Multiple Dependent State Sampling Inspection Plan for Lindley Distributed Quality Characteristic

This article develops multiple dependent state (MDS) sampling inspection plans based on the mean of lifetime quality characteristic that follows non-normal distributions viz., exponential and Lindley distribution. In this plan, the lot quality is measured by the lot mean (𝜇). We have estimated the optimal plan parameters of the proposed technique by non-linear optimization approaches considering acceptable quality level and rejection quality level. We have compared the sample size between the MDS sampling inspection plan and the single sampling inspection plan for the variable. Finally, we have taken two examples to illustrate the proposed technique.

Bayesian Two-sided Complete Group Chain Sampling Plan for Binomial Distribution using Beta Prior through Quality Regions

Acceptance sampling is a technique for statistical quality assurance based on the inspection of a random sample to decide the lot disposition: accept or reject. Producer’s risk and consumer’s risk are inevitable in acceptance sampling. Most conventional plans only focus on minimizing the consumer’s risk. This study focused on minimizing both producer’s and consumer’s risks through the quality region. Experts from available historical knowledge concurred that Bayesian is the best approach to make the correct decision. In this study, a Bayesian two-sided complete group chain sampling plan (BTSCGChSP) was proposed for the average probability of acceptance. The binomial distribution was used to derive the probability of lot acceptance, and the beta distribution was used as the prior distribution. For selected design parameters in BTSCGChSP, the acceptable quality level and limiting quality level were considered to estimate quality regions that were directly associated with producer’s and consumer’s risks, respectively. Four quality regions: (i) quality decision region , (ii) probabilistic quality region (PQR), (iii) limiting quality region, and (iv) indifference quality region, were evaluated. To compare with the existing Bayesian group chain sampling plan (BGChSP), operating characteristic curves were used for the same parameter values and probability of lot acceptance. The findings explained that BTSCGChSP provided a smaller proportion of defectives than BGChSP for the same probability of acceptance. If quality regions were found for the same values of consumer and producer risks, then the BTSCGChSP region would contain fewer defectives than in the BGChSP region. Therefore, for industrial practitioners, the proposed plan is a better substitute for existing BGChSP and other conventional plans.

Mejoramiento de la calidad en la recepción de materias primas, mediante la implementación de un plan de muestreo por variables

Objective: To guarantee the adequate inspection of raw material and packaging material by the inspection-receipt area by implementing an effective sampling plan that allows reducing the percentage of defective raw material and its impact on the production process. Methodology: It is based on the continuous improvement cycle (PHVA) within which a diagnosis is made to determine the current state of the inspection process, which will allow defining new action strategies aimed at standardizing the inspection process, through the implementation of a sampling plan by variables based on the MIL-STD 414 standard, once implemented, this process is documented, at the same time the integration of suppliers is carried out, through periodic evaluations in order to know the dynamics of the new inspection process and work only with those suppliers committed to the quality of their inputs. Contribution: Reduction of the percentage of defective raw material, from 21% to 13%, which means an acceptable quality level of the materials of 87%.

Critical Success Factors of Logistics Organizations

To sustain the growing competition by the domestic players and the international players, these firms are implementing customer service standards to be wary of adopting easily achievable performance standards. But setting standards at unrealistically low levels will not help to establish a competitive advantage. Emphasis on total quality or on creation of the perfect order are very critical to any acceptable quality level set below 100%; the firm should develop customer service policies and standards through customer consultation; the firm should develop procedures to measure, monitor, and control the customer service quality called for by the firm's performance measures and standards. The chapter discusses the various tools, techniques, and highlights the critical success factors for a logistic firm by studying the five top-most logistics organizations on the globe.

On Inspection Strategy Based on Sample Inspection Reliability

As we all known, for a certain product, inspection reliability for the product can be considered and calculated by a famous reliability index as POD (Probability of Detection). While, for a batch of product, Stat. character should be added into POD because one-by-one inspection always is not economic, and POD without any consideration of Stat. character is not used directly for inspection reliability of a batch of product. So, inspection reliability for a batch of products can be considered and divided into reliability of inspecting objective (could be defined as POD) and reliability induced by sample inspection (Rs). In this present paper, inspection strategy based on sample inspection reliability is considered, together with some factors influent to Rs is analyzed such as sample size, producer risk and consumer risk, acceptable quality level (AQL), rejectable quality level (RQL).

Optimal Designing of Variables Chain Sampling Plan by Minimizing the Average Sample Number

We investigate the optimal designing of chain sampling plan for the application of normally distributed quality characteristics. The chain sampling plan is one of the conditional sampling procedures and this plan under variables inspection will be useful when testing is costly and destructive. The advantages of this proposed variables plan over variables single sampling plan and variables double sampling plan are discussed. Tables are also constructed for the selection of optimal parameters of known and unknown standard deviation variables chain sampling plan for specified two points on the operating characteristic curve, namely, the acceptable quality level and the limiting quality level, along with the producer’s and consumer’s risks. The optimization problem is formulated as a nonlinear programming where the objective function to be minimized is the average sample number and the constraints are related to lot acceptance probabilities at acceptable quality level and limiting quality level under the operating characteristic curve.

Equal-Risk Acceptance Sampling Plan

The common acceptance sampling plan needs to specify two points in the operating characteristic curve, which represent the producers and customers risks, respectively. The points are determined in a subjective way and hence may result in unfairness. This paper presents a new approach, which determines the acceptance sampling plan based on the condition that the risks of producer and customer at acceptable quality level are the same. Another condition is introduced to control the mean risk of producer. The approach can considerably simplify the design of acceptance sampling plan, and is illustrated by an example.

VARIABLES QUICK SWITCHING SYSTEM WITH DOUBLE SPECIFICATION LIMITS

This paper investigates a variables quick switching sampling system when a measurable quality characteristic has double specification limits beyond which an item is considered to be a nonconforming. The quality characteristic of interest is assumed to follow a normal distribution. The optimal parameters of the variables quick switching system are determined for both known and unknown standard deviations which satisfy the producer's and consumer's risks at the corresponding specified quality levels. Symmetric and asymmetric cases based on the fraction nonconforming by the lower and the upper specification limits are also considered. The problem is formulated as a nonlinear programming where the objective function to be minimized is the average sample number and the constraints are related to a lot of acceptance probabilities at acceptable quality level and limiting quality level under the operating characteristic curve.