We plan to start selling home and garden products in the USA.
The supplier is located in Brazil in Rio De Janeiro.
We will consider the supply chain with 70 customers on the territory of the United States.
The supply chain deals with five products:
- Large home appliances
- Gardening equipment
- Small appliances
Demand is periodic, i.e., certain volume of products is ordered every 5 days:
- 16 customers order 16 furniture
- 18 customers order 4 gardening equipment
- 18 customers order 12 large home appliances
- 15 customers order 8 lightning
- 17 customers order 20 small appliances
We used the GFA experiment to find the following areas for regional distribution centers:
- West (Nevada)
- South (Texas)
- East (border of Virginia, West Virginia and the District of Colombia)
Then we used the NO experiment to find port for transshipment and the most suitable warehouse locations in every area:
- DC Lynchburg
- DC Reno
- DC Austin
- Port of New Orleans
anyLogistix offers two scenarios:
- Example with estimated demand without variation
- Example with triangular demand distribution and with demand variation (in Variation experiment)
By using the simulation experiment we can review each network budgeting detail for both scenarios. By default, the following details are available in the Profit and Loss Statement page of the experiment results:
- Graph “Revenue, Total Cost”
- Schedule “Profit and Loss Statement”
- Histogram “Profit, Revenue, Total Cost”
More information can be found in the next tabs:
- Service Level
- Lead Time
- Available Inventory
- The output of other efficient data can be customized
By comparing scenarios (1) and (2) we can answer the following question:
How will the changes in demand affect the company budget?
The Profit and Loss Statement table shows budget changes when demand is variable.
By running the Variation experiment in scenario (2) we can answer the following question:
What will happen if demand is either increased by 20% or defined by the triangular distribution?
The graphs below show that the Service Level by Products is sensitive to variations in demand.
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