---
# Automated Master Production Schedule across Manufacturing Sites
**URL:** https://www.sigmoid.com/case-studies/automated-master-production-schedule/
Date: 2022-03-28
Author: Sigmoid
Post Type: page
Summary: 15% Increase in Capacity Utilization with Automated Master Production Schedule Robust ML solution enabled near real-time visibility of operational functioning and other...Read More...
Featured Image: https://www.sigmoid.com/wp-content/uploads/2023/02/automated-master-production-schedule-cs-thumbnail-opt.jpg
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# 15% Increase in Capacity Utilization with Automated Master Production Schedule
Robust ML solution enabled near real-time visibility of operational functioning and other KPIs.
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Robust ML solution enabled near real-time visibility of operational functioning and other KPIs
### Business Challenge
The customer is a Fortune-500 biopharmaceutical company, who wanted to develop a Master Production Schedule that would automate the process for their various manufacturing plants and generate faster, accurate schedules. Their existing production schedules were designed manually and took nearly two days to alter, in case of changes in demand, raw material availability or safety stock requirements. They wanted a system that would synchronize operations within the organization, improve productivity and capacity planning.
### Sigmoid Solution
Sigmoid built a linear programming model for the different sites based on certain unique site-specific requirements and constraints. The process of generating a Master Production Schedule was automated using Constraint Programming having blended KPIs as the objective function. Google OR-Tools and other optimization tools were used to generate various scenarios based on fluctuating demand and inventory levels, considering the constraints such as production run rate, timelines, and machinery assignment. Amazon SageMaker was used to build, train and deploy scalable ML models. The resulting solution could be generated within a few minutes, making it relatively easier to make schedule changes as compared to the earlier process. Sigmoid also developed schedule adherence visualization, notifications and trend highlights.
[Download case study](#case-study-download)
### Business Impact
Sigmoid’s model ensured an optimal solution was reached through generation of multiple scenarios and schedules. It automatically adjusts the rest of the schedule based on a single change to ensure optimality of the schedule, which was a major improvement from the existing labour-intensive process.
15%
Increase in capacity utilization
98%
Reduction in time for schedule generation
Near real-time
visibility to operational metrics and KPIs
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