Phase 2. Specify Expenses and Compare Results

In the previous phase we analyzed the locations offered by GFA experiment, found and defined alternative DC locations considering infrastructure, specified product supplier, set assets constraints.
 
In this phase we will add financial information which will mostly refer to the expenses the supply chain incurs. We will specify the cost of opening a DC for every potential location. The cost will obviously differ from city to city due to numerous factors. Alongside this, we will specify the cost of processing the outgoing shipments for every potential location. We will also set the cost of obtaining a product as well the selling price. The Network Optimization experiment will consider the new data which will make the results it offers more accurate.

Let us start with specifying the cost of obtaining one item of PS4 console and its selling price. These two product parameters can be found in the Products and Demand tables respectively.

Specify the self-value and the selling price of a PS4
  1. Navigate to the Products table and specify 381 in the Cost column cell. This is the cost of obtaining one PS4 console.
  2. Navigate to the Demand table and specify 399 in the Revenue column cells for all customers. This is the retail price of PS4 console.

As you might have noticed, the difference between the cost of obtaining a PS4 unit and the price of selling it constitutes 18$ only. This is due to the fact that Sony is making money not on selling its console but on selling software (games) for its console. Let us run the Network Optimization experiment to see how these changes affect the result we will receive.

Run the Network optimization experiment
  1. Navigate to the Experiments section and click NO experiment. 
  2. Click  Run in the opened experiment's view. The results will be available in the Results 2 sub-item of the NO experiment tree branch.

  3. Analyze the received data in the Optimization results tab below the experiment view.
    The top result still offers the same set of DCs, but the Profit(NetOpt) column value is now positive.

Now we will specify the Initial cost for all potential DC locations. This will inevitably affect the Profit(NetOpt) column value, but we are mostly interested in the locations the experiment will offer us considering the additional expenses.

Specify Initial cost values

  1. Navigate to the Facility Expenses table and click Add to create a new record. 

  2. Double-click the Facility column cell and select Carson City DC from the drop-down list.
  3. Double-click the Expense Type column cell and select Initial cost from the drop-down list.
  4. Click the Value column cell and set 12,092,000 as the cost of opening a facility in Carson city.
    The record should look like this:

  5. In the same way specify the cost of opening a facility for each DC location that is not marked as Excluded, as shown on the screenshot below.

When done, proceed to specify the cost of processing outgoing shipments.

Specify cost of processing outgoing shipments

  1. Navigate to the Processing cost table and create six table records by clicking Add.
  2. Now specify the product and the cost of processing one console unit in outgoing shipments for every DC, as shown on the screenshot below.

Now let us go back to the Network Optimization view and run our experiment again. 

Run the experiment and compare results

  1. Navigate to the Experiments section and click NO experiment. 
  2. Click  Run in the opened experiment's view. The results will be available in the Results 3 sub-item of the NO experiment tree branch.
  3. Analyze the received data in the Optimization results tab below the experiment view.
    The top result still offers the same set of DCs in the top result, but the Profit(NetOpt) column value is now positive.

  4. Compare the received results by switching between the Result 2 and Result 3 sub-items of the of the NO experiment tree branch.

    As you can see, the top result differs now. It means that the best DC locations in terms of the specified costs are: Hazleton DC, Vicksburg DC and Elko DC.

  5. Click the top result in the Optimization results tab of the Result 3 to have it displayed on the GIS map.

You may enable visualization of sourcing paths and zoom in to each offered DC location to examine its paths to the customers it services, as we did on the example of Hazleton DC below. 

We have found the best supply chain configuration using analytical optimization methods. The results we observe take into account: 

Now we need to convert this result to a scenario to be able to modify it later and use it in the Simulation experiment.

Convert Network Optimization experiment results to a scenario

  1. Right-click the top result in the Optimization results tab of the Result 2 sub-item. A pop-up menu will open.

  2. Click the Create scenario for selected item option in the popped-up menu. The current results will be copied to a newly created scenario. The name of the  new scenario with contain the ordinal number of the experiment result that the data was derived from.

You can rename a scenario if needed to give it a meaningful name. We will leave the scenario name as it is.

Now, that you have converted the experiment result to the NO scenario, you can observe the results in ALX tables. Basically we have two tables that acquired additional data as compared to the initial scenario without the results of network optimization.

Observe experiment results in tables

  1. Navigate to the Site States Changes table of the newly created scenario. This table contains the best DC locations additionally to the included Hazleton.

  2. Now move to the Product Flows, which now contains significantly more records. Filter the data by typing Hazleton into the empty field below the Source column name.
    The table contains the following set of records:

We have successfully completed the Network Optimization tutorial.

This new scenario can now be used in the Simulation experiment for further supply chain optimization.

  Phase 1. Configure and run Network Optimization experiment