions can have a drastic impact on the
profitability and success of a company. Whether to have one warehouse or two, close a factory or rent a
new one, or to choose one network path over another are all consequential decisions a supply chain (SC)
manager must make. However, these decisions must be the result of more than experience or intuition, and,
as a result, research in SC management (SCM) is geared towards providing the data, tools, and models
necessary for supporting SC managers analytical decisions. One of these decision-supporting tools is
anyLogistix, a software which facilitates Greenfield Analysis, Network Optimization, and Simulation.
anyLogistix has become more and more popular with the provision of the free PLE version, and because it is
an easy-to-use software, includes simulation and optimization, and covers all standard teaching topics
(center-of-gravity, efficient vs responsive SC design, SC design through network optimization, inventory
control simulation with safety stock computations, sourcing (single vs. multiple) and shipment (LTL vs FTL)
policy simulation, and milk-run optimization).
The ALX exercise book addresses the application of quantitative analysis methods and software to decisionmaking in global supply chains and operations. Understanding of optimization and simulation methods in
SCM is the core of the course. Technical skills for using simulation and optimization software in praxis can
be acquired with the help of anyLogistix software. This case study is designed to stimulate and enhance
conceptual and analytical decision-making skills in actual operating situations. The case method requires
you to prepare a decision based on careful evaluation of case facts and numbers to the extent possible. As
with all business situations, there may be insufficient facts, ambiguous goals, and dynamic environments.
This case seeks to convey the following skills:
Analytical Skills: Students will possess the analytical and critical thinking skills to evaluate issues faced in
business and professional careers.
Technical Skills: Students will possess the necessary technological skills to analyze problems, develop
solutions, and convey information using optimization and simulation software.
Along these lines, throughout the course we will examine two scenarios:
Building a new SC from scratch -a case study of the Polarbear Bicycle company, which
must create and optimize its SC in order to maintain profitability and keep its competitive
edge in an increasingly global market where sales prices are driven down while costs re
main stable and seeks to analyze the performance of their existing SC and optimize its distribution
network, while considering the risks and ripple effect.
Using the models available in anyLogistix, we will conduct analyses to (1) determine an optimal location
using Greenfield Analysis (GFA) for a new warehouse, given the location of their current customers and those
customers relative demands, (2) compare alternative network designs using Network Optimization (NO).
2. Case study
2.1 Description of Case Study
We consider a company called Polarbear Bicycle. Polarbear Bicycle was founded as an e-commerce start-up
selling bicycles, however they were just purchased by the company you work for as an analyst Global
Bikes (GBI). With this new purchase, the board of directors of GBI is asking a number of questions that you
as an analyst for GBI need to answer. Polarbears portfolio includes four different types of bicycles: x-cross,
urban, all terrain, and tour bicycles. You have been assigned the task to find the best location for one or two
Customer
Cologne
Cologne
Cologne
Cologne
Bremen
Bremen
Bremen
Bremen
Frankfurt am Main
Frankfurt am Main
Frankfurt am Main
Frankfurt am Main
Stuttgart
Stuttgart
Stuttgart
Stuttgart
Bicycle Type
x-cross
urban
all terrain
tour
x-cross
urban
all terrain
tour
x-cross
urban
all terrain
tour
x-cross
urban
all terrain
tour
Costs
Factory Nuremberg: fixed (other) costs, per day
Factory Poland: fixed (other) costs, per day
DC Germany: fixed (other) costs, per day
DC Germany: carrying costs (per bicycle)
DC Czech Republic: fixed (other) costs, per day
DC Czech Republic: carrying costs
DC Germany: processing costs (inbound and outbound shipping
per pcs)
DC Czech Republic: processing costs (inbound and outbound
shipping per pcs)
Factory Nuremberg: production costs (per bicycle)
Factory Poland: production (per bicycle)
All bicycles: product purchasing costs
Transportation costs; Paths: from factory -to DCs
Transportation costs; Paths: from DCs -to customers
Unit revenue
Table 1
Demand per day
2
50
15
10
7
30
20
20
6
5
4
5
15
15
1
40
Value in USD
15,000
5,000
15,000
3.00
5,000
2.00
2.00
1.00
250
150
30
0.01 * product(pcs) * distance
0.01 * product(pcs) * distance
499
new distribution centers (DC). First, you estimate customer demand based on Table 1 above. Polarbear
distributes their bicycles to four locations throughout Germany: Cologne, Bremen, Frankfurt am Main, and
Stuttgart. Table 1 shows customer demand, which is equal to 245 bicycles per day.
GBI now needs you to analyze supply and distribution network alternatives and to develop a best-case
scenario for Polarbear-GBI Bicycle. You are charged with conducting a GFA to determine the possible
location of a new DC or DCs in Germany, as well as a network optimization to compare several options for
network paths.
2.2 Greenfield Analysis (GFA) for Facility Location Planning: Selecting the Best
Warehouse Location for Polarbear-GBI Bicycle
Now we conduct a GFA for the outbound network of Polarbear-GBI Bicycle considering the four customers
located in Cologne, Bremen, Frankfurt am Main, and Stuttgart. The aim of this GFA is to determine the
optimal location of one (or two) new DCs in Germany subject to total minimum transportation costs. Note:
for the purposes of this analysis we are not considering current GBI customers or DCs within Europe.
Polarbear-GBI makes and sells very unique bicycles that currently are not a good fit within the GBI
network, therefore we consider a completely separate distribution network.
Creating an ALX model.
Step 1. Open Anylogistix. Click on New Scenario, click OK. Next click on import scenario then select the
file you downloaded, PB GFA Level 2 with Solutions.xlsx. Change the scenario name to your name
Note: You may receive a warning about old data file. You should be able to say OK and just ignore it.
Performing experiments. Data from Table 1 has already been entered for Customers, Demand, and
Products.
Step 2. Go to GFA Experiment and run it for Number of sites = 1 and the period of two months.
Select custom periods and make sure the default dates 11/1/17 12/31/17 are set.
Step 3. Analyze the results using statistics Flows and New Sites:
Note: Use the Polarbear-GBI case study answer sheet to submit ALL of your answers.
1. What are the optimal coordinates of the DC?
2. What is the maximum distance from the optimal DC location to a customer?
3. What is the minimum distance from the optimal DC location to a customer?
4. What are the total costs of the SC? (Note: to compute the sum of costs or flows in GFA Results, just
slightly drag the heading of the column Period in table Product flows in the space over the
table.
5. Compare the data in statistics Flowsand TableDemand. Do we satisfy all customer demands
from the optimal DC location? If Yes, why? If no, why?
Step 4. Go to GFA Experiment and run it for Number of sites = 2.
Step 5. Analyze the results using statistics Flows and New Sites:
6. What are the total costs of the SC?
7. Compare the results with one and two DCs in terms of costs and responsiveness.
8. What other costs were not considered in selecting the optimal facility location in the GFA?
2.3 Network Optimization (NO) for Facility Location Planning: Comparing Po
larbears Supply Chain Design Alternatives
After selling the bicycles from the newly established DC(s) according to the GFA results, Polarbear-GBI
decided to produce their own bicycles. Their production facility has now been established in Nuremberg and
250 bikes are produced each day. Recently, they have received an offer from a Polish production factory to
rent a DC in the Czech Republic at a reasonable price. The same company also wants to offer them rental of
a factory in Warsaw, Poland, even though they already have one factory in Germany. Polarbear-GBI must
now decide which SC design is more profitable:
Option 1: DC in Germany and Factory in Germany
Option 2: DC in Germany and Factory in Poland
Option 3: DC in Czech Republic and Factory in Poland
Option 4: DC in Czech Republic and Factory in Germany
In Fig. 1, the different possibilities for the path networks are shown. The dotted lines show possible
alternatives and the solid lines the existing structure of Polarbears SC.
Figure
1.
Network
optimization
alternatives
The aim of the
NO
is
to
determine which network design is optimal based on Polarbears selected KPIs, e.g., profit.
Therefore, the factory in Warsaw, Poland, the DC in the Czech Republic, and the DC in Steimelhagen were
added as inputs to the model along with the Nuremburg factory. To enable the models calculation, the
reality of the case must be simplified: all demand is assumed to be deterministic without any uncertain
fluctuations. To define the two-stage NO problem (transport between factories and DCs and between DCs
and customers) from a mathematical perspective, several parameters must be input as data. These are
shown in Table 2.
The costs of the rent for the factory in Poland and the DC in Czech Republic are included in othercosts.
For transport, it is always assumed that each truckload fits 80 bicycles, and trucks travel at a speed of 80
km/h.
Creating an ALX model
Step 0. Probably best to close and re-open ALX at this point. Now create an new scenario as you did in
Step 1 above and import the file PB NO Level 2 Solution.xlsx. Rename it so that it has your name or
initials as the scenario name: Note: Data from Table 2 has been entered for you.
Note: You may receive a warning about old data file. You should be able to say OK and just ignore it.
Performing experiments Step 1. Go to NO Experiment and run it with the Demand variation type 95100%.
3
NOTE! In order to run the NO experiment, make sure the units in experiment settings is set from m to
pcs to align it with product data.
Step 2. Analyze the results using statistics Optimization Results, Flow Details, Production Flows,
Demand, and Overall Stats:
b. place a screen shot here clearly showing your new NO results with your name or initials in the
scenario name.
9. What is the most profitable SC design?
10. Is demand for all customers satisfied? Why or Why not?
11. What is the total revenue of the most profitable SC?
12. What is total profit of the most profitable SC?
13. Compare the data in statistics Production Flows and Table Demand. Does the production
quantity correspond to the total demand? Explain.
14. Compare the optimal SC design as computed in the NO and the initial SC design (factory and DC in
Germany) in terms of profit.
15. What other costs should be considered when redesigning the SC according to NO results?
16. What other factors, apart from costs, should be considered when re-designing the SC according to
the results of the NO?
description Copy of scenario PB GFA Level 1.Description of the original scenario:
type
GFA
name
PB GFA Level 2 with Solutions
creationDate
2018-06-19
Name
Type
Cologne Customer
Bremen Customer
Stuttgart Customer
Frankfurt am
Customer
Main
Location Inclusion Type
Icon
Cologne location
Include
Bremen location
Include
Stuttgart location
Include
Frankfurt am
Include
Main location
4
4
4
4
Name
Type
Location
Inclusion Type
Icon
Customer Product Demand Type
Time Period
Cologne bicycle “all terrain”
PeriodicDemand[period::1.0;quantity::15.0]
(All periods)
Cologne bicycle “tour”
PeriodicDemand[period::1.0;quantity::10.0]
(All periods)
Cologne bicycle “urban”
PeriodicDemand[period::1.0;quantity::50.0]
(All periods)
Cologne bicycle “x-cross”
PeriodicDemand[period::1.0;quantity::2.0]
(All periods)
Bremen bicycle “all terrain”
PeriodicDemand[period::1.0;quantity::20.0]
(All periods)
Bremen bicycle “tour”
PeriodicDemand[period::1.0;quantity::20.0]
(All periods)
Bremen bicycle “urban”
PeriodicDemand[period::1.0;quantity::30.0]
(All periods)
Bremen bicycle “x-cross”
PeriodicDemand[period::1.0;quantity::7.0]
(All periods)
Frankfurt am
bicycle
Main “all terrain”
PeriodicDemand[period::1.0;quantity::4.0]
(All periods)
Frankfurt am
bicycle
Main “tour”
PeriodicDemand[period::1.0;quantity::5.0]
(All periods)
Frankfurt am
bicycle
Main “urban”
PeriodicDemand[period::1.0;quantity::5.0]
(All periods)
Frankfurt am
bicycle
Main “x-cross”
PeriodicDemand[period::1.0;quantity::6.0]
(All periods)
Stuttgart bicycle “all terrain”
PeriodicDemand[period::1.0;quantity::1.0]
(All periods)
Stuttgart bicycle “tour”
PeriodicDemand[period::1.0;quantity::40.0]
(All periods)
Stuttgart bicycle “urban”
PeriodicDemand[period::1.0;quantity::15.0]
(All periods)
Stuttgart bicycle “x-cross”
PeriodicDemand[period::1.0;quantity::15.0]
(All periods)
id
date
quantity
Name
DescriptionCustomers Sites
Suppliers Groups
Name
Locations
Code
Name
City
Region
Cologne location
Bremen location
Stuttgart location
Frankfurt am Main location
Country
Address
Latitude
50,8337
53,05442
48,77791
50,07829
Longitude Autofill Coordinates
6,855469 FALSE
8,85498 FALSE
9,294434 FALSE
8,635254 FALSE
Autofill Coordinates
Name
Product
Amount from
Amount to Unit to
Name
Periods
Name
Start
End
Demand Coefficient
Basic period2017-01-012017-12-31
1
Name
Products
Name
Unit
bicycle “all terrain”
pcs
bicycle “tour”
pcs
bicycle “urban”
pcs
bicycle “x-cross”
pcs
Delivery Destination
Product
Source
Time PeriodInclusion Type
Name
Type
Location
Products
Inclusion Type
Icon
Currency
Volume
Distance
Time
USD
m³
km
day
maxDist
20
minimizeSitesNumber
FALSE
destinations(All customers)
nSitesConstr
2
distanceUnitkm
productUnitpcs
sourcingPriority
FALSE
toSiteTranspCoeff 0,9
statsDistanceStep 100
latLonOffset
1
newSiteIcon
2
name
type
GFA
scenario PB GFA Level 2 with Solutions
statisticsSettings
GFA_FLOWS::true;d;f
GFA_NEW_SITES::true;d;f
GFA_DISTANCE_BY_DEMAND::true;d;f
GFA_DEMAND_BY_DISTANCE::true;d;f
GFA_TOTAL_DEMAND_BY_DISTANCE::true;d;f
Units settings
Currency::USD
Volume::m³Time::day Distance::km
timeType Custom period
startPeriod
endPeriod
startDate 2017-11-01T00:00
stopDate 2017-12-31T00:00
dashboardData
Page name Chart type Accumulative
Stats namesLayout dataDetalizationFilters
Chart name
dashboardData
Flows
CUSTOM_TABLE
TRUE GFA_FLOWS0,0,36,8
Flows
dashboardData
New sites CUSTOM_TABLE
TRUE GFA_NEW_SITES
0,0,36,8
New sites
dashboardData
Distance byCUSTOM_TABLE
demand
TRUE GFA_DISTANCE_BY_DEMAND
0,0,36,8
Distance by demand
dashboardData
Demand byCUSTOM_TABLE
distance
TRUE GFA_DEMAND_BY_DISTANCE
0,0,18,8
Demand by distance
dashboardData
Demand byCUSTOM_TABLE
distance
TRUE GFA_TOTAL_DEMAND_BY_DISTANCE
0,0,18,8
Total demand by distance
preProcessor
postProcessor
Total demand by distance
minPopulation 50000
destinations(All customers)
ignoreNotRealRoutes
FALSE
nSitesConstr
1
distanceUnitkm
productUnitm³
sourcingPriority
FALSE
toSiteTranspCoeff 0,5
statsDistanceStep 100
latLonOffset
100
newSiteIcon
2
name
type
GFAWithRoads
scenario PB GFA Level 2 with Solutions
statisticsSettings
GFA_FLOWS::true;d;f
GFA_NEW_SITES::true;d;f
GFA_DISTANCE_BY_DEMAND::true;d;f
GFA_DEMAND_BY_DISTANCE::true;d;f
GFA_TOTAL_DEMAND_BY_DISTANCE::true;d;f
Units settings
Currency::USD
Volume::m³Time::day Distance::km
timeType All periods
startPeriod
endPeriod
startDate 2018-01-01T00:00
stopDate 2018-12-31T00:00
dashboardData
Page name Chart type Accumulative
Stats namesLayout dataDetalizationFilters
Chart name
dashboardData
Product Flows
CUSTOM_TABLE
TRUE GFA_FLOWS0,0,36,8
Product Flows
dashboardData
New Site Locations
CUSTOM_TABLE
TRUE GFA_NEW_SITES
0,0,36,8
New Site Locations
dashboardData
Distance Coverage
CUSTOM_TABLE
by Demand
TRUE GFA_DISTANCE_BY_DEMAND
0,0,36,8
Distance Coverage by Demand
dashboardData
Demand Coverage
CUSTOM_TABLE
by Distance
TRUE GFA_DEMAND_BY_DISTANCE
0,0,18,8
Demand Coverage by Distance
dashboardData
Demand Coverage
CUSTOM_TABLE
by Distance
TRUE GFA_TOTAL_DEMAND_BY_DISTANCE
0,0,18,8
Total Demand Coverage by Dista
preProcessor
postProcessor
Total Demand Coverage by Distance
customType
name
type
Custom
scenario PB GFA Level 2 with Solutions
statisticsSettings
GFA_FLOWS::true;d;f
GFA_NEW_SITES::true;d;f
GFA_DISTANCE_BY_DEMAND::true;d;f
GFA_DEMAND_BY_DISTANCE::true;d;f
GFA_TOTAL_DEMAND_BY_DISTANCE::true;d;f
LINEAR_COSTS::true;d;f
Flows Details::true;d;f
Sites Initial::true;d;f
Units settings
Currency::USD
Volume::m³Time::day Distance::km
timeType All periods
startPeriod
endPeriod
startDate 2019-01-01T00:00
stopDate 2019-12-31T00:00
dashboardData
Page name Chart type Accumulative
Stats namesLayout dataDetalizationFilters
Chart name
dashboardData
Log
preProcessor
postProcessor
Sites Fix::true;d;f
Storage by Product::true;d;f
Production Production
cost::true;d;f
Multiple
flows::true;d;f
Flows
Working
Constraints::true;d;f
Sites::true;d;f
Multiple Storages
Demand::true;d;f
Constraints::true;d;f
VEHICLES_FLOWS::true;d;f
Chart name
Named Expressions::true;d;f
Objective Members::true;d;f
Overall Stats::true;d;f
Flows Amount::true;d;f
DAILY_VEHICLES_SHIPPED::true;d,Type,Object,Vehicle
DAILY_VEHICLES_USAGE::true;d,Type,Object,Vehicle
TRAVELLED_DISTANCE::true;d,Type,Object,Vehicle
DAILY_PRODUCTS_SHIPPED_INTERNAL::true
AVAILABLE_INVENTORY_AMOUN
type;f
type;f type
CURRENT_BACKLOG_PRODUCTS::true;d,Type,Object,Product,Period;f
MAX_CAPACITY_VOLUME::true;d,Type,Object;f
ON_HAND_INVENTORY_VOLUME::true;d,Type,Object,Product,Period;f
FACILITY_CO2_STATS::true;d,Type,Object,Period;f
STORING_CO2_PER_M3_STATS::true;d,Type,Object,Product,Period;f
MAX_CAPACITY_INTERNAL2::true;d,Type,Object,Period;f
FACILITY_COSTS::true;d,Type,Object,Period;f
CARRYING_COSTS::true;d,Type,Object,Produ
TRANSPORTATION_COSTS::true;d
OTHER_COSTS::true;d,Type,Object;f
REVENUE::true;d,Type,Object,Product;f
PRODUCTS_LOST::true;d,Type,Object,Product;f
ORDERS_LOST::true;d,Type,Object,Product;f
DAILY_INCOMING_REPLENISHMENT_PRODUCTS::true;d,Type,Object,Product,P
DAILY_INCOMING_REPLENISHMENT_ORDERS::true;d,Type,Object,P
DAILY_PRODUCTS_SHIPPED::true;d,Type,Object,Produc
PRODUCT_FLOWS_TABLE::true;d,Object;f
INVENTORY_PURCHASES::true;d,
INITIAL_COSTS::true;d,Type,Object;f
DAILY_ITEMS_RECEIVED::true;d,Type,Object,Product,Period;f
DAILY_ORDERS_RECEIVED::true;d,Type,Object,Product,Period;f
INTERESTS_STATS::true;d,Type,Object;f
DAILY_OUTGOING_REPLENISHMENT_PRODUCTS::true;d,Type,Object,Product,P
DAILY_OUTGOING_REPLENISHMENT_ORDERS::true;d,Type,Object,P
DAILY_ORDERS_SHIPPED::true;d,Type,Object,Product,V
LOADING_TIME_VEHICLE::true;d,Type,Objec
UNLOADING_TIME_VEHICLE::tru
GATES_BUSY::true;d,Type,Object,Staff
GATES_IDLE::true;d,Type,Object,Staff
CURRENT_BACKLOG_ORDERS::true;d,Type,Object,Product,Period;f
CLOSURE_COSTS::true;d,Type,Object;f
type;f ORDERED_PRODUCTS_SENT::true;d,Type,Object,Product,Period;f
type;f PRODUCTS_BULLWHIP_EFFECT::true;d,Type,Object,Product;f
INPUT_PROCESSING_COSTS::true;d,Type,Object,Produc
PROCESSING_CO2_INPUT_STATS::true;d,Typ
PROCESSING_CO2_OUTPUT_STA
CLOSURE_CO2::true;d,Type,Object;f
INITIAL_CO2::true;d,Type,Object;f
OTHER_CO2::true;d,Type,Object;f
TRANSPORTATION_CO2::true;d,Type,Object,Vehicle
TOTAL_COSTS::true;d,Type,Object;f
OUTPUT_PROCESSING_COSTS::true;d,Type,Object,Product,Period;f
Shipments schedule::false;d,Object;f
PRODUCTION_COSTS::true;d,Type,Object,Pr
type,Destination;f
PRODUCED::true;d,Type,Object,P
PRODUCTION_REQUESTS::true;d,Type,Object,Product,Period;f
PRODUCTION_LINE_BUSY_TIME::true;d,Type,Object,Product,Period;f
PRODUCTION_LINE_IDLE_TIME::true;d,Type,Object,Product,Period;f
PRODUCED_ORDERS::true;d,Type,Object,Product,Period;f
PRODUCTION_REQUEST_ORDERS::true;d,Type,Object,Product,Period;f
PRODUCTION_CO2::true;d,Type,Object,Product,Period;f
STAFF_BUSY_TIME::true;d,Type,Object;f
STAFF_IDLE_TIME::true;d,Type,Object;f
Utilized Volume (Yogurt Factory):
Delayed Batches
DC Rating
(Yogurt
(Online
ZONE_LOAD::true;d,Type,Object,Zone;f
Factory)::true;d,Type,Object;f
Shop)::true;d,Type,Object;f
BUSY_STAFF::true;d,Type,Object,Staff
ZONE_CAPACITY::true;d,Type,Object,Zone;f
STAFF_TOTAL::true;d,Type,Object,Staff
CUSTOMER_REVENUE::true;d,Type,Object,Product,Perio
type;f CUSTOMER_DELAYED_ORDERS::true;d,Type
CUSTOMER_IN_TIME_ORDERS::t
type;f
CUSTOMER_ORDERS_TOTAL::true;d,Type,Object,Product,Period;f
CUSTOMER_DELAYED_PRODUCTS::true;d,Type,Object,Product,Source,Period;f
CUSTOMER_IN_TIME_PRODUCTS::true;d,Type,Object,Product,Source,Period;f
CUSTOMER_PRODUCTS_TOTAL::true;d,Type,Object,Product,Period;f
ORDERS::true;d,Type,Object,Product,Period;f
ORDERED_PRODUCTS::true;d,Type,Object,Product,Period;f
DROPPED_ORDERS::true;d,Type,Object,Product,Source,
DROPPED_ORDERED_PRODUCTS::true;d,Typ
LEAD_TIME::true;d,Type,Object,P
SUCCESSFUL_ORDERS_SIZE::true;d,Type,Object,Product,Source,Period;f
SUCCESSFUL_ORDERS::true;d,Type,Object,Product,Source,Period;f
UNSUCCESSFUL_ORDERS_SIZE::true;d,Type,Object,Product,Source,Period;f
UNSUCCESSFUL_ORDERS::true;d,Type,Object,Product,Source,Period;f
Negative Feedback
Positive (Online
Feedback
No Feedback
Shop)::true;d,Type,Object;f
(Online
Demand
(Online
Shop)::true;d,Type,Object;f
Placed
Shop)::true;d,Type,Object;f
PRODUCT_VOLUMES::true;d,Pro
(Dropped Late Orders) by Cu
PRODUCT_COSTS::true;d,Product;f
PRODUCT_PRICES::true;d,Product;f
VEHICLE_VOLUMES::true;d,Vehicle
CASH::true;d,Period,Account;f
INTERESTS::true;d,Period,Account;f
ACCOUNT_PAYABLE::true;d,Period,Account;f
type;f
LOAN::true;d,Period,Account;f
ACCOUNT_RECEIVABLE::true;d,Period,Accou
EVENTS_TABLE::true;d,Object;f
TOTAL_INTERESTS_PAID::true;d,Period,Account;f
TOTAL_CO2::true;d,Object;f
EBITDA::true;d,Object;f
FACILITY_CO2::true;d,Object,Period;f
CARRYING_CO2::true;d,Object,Product,Period;f
INVENTORY_MINUS_BACKLOG_AMOUNT::true;d,Object,Product;f
INPUT_PROCESSING_CO2::true;d,Object,Product,Period
OUTPUT_PROCESSING_CO2::true;d,Object,P
GENERAL_ORDERS_SERVICE_LEV
GENERAL_PRODUCTS_SERVICE_LEVEL_ALPHA_TYPE::true;d,Object,Product,Source,Period;f
GENERAL_MONEY_SERVICE_LEVEL_BETA_TYPE::true;d,Object,Product,Source,Period;f
OPPORTUNITY_COSTS::true;d,Object,Product;f
GENERAL_COST_PER_ORDER::true;d,Object;f
GENERAL_COST_PER_PRODUCT::true;d,Object;f
GENERAL_ORDERS_SERVICE_LEVEL_BY_ELT::true;d,Object,Product,
GENERAL_PRODUCTS_SERVICE_LEVEL_BY_ELT::true;d,O
AVERAGE_ON_HAND_INVENTORY_DAYS::tru
AVERAGE_ON_HAND_INVENTOR
ELT_SERVICE_LEVEL_BY_REVENUE::true;d,Object,Product,Source,Period;f
AVAILABLE_INVENTORY_VOLUME_INTEGRAL::true;d,Object,Product,Period;f
MEAN_LEAD_TIME::true;d,Object,Product,Source;f
PRODUCTION_UTILIZATION::true;d,Object,Product,Period;f
TRANSPORT_UTILIZATION::true;d,Type,Object,Vehicle
VEHICLES_USAGE::true;d,Object,Vehicle
MAX_VEHICLES_USAGE::true;d,Object,Vehicle
Max lead time::true;d,Object,Product,Source
GATES_UTILIZATION::true;d,Obje
type;f
type;f
type;f
AVAILABLE_INVENTORY_CUSTOM::true;d,Object,Product,Period;f
AVAILABLE_INVENTORY_INTEGRAL_CUSTOM::true;d,Object,Product,Period;f
PRODUCED_CUSTOM::true;d,Type,Object,Product,Period;f
CASH_MINUS_LOAN::true;d,Period,Account;f
CASH_MINUS_INTERESTS::true;d,Period,Account;f
CASH_MINUS_LOAN_INTERESTS::true;d,Period,Account;f
LOAN_PLUS_INTERESTS::true;d,Period,Account;f
STAFF_UTILIZATION_DC_WITH_STAFF::true;
STAFF_UTILIZATION_EXTENDED_
SPACE_UTILIZATION::true;d,Object,Zone;f
PROFIT_AND_LOSS_STATEMENT::true;d,Object;f
SAFETY_STOCK_INVENTORY_BASED::true;d,Type,Object,Product,Period;f
DC
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