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Phase 1. Import Scenario and Run the Experiment

We shall start with obtaining the data that will serve as the basis for our experiment. You may either provide your own data or you can instantly import the scenario. Either way, you should have a supply chain scenario containing the following:

  • Customers
  • Distribution center(s) located in the optimal places
  • Properly configured sourcing and paths
  • Vehicle type(s)

The numbered lists in tutorials are actually checklists. Click the numbers to save your progress!

Download and import the scenario

  1. Download the scenario. No internet connection is required, since the scenario is supplied with anyLogistix.
  2. Import the downloaded scenario. The GIS map will appear showing the content of the imported scenario.
  3. Explore the connections by clicking the Show filters button to open the filter options, and then clicking the Show connections button.

The imported scenario contains a supply chain with 7 Nike factory stores, two distribution centers, and Port Jersey.

Let us observe the data the experiment will work with. Primarily we are interested in the Paths and the Sourcing tables.

Observe the data of the scenario

  1. Navigate to the Groups table. It contains groups of customers per each DC, and a group of DCs. The groups of customers are used in the sourcing policies. In this way we guarantee that all customers will be visited, and no customer will be excluded from the experiment. Click the Customers column cells to see the list of customers for each DC:
    • Berlin DC Customers — contains factory stores serviced by the DC in Berlin, namely: Atlantic City — Factory Store, Jackson — Factory Store, Gloucester — Converse Factory Store.
    • Newark DC customers — contains customers served by the DC in Newark, namely: Jersey Gardens — Factory Store, Paramus — Factory Store, Jersey Shore Premium Outlets — Factory Store, Newark — Factory Store.
  2. Move on to the Paths table. It contains 1 record, allowing all connections between the objects of our supply chain. If you need to edit this record, make sure that the resulting record(s) (with Fixed delivery cost or Distance based cost calculation formulas only) allow a vehicle to go from:
    • Site to Customer
    • Customer to Customer
    • Customer to Site
  3. Now switch to the Sourcing table. It contains three records, describing the sourcing of products from supplier to DCs and from DCs to the customers:
    • The first two records define source of products for our customers. The Sources column contains DCs which will be servicing customers that are defined in the Delivery Destination groups. The content of the groups can be seen in the Groups table, which we have previously observed.
    • The third record defines the source of products for our DCs.

Configure parameters of the TO experiment

  1. Navigate to the experiments section and click TO experiment.

    You will be taken to the experiment's view with its settings, where:

    • Max customers in a route — the required number of customers per route. The default value is 10, which is enough to cover all customers within 1 route, since we have just 7 of them.
    • Vehicle types — the type of vehicle delivering the products. The selected here vehicle type(s) must correspond to the vehicles defined in the Paths table.
    • Travel segment limit — the maximum remoteness of the network objects from each other in the specified Distance unit.
    • Returning segment limit — the maximum remoteness of the last customer of the route to the DC from which the vehicle set off.

  2. Click Run in the toolbar of the TO experiment.
    The results will be available in the Result sub-item of the TO experiment tree branch.

Let us analyze the received data.

Analyze the received results

  1. Navigate to the Optimization results page below the experiment view.
    The page contains a table with 2 records, 1 per each DC.

    The Destinations column lists the customers in the order, in which our vehicle visits them.

  2. Expand the record borders to observe the order of the customers for each DC.

  3. Now click the first record. The GIS map will appear, showing a route comprising 4 customers, with Newark — Factory Store being the first customer our vehicle visits on its way from the Newark DC.

    Transportation optimization experiment considers actual roads. The GIS map, however, depicts the results with straight lines by default. Disable the Show Straight toggle button to depict actual routes.

  4. Now click the second record to observe the second route on the GIS map. This time three customers are visited, with Gloucester Premium Outlets — Converse Factory Store being the first customer our vehicle visits on its way from the Berlin DC.

  5. Navigate to the Generated Paths page of the experiment results to observe transportation cost per route.

  6. For more details you can navigate to the Generated Path Segments page, which contains details on segments of every route:
    • Lines 1-5 describe the visiting order and the cost of a route per segment for the DC in Newark.
    • Lines 6-9 describe the visiting order and the cost of a route per segment for the DC in Berlin.

That's it we have completed the first phase of this tutorial. We imported the scenario, created cost-efficient routes, and analyzed the cost of each route. In the next phase we will export the results into a new SIM scenario and observe them in the tables.

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