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BLENDING OR/MS, JUDGEMENT, AND GIS: RESTRUCTURING THE P&G’S SUPPLY CHAIN
Presented by Assel Gubaidullina and Carolina Argenté
The case study analyzes in great details how by utilizing integer programming and network optimization worked in concert with Geographical Information System (GIS) to re-engineering product sourcing and distribution system for North America.
Procter & Gamble is the biggest company in “Fast Moving Consumer Goods”. The company produces 300 brands worldwide in 140 countries and has operating in 58 locations around the world. It has grown continuously over the past 159 years, and the challenge is to keep growing globally over the years.
Problem
It became noticeably difficult for P&G be flexible and fast enough operating with hundreds of suppliers, 50 product categories, 60 plants, 15 distribution centers and 1000 consumers. In 1993, P&G conducted the study which had to help such question as: the amount of product categories that should be produced in a plant, the number of plants that should there be built or closed, where to locate new plants, where to locate DCs so it will be possible to deliver the products to the customers faster.
OR/MS team realized that they need a system that provides fast and optimal solution while being easy to understand for all users, especially the top level managers that would validate its usefulness. The overall supply chain problem was divided in two subproblems: * Product sourcing problem (for each product category) * Distribution-location problem
Model
The distribution-location problem was solved using an ordinary uncapacitated facility-location model. The product sourcing problem was solved by a simple transportation model.
Solution
OR/MS team solved Distribution Location model on a 486 33 Mhz computer using Lindo with 2000 variables and 2200 constraints. In comparison, the Product-Sourcing model was not just OR, but the objective was to have something that could
Presented by Assel Gubaidullina and Carolina Argenté
The case study analyzes in great details how by utilizing integer programming and network optimization worked in concert with Geographical Information System (GIS) to re-engineering product sourcing and distribution system for North America.
Procter & Gamble is the biggest company in “Fast Moving Consumer Goods”. The company produces 300 brands worldwide in 140 countries and has operating in 58 locations around the world. It has grown continuously over the past 159 years, and the challenge is to keep growing globally over the years.
Problem
It became noticeably difficult for P&G be flexible and fast enough operating with hundreds of suppliers, 50 product categories, 60 plants, 15 distribution centers and 1000 consumers. In 1993, P&G conducted the study which had to help such question as: the amount of product categories that should be produced in a plant, the number of plants that should there be built or closed, where to locate new plants, where to locate DCs so it will be possible to deliver the products to the customers faster.
OR/MS team realized that they need a system that provides fast and optimal solution while being easy to understand for all users, especially the top level managers that would validate its usefulness. The overall supply chain problem was divided in two subproblems: * Product sourcing problem (for each product category) * Distribution-location problem
Model
The distribution-location problem was solved using an ordinary uncapacitated facility-location model. The product sourcing problem was solved by a simple transportation model.
Solution
OR/MS team solved Distribution Location model on a 486 33 Mhz computer using Lindo with 2000 variables and 2200 constraints. In comparison, the Product-Sourcing model was not just OR, but the objective was to have something that could