Situation: A manufacturer for power tools has 3 plants and delivers a set of 30 products to 5 distribution centers. Each distribution center is assigned to a set of between 40 and 60 hardware stores. Each store has a demand series for each of the products.
What's given:
- We have the demand series for each product in each hardware store
- We have a mapping table that uniquely assigns each distribution center to a hardware store. This assumption is a simplification as it is possible that one hardware store obtains products from different distribution centers
- We have a table that assignes the costs of shipping a product from each plant to each distribution center
- We have a table of the maximum quantities that can be produced by and shipped from each plant to each of the distribution centers
We proceeed in 2 steps:
- Demand Forecasting: Derive next week's demand for each product and distribution center:
- For the demand series for each product within each store we generate a one-week-ahead forecast
- For distribution center, we derive next week's estimated demand for each product
- Minimization of transportation costs: From the cost and constraints tables of producing by and shipping from a plant to a distribution center we derive cost-optimal transportation.
Author Max Köhler max.kohler@databricks.com
© 2022 Databricks, Inc. All rights reserved. The source in this notebook is provided subject to the Databricks License [https://databricks.com/db-license-source]. All included or referenced third party libraries are subject to the licenses set forth below.
| library | description | license | source |
|---|---|---|---|
| pulp | A python Linear Programming API | https://github.com/coin-or/pulp/blob/master/LICENSE | https://github.com/coin-or/pulp |
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