Academic literature on the topic 'Average Outgoing Quality (AOQ)'

To stay competitive in today’s global market, manufacturers must ensure products released to consumers, meet international regulations and standards and this can only be done by putting in place sampling plans to guarantee the release of quality lots of products into the market. Hence, the objective of this paper is to design a variables sampling plan for the released of packed yam flour in view of international regulations on the net content of packaged goods. Probability plots, operating characteristic curves, the average outgoing quality (AOQ), average outgoing quality limit (AOQL) and average total inspection (ATI) were useful measures to evaluate the fitness of the sampling plan using the Minitab 2021 statistical software package. The packing process net weight, was found to be normally distributed with a p-value of 0.075 and a process standard deviation of 2.16. A comparative analysis on sample size, sampling plan measures, such as the AOQ, AOQL, and ATI and in view of best practice, were decisive in selecting a sampling plan with a sample size of 31 packs per lot as the most economic plan for lot sentencing. A practical demonstration on this sampling plan usage was also showcased. This sampling plan elevates and improves the net content of the packed product released into the market in view of international regulatory laws.

Increasing the amount of inspection activities and inspecting a large fraction of the items are two approaches that are used to improve products' quality. In this paper, as in a precedent article by Jaraiedi, et al.[5], 100% inspection in combination with multiple-criteria decision (MCD) response is considered. Three different inspection procedures for the multi-stage inspection are presented. Performance of these three procedures are derived and the Average Outgoing Quality (AOQ), Average False Rejected (AFR), and Overall Average Fraction Inspected (SAFI) for all procedures are compared. Two examples are discussed in depth to illustrate numerical comparisons of these procedures.

Today, prefabricated concrete elements are used in many construction areas, including in industrial, public, and residential construction; this was confirmed via questionnaire research. In the article, the prospects for precast concrete development are presented, and the factors determining the use of this technology are defined. Based on a review of the literature, it was shown that currently, higher-quality prefabricated elements are primarily created through the implementation of innovative materials and production technologies. For this reason, the lack of research regarding quality control in prefabricated elements based on statistical quality control is particularly noticeable. The quality control process is one of the most important distinguishing features in prefabrication due to the increasingly stringent expectations of customers; it helps to ensure that the desired durability of implemented constructions is achieved. Issues related to assessing the effectiveness of standard procedures presented in this paper were analyzed using statistical methods in the form of OC (operating characteristic) and AOQ (average outgoing quality) curves. Thus, a new approach was proposed because these methods have not been previously used in precast concrete. The shape of the curves obtained confirmed the significant dependence of the value of the acceptance probability on the defectiveness of production. In AQL control systems based on OC and AOQ curves, it is necessary to calculate the current average defectiveness, which should be treated as a basis for the decision to switch from one type of control (normal, tightened, or reduced) to another. In this respect, the standard requirements of quality control have been simplified, and it has not been considered necessary to determine the average defectiveness value in production processes. The examples included in this study, including the analysis of curb production data, clearly show the harmful effects of ignoring the actual process defectiveness. As a result of the calculations, it was found that the average actual defectiveness of the curbs produced could not be equated with batch defectiveness. The analyses carried out in this study prove that equating batch defectiveness with process defectiveness is not an appropriate approach, which was confirmed through the producer’s/customer’s risk analysis. The approach proposed in this study, the analysis of OC and AOQ curves, is an innovative solution in prefabrication and can be an effective tool for managing the quality of prefabricated products, taking into account economic boundary conditions.

Purpose – The purpose of this paper is to introduce a hybrid framework (suppliers, inputs, process, output and customers+define, measure, analyze, improve and control (SIPOC+DMAIC)) aimed at improving supply chain management (SCM) process dimensions in a supply chain (SC) network. Design/methodology/approach – Based upon the critical review of literature, process dimensions (average outgoing quality limit (AOQL), average outgoing quality (AOQ), process Z, defect per million opportunity) critical to SCM performance were identified. A framework consisting of three phases, i.e., design, implementation and results has been conceptualized and a case from paint industry is investigated. Implementation framework makes use of SIPOC model and Six Sigma DMAIC methodology. The goals of the study were achieved by using Six Sigma tools such as brainstorming sessions; root cause analysis, histograms, statistical tools such as control charts and process capability analysis. Findings – Authors made an attempt to propose a conceptual framework for improving process dimensions in a SC network. It is observed from the results that selection of appropriate strategies for improving process performance based upon experiences, and use of statistical tools by cross-functional teams with an effective coordination, guarantees success. Metrics such as AOQL shows the maximum worst possible defective or defect rate for the AOQ. Process Z helps to know about sigma capability of the process. Research limitations/implications – The framework so developed is tested in a single company manufacturing batches of paint. The study has important implications for the industry since it tries to integrate SCM process dimensions which would help in successful implementation of SCM practices in firm by following the DMAIC process. The framework enables the practitioners to investigate the process and demonstrate improvements using DMAIC which makes use of statistical tools. Originality/value – Although process dimensions related to SCM are critical to organization competitiveness, research so far has tended to focus on supply chain operations and reference model, balanced scorecard, total quality management, activity-based costing, just in time, etc., but in literature hardly any description of the SIPOC-DMAIC model to improve SCM process performance is provided. The use of statistics in DMAIC provides better insight into the process performance, and process control.