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Summary
This summary is machine-generated.

This study introduces a risk assessment method for machine inspections and production planning. It improves reliability and uses a maximum likelihood approach for accurate scheduling, minimizing production disruptions and costs.

Keywords:
MTTFSix Sigmanormal distributionpredictive maintenanceproduction planningreliability theory

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Area of Science:

  • Industrial Engineering
  • Operations Research
  • Reliability Engineering

Background:

  • Unpredicted machine failures disrupt production plans, leading to rescheduling, increased costs, and reduced quality.
  • Accurate machine reliability assessment is crucial for effective production planning and risk management.

Purpose of the Study:

  • To develop a method for risk assessment and planning of technical inspections for machines.
  • To optimize production tasks by integrating reliability characteristics and Six Sigma principles.
  • To enhance the prediction of scheduling problems in job shop environments.

Main Methods:

  • Describing machine failure frequency using a normal distribution truncated to the positive half-axis.
  • Extending the Six Sigma concept for continuous monitoring and control of production processes.
  • Utilizing reliability characteristics to develop predictive schedules and assessing them for robustness.
  • Comparing parameter estimation methods for disturbances in job shop scheduling problems.

Main Results:

  • The maximum likelihood estimation method provided more accurate predictions for scheduling problems.
  • The proposed method integrates risk assessment, reliability, and Six Sigma for improved production planning.
  • The developed approach was practically applied to electric steering gears, demonstrating its effectiveness.

Conclusions:

  • The proposed method effectively addresses machine reliability and production scheduling challenges.
  • Implementing this approach can lead to more robust and efficient production processes.
  • Accurate parameter estimation is key to minimizing the impact of machine failures on production schedules.