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Related Concept Videos

Range00:59

Range

11.2K
The range is one of the measures of variation. It can be defined as the difference between a dataset's highest and lowest values. For example, in the study of seven 16-ounce soda cans, the filled volume of soda was measured, thus producing the following amount (in ounces) of soda:
15.9; 16.1; 15.2; 14.8; 15.8; 15.9; 16.0; 15.5
Measurements of the amount of soda in a 16-ounce can vary since different subjects record these measurements or since the exact amount - 16 ounces of liquid, was not...
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Midrange01:07

Midrange

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A somewhat easy to compute quantitative estimate of a data set’s central tendency is its midrange, which is defined as the mean of the minimum and maximum values of an ordered data set.
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to...
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Interpreting R Charts01:22

Interpreting R Charts

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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.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
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Electronic Distance Measuring Instruments01:30

Electronic Distance Measuring Instruments

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Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over short...
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The R Chart01:02

The R Chart

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In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
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Response Surface Methodology01:16

Response Surface Methodology

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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
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Assessing the logistics industry efficiency with a modified range adjusted measure.

Chongyu Ma1, Jianwei Ren2, Chunhua Chen3

  • 1Transportation Institute, Inner Mongolia University, Hohhot, 010000, China.

Scientific Reports
|July 31, 2024
PubMed
Summary
This summary is machine-generated.

A new Modified Range Adjust Measure (MRAM) model improves logistics efficiency assessment. This enhanced method provides more accurate results compared to existing models, aiding in production issue identification and optimization.

Keywords:
Data envelopment analysisLogistics industry efficiencyModified boundsRange adjust measure

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Area of Science:

  • Operations Research
  • Logistics Management
  • Data Envelopment Analysis

Background:

  • Accurately assessing logistics efficiency is crucial for identifying production issues and optimizing operations.
  • The existing Range Adjust Measure (RAM) model is limited to variable returns to scale conditions.
  • There is a need for a more versatile efficiency assessment method applicable under different scale conditions.

Purpose of the Study:

  • To develop a Modified Range Adjust Measure (MRAM) model for enhanced logistics efficiency assessment.
  • To adapt the RAM model for constant returns to scale (CRS) conditions.
  • To validate the practicality and accuracy of the MRAM model using real-world logistics data.

Main Methods:

  • Development of a RAM-CRS model under constant returns to scale.
  • Introduction of radial models to define input lower bounds and output upper bounds for the MRAM.
  • Application and comparison of the MRAM model with RAM-CRS and an additive model using logistics data from 18 provinces.

Main Results:

  • The RAM-CRS model's efficiency values were found to be relatively low due to overly restrictive range bounds.
  • The MRAM model, with its modified bounds, effectively alleviates the restrictions of the RAM-CRS model.
  • Comparative analysis indicated that the MRAM model provides more accurate efficiency measurements than the additive model.

Conclusions:

  • The MRAM model offers a more flexible and accurate approach to logistics efficiency assessment, particularly under constant returns to scale.
  • The modified bounds in MRAM allow for a more realistic evaluation of efficiency compared to RAM-CRS.
  • The MRAM model is a valuable tool for identifying production issues and supporting logistics optimization efforts.