Optimizing production schedules for flexible manufacturing (MFG101)

Optimizing Production Scheduling for Flexible Manufacturing

Industry Challenges

  • Increasing complexities in manufacturing operations due to real-time fluctuations in demand, cycle times, and resource availability
  • Legacy systems and manual processes still prevalent, making it difficult to integrate and bring dynamic shop floor operations to life
  • Evolving business priorities, such as cost reduction, quality, maintenance, and sustainability goals, creating the need for optimized solutions

The Power of Optimization

  • Optimization is a proven mathematical approach in the field of prescriptive analytics that uses specialized software (solvers) to simplify decision-making
  • Optimization models translate key business problem characteristics into mathematical terms, including objective functions, decision variables, and business constraints
  • Optimization can be applied to various production scheduling scenarios, such as:
    • Optimal economic order quantity
    • Integrating equipment data and performance metrics
    • Optimizing energy consumption and sustainability goals

Customer Scenario: Automotive Manufacturer

  • The customer, a large automotive manufacturer, faced significant production scheduling challenges due to:
    • Competing and changing demand
    • Inaccurate cycle times
    • Labor and resource availability issues
  • These challenges resulted in financial impacts, such as increased overtime costs, expedited material costs, and lost opportunity costs due to downtime
  • AWS can help by enabling more adaptive scheduling, maximizing efficiencies, and reducing costly downtime
  • Key goals for the customer include:
    • Gaining full visibility over production planning
    • Reducing expected overtime
    • Increasing on-time delivery
    • Reducing expedited parts costs

Toward a Smart Factory

  • The vision is to have manufacturing operations that optimize themselves in real-time, with the ability to:
    • Instantly detect issues
    • Analyze millions of scenarios in seconds
    • Implement optimal solutions before problems arise
    • Continuously learn and predict maintenance needs
    • Adapt to demand changes and optimize energy usage
  • This transformation from a traditional factory to a "smart factory" can be enabled by leveraging the power of AWS services and solutions

The AWS Solution Approach

  • Key components of the AWS solution include:
    • Edge collector to gather and contextualize equipment data
    • Optimization engine to generate alternate scheduling solutions
    • Integration with production scheduling systems to implement optimal schedules
  • Leveraging AWS IoT services, event-driven architecture, and open-source/commercial optimization models to enable real-time, adaptive production scheduling

Next Steps

  • Interested parties are invited to visit the AWS kiosks to discuss their specific challenges and explore potential use cases, high-level architectures, and pilot projects
  • Contact information for the presenter is provided for further discussion and collaboration

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