Moving average control chart under neutrosophic statistics

Muhammad Aslam; Khushnoor Khan; Mohammed Albassam; Liaquat Ahmad; Muhammad Aslam; Khushnoor Khan; Mohammed Albassam; Liaquat Ahmad

doi:10.3934/math.2023357

AIMS Mathematics

2023, Volume 8, Issue 3: 7083-7096. doi: 10.3934/math.2023357

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Moving average control chart under neutrosophic statistics

1.
Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21551, Saudi Arabia
2.
Department of Statistics and Computer Science, University of Veterinary and Animal Sciences, Lahore 54000, Pakistan

Received: 19 September 2022 Revised: 26 December 2022 Accepted: 29 December 2022 Published: 11 January 2023
MSC : 62A86

Continuous monitoring and improving the production process is a crucial step for the entrepreneur to maintain its position in the market. A successful process monitoring scheme depends upon the specification of the quality being monitored. In this paper, the monitoring of temperature is addressed using the specification of moving average under uncertainty. We determined the coefficients of the proposed chart utilizing the Monte Carlo simulation for a different measure of indeterminacy. The efficiency of the proposed chart has been evaluated by determining the average run lengths using several shift values. A real example of weather-related situation is studied for the practical adoption of the given technique. A comparison study shows that the proposed chart outperforms the existing chart in monitoring temperature-related data.
- control chart,
- process monitoring,
- temperature,
- neutrosophic statistics,
- Monte Carlo simulation,
- average run lengths
Citation: Muhammad Aslam, Khushnoor Khan, Mohammed Albassam, Liaquat Ahmad. Moving average control chart under neutrosophic statistics[J]. AIMS Mathematics, 2023, 8(3): 7083-7096. doi: 10.3934/math.2023357

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Abstract

Continuous monitoring and improving the production process is a crucial step for the entrepreneur to maintain its position in the market. A successful process monitoring scheme depends upon the specification of the quality being monitored. In this paper, the monitoring of temperature is addressed using the specification of moving average under uncertainty. We determined the coefficients of the proposed chart utilizing the Monte Carlo simulation for a different measure of indeterminacy. The efficiency of the proposed chart has been evaluated by determining the average run lengths using several shift values. A real example of weather-related situation is studied for the practical adoption of the given technique. A comparison study shows that the proposed chart outperforms the existing chart in monitoring temperature-related data.

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