Building Optimization Model For Supply Chain Management
Lafarge India Ltd. (LIL) is a subsidiary of the Lafarge Group France, with a total production capacity of 5 million tons and a clinker capacity of 3 million tons. Its product range includes Portland Slag Cement, Ordinary Portland Cement and Portland Pozzolana Cement and operates in a market spread in 14 states through a huge dealer network.
Our client Lafarge India Ltd. has multiple raw materials input and finished goods output points, which has created many bottlenecks in their supply chain processes. To improve the performance of their supply chain management system they were in urgent need of optimization of their existing system.
The challenge was to collect data from scattered resources and process them to give quantifiable real time data/reports to facilitate the production and distribution related decision-making.
LIL invited Binary Semantics to assist them in defining and constructing this supply chain solution.
- Optimization of over all supply chain on the basis of given constraints like: raw material inventory, production capacity, market demand & status, transportation cost and production cost.
- Development and implementation of a web interface to accept inputs from varied sources and provide various input, output and analysis reports across the company's network.
We suggested the use of OR (Operations Research) technique and LINGO 9.0 modeling software. With the help of these technique and tool, we created a mathematical model, incorporating various constraints of time, costs, inventory and distance; and Binary’s Optimization solution was designed.
The Binary Optimization solution consisted of Lingo engine, optimization model and web interface to send and receive data across LIL. The decision variables in the optimization engine were designed to decide on the exact quantity of raw material and finished goods to be shipped to different stock keeping units (SKUs).
This optimization solution implementation has improved LIL's planning capability for manufacturing and distribution processes, which have helped in creating a more robust supply chain.