Data Aggregation – Warehouse and Retail Stores
The analysis outline:
Data collection:
Location of retail stores, existing warehouses (5 warehouses located in Atlanta, Boston, Chicago, Dallas and Los Angeles), manufacturing facilities (a single manufacturing facility in San Jose), and suppliers.
Candidate locations for the new warehouse.
Information about products, i.e., their sizes, shapes and volumes.
Annual demand (past actuals and future estimates) and service level requirements for each product by customer location.
Transportation rates by available modes.
Specify maximum transportation distances from candidate warehouse locations to retail stores.
Handling warehousing costs, including labor, inventory carrying charges, and fixed operating costs.
Fixed ordering costs, order frequencies and sizes by product or product family.
Data aggregation:
Demand needs to be aggregated based on distribution patterns and/or product types. Customers located in close proximity to each other should be aggregated using a grid network or clustering technique.
Products will also be aggregated into a reasonable number of product groups, based on the Distribution pattern and the Product type.
Model and Data validation based on proposed network structure.
After the data is collected, tabulated, and cleaned for the specific network model, we have to make sure that the model make sense, the data is consistent, the model results can be fully explained, and sensitivity analysis has to be performed.
Selection of a few low cost alternatives based on the mathematical model.
Before the final decision, incorporate qualitative factors that were disregarded in the mathematical model, e.g., specific regulations, environmental factors, etc.
Optionally, build a detailed simulation model to evaluate these low cost solutions and perform micro level analysis that may include individual ordering pattern, specific inventory policies and inventory movement inside the warehouse.
Advantages of the newly suggested distribution strategy:
One of the