An economic design of the zone control chart for jointly monitoring process centering and variation

Ho, Chuanching; Case, Kenneth E.

doi:10.1016/0360-8352(94)90056-6

Cited by 15 publications

(5 citation statements)

References 14 publications

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“…The Zones graph consists of eight zones (four on each side of the center line) (Zhang et al, 2018), bounded by a central line, the limits: 1-sigma, 2-sigma and 3-sigma. Its use is recommended for practical use because of its performance, simplicity, efficiency, ease of use and understanding (Ho & Case, 1994). For the interpretation of the Zones graphs, the scoring rules (Davis et al, 1990), described in Table 2, were used, and a graph is considered out of statistical control when it reaches 8 points.…”

Section: Introductionmentioning

confidence: 99%

Statistical Quality Control in Uniformity of Drip Irrigation With Different Slopes

Lopes¹,

Boas²,

Pazuch³

et al. 2019

JAS

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The topography directly influences the functioning of an irrigation system, being necessary the determination of the uniformity to verify its performance. Statistical quality control is a powerful tool for verifying the quality of a process. Thus, it was aimed to use the statistical control of quality in the evaluation of the uniformity of a drip irrigation system in different slopes. The Christiansen’s uniformity coefficient (UC) and Uniformity of Distribution (UD) were determined and analyzed by the control graphs of Shewhart, Zones and CUSUM and by the indices of process capacity (Cp, Cpl and Cpk), in different slopes (0%, 2% and -2%). The slope irrigation was more uniform (UC = 99.03% and UD = 98.45%), however, for all the graphs studied it was out of statistical control. Zone charts were more sensitive than the CUSUM and Shewhart charts.

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Section: Introductionmentioning

confidence: 99%

Statistical Quality Control in Uniformity of Drip Irrigation With Different Slopes

Lopes¹,

Boas²,

Pazuch³

et al. 2019

JAS

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“…Assumptions 1-4 used in our cost model are similar to those given in Ho and Case (1994). Assumptions 5-10 are related to the expected time of occurrence of assignable causes and shifts in the process mean and variance for the interested quality characteristic.…”

Section: Assumptionsmentioning

confidence: 99%

An optimal design of joint X‐ and S control charts using quadratic loss function

Yang

1997

International Journal of Quality &Amp; Reliability Management

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Cost models for the design of control charts based on Duncan’s approach have been studied in recent years. Presents a double assignable‐cause cost model, which is in terms of Taguchi’s loss imparted to society from the time a product is shipped, using renewal theory approach. The expression for the expected cycle length and the expected cost per cycle are easier to obtain by the proposed approach, and the cost model, including the customer’s voice, reveals the importance of quality. Sensitivity analysis performed on a large number of numerical examples illustrates that the cost of repair or replacement and customers’ tolerance, which are related to loss function, are critical when designing economically based on X‐ and S control charts.

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“…Davis et al studied the performance of the zone control chart while Jin and Davis provided the average run length calculation for the zone control chart using a Fortran program. Davis et al improved the performance of the zone control chart, and Ho and Case proposed an economic design of the zone control chart for simultaneously monitoring the process mean and variance. Champ and Rigdon provided an in‐depth analysis of the RS chart.…”

Section: Introductionmentioning

confidence: 99%

The Run Sum Hotelling's χ² Control Chart with Variable Sampling Intervals

Chew

Khoo

Teh

et al. 2016

Quality & Reliability Eng

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Control charts are widely used for process monitoring and quality control in manufacturing industries. Implementing variable sampling interval (VSI) control schemes on control charts rather than traditional fixed sampling interval procedure can significantly improve the control chart's efficiency. In this paper, the VSI run sum (RS) Hotelling's χ2 chart is proposed. The optimal scores and parameters of the proposed chart are determined using an optimization technique to minimize the following: (i) out‐of‐control average time to signal (ATS); (ii) adjusted ATS (AATS), when the exact shift size can be specified; (iii) expected ATS; or (iv) expected AATS, when the exact shift size cannot be specified. The Markov chain method is used to evaluate the zero‐state ATS and expected ATS, and steady‐state AATS and expected AATS of the proposed chart. The results show that the VSI RS Hotelling's χ2 chart significantly outperforms the standard RS Hotelling's χ2 chart and the former also performs well compared with other competing charts. By adding more scoring regions, the efficiency of the VSI RS Hotelling's χ2 chart can be further enhanced. An illustrative example using data from a manufacturing process is presented to demonstrate the application of the VSI RS Hotelling's χ2 chart. The application of the proposed chart in a quality improvement program can be extended to management and service industries. Copyright © 2016 John Wiley & Sons, Ltd.

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An economic design of the zone control chart for jointly monitoring process centering and variation

Cited by 15 publications

References 14 publications

Statistical Quality Control in Uniformity of Drip Irrigation With Different Slopes

Statistical Quality Control in Uniformity of Drip Irrigation With Different Slopes

An optimal design of joint X‐ and S control charts using quadratic loss function

The Run Sum Hotelling's χ² Control Chart with Variable Sampling Intervals

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An economic design of the zone control chart for jointly monitoring process centering and variation

Cited by 15 publications

References 14 publications

Statistical Quality Control in Uniformity of Drip Irrigation With Different Slopes

Statistical Quality Control in Uniformity of Drip Irrigation With Different Slopes

An optimal design of joint X‐ and S control charts using quadratic loss function

The Run Sum Hotelling's χ2 Control Chart with Variable Sampling Intervals

Contact Info

Product

Resources

About

The Run Sum Hotelling's χ² Control Chart with Variable Sampling Intervals