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Sequential and systematic sampling plans for the Markov‐dependent production process

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  • P. Vellaisamy
  • S. Sankar

Abstract

Acceptance sampling plans are used to assess the quality of an ongoing production process, in addition to the lot acceptance. In this paper, we consider sampling inspection plans for monitoring the Markov‐dependent production process. We construct sequential plans that satisfy the usual probability requirements at acceptable quality level and rejectable quality level and, in addition, possess the minimum average sample number under semicurtailed inspection. As these plans result in large sample sizes, especially when the serial correlation is high, we suggest new plans called “systematic sampling plans.” The minimum average sample number systematic plans that satisfy the probability requirements are constructed. Our algorithm uses some simple recurrence relations to compute the required acceptance probabilities. The optimal systematic plans require much smaller sample sizes and acceptance numbers, compared to the sequential plans. However, they need larger production runs to make a decision. Tables for choosing appropriate sequential and systematic plans are provided. The problem of selecting the best systematic sampling plan is also addressed. The operating characteristic curves of some of the sequential and the systematic plans are compared, and are observed to be almost identical. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 451–467, 2001

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  • P. Vellaisamy & S. Sankar, 2001. "Sequential and systematic sampling plans for the Markov‐dependent production process," Naval Research Logistics (NRL), John Wiley & Sons, vol. 48(6), pages 451-467, September.
    • Handle: RePEc:wly:navres:v:48:y:2001:i:6:p:451-467
    DOI: 10.1002/nav.1028
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    Cited by:

    1. P. Vellaisamy & S. Sankar, 2005. "A Unified Approach for Modeling and Designing Attribute Sampling Plans for Monitoring Dependent Production Processes," Methodology and Computing in Applied Probability, Springer, vol. 7(3), pages 307-323, September.
    2. P. Vellaisamy & S. Sankar & M. Taniguchi, 2003. "Estimation and Design of Sampling Plans for Monitoring Dependent Production Processes," Methodology and Computing in Applied Probability, Springer, vol. 5(1), pages 85-108, March.

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