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Related Concept Videos

Interpreting X̄ Charts01:13

Interpreting X̄ Charts

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Interpreting x̄ charts, a type of control chart used in statistical process control helps monitor the variation in processes over time. The x̄ chart is based on the sample mean and allows for monitoring variations in the process mean over time. These charts are pivotal for quality assurance in manufacturing and other sectors.
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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
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Steel manufacturing is a multi-stage process that begins by smelting iron ore into cast iron in a blast furnace. This initial stage involves layering iron ore with coke, a type of fuel, and crushed limestone within the furnace. The coke is ignited with a high volume of air, leading to the creation of carbon monoxide, which acts to reduce the iron ore to pure iron.
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The workability of concrete is a critical characteristic that influences the ease of mixing, handling, and finishing the concrete. It is affected by several factors including water content, aggregate properties, and admixtures like air entrainment. Water plays a fundamental role as it lubricates the concrete mix, facilitating easier movement and placement. However, the water requirement varies depending on the texture and shape of aggregates. Finer particles and angular, rough-textured...
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Systemic analysis of a manufacturing process based on a small scale bakery.

Radosław Drozd1, Radosław Wolniak2, Jan Piwnik3

  • 1Department of Informatics in Management, Faculty of Management and Economics, Gdansk University of Technology, Traugutta 79, 80-233 Gdansk, Poland.

Quality & Quantity
|May 24, 2022
PubMed
Summary

This study introduces two novel reliability concepts for manufacturing systems, focusing on small-scale bakeries. Robotization significantly enhances system reliability and operational efficiency in bread production.

Keywords:
Analysis of relations streamsBread bakingManufacturing process reliabilityQuality

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Area of Science:

  • Industrial Engineering
  • Operations Research
  • Manufacturing Systems Analysis

Background:

  • Manufacturing systems require robust reliability metrics for operational efficiency.
  • Small-scale bakeries face unique challenges in maintaining consistent production reliability.
  • Existing reliability models may not fully capture the complexities of integrated manufacturing streams.

Purpose of the Study:

  • To introduce two innovative concepts for assessing the reliability of functioning manufacturing systems.
  • To develop a new scalar value, Pss, representing global reliability as a product of system stream reliability.
  • To analyze the impact of robotization on manufacturing process reliability in small-scale bakeries.

Main Methods:

  • Development of two reliability representations: global reliability P g (t) and systemic stream reliability Pss.
  • Analysis of system streams including energy, matter, information, time, and finances.
  • Defining and quantifying important (reliability=1) and supportive (reliability 0-1) relations between stream elements.
  • Application of the Pss concept to a small-scale bakery for efficiency analysis.

Main Results:

  • Identified a significant increase in reliability when transitioning from traditional to robotized bakeries.
  • Demonstrated that Pss, a scalar value, characterizes the failure-free operation of a whole system.
  • The Pss formula provides a method for process optimization by increasing important relations between input stream components.

Conclusions:

  • The systemic-stream reliability concept (Pss) offers a quantifiable method for analyzing and optimizing any technological or manufacturing process.
  • Robotization in small-scale bakeries leads to substantial improvements in bread production reliability.
  • The developed reliability concepts are applicable beyond bakeries to diverse manufacturing environments.