Part 1: Greenfield Analysis Screen Capture 1: DC’s and Customers To receive credit, the scenario and results MUST include your name and initials. The file must also include the map inclusive of your customers and DCs. Part 2: Network Optimization Screen Capture 2: Customer Groups To receive credit, the scenario and results MUST follow the naming convention outlined in the assignment guidelines. Screen Capture 3: Product Flows Take a screen capture of the sources included in the Flow table. To receive credit, the naming convention of the supplier must be followed. Screen Capture 4: Profitability (Operating Loss) Take a screen capture of the profit (Operating Loss). The screen capture MUST include the scenario name inclusive of “YOUR NAME” Screen Capture 5: Profitability Take 2 Take a screen capture of the profit. The screen capture MUST include the scenario name inclusive of “YOUR NAME” Screen Capture 6: Profitability Take 3 Take a screen capture of the profit. The screen capture MUST include the scenario name inclusive of “YOUR NAME” Part 3: Simulation Screen Capture 7: Dashboard; Annual Performance • After running the simulation for a complete year, take a screen capture of the dashboard. The screen capture MUST include the scenario name inclusive of “YOUR NAME” Screen Capture 8: Dashboard; Annual Performance (Take 2) • After altering parameters and rerunning the simulation for a complete year, take a screen capture of the dashboard. The screen capture MUST include the scenario name inclusive of “YOUR NAME” Part 4: Independent Simulation and Summary Screen Capture 9: Dashboard; Annual Performance (Independent Simulation) • After altering parameters and rerunning the simulation for a complete year, take a screen capture of the dashboard and place the results here. The screen capture MUST include the scenario name inclusive of “YOUR NAME”. Additionally, your reflection (a few paragraphs) should be included. AnyLogistix Assignment(s) AnyLogistix™ (ALX™) is software for designing supply chains and managing them with a digital twin. It integrates supply chain design, optimization, and simulation with operations data enabling network analysis and improvement. Prior to completing the four assignments outlined below, you must download a free version of the Personal Learning Edition of the software using the link below: https://www.anylogistix.com/personal-learning-edition/ NOTE: ALX is Java based and has been tested on the following platforms: Microsoft Windows 10 x64, Microsoft Windows 8 x64, Microsoft Windows 7 SP1 x64. Full System Requirements are accessible via the link included above. Part 1: Greenfield Analysis (GFA) A Greenfield Analysis is used to find optimal number of distribution centers as well as for defining the approximate locations for the supply chain sites taking into account the following data: Locations of the customers/sites, list of products, aggregated demand for each customer and product, and direct distance between customers and DCs/Warehouses or number of facilities we need to find. (Source: https://www.anylogistix.com/help/index.jsp?topic=%2Fcom.anylogic.anyLogistix.help%2Fhtml%2Fexperiments%2FGFA.html) Part 1 is associated with Video 1 through the 12 minute mark (accessible via Canvas). Once ALX is downloaded on your machine, launch the application to perform the following functions: • Create New Scenario o Click New Scenario o Select Scenario Type: GFA o Rename the Scenario NOTE: Your name must be included in the scenario (see below) o Click OK 1 • Add customers o Add customer via GIS map ▪ Click the Create Customer icon (blue icon) o o ▪ Double-click on Washington, DC on the map (re-center and zoom in on the map) ▪ In the Customer table, rename the customer “Washington, DC” Download the AnyLogistix Data.xlsx file to your machine to facilitate the import below Add customers via the Import function ▪ Click Import Scenario ▪ ▪ Select: • Select the AnyLogistix Data.xlsx file from your machine • Open Advanced options • Sheets to import: Customers, Demand, Locations • Create new scenario: off • Scenario name: Choose your scenario • Import experiments: no Click OK NOTE: If you receive a warning message, simply click “OK” 2 • Modify product – a product is in the scenario by default. You will customize accordingly. o Navigate to the Products table o Double-click the Name of the existing product and change it to “PS4” o Double-click the Unit and select “pcs” • Add demand o Demand was automatically populated via the import. To confirm, perform the following steps: ▪ Navigate to the Demand table ▪ Double-click the Parameters for Washington, DC o ▪ View the current settings and click OK Define the Product ▪ Press Ctrl and click the Product column title ▪ ▪ Press the spacebar OR (Note: this may vary on your PC) Select PS4 from the drop down menu ▪ Click OK 3 • Configure and run the GFA experiment o Click on the name of your experiment under GFA, then click on GFA experiment o Enter: ▪ Number of sites: 4 ▪ Product measurement unit: pcs o Click the Run icon (red triangle) o Click the Filter icon (funnel) and then click Show Connections (four connected squares) to see which distribution center is serving which customers. o Repeat the previous steps with 3 sites, and rerun the experiment. o To hold on to settings, rename the results. ▪ Right-click the word Results and change the name of the experiment to “Your Initials_4DCs” ▪ Right-click the word Results 2 and change the name of the experiment to “Your Initials_3DCs” Right-click the name 3DCs and click Convert to GFA scenario. Repeat this step for the 4DCs experiment Screen Capture 1: Take a screen capture of your results and place in the separate results file. Rename AnyLogistics_Name. Note: To receive credit, the scenario and results MUST include your name and initials. The file must also include the map inclusive of your customers and DCs. o o 4 i.e. SamJones_GreenfieldAnalysis i.e. SJ_4DCs and SJ_3DCs 5 Part 2: Network Optimization (NO) Network optimization experiment is used to provide the most optimal locations for distribution or production facilities, product flows, and sourcing options. The optimal solution is the best set of flows and facilities considering a profit maximizing objective and adherence to all constraints (i.e. Production). The experiment considers the following data: Demand; Location of suppliers, customers, existing and potential facilities, and paths between the supply chain elements; Product flows; Product storages; DC fixed/variable costs, and transportation costs; and Time periods. (Source: https://www.anylogistix.com/help/index.jsp?topic=%2Fcom.anylogic.anyLogistix.help%2Fhtml%2Fexperiments%2FGFA.html ) Part 2 is associated with Video 1 (12 through end) and Video 2 (accessible via Canvas). Part 2 is dependent on Part 1. To complete Part 2 perform the following steps: • • Right-click the 3DCs scenario name and select Create Copy as NO Investigate the GFA DC locations o Click on GFA DC and zoom in o Select a more appropriate DC location (based on roads, expected costs, etc.) and move the DC location. Refer to the end of Video 1 and beginning of Video 2. Rename the DC based on the location. Ensure your Initials are included (see below): o Repeat this step for GFA DC 2 & 3 • Rename Groups o Navigate to the Groups table o Rename each of the groups as follows: ▪ GFA_Your Initials_DC#_City Name_ Customers (e.g. GFA_SJ_ DC1_ Bayonne_Customers) o Screen Capture 2: Take a screen capture of your results and place in the separate results file. Note: To receive credit, the naming convention above must be followed. 6 • Add a supplier o In the map, navigate to the Port of Los Angeles o Click the create a supplier icon (green circle) o Double-click the port to add a supplier o Navigate to the Suppliers table and rename the supplier Your Initials_Port of LA o o Navigate to the Product Flows table Click Add, o o o In the Source column, select YourInitials_Port of LA In the Destination column, select All sites In the product column, select PS4 Screen Capture 3: Take a screen capture of the sources included in the Product Flows table and place in the separate results file. Note: To receive credit, the naming convention of the supplier must be followed. 7 • Modify the cost calculation o Navigate to the Paths table o Double-Click the first cell in the “From” column and change it to “All locations”. The “To” cell should also be “All locations” o • Double-click the Cost Calculation Parameters cell. Change the Amount unit to pcs and change the Cost per unit to 0.002 Test run the experiment o Click NO experiment o Change the Product statistics unit to pcs o Click the run icon (red triangle) o The profit result will be a loss Screen Capture 4: Take a screen capture of the profit and place in the separate results file. The screen capture MUST include the scenario name inclusive of “YOUR NAME” 8 • Specify the product cost and price o Navigate to the Products table and specify 399 for Revenue and 381 for Cost • Re-run the experiment Screen Capture 5: Profitability Take 2 Take a screen capture of the profit and place the results in the separate results file. The screen capture MUST include the scenario name inclusive of “YOUR NAME” • • Specify cost of processing outgoing shipments o Navigate to the Processing cost table and create a table records for each DC o Specify the product and the cost of processing shipments. Enter: ▪ Product: PS4 ▪ Unit: pcs ▪ Cost: enter $0.58 for GFA DC’s 1 & 3, and $0.52 for GFA DC 2 Re-run the experiment Screen Capture 6: Profitability Take 3 Take a screen capture of the profit and place the results in the separate results file. The screen capture MUST include the scenario name inclusive of “YOUR NAME” 9 Part 3: Simulation Simulation experiment is used to model the actual products delivery on the GIS map with detailed statistics generated real-time. It is used as well for what-if scenarios to see how the changes you make affect the outcome. Simulation experiment works with the same set of data that is used for GFA and Network optimization experiments alongside the additional data provided for this type of experiment: Suppliers, Sourcing of products, Inventory policies, and Expenses incurred. Source: https://ww.anylogistix.com/help/index.jsp?topic=%2Fcom.anylogic.anyLogistix.help%2Fhtml%2Fexperiments%2FGFA.html Part 3 is associated with Video 3 ((accessible via Canvas). Part 3 is dependent on Parts 1 and 2. To complete Part 3 perform the following steps: • • Right-click the scenario name (within NO) and select Create Copy as SIM Prior to creating an inventory policy (below), add a group for DCs only. It should be the same as GFA group but will be named Your Initials_DCs and have the 3 distribution centers selected as sites. • Create inventory policy o Navigate to the Inventory table o Set all policies to Exclude o o o o Change the Product to PS4 Press Ctrl and click the word Policy Parameters to highlight the column. Press spacebar Set the values to Min: 300; Max: 3000 Highlight the Initial Stock column and set the values to 1000 10 • Create simulation experiment o Click Simulation experiment o Change the Product statistics unit to pcs o Right-click the empty space next to the word Dashboard and select Add item o In the selection menu, choose Revenue, Total Cost, and Profit and then click OK o o o Repeat the Add item step. Select Products and then Available Inventory. Click OK Repeat the Add item step. Select Demand Received Dropped Orders metric. Click the Run icon Screen Capture 7: Dashboard; Annual Performance • After running the simulation for a complete year, take a screen capture of the dashboard and place the results in the separate results file. The screen capture MUST include the scenario name inclusive of “YOUR NAME” • Run what-if scenarios o Navigate to the Inventory table o Adjust the Policy parameters cell in the GFA group row: Min: 2000; Max: 5000 o Set the Initial Stock, units to 2000. o Navigate back to the Simulation experiment and run it. o Compare the results of the two experiments Screen Capture 8: Dashboard; Annual Performance (Take 2) • After altering parameters and rerunning the simulation for a complete year, take a screen capture of the dashboard and place the results in the separate results file. The screen capture MUST include the scenario name inclusive of “YOUR NAME” 11 Part 4: Independent Simulation Video 4 is associated with Part 4 of this assignment. While not required (to watch), much of the information contained within will aide in the completion of the assignment. 1. Adjust the number and locations of the DC’s to only 2 • • • Return to the NO scenario and create a copy (use the initial scenario as a backup) Rename (right click and select properties) as follows: YourInitials_Independent Simulation Move, create, or delete warehouses o Make sure that you adjust the customer lists in the Product Flows table so each customer is served by the one closest DC o Adjust the Processing Costs as follows: ▪ Large metropolitan areas: $0.60 per piece ▪ Small metropolitan or outskirts of large metropolitan areas: $0.55 per piece ▪ Rural areas: $0.50 per piece 3. Create a new SIM and attempt to maximize your profitability by adjusting the inventory settings 4. Select the appropriate metrics (including profitability) and display on your dashboard 5. Summarize your results in a few paragraphs Screen Capture 9: Dashboard; Annual Performance (Independent Simulation) • After altering parameters and rerunning the simulation for a complete year, take a screen capture of the dashboard and place the results in the separate results file. The screen capture MUST include the scenario name inclusive of “YOUR NAME”. Additionally, your reflection (a few paragraphs) should be included. 12 startDate endDate description type GFA name My Supply Chain creationDate 2017-05-31 Name Type Phoenix Customer Dallas Customer Boston Customer Nashville Customer Orlando Customer San AntonioCustomer Denver Customer Seattle Customer PhiladelphiaCustomer Los AngelesCustomer Chicago Customer Detroit Customer JacksonvilleCustomer San Jose Customer Kansas City Customer Atlanta Customer New York Customer Salt Lake City Customer San Diego Customer Location Inclusion Type Icon Phoenix location Include Dallas location Include Boston location Include Nashville location Include Orlando location Include San AntonioInclude location Denver location Include Seattle location Include PhiladelphiaInclude location Los AngelesInclude location Chicago location Include Detroit location Include JacksonvilleInclude location San Jose location Include Kansas City Include location Atlanta location Include New York location Include Salt Lake City Include location San Diego location Include 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 Name Type Location Inclusion Type Icon Customer Product Phoenix Dallas Boston Nashville Orlando San Antonio Denver Seattle Philadelphia Los Angeles Chicago Detroit Jacksonville San Jose Kansas City Atlanta New York Salt Lake City San Diego Demand Type PeriodicDemand[period::2.0;quantity::108.0] PeriodicDemand[period::5.0;quantity::225.0] PeriodicDemand[period::5.0;quantity::10.0] PeriodicDemand[period::3.0;quantity::68.0] PeriodicDemand[period::2.0;quantity::18.0] PeriodicDemand[period::3.0;quantity::152.0] PeriodicDemand[period::3.0;quantity::70.0] PeriodicDemand[period::5.0;quantity::10.0] PeriodicDemand[period::3.0;quantity::165.0] PeriodicDemand[period::1.0;quantity::138.0] PeriodicDemand[period::3.0;quantity::287.0] PeriodicDemand[period::2.0;quantity::48.0] PeriodicDemand[period::1.0;quantity::30.0] PeriodicDemand[period::5.0;quantity::179.0] PeriodicDemand[period::5.0;quantity::83.0] PeriodicDemand[period::3.0;quantity::48.0] PeriodicDemand[period::1.0;quantity::300.0] PeriodicDemand[period::5.0;quantity::34.0] PeriodicDemand[period::1.0;quantity::50.0] Time Period (All periods) (All periods) (All periods) (All periods) (All periods) (All periods) (All periods) (All periods) (All periods) (All periods) (All periods) (All periods) (All periods) (All periods) (All periods) (All periods) (All periods) (All periods) (All periods) id date quantity Name DescriptionCustomers Sites Suppliers Groups Name Locations Code Name Region Denver location Salt Lake City location Chicago location Boston location Dallas location San Antonio location Kansas City location Jacksonville location San Diego location Seattle location San Jose location Detroit location Los Angeles location Atlanta location Nashville location Phoenix location Orlando location New York location Philadelphia location Country USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA Latitude Longitude Autofill Coordinates 39.73915 -104.985 FALSE 40.76701 -111.89 FALSE 41.87555 -87.6244 FALSE 42.36048 -71.0596 FALSE 32.77627 -96.7969 FALSE 29.4246 -98.4951 FALSE 39.08447 -94.563 FALSE 30.33218 -81.6557 FALSE 32.71742 -117.163 FALSE 47.60383 -122.33 FALSE 37.33619 -121.891 FALSE 42.34866 -83.0567 FALSE 34.05439 -118.244 FALSE 33.7491 -84.3902 FALSE 36.16223 -86.7744 FALSE 33.44859 -112.077 FALSE 28.54212 -81.379 FALSE 40.7306 -73.9866 FALSE 39.9524 -75.1636 FALSE 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 Product Unit m³ Delivery Destination Product Source Time PeriodInclusion Type Name Type Location Products Inclusion Type Icon Currency Volume USD m³ nSitesConstr 1 maxDist 200 distanceUnitkm minimizeSitesNumber FALSE destinations(All customers) productUnitm³ sourcingPriority FALSE toSiteTranspCoeff 0.5 statsDistanceStep 100 realRoads FALSE latLonOffset 100 minPopulation 50000 newSiteIcon 2 name type GFA scenario My Supply Chain 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 2017-01-01T00:00 stopDate 2017-12-31T00:00 preProcessor postProcessor 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 Total Demand Coverage by Distance customType name type Custom scenario My Supply Chain statisticsSettings 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;d,Type,Object,Vehicle AVAILABLE_INVENTORY_AMOUNT::true;d,Type,Object,Product,Per type;f CURRENT_BACKLOG_PRODUCTS::true;d,Type,Object,Pro type;f MAX_CAPACITY_VOLUME::true;d,Type,Obje type;f ON_HAND_INVENTORY_VOLUME type;f Units settings Currency::USD Volume::m³Time::day Distance::km timeType All periods startPeriod endPeriod startDate 2017-01-01T00:00 stopDate 2017-12-31T00:00 preProcessor postProcessor dashboardData Page name Chart type Accumulative Stats namesLayout dataDetalizationFilters Chart name dashboardData Log MAX_CAPACITY_INTERNAL::true;d,Type,Object,Period;f STORING_COST_PER_M3_STATS::true;d,Type,Object,Product,Period;f FACILITY_COST_STATS::true;d,Type,Object,Period;f TRANSPORTATION_COSTS::true;d,Type,Object,Vehicle 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 type,Destination;f DAILY_INCOMING_REPLENISHME Chart name DAILY_INCOMING_REPLENISHMENT_ORDERS::true;d,Type,Object,Product,Period;f DAILY_PRODUCTS_SHIPPED::true;d,Type,Object,Product,Vehicle PRODUCT_FLOWS_TABLE::true;d,Object;f INVENTORY_PURCHASES::true;d,Type,Object,Product;f 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,P type,Period,Destination;f PROCESSING_COST_INPUT_STATS::true;d,Ty PROCESSING_COST_OUTPUT_STA INTERESTS_STATS::true;d,Type,Object;f DAILY_OUTGOING_REPLENISHMENT_PRODUCTS::true;d,Type,Object,Product,Period;f DAILY_OUTGOING_REPLENISHMENT_ORDERS::true;d,Type,Object,Product,Period;f DAILY_ORDERS_SHIPPED::true;d,Type,Object,Product,Vehicle LOADING_TIME_VEHICLE::true;d,Type,Object,Vehicle UNLOADING_TIME_VEHICLE::true;d,Type,Object,Vehicle GATES_BUSY::true;d,Type,Object,Staff GATES_IDLE::true;d,Type,Object,Staff CURRENT_BACKLOG_ORDERS::tr type,Period,Destination;f type;f type;f type;f type;f CLOSURE_COSTS::true;d,Type,Object;f ORDERED_PRODUCTS_SENT::true;d,Type,Object,Product,Period;f PRODUCTS_BULLWHIP_EFFECT::true;d,Type,Object,Product;f Shipments schedule::false;d,Object;f PRODUCTION_COSTS::true;d,Type,Object,Product;f PRODUCED::true;d,Type,Object,Product;f PRODUCTION_REQUESTS::true;d,Type,Object,Product;f PRODUCTION_LINE_BUSY_TIME::true;d,Type PRODUCTION_LINE_IDLE_TIME:: PRODUCED_ORDERS::true;d,Type,Object,Product;f PRODUCTION_REQUEST_ORDERS::true;d,Type,Object,Product;f Cash (Cash-to-Serve)::true;d,Period;f Interests (Cash-to-Serve)::true;d,Period;f Account Payable Loan (Cash-to-Serve)::true;d,Period;f (Cash-to-Serve)::true;d,Period;f STAFF_BUSY_TIME::true;d,Type,Object;f STAFF_IDLE_TIME::true;d,Type,Object;f ZONE_LOAD::true;d,Type,Object, BUSY_STAFF::true;d,Type,Object,Staff ZONE_CAPACITY::true;d,Type,Object,Zone;f STAFF_TOTAL::true;d,Type,Object,Staff DC rating::true;d,Type,Object;f type;f CUSTOMER_REVENUE::true;d,Type,Object,Product,Period;f CUSTOMER_DELAYED_ORDERS::true;d,Type,Object,Product,Period; type;f CUSTOMER_IN_TIME_ORDERS::true;d,Type,Object,Prod CUSTOMER_ORDERS_TOTAL::true;d,Type,Ob CUSTOMER_DELAYED_PRODUCT CUSTOMER_IN_TIME_PRODUCTS::true;d,Type,Object,Product,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,Period;f DROPPED_ORDERED_PRODUCTS::true;d,Type,Object,Product,Perio LEAD_TIME::true;d,Type,Object,Product;f SUCCESSFUL_ORDERS_SIZE::true;d,Type,Obj SUCCESSFUL_ORDERS::true;d,Typ UNSUCCESSFUL_ORDERS_SIZE::true;d,Type,Object,Product,Period;f UNSUCCESSFUL_ORDERS::true;d,Type,Object,Product,Period;f Account Receivable PRODUCT_VOLUMES::true;d,Product;f (Cash-to-Serve)::true;d,Type,Object,Product;f PRODUCT_COSTS::true;d,Product;f PRODUCT_PRICES::true;d,Product;f VEHICLE_VOLUMES::true;d,Vehicle TOTAL_COSTS::true;d,Object;f EBITDA::true;d,Object;f type;f FACILITY_COSTS::true;d,Object,Period;f CARRYING_COSTS::true;d,Object,Product,Period;f INVENTORY_MINUS_BACKLOG_AMOUNT::true;d,Object,Product;f INPUT_PROCESSING_COSTS::true;d,Object,Product,Period;f OUTPUT_PROCESSING_COSTS::true;d,Object,Product,Period;f GENERAL_ORDERS_SERVICE_LEVEL_ALPHA_TYPE::true;d,Object,Pro GENERAL_PRODUCTS_SERVICE_LEVEL_ALPHA_TYPE::tru GENERAL_MONEY_SERVICE_LEVEL_BETA_TY OPPORTUNITY_COSTS::true;d,Ob 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,Period;f GENERAL_PRODUCTS_SERVICE_LEVEL_BY_ELT::true;d,Object,Product,Period;f AVERAGE_ON_HAND_INVENTORY_DAYS::true;d,Object,Product,Period;f AVERAGE_ON_HAND_INVENTORY_IN_PRODUCT_UNITS_DAYS::true ELT_SERVICE_LEVEL_BY_REVENUE::true;d,Object,Produ AVAILABLE_INVENTORY_VOLUME_INTEGRA MEAN_LEAD_TIME::true;d,Objec PRODUCTION_UTILIZATION::true;d,Object,Product;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;f GATES_UTILIZATION::true;d,Object,Staff type;f type;f AVAILABLE_INVENTORY_CUSTOM::true;d,Object,Produc AVAILABLE_INVENTORY_INTEGRAL_CUSTOM type;f PRODUCED_CUSTOM::true;d,Typ type;f STAFF_UTILIZATION_DC_WITH_STAFF::true;d,Object;f STAFF_UTILIZATION_EXTENDED_DC::true;d,Object,Staff SPACE_UTILIZATION::true;d,Object,Zone;f PROFIT_AND_LOSS_STATEMENT::true;d,Object;f LINEAR_COSTS::true;d;f Flows Details::true;d;f type;f Sites Initial::true;d;f 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 Named Expressions::true;d;f Objective Members::true;d;f Overall Stats::true;d;f Flows Amount::true;d;f 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 _BY_DISTANCE::true;d;f 3d edition Supply Chain Simulation and Optimization with design optimize experiment innovate Decision-oriented teaching notes for model-based management decision making Prof. Dr. Dmitry Ivanov Berlin School of Economics and Law Professor of Supply Chain Management To be cited as: Ivanov D. (2019). Supply chain simulation and optimization with anyLogistix: Teaching notes. Berlin School of Economics and Law. © Prof. Dr. Dmitry Ivanov, 2019. All rights reserved. Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 1 Table of Contents About the Author .................................................................................................................................... 6 Foreword ................................................................................................................................................ 7 Introduction.......................................................................................................................... 8 How to use this book .............................................................................................................................. 8 Theoretical Background and Principles of Decision-making Support in Supply Chain Management using anyLogistix ................................................................................................................................. 10 Supply Chain Management .............................................................................................................................. 10 Model-based Decision-Making in Supply Chain Management ......................................................................... 10 Principles of Supply Chain Simulation and Optimization in anyLogistix ........................................................... 11 Simulation and Optimization for Decision-Making Support in Supply Chain Management .............................. 12 Introducing anyLogistix ........................................................................................................................ 17 Understanding Projects .................................................................................................................................... 17 Understanding Scenarios ................................................................................................................................. 17 Option 1: Setting Up a Greenfield Analysis Experiment ................................................................................... 21 Option 2: Setting Up a Network Optimization Experiment ................................................................................ 22 Option 3: Setting Up a Simulation Experiment ................................................................................................. 23 Chapter 1: Greenfield Analysis and Basics of Simulation for Two-stage Supply Chain ...... 25 Our Learning Objectives .................................................................................................................................. 25 Theoretical background.................................................................................................................................... 25 Performing a Greenfield Analysis (GFA) for a New Facility................................................................. 27 Our Greenfield Analysis Case Study: Facility Location Planning ..................................................................... 27 Creating a Scenario ......................................................................................................................................... 28 Defining Supply Chain Structure and Parameters ............................................................................... 29 Adding Customers and their Locations ............................................................................................................ 29 Defining Products and Customer Demand ....................................................................................................... 30 Importing Data from Microsoft Excel workbooks .............................................................................................. 34 Creating Groups ............................................................................................................................................... 34 New GFA Experiment .......................................................................................................................... 35 Creating a New Experiment ............................................................................................................................. 35 Determining the Optimal Location for a Single Warehouse.............................................................................. 35 Determining the Minimal Number of Warehouses and their Locations ............................................................ 36 Discussion Questions....................................................................................................................................... 37 New Simulation Experiment ................................................................................................................. 37 What is a simulation experiment? .................................................................................................................... 37 KPI Dashboard................................................................................................................................................. 38 KPI System ...................................................................................................................................................... 39 Revenue, Costs, Service Level, Lead Time and On-time Delivery ................................................................... 40 Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 2 Inventory control policy .................................................................................................................................... 41 Transportation Distance and Costs .................................................................................................................. 42 Sourcing Policy Definition ................................................................................................................................ 44 Experiments and Analyses .................................................................................................................. 45 Simulation Experiments for Multiple Warehouses with Real Routes ................................................................ 45 Simulation Experiments for Single Warehouses with Real Routes .................................................................. 50 Chapter 2. Network Optimization and Advanced Simulation with Inventory and Transportation Control: Three-stage Supply Chain ............................................................ 53 Our Learning Objectives ...................................................................................................................... 53 Theoretical background ....................................................................................................................... 53 Supply chain design and network optimization ................................................................................................ 53 Combining optimization and simulation in supply chain design........................................................................ 55 Inventory control .............................................................................................................................................. 56 Transportation policies and routing .................................................................................................................. 63 Our Case Study: Distribution Network Design, Inventory Control and Transportation Policies .......... 64 Network Optimization ........................................................................................................................... 64 Starting the Case Study ................................................................................................................................... 64 Demand and Expected Lead Time ................................................................................................................... 65 Transportation Policy and Costs ...................................................................................................................... 66 Stochastic demand and lead time .................................................................................................................... 66 Reviewing the Path Table’s Parameters .......................................................................................................... 67 Grouping Supply Chain Elements .................................................................................................................... 68 New Network Optimization Experiment ............................................................................................... 68 Preparing Data ................................................................................................................................................. 68 Performing the NO experiment ........................................................................................................................ 72 Capacitated Network Optimization Experiment ................................................................................................ 75 Transportation Network Optimization (TO) .......................................................................................... 76 Creating a new TO scenario ............................................................................................................................ 76 Performing TO experiment ............................................................................................................................... 78 Simulation Experiment ......................................................................................................................... 79 Inventory Control Policy ................................................................................................................................... 79 Sourcing Policy ................................................................................................................................................ 80 Defining Operational Costs at Distribution Centers .......................................................................................... 80 Creating a KPI Dashboard ............................................................................................................................... 80 Tab 1: Financial and Customer Performance KPI ............................................................................................ 81 Tab 2: Operational Performance KPI ............................................................................................................... 84 Tab 3: Inventory and Capacity Dynamics......................................................................................................... 87 Experiment and Result Analysis .......................................................................................................... 89 Experimental Results ....................................................................................................................................... 89 Result Analysis ................................................................................................................................................ 92 Impact of Inventory Control Policy ................................................................................................................... 93 Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 3 Experiment ....................................................................................................................................................... 94 Results Analysis ............................................................................................................................................... 97 Using AnyLogic to Extend anyLogistix ............................................................................................................. 97 Impact of Transportation Policy ........................................................................................................... 99 Experiment ..................................................................................................................................................... 100 Results Analysis ............................................................................................................................................. 101 Chapter 3. Simulation with Production Factories and Sourcing Policies: Four-Stage Supply Chain ............................................................................................................................... 104 Our Learning Objectives ................................................................................................................................ 104 Theoretical background.................................................................................................................................. 104 Production Factories .......................................................................................................................... 105 Case Study: Smartphone Supply Chain ......................................................................................................... 105 Assessment Questions: ................................................................................................................................. 106 Supply Chain Design ......................................................................................................................... 106 Multi-stage Supply Chain Design ................................................................................................................... 106 Transportation, Sourcing and Inventory Policy............................................................................................... 106 Production Policy and Bill of Materials (BOM)................................................................................................ 108 Production and Sales Batches ....................................................................................................................... 108 AS-IS Simulation ................................................................................................................................ 108 Experiment Preparation and KPI Dashboard ................................................................................................. 108 Experimental Result for Pessimistic Scenario ................................................................................................ 109 Experimental Result for Optimistic Scenario .................................................................................................. 110 Result Analysis .............................................................................................................................................. 111 Sourcing Policies ............................................................................................................................... 112 Our Case Study: Extended Supply Chain for Smartphones ........................................................................... 112 Improvement Action: Single Distribution Center - Increased Capacity ........................................................... 112 Result Analysis .............................................................................................................................................. 113 Improvement Action: New Distribution Center - Dual Sourcing ...................................................................... 114 Comparison to New Distribution Center – Single Sourcing ............................................................................ 118 Chapter 4. Risk Management in Supply Chains ............................................................... 122 Our Learning Objectives .................................................................................................................... 122 Theoretical Background ..................................................................................................................... 122 Operational and disruption risks: Bullwhip effect and Ripple effect ................................................................ 122 Simulation and optimization applications to supply chain risk management .................................................. 123 Bullwhip Effect in the Supply Chain: Our Case-Study ....................................................................... 128 Experiment and Bullwhip Effect Analysis ........................................................................................... 129 Supply Chain Design and Policies ................................................................................................................. 129 KPI Dashboard............................................................................................................................................... 130 Experiments and Result Analysis................................................................................................................... 132 Batching and Ordering Rules ............................................................................................................. 135 Transportation Batches .................................................................................................................................. 135 Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 4 Sales and Production Batches ....................................................................................................................... 136 Ordering Rules ............................................................................................................................................... 136 Impact of Batching and Ordering Rules on Bullwhip Effect ............................................................................ 137 Comparison Experiment .................................................................................................................... 141 Ripple Effect in the Supply Chain ...................................................................................................... 142 Case Study: A Distribution Center Stops Working for a Month ...................................................................... 142 Events ............................................................................................................................................................ 142 Simulation Experiment for Ripple Effect ......................................................................................................... 143 Analysis of Proactive and Reactive Policies ...................................................................................... 145 Impact of Inventory Increase .......................................................................................................................... 145 Impact of a Backup Distribution Center .......................................................................................................... 145 Impact of Recovery Strategies ....................................................................................................................... 147 Safety Stock Estimation Experiment .................................................................................................. 147 Variation Experiment .......................................................................................................................... 148 Create New Variation Experiment .................................................................................................................. 149 Performing a Variation Experiment ................................................................................................................ 150 Risk Analysis Experiment .................................................................................................................. 150 Create New Risk Analysis Experiment ........................................................................................................... 151 Performing New Risk Analysis Experiment .................................................................................................... 151 Literature ......................................................................................................................... 155 Summary and Discussion Questions ............................................................................... 156 Avoiding Typical Conceptual Mistakes............................................................................. 159 Convenience Hints .......................................................................................................... 161 Appendix 1: Examples of Case Study Problem Statements ............................................. 163 Example 1 .......................................................................................................................................... 163 Example 2 .......................................................................................................................................... 168 Example 3 .......................................................................................................................................... 168 Example 4 .......................................................................................................................................... 168 Example 5 .......................................................................................................................................... 168 Appendix 2: Case-Studies on Combined Usage of Optimization and Simulation for Supply Chain Design ................................................................................................................... 171 Case Study 1: Multi-Product Supply Chain Redesign ....................................................................... 171 Scenario Settings ........................................................................................................................................... 174 Simulation Experiments ................................................................................................................................. 174 AS-IS Supply Chain Simulation ...................................................................................................................... 175 Supply Chain Redesign.................................................................................................................................. 176 Case Study 2: Network Optimization Approach and Optimization-based Simulation ....................... 180 Case Study .................................................................................................................................................... 180 Simulation Experiment ................................................................................................................................... 181 Optimization Experiment ................................................................................................................................ 181 Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 5 Optimization-based Simulation Experiment ................................................................................................... 183 Case-study 3: Simulation and network optimization .......................................................................... 185 Case-Study 4: Three-stage, one-period supply network design........................................................ 193 Problem statement ......................................................................................................................................... 193 Input data ....................................................................................................................................................... 194 Network optimization experiments ................................................................................................................. 197 How to analyze the optimization results and make a management decision ................................................. 199 Variation experiment ...................................................................................................................................... 201 Case-Study 5: Four-stage, multi-period supply chain planning with capacity disruptions, inventory, and transportation constraints............................................................................................................ 202 Problem statement ......................................................................................................................................... 202 Setting the management problem in anyLogistix Network Optimizer ............................................................. 203 Network optimization results .......................................................................................................................... 206 Additional features ......................................................................................................................................... 207 Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 6 About the Author Dr. Dr. habil. Dmitry Ivanov is professor of Supply Chain Management at Berlin School of Economics and Law (BSEL). For over 15 years, he has taught courses in operations management, production and supply management, supply chain management, logistics, management information systems, and strategic management at undergraduate, master's, PhD and executive MBA levels at universities in Germany, Russia, UK, US and China. Before he became an academic, Dr. Ivanov was engaged in industry and consulting, especially on process optimization in manufacturing, logistics and ERP systems. His practical expertise includes many projects on application of operations research and process optimization methods for operations design, logistics, scheduling and supply chain optimization. His research explores supply chain structure dynamics and control, with an emphasis on global supply chain design with disruption management consideration, distribution planning, and dynamic rescheduling. He is (co)-author of structure dynamics control method for supply chain management. He applies mathematical programming, simulation and control theoretic methods. Based on the triangle “process-model-technology”, he investigates the dynamics of complex networks in production, logistics and supply chains. Most of his courses and research take place at the intersection of supply chain management, operations research, industrial engineering and information technology. He is the author or coauthor of more than 300 publications, including a textbook, “Global Supply Chain and Operations Management” and a monograph, “Adaptive Supply Chain Management”. Professor Ivanov’s research has been published in a variety of academic journals, including the Annals of Operations Research, Annual Reviews in Control, Computers and Industrial Engineering, European Journal of Operational Research, IEEE Transactions on Engineering Management, International Journal of Production Research, International Journal of Production Economics, International Journal of Technology Management, International Journal of Systems Science, Journal of Scheduling, Omega, Transportation Research: Part E, etc. He has been a guest editor different journals, including International Journal of Production Research, International Transactions on Operations Research and International Journal of Integrated Supply Management. He is an associate editor of International Journal of Systems Science and Editorial Board member of several international and national journals such as International Journal of Systems Science: Operations and Logistics. He is Chair of IFAC Technical Committee 5.2 “Manufacturing Modelling for Management and Control”. He is General Conference Chair of 9th IFAC Conference MIM 2019 “Manufacturing Modelling, Management and Control”. He has been member of numerous associations, including INFORMS, POMS, CSCMP, VHB, GOR. He regularly presented his research results and has been co-chairman and IPC member of many international conferences where he has organized numerous tracks and sessions (including IFAC MIM, INCOM, EURO, INFORMS, IFORS, ICPR, OR, POMS, IFAC World Congress, IFIP PRO-VE). Contact: Dr. Dmitry Ivanov Professor of Supply Chain Management Berlin School of Economics and Law https://blog.hwr-berlin.de/ivanov Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 7 Foreword anyLogistix is an easy-to-understand tool students and professionals can use to address a wide range of supply chain management (SCM) problems. This guide explains how to use anyLogistix to create supply chain models, conduct experiments and analyze the results. By reducing technical complexity to a minimum, anyLogistix allows students to focus on management decision analysis and use KPIs for operational, customer and financial performance measurement and decision-making. This guide groups the content into three parts regarding facility location planning using GFA (Greenfield analysis), network optimization and simulation that correspond to three basic process structures — two-stage, three-stage and four-stage supply chains — as well supply chain-based risk management. It presents simulation and optimization examples by describing how to develop and build models and evaluate KPI. It also discusses how to use these models and their simulation and optimization results to improve management decision-making. Because this guide is focused on management issues, it uses simple terms to describe model developments. If you want to import sample models and use them to perform experiments, you can point to anyLogistix’s File menu and then click Import. Please excuse any errors in the text and formatting. This guide is a work in progress and we welcome any comments and suggestions that may help us improve it. This guide’s author has also co-authored the textbook “Global Supply Chain and Operations Management” by Springer (http://www.springer.com/us/book/9783319242156) and its companion web site http://global-supply-chain-management.de where additional AnyLogic and AnyLogistix models can be found. In addition, he has also authored the e-book “Operations and Supply Chain Simulation with AnyLogic” (http://www.anylogic.com/books). The author deeply thanks the AnyLogic Company for their valuable feedback and improvement suggestions. Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 8 Introduction How to use this book The ALX book aims to provide an overview of how to use anyLogistix to solve practical problems in supply chain management (SCM) and logistics. In doing so, the ALX book: - provides an overview of anyLogistix; explains how to develop anyLogistix models with different degrees of complexity degrees; - suggests a set of practical problem settings in supply chain management and logistics that can be modelled using anyLogistix; - describes step-by-step how to use anyLogistix for decision-making support in supply chain management and logistics problem settings; - figures out some cases for further development using anyLogistix. The ALX book can be used as a self-study guide or in the classroom for exemplifying different SCM and logistics topics or guiding students as they create their own models. The book is structured as follows (Table I-1). Table I-1: ALX book structure Section Content Introduction Scenario as Excel file Corresponding chapter in the textbook Global Supply Chain and Operations Management Complexity level Principles of anyLogistix Chapter 1 Basic Basics of technical work with anyLogistix Chapter 3 Basics of applying simulation and optimization to supply chain management Chapter 1 Scenarios for Chapter 1 Chapter 7 Basic Scenarios for Chapter 2 Chapter 8 Advanced I Advanced Simulation (Production and Sourcing Policies) Scenarios for Chapter 3 Chapter 5 Risk Analysis in the Supply Chain (Bullwhip Effect and Ripple Effect) Scenarios for Chapter 4 Chapter 15 Greenfield Analysis Simple Simulation Chapter 2 Network Optimization Advanced Simulation (Inventory Control and Shipment Policy) Chapter 13 Chapter 14 Vehicle Routing Optimization Chapter 3 Chapter 4 Advanced I Chapter 12 Advanced II Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 9 Variation and Comparison Experiments Risk Analysis Experiment Appendix 1 Examples of case-studies that can be developed using anyLogistix (without solutions) Scenarios for App. 1 Advanced IIII Appendix 2 Advanced examples of casestudies with simulation and optimization (with solutions) Scenarios for App. 2 Advanced IIII We recommend starting the ALX book by reading the Introduction. Next, the examples from Chapter 1 should be studied using the supplementary Excel files (cf Table I1). How to import scenarios is explained in Chapter 1 in the form of Excel files and follows step-by-step explanations in the ALX Handbook. At the same time, we also recommend watching the Webinar and educational videos provided by The AnyLogic Company as well as the standard model samples which come with anyLogistix software (you will find them in Help). In Help – ALX Documentation, you will find detailed explanations for all tables, parameters, and statistics used in anyLogistix. After completing Chapter 1, you will be able to perform Greenfield Analysis and some simple simulations on a basic level. Chapter 2 introduces network optimization and transportation optimization. It also extends the Chapter 1 materials on simulation, and explains inventory control policies and shipment policies. After completing Chapter 2, you will be able to perform network optimization and advanced supply chain simulations. Chapter 3 extends the materials of Chapter 2 on simulation and explains production and sourcing policies in the framework of a multi-echelon supply chain. After completing Chapter 3, you will be able to perform advanced supply chain simulations. Chapter 4 focuses on supply chain risks and explains how anyLogistix can be used to analyze the bullwhip and ripple effects in the supply chain. It also introduces variation, comparison and risk analysis experiments. After completing Chapter 4, you will be able to perform risk analysis for supply chains. Appendix 1 contains some example supply chain problems that can be solved using anyLogistix (without solutions). Appendix 2 contains more advanced example problems and their corresponding simulation and optimization solutions. The respective chapters of the textbook Ivanov D., Tsipoulanidis, A., Schönberger, J. (2019) Global Supply Chain and Operations Management: A decision-oriented introduction into the creation of value, 2nd Edition, Springer Nature, Cham are depicted in Table I-1. Short theoretical background information is given about the relevant problem settings in each chapter. Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 10 Theoretical Background and Principles of Decision-making Support in Supply Chain Management using anyLogistix Supply Chain Management A supply chain is a network of organizations and processes where enterprises (suppliers, manufacturers, distributors and retailers) cooperate and coordinate along the value chain to acquire raw materials, to convert these raw materials into products, and to deliver these products to customers (Ivanov et al. 2017). Supply chain management (SCM) is a cross-department and cross-enterprise integration and coordination of material, information and financial flows to use the supply chain resources in the most rational way along the value chain, from raw material suppliers to customers (Ivanov et al. 2017). Supply chain management integrates production and logistics processes at several levels. Strategic issues include decisions such as the size and location of manufacturing plants or distribution centers, the structure of service networks and designing the supply chain. Tactical issues include production, transportation and inventory planning. Finally, operative issues address production scheduling and control, inventory control and vehicle routing. Model-based Decision-Making in Supply Chain Management Decision-making in supply chain management implies the use of qualitative and quantitative methods. Quantitative methods are typically based on optimization or simulation. Model-based decision-making process is shown in Figure I-1. Figure I-1: Model-based decision-making process (Ivanov et al. 2017) We can observe that a real management problem is the initial point of the decisionmaking process. For example, this could be a facility location problem where we are trying to decide where to locate the facilities and which quantities should be shipped from the facilities to the markets. The next step is to transform the real problem into a mathematical model. For this transformation, we need to reduce the complexity of reality or in other words simplify the reality. For example, we aggregate demand into fixed quantities instead of considering fluctuations in demand. The simplifications are necessary to represent the management problem as a mathematical model. This model can then be solved with the help of existing algorithms in a reasonable time. In our example, we formulate the facility location problem as a mixedinteger linear programming model that can be solved with the help of simplex and branch&bound algorithms. Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 11 For implementation of the mathematical model, software is needed. For example, the professional solver CPLEX is used in anyLogistix. Software will calculate the solution. In our example, the solution would include suggestions on where to open facility locations and which product quantities should be shipped from each opened facility to each of the markets so that total production and logistics costs are minimal. However, it is important to consider whether this solution is automatically our decision. NO! This is a solution to the mathematical problem. Management expertise is needed to transfer this mathematical solution into managerial decisions. First, the simplifications of reality should be reviewed. Second, so called soft facts such as risks, flexibility, etc. should be included in the analysis. This need for managerial expertise is why we call these models decision-supporting quantitative methods. To understand the application of quantitative methods to SCM in practice, SCM courses are often enhanced by decision-support software such as anyLogistix. Universities can use anyLogistix to support SCM, operations and logistics courses. Principles of Supply Chain Simulation and Optimization in anyLogistix anyLogistix makes it possible to develop real-life examples for many of the most important supply chain management domains, including: ● Facility Location Planning ➢ Center-of-Gravity Method for Single and Multiple Locations ➢ Network Optimization using Mixed-Linear Programming ● Capacity Planning of Distribution Centers ● Inventory Control Policies and Ordering Rules ● Sourcing Policies (Single and Multiple Sourcing) ● Transportation Policies (Full Truckload/FTL and Less-Than-Load/LTL) ● Batching in Transportation, Production, and Sales ● Bullwhip Effect and Ripple Effect Analysis in the supply chain You can use KPI (key performance indicators) to assess the quality of your decisions in these areas as well as their impact on financial, operational and customer performance in the supply chain. The anyLogistix software can assess the impacts and interfaces of decisions and KPIs in all these domains to help you better answer the following questions: ● Where are the best locations for our warehouses, distribution centers and production sites? ● What are the best policies for replenishment, sourcing and transportation? ● How robust is our supply chain? ● What will happen if we change our inventory policy? ● What will happen if we increase a distribution center’s capacity? ● What will happen if demand changes? ● What will happen if we add a new product? ● What does an out-of-stock event cost? You can model the supply chain in two ways (Figure I-2): ● Analytical modeling that uses optimization models to investigate the supply chain ● Simulation modeling that uses a set of objects and rules that describe their dynamic behavior and their interaction to represent the supply chain Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 12 Figure I-2: Analytical and Simulation methods in anyLogistix Simulation and Optimization for Decision-Making Support in Supply Chain Management Both optimization and simulation have certain application areas, advantages and disadvantages. anyLogistix uses both and helps to understand differences and application issues. For example, you can optimize the supply chain’s facility locations and then simulate their inventory control policies, transportation and sourcing rules (cf. Figure I-1 and I-2). You’ll usually start the first stage of a project (i.e., a scenario in anyLogistix) at the strategic level by using a Greenfield analysis (GFA), sometimes called a center-ofgravity analysis, to define the optimal locations of distribution centers. At this stage, a high level of abstraction with a minimum number of details is used. Existing data, such as customer locations, demand per customer, the number and location of DCs, and/or service distances, are used as inputs to the analysis. The output of the analysis is an Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 13 approximate, optimal location for a production or warehousing facility at which the cost of all in- and outbound transportation is minimized. During the second stage – the NO (network optimization), you’ll extend the problem setting by including feasible facility locations and use other parameters — such as fixed facility costs, inventory carrying costs, facility opening/closure costs, CO2 emissions, many periods — and perform network optimizations. Network optimization is a decision-supporting quantitative model for supply chain management (SCM), which allows a supply chain manager to easily compare alternative network designs according to a customizable cost objective function. In contrast to the GFA, through an optimization analysis many alternative network designs and paths can be compared according to their impact on supply chain efficiency. The results also allow the maximal profitability of each potential alternative network design to be compared with one another. However, a real supply chain is complex and subject to uncertainty, and it is difficult to include many time-dependent, dynamic factors in optimization. As your problem becomes more detailed, we extend the analysis in the third stage using simulations which provide an overview of the effects of different combinations of inventory control, sourcing, transportation, and production policies (Figure I-3). Figure I-3: A pyramid of supply chain design and analysis problems. According to Ivanov et al. (2017, p.61), “Simulation is imitating the behavior of one system with another”. In a simulation, supply chain processes in time can be observed and improved. By changing input parameters, the goal of the simulation is to understand the dynamics and material flow of the supply chain: “Simulation is an ideal tool for further analysing the performance of a proposed design derived from an optimization model” (Ivanov et al. 2017, p. 61). To run a simulation, some critical data is needed, such as inventory control policy, sourcing policy, shipment policy, bills of material, production policy, etc. Supply chain simulation can be of strategic and operational support. Strategic support might include decisions concerning the number and location of facilities, stock levels, and transportation and supply planning. Operational Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 14 support might include process control, predictions of developments in upcoming periods, trends detection, or decision support for choosing alternatives in unexpected situations such as operational risks of demand fluctuations (i.e., bullwhip effect) or disruption risks of facility breakdowns (i.e., ripple effect). Finally, you will use the results of GFA, NO and Simulation for decision-making. In doing so, it will be important task to validate the results using sensitivity analysis and compare different scenarios subject to some KPIs. This will be done using Variation and Comparison analysis in anyLogistix. Conducting a sensitivity analysis with different iterations, a so called “variation” analysis, highlights the best result in the model and provides a check for robustness (Watson et al. 2013, p. 63-77). This can best be done by altering various key input parameters such as demand, inventory, or costs. The results then show whether any changes will have severe impacts on the network with regards to cost increases and savings decreases (Watson et al. 2013, p. 77). How simulation and optimization are combined depends on the modeling objective. Three major combinations can be distinguished as follows (Figure I-3): ● Optimization as a starting point and simulation as an extended analysis method, e.g., for précising solutions obtained analytically using dynamic process analysis, ● Simulation as a starting point and optimization as an extended analysis method, e.g., for obtaining optimal parameters values in supply chain design, and ● Hybrid simulation-optimization techniques, e.g., simulation-based optimization, i.e., for iterative improvement of supply chain performance. Figure I-3: Optimization and simulation combination variants Optimization seeks the best solution for an operations or supply chain problem. It works by representing problem choices as decision variables and seeking values that extremized objective functions of the decision variables subject to constraints on variable values expressing the limits on possible decision choice. Optimization is an analysis method that determines the best possible option for solving a particular supply chain management problem. An optimization model comprises an objective function, a constraint system, and a set of decision variables and input parameters. Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 15 The drawback is the difficulty in developing a model with the detail to represent complexity and uncertainty that is also simple enough to be solved. What’s more, most optimization models are deterministic and static. Unless there are mitigating circumstances, optimization is the preferred approach. However, most supply chain and operations problems are dynamic. Their mutually dependent parameters and variables are difficult to restrict to an optimization model. Simulation imitates the dynamic behavior of one system with another. By changing the simulated supply chain, one expects to better understand the physical supply chain’s dynamics. Rather than deriving a mathematical solution, you experiment by changing the system’s parameters and studying the results. Another advantage of simulation is to visualize the processes and structures. However, since simulation works on the “what happens if..?” principle, the questions of result extremity, completeness and consistency remain open. That’s why simulation can be an ideal tool for analyzing the performance of a proposed supply chain design you derive from an optimization model. Optimization-based simulation is a promising area to support supply chain and operations managers. An optimal decision is the best decision which can be made according to some goal, criteria or objectives. Note: The drawback of using optimization is the difficulty in developing a model that is sufficiently detailed and accurate in representing the complexity and uncertainty of the SCM, while keeping the model simple enough to be solved. Optimal decisions are “fragile” and presume certain problem dimensionality, fullness, and certainty of the model. In addition, the optimal solutions are usually very sensitive to deviations. Moreover, decision-making is tightly interconnected with dynamics and should be considered as an adaptive tuning process and not as a “one-way” optimization. Optimization can also be applied as a validation tool for simulation models which can be run using the optimization results. Analytical optimization methods are used to define the supply chain design with aggregate parameters such as annual capacities, demands, etc. Using a number of parameters such as transportation costs, real routes, and feasible facility locations, it becomes possible to perform network optimization. By reducing the aggregation and abstraction level, we extend the analytical network optimization models through simulation. In comparison to analytical closed form analysis, simulation has the advantage that it can handle complex problem settings with situational behavior changes in the system over time. The simulations in anyLogistix can be run using the optimization results and include additional, time-dependant inventory, production, transportation, and sourcing control policies which are difficult to implement at the network optimization level. In addition to the standard functionality you’ll find in anyLogistix, you can use AnyLogic to extend a policy or structural object (Figure I-4). Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 16 Figure I-4: An AnyLogic extension helps improve anyLogistix’s supply chain modeling. You can use AnyLogic’s agent-based, discrete-event and system dynamics simulation models to customize inventory control, sourcing, transportation and production policies as well as distribution centers, customers and suppliers. As an example, you might decide to not define a distribution center’s processing time as a fixed time. Instead, you could embed a simulated distribution center you built in AnyLogic that uses details such as forklift capacities, real layouts and loading and unloading times. We think you will find working with anyLogistix to be intuitive, and you’ll find helpful descriptions of the program’s features throughout this book. Enjoy your supply chain simulation and optimization with anyLogistix! Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 17 Introducing anyLogistix Understanding Projects The anyLogistix software uses projects to organize data and experiments. Each project can include any number of scenarios and experiments. When you create a project, anyLogistix creates a dedicated database to store your project information. Note: You can only work on one anyLogistix project at a time. Understanding Scenarios Your simulation and optimization starts when you create a scenario or import one from a Microsoft Excel workbook. A scenario is made up of the supply chain’s: ● Design structure ● Sourcing, transportation, inventory control and production policies ● Parameters of the structural elements and policies After you’ve created or imported a scenario, you can perform the following experiments (Figure I-5): ● Supply Chain Optimization: Greenfield Analysis (GFA) and Network Optimization ● Supply Chain Analysis: Optimization-based simulation, simulation, variation, and comparison Figure I-5: An overview of the anyLogistix process that starts when you create a scenario and ends with your experiment’s results. The following illustrations introduce you to anyLogistix’s user interface and show you how to create new project. If you’re using the program for the first time, the Projects dialog box will open automatically. To open it at any other time, point to the File menu and click Select Project. Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 18 Figure I-6: Using anyLogistix’s Projects Menu. Figure I-7: Creating a project in anyLogistix. Figure I-8 shows the basic steps you’ll use to log on to anyLogistix’s project database. If you haven’t created a user account, the program will prompt you to set up a username and password. Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 19 Figure I-8: Logging on to anyLogistix’s project database. As you’ve seen, your anyLogistix project contains scenarios that describe the supply chain. Figure I-9 shows the basic steps you’ll need to perform to create a scenario. Figure I-9: Creating a scenario. After you select a scenario from the list that displays on the left part of your screen (Figure I-10), you’ll see a list of options for that scenario. For example, you may see options such as Scenario Data and Experiment Settings. If you click Data for the selected scenario, a map with your supply chain objects will display in the right part of your screen. You can use the toolbar on top of the map to add objects to your supply chain, show or hide sourcing paths and show or hide object Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 20 names. At the bottom of the screen, you’ll see a list of tables you’ll use to set up the supply chain. Figure I-10: A sample of anyLogistix’s graphical user interface. Figure I-11 shows how you can change scenario data. Figure I-11: A detailed look at anyLogistix’s scenario data view. Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 21 Figure I-12 helps you understand anyLogistix’s navigation menus. Figure I-12: An overview of anyLogistix’s menus. Option 1: Setting Up a Greenfield Analysis Experiment The image below (Figure I-13) shows you how to prepare a Greenfield analysis (GFA) experiment. In anyLogistix’s left pane, click the GFA heading, click Simple GFA, and then click GFA experiment. Afterward, you’ll need to select your experiment’s settings. Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 22 Figure I-13: A Greenfield analysis (GFA) experiment’s settings. Option 2: Setting Up a Network Optimization Experiment The following image (Figure I-14) shows you how to set up a network optimization experiment. In anyLogistix’s left pane, click the NO heading, click Simple NO to select the network optimization scenario, and then click NO experiment. Figure I-14: Network optimization experiment settings. Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 23 Option 3: Setting Up a Simulation Experiment The image below (Figure I-15) shows you how to set up a simulation experiment. In anyLogistix’s left pane, click the SIM heading, click Simulation Experiment and then decide which statistics you want AnyLogistix to collect during the experiment. Figure I-15: Simulation experiment settings. Figures I-16 and I-17 show you how to work with anyLogistix’s dashboard. You’ll use this dashboard—which may include one or many pages—to display the statistics the program collects during your experiment. Figure I-16: Simulation experiment settings: dashboard (1 of 2). Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 24 Figure I-17: Simulation experiment settings: dashboard (image 2 of 2). Figure I-18 shows you the steps you need to complete to set up a variation experiment. You’ll start by navigating to the right to the experiments tree and clicking Variation experiment. Afterward, you must select the scenario you want, define the variations and then select the statistics you want anyLogistix to collect. Figure I-18: Variation experiment settings. If you want more information about anyLogistix’s user interface, you can open the program’s Help feature by pointing to the Help menu and clicking anyLogistix Help. Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 25 Chapter 1: Greenfield Analysis and Basics of Simulation for Two-stage Supply Chain Our Learning Objectives 1. Develop the analytical and management skills to use the center-of-gravity method and simulation to select the optimal locations for your company’s facilities 2. Develop the technical skills you need to use anyLogistix to create two-stage supply chain models, perform experiments and measure performance 3. Understand the major trade-offs in facility location planning that affect the number of sites, lead time and demand uncertainty 4. Understand the areas of simulation and optimization Theoretical background The theoretical background described in this and further chapters is based on the textbook Ivanov D., Tsipoulanidis, A., Schönberger, J. (2019) Global Supply Chain and Operations Management: A decision-oriented introduction into the creation of value, Springer Nature, Cham. The objective of the Greenfield analysis (GFA) is to determine the best location for our distribution center. We want to find the location that allows us to fulfill our customer demands at the lowest total transportation cost. GFA, also known as center-of-gravity analysis, is a common method for determining optimal locations for new facilities (Ivanov et al. 2019). The issues we need to consider during a Greenfield analysis are our customers’ locations, the distances from our warehouse(s) to our customers, and our customers’ demands for our products. The GFA is used to find the optimal location within a network to setup a new production facility or warehouse, while a “brown” field analysis, utilizing the same technique, can be used to adjust existing networks (Ivanov et al. 2019). Identifying the optimal location for a production or warehousing facility is determined by finding the point at which the sum of the distances from all suppliers to the factory (demand point), weighted by the volume of product flow between each supplier and the potential factory, is minimal. Likewise, to determine the optimal location for a warehouse, the distances from the customers to the warehouse, weighted by their respective demands, are calculated. To conduct the GFA, a high level of abstraction with a minimum number of details is used. Existing data, such as customer locations, demand per customer, the number and location of DCs, and/or service distances, are used as inputs to the analysis. Program parameters for the GFA include how many possible results the program should calculate and whether the program should use real roads. The output of the analysis is an approximate, optimal location for a production or warehousing facility (Ivanov 2017). This optimal point is called the “center or gravity” (Ivanov et al. 2019). As explained, these so called “Gravity models” determine the location at which the cost of all in- and outbound transportation is minimized (Chopra and Meindl, 2016). In technical terms, an ordered pair of (x;y)-coordinates represents each customer location. You can’t change these data; they are input data or problem parameters. Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 26 By contrast, your new warehouse’s (x;y)-coordinates (px;py) are variable. We will determine them after it calculates the data you provide in a way that matches the parameters you set. As a result, we say px and py are this scenario’s decision variables. We also assume our transportation cost is linearly proportional to the distance and the transportation volume (that is, the demand). We can see the total transportation costs will depend on the coordinates (px;py) of our prospective warehouses and distances. We assume the transportation costs from the prospective warehouse (px;py) to a customer location (xi;yi) is more or less equal to the distance and demand. With that in mind, we need to determine the distances d((px;py); (xi;yi)) between the i-customer location and the warehouse to calculate transportation costs. To minimize the payments to the forwarding company, you must vary px as well as py as long as Z(px;py) becomes minimal. Total costs Z(px;py) is a determinant in GFA since we seek to find optimal location of a warehouse subject to total costs minimization to serve all customer demands from the warehouse. We assume that the total transportation cost sum is proportional to the distance and the transportation volume (i.e., the demand). This leads us to the formulation of the objective function, as shown in Eq. (1.1): (1.1) We can observe that the total transportation costs depend on the coordinates px and py of the prospective warehouses and distances. We assume that total transportation cost sum from the prospective warehouse location (px;py) to a customer location (xi;yi) is more or less equivalent to the distance and demand. Therefore, the distance d((px;py); (xi;yi)) between the i-th customer location and the warehouse should be determined to calculate transportation costs. To minimize the payments to the forwarding company, it is necessary to vary px as well as py as long as Z(px;py) becomes minimal. The function Z is continuous and differentiable and the decision variables are unrestricted. Hence, we can determine the optimal point of Z by differential calculus. The following consecutive steps have to be executed in the given order. The first derivative Z′ of Z is determined and the zero of Z′ is determined. Then we have (1.2) (1.3) The model (1.1) is called the center-of-gravity model of location analysis. Using demand data, formulas (1.4) and (1.5) are used to calculate optimal coordinates of the warehouse. Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 27 (1.4) (1.5) The determination of an optimal pair of coordinates for the warehouse again requires the determination of the directional derivatives. These two functions are then set equal to 0 and we get the expressions (1.4) and (1.5), respectively, to express px and py. Note that the model (1.1)-(1.5) is valid for determining the location of a single warehouse’s location. In anyLogistix, we can determine multiple locations and even the number of locations needed subject to a maximum service distance from warehouse to customer. This can be useful for comparing the costs of efficient vs responsive (short maximum distances to customers) supply chains. In addition to the mathematical result of the GFA, supply chain managers should consider several other variables: a potential increase in production volume and future expansion needs; quality of the potential infrastructural network; qualifications of prospective employees; options for suppliers; and the regional availability of logistics service providers who could handle inbound and outbound transport. Certain taxation benefits provided by local government can also influence a company’s decision about where to locate a facility (Ivanov et al. 2019). Performing a Greenfield Analysis (GFA) for a New Facility Our Greenfield Analysis Case Study: Facility Location Planning Suresh, a supply chain manager at a German-based retail network, needs to decide where his company should build their new distribution centers and how many centers they need to open to minimize supply chain costs. The data he needs for his analysis are the company’s: ● Customers and their geographical locations ● Products and measurement units ● Customer demand ● Per-kilometer transportation costs ● Distances in the supply network He began gathering the data by asking sales and marketing managers to estimate the annual demand from customers in different regions and then grouping those regions into ten major markets. Afterward, Suresh asked the transportation manager to estimate the company’s shipment costs. Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 28 In this case study, we’ll use anyLogistix to help Suresh improve the distribution center network. The following steps will show you how to: 1. Create a scenario and define the supply chain’s structure and parameters 2. Define the supply chain’s customer demand, transportation and sourcing policies 3. Parametrize the sites and policies 4. Perform the Greenfield Analysis experiment to determine the best locations for one or many warehouses 5. Create a KPI dashboard and collect statistics on supply chain performance 6. Simulate the supply chain design with the new greenfield locations and determine their impact Creating a Scenario The first step in building a decision-support model for facility location planning is to create a new scenario. Figure 1, below, shows you the basic steps you need to complete to create a scenario and make it available in anyLogistix’s central panel. Each scenario has a supply chain structure and parameters you can use during your simulation and optimization experiments. Figure 1: Creating a scenario. You can modify a scenario’s properties by right-clicking the scenario’s name to open the context menu, and then clicking Properties. You can also import a scenario from a Microsoft Excel workbook and use it to perform an experiment. Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 29 Figure 2: Using the Start window to prepare a new scenario. We’ve named our new scenario Greenfield Analysis (GFA), and it now displays in the program’s list of scenarios. Our next step is to define the supply chain’s structure and parameters. Defining Supply Chain Structure and Parameters Adding Customers and their Locations Our first step in defining the supply chain’s structure is to define our customer locations. To define a location, right-click on the map, click Create Customer and enter the required information (Figure 3). Afterward, anyLogistix adds the customer location and its latitude and longitude to the list of customers (Figure 4). Figure 3: Defining a new customer. Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 30 Figure 4: A view of anyLogistix’s list of Customers. Defining Products and Customer Demand Before we define customer demand, we need to use the Products table to add and define the products we will ship to our customers. In our example, we’ll define a new product (Water) by opening the Products table and clicking Add (Figure 5). Figure 5: Adding and defining a product. To set the product’s demand parameters, click the Demand heading on the screen’s left pane. The Demand table that opens lists our customers and allows us to select each customer’s demand type and demand parameters. In time, anyLogistix will use these values to compute our service levels. Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 31 Figure 6: Selecting product demand data. For now, we’ll use two parameters—Order Interval and Quantity—to define customer periodic demand. By setting the Order Interval value to five days and the Quantity value to eight, we’ve ensured our simulated customers will send a new eight-unit order to the distribution center every five days. You can set customer demand to be deterministic or stochastic by using the Demand table’s Demand Type column to select Periodic demand or Historic demand. You can use periodic demand if you know the sales quantity that takes place during a given period. In this example, we know we can expect to sell five water pallets within ten days. By contrast, historical demand assumes you use data about sales over a longer period such as the previous year. To define our historical data, we’ll select the Historic demand option and click Add (Figure 7). Figure 7: Setting up historical demand. To define periodic demand data, we select the Periodic demand option and then define the customer’s demand for a given period. For example, Figure 8 shows you how to set Customer #1’s demand for five water pallets over a ten-day period. Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 32 Figure 8: A Periodic demand setup. To make our analysis more valuable, we’ll change the default customer names—for example, Customer 1 and Customer 2—to the names of the markets we serve such as Hamburg and Berlin. To do this, open the Customer table and change the Name values as needed. Figure 9 below shows the results of our renaming process. Figure 9: Renaming customers. Now, we’ll define the periodic demand for each customer (Figure 10). Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 33 Figure 10: Setting the experiment’s demand data. Note: If you want a flexible approach to demand data, you can define Time Periods (for example, spring, summer, winter and fall) and use the Demand Forecast table to define demand coefficients (Figure 11). → You can define stochastic demand, we can select different types of distributions clicking the arrow in the respective parameter (that is, order interval or quantity): Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 34 Figure 11: Defining Periods Note: Parameters Period (order interval) and Quantity will determine the customer ordering logic in our future simulation experiment. For example, in case of Period=5 and Quantity=10, the customer will order at a DC 10 units every 5 days. Importing Data from Microsoft Excel workbooks If you have a long list of customers and products or you want to avoid manually entering demand data, you can import this data from a Microsoft Excel workbook. To do so, point to the File menu and then click Import. You can import sample ALX scenarios and your own scenarios with experiments. You can also accelerate the scenario creation process by using a Microsoft Excel workbook to create a scenario. After your scenario is complete, you can import it into anyLogistix. Creating Groups The problem in this example is simple, but other problems can be complex. To simplify your simulation modeling and experiments, you might want to group similar objects, such as distribution centers, customers or suppliers. You’ll do this in the Groups table (Figure 12). Figure 12: Creating a group. To create a group, click Add and then enter the new group’s name (for example, Customers). Second, we open the list of all customers in the new Customers table and Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 35 activate those we need in the group. For distribution centers and factories, we activate objects in the Sites column. Supplier groups are created in the Suppliers column. After you create your groups, you can use them in sourcing, transportation, inventory and production policy definitions instead of individual objects. In the Product groups table, you can group individual products in a similar way. This helps to reduce modeling complexity and your time when setting up different sourcing and transportation policies in future. With our data set up, we are ready to perform our first experiment. New GFA Experiment Creating a New Experiment In Experiments, we select Greenfield Analysis. We select our new Greenfield Analysis scenario (Figure 13). Figure 13: Setting data for a Greenfield Analysis experiment. We’ll start by selecting the locations and customers we want to include in our analysis. In this example, we’ll include all our customers. Second, we can perform the computation in two modes: - Define optimal location for a single warehouse Define minimal number of warehouses and their locations subject to a maximum service distance. Determining the Optimal Location for a Single Warehouse In a Greenfield Analysis experiment, the default value for the Desired number of sites parameter is 1. While you can easily change the default value if you want to consider more than one location, we’ll continue our work to determine the optimal location for a single warehouse (Figure 14). Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 36 Figure 14: Computed optimal location for single warehouse. Determining the Minimal Number of Warehouses and their Locations In our experiment, we select the Minimize sites number option and enter a value in the Maximum service distance box. In this example (Figure 15), the maximum service distance is 300 kilometers. Figure 15: Settings to determine minimal number of warehouses and their locations based on the value we enter for the maximum service distance. Ivanov D. (2019) Supply Chain Simulation and Optimization with anyLogistix 37 Figure 16: Computation result for the minimal number of warehouses and their locations that meets our need for a maximum service distance of 300 km. The information in Figure 16 shows us the company needs to install two distribution centers if they want their maximum service distance to be 300 km. This would result in transportation costs reduction from $1,580,871 in the case with 1 DC to $1,141,504 in case with 2 DCs. Note: You can export the results of your Greenfield analysis to a new scenario as NO or SIM. Doing so will help you perform optimization and simulation experiments. 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 tabl...