scholarly journals TIME CONTROL CHARTS THROUGH NHPP BASED ON NEW WEIGHTED EXPONENTIAL DISTRIBUTION

2022 ◽  
Vol 72 (1) ◽  
pp. 97-111
Author(s):  
Anil Arepalli ◽  
B. Srinivasa Rao
2021 ◽  
Vol 23 (11) ◽  
pp. 825-836
Author(s):  
Anil Arepalli ◽  
◽  
B. Srinivasa Rao ◽  

It is assumed that the probabilistic model of the quality characteristics follows the new weighted exponential distribution. Control charts based on each subgroup’s extreme values are established. The constants in the control chart are determined by the probability distribution of the extreme value order statistics of the sub-group and the sub-group size. The proposed chart is thus referred to as an extreme values chart. A biased overall mean analysis method (ANOM for truncated population) is used for the new weighted exponential distribution. Examples based on real time data are used to explain the findings.


1952 ◽  
Vol 6 (2) ◽  
pp. 7-9 ◽  
Author(s):  
R. Schmidt

The reliability of chemical analysis is discussed in a general way, stress being laid on the necessity of using the methods of mathematical statistics in the design as well as in the evaluation of experiments (factorial design; analysis of variance; randomisation in time; control charts), carried out to develop analytical methods and procedures, in order to obtain reliable results in the application of the methods in practical analysis.


Hand ◽  
2019 ◽  
Vol 15 (5) ◽  
pp. 659-665 ◽  
Author(s):  
Michael T. Milone ◽  
Heero Hacquebord ◽  
Louis W. Catalano ◽  
Steven Z. Glickel ◽  
Jacques H. Hacquebord

Background: No study exists on preparatory time—from patient’s entrance into the operating room to skin incision—and its role in hand surgery operating room inefficiency. The purpose of this study was to investigate the length and variability of preparatory time and assess the relationship between several variables and preparatory time. Methods: Consecutive upper extremity cases performed for a period of 1 month by hand surgeons were reviewed at 3 surgical sites. Preparatory time was compared across locations. Cases at one location were further analyzed to assess the relationship between preparatory time and several variables. Both traditional statistical methods and Shewhart control charts, a quality control tool, were used for data analysis. Results: A total of 288 cases were performed. The mean preparatory times at the 3 sites were 25.1, 25.7, and 20.7 minutes, respectivley. Aggregated preparatory time averaged 24.4 (range 7-61) minutes, was 75% the length of the surgical time, and accounted for 34% of total operating room time. Control charts confirmed substantial variability at all locations, signifying a poorly defined process. At a single site, where 189 cases were performed by 14 different surgeons, there was no difference in preparatory time by case type, American Society of Anesthesiologists status, or case start time. Preparatory time varied by surgeon and anesthesia type. Conclusions: Preparatory time was found to be a source of inefficiency, independent of the surgical site. Control charts reinforced large variations, signifying a poorly designed process. Surgeon seemingly plays an important, albeit likely indirect, role. Efforts to improve operating room workflow should include preparatory time.


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