AI can analyze thousands of Production Scenarios and create valid and optimal plans/schedules that maximizes yield while improving other important KPIs.
Optimize Product Transitioning/Sequencing (switching from manufacturing product A to product B on the same line), cycle length, while minimizing the costs of transitions and maximizing product availability for customers (taking demand into consideration) – all whilst minimizing inventory on hand
Understand the Use-case under 5 minutes
Article & Video
AI Production Schedule Optimization uses AI to generate accurate demand forecasts and optimal schedules for complex operations to minimize production costs and maximize OTIF planning and scheduling performance.
Visit
Get to know more Business and Technical details about the use-case (15-30 minutes)
More detailed introduction covering business and technical aspects
White Paper
How AI could be used to optimize manufacturing planning by bringing: Optimized planning parameterization, Production sequence abstraction, & more
Read
Article (7 minutes)
AI-powered capacity planning strategies take into account all the relevant aspects of production including workforce, facilities, production schedules, budgets, and supplies - to recommend best possible plans and schedules
ReadCase studies, Organizational Aspects, Return on Investment examples
Case Study (3 minutes)
By implementing the BHC3™ Process Optimization application, the manufacturer reduced average product transition times by more than 30%, saving millions of dollars
Visit
Case Study (3 minutes)
Lenovo Research created its own AI solution to overhaul and optimize production. The result? Planning processes cut from six hours to just 90 seconds.
Visit
Case Study (2 minutes)
How a large hydrocarbon processing company configured the C3 AI® Production Schedule Optimization application on a large polypropylene plant to optimize production schedules and minimize manufacturing costs.
VisitMore details on the technical aspects of the use-case
Video (23 minutes)
Reinforcement learning trained agents that optimize a production planning problem at Dow, Inc and deliver measurable business impact
Watch
Article (2 minutes)
Overview of the main technical components of Lenovo’s AI Smart PProduction Planning System: Heuristic search, Incremental iterative optimization, & Optimization algorithm based on deep graph network
Visit
Technical resources that will help you implement the use-case (notebooks, tutorials..)
Github Repo
This framework provides an integrated simulation and reinforcement learning model to investigate the potential of data-driven reinforcement learning in production planning and control of complex job shop systems.
VisitGithub Repo
Optimal Usage of manufacturing lines based on sales orders, backlogs and demand forecast minimizing cost and chances of delay thus producing working factory layout.
Visit
Video (20 minutes)
This video you will learn how to build a mathematical model for dynamic scheduling in smart manufacturing including assumptions, tasks, services, optimization capabilities and constraints.
Watch
Article (12 minutes)
Given a set of heterogeneous machines and a set of heterogeneous production jobs, compute the processing schedule
Visit
Using Reinforcement Learning for Scheduling Optimization: The system automatically develops a scheduling solutions, which is on a par with the expert benchmark, without human intervention or any prior expert knowledge
Read
The implementation of a deep reinforcement learning based agent to automatically make scheduling decisions for a continuous chemical reactor currently in operation
Read
Data Sets you can use to build Demos, POCs, or test Algorithms
Stay tuned
Off-the-Shelf Products using AI for Optimizing Production Planning
Got a Question or a Resource to share with the Community? Please do!
Copyright © 2024 AI Cases. All rights reserved
Session expired
Please log in again. The login page will open in a new tab. After logging in you can close it and return to this page.
