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.
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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
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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
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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.
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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
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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
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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.
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Optimal Usage of manufacturing lines based on sales orders, backlogs and demand forecast minimizing cost and chances of delay thus producing working factory layout.
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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.
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Article (12 minutes)
Given a set of heterogeneous machines and a set of heterogeneous production jobs, compute the processing schedule
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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
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The implementation of a deep reinforcement learning based agent to automatically make scheduling decisions for a continuous chemical reactor currently in operation
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Data Sets you can use to build Demos, POCs, or test Algorithms
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Off-the-Shelf Products using AI for Optimizing Production Planning
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