Leading global pharmaceutical company that discovers, develops, and distributes a wide range of medicines, vaccines, and innovative healthcare products.
The client wanted to transform the capacity management process across manufacturing sites, warehouses and distribution centers. The client was using legacy systems which made the process less fluid and difficult to optimize.
SG Analytics' data scientists developed a framework for effective capacity planning using the following approach:
At the outset, SG Analytics' team reviewed the existing system. It observed that data was maintained in various inconsistent formats and there were data entry errors. The data was stored in row-based storage due to which scenario management and optimization was not possible.
The team built an application interface that placed the data in a Linux environment and triggered the shell script for automating the data quality checks and standardizing the data hierarchy across locations.
We built a Qlikview based solution to enable scenario planning and management.
SG Analytics implemented Spark Graph X which could store data by nodes, followed by optimization based on distance and number of nodes/locations.
Tools and Technologies used
Reduced the time taken for the data standardization process.
Designed predictive scenarios and observing change in demand & supply profile.
Ranked the top five route options available, and allowed business users decide the best course of action.