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Recent literature about quality control has investigated the continuous surveillance of the ratio of two normal random variables under the assumption of no measurement error. However, in practice, measurement errors always exist in quality control applications and may considerably affect the performance of control charts. In this paper, the performance of the Shewhart-RZ control chart is investigated in the presence of a measurement error and modelled by a linear covariate error model. Several figures and tables are generated and commented to show the statistical performance of the Shewhart-RZ control chart for different sources of the measurement error. Two examples illustrate the use of this chart on a quality control problem simulated from the food industry and a real industrial case from a plant treating batteries for recyclement. [ABSTRACT FROM AUTHOR]

Today, many companies use job production to produce small batches of work output according to customised specifications required by customers. With job production, the quantity of parts to be produced is finite and can be limited to a few thousands or hundreds (low-volume production). Usually, job production requires high-quality standards. When 100% sampling is not possible, online monitoring is a viable solution to immediately start process quality control. However, knowledge about the process distribution is frequently unavailable. Recently, it has been shown that the distribution-free Shewhart sign control chart is an efficient means to monitor processes performing high-volume production within a finite production horizon. In this paper, we reconsider the definition of the Shewhart sign chart statistic when the population size is small and we propose an approach that extends its implementation to finite batch sizes of work to be produced. Tables presenting the statistical properties of the Shewhart sign control chart for low-volume production are shown and commented. An illustrative example is discussed on a simulated data-set. [ABSTRACT FROM PUBLISHER]

Recent studies show that Shewhart-type control charts monitoring the ratio of two normal random variables are useful to perform continuous surveillance in several manufacturing environments; anyway, they have a poor statistical sensitivity in the detection of small or moderate process shifts. The statistical sensitivity of a Shewhart control chart can be improved by implementing supplementary Run Rules. In this paper, we investigate the performance of Phase II Run Rules Shewhart control charts monitoring the ratio with each subgroup consisting ofsample units. A Markov chain methodology coupled with an efficient normal approximation of the ratio distribution is used to evaluate the statistical performance of these charts. We provide an extensive numerical analysis consisting of several tables and figures to discuss the statistical performance of the investigated charts for deterministic and random shift sizes affecting the in-control ratio. An illustrative example from the food industry is provided for illustration. [ABSTRACT FROM PUBLISHER]

In this work, a new distribution-free Phase II EWMA-type chart based on the Wilcoxon signed rank statistic is introduced and its exact Run Length properties are discussed. A proper discrete Markov-chain approach is used for the determination of the Average Run Length in the in-control and out-of-control process operating conditions and its exact performance is derived without any knowledge of the distribution of sample observations. In addition, an algorithm for the determination of the chart's optimal design has been developed. Moreover, the performance of our proposed chart is compared with several existing nonparametric schemes available from the literature. Finally, two illustrative examples are provided to show the practical implementation of our proposed chart. [ABSTRACT FROM AUTHOR]

Agricultural crop production can generate significant residues and their management is generally a disposal problem. Composting can be a solution. It is essentially an efficient bioconversion technique to process organic wastes (manure, food, crop and forestry residues, solid digestate) into humus-like substances destined to different uses (soil amendment, organic fertilization, plant nursery substrates). Life Cycle Assessment, Energy Analysis, and Life Cost Costing were used to perform a cross-analysis aimed to assess the environmental, economic, and energy sustainability of some procurement systems of compostable materials (bulking agents and agricultural crop residues) to be allocated to composting on the farm. The procurement systems were characterized by different mechanization levels in order to explore the possible scenarios related to the availability of vehicles within the farm and increasing transport distances. Our findings underline that the most sustainable option for the environment it was not often the most cost-effective choice. Therefore, the cross-analysis of these relevant data sets allowed us to detail the different operative steps and guide the procurement system choice toward those showing the best compromise for both the environment and farmers (cheaper, with no or low impactful). [ABSTRACT FROM AUTHOR]

In this paper the scheduling of batches of parts within a manufacturing system consisting of M unrelated parallel CNC manufacturing cells and employing a limited workforce capacity is investigated: the objective is to maintain the number R of available workers lower than the number M of the cells leaving unchanged the whole system output. Each cell of the system consists of a CNC machining centre which works batches of small mechanical parts, grouped together on a common pallet in accordance to the group technology principles. Batch splitting among CNC cells is not allowed and the setup time required by each batch is both sequence and cell dependent. The setup activities are fully performed by the workers employed within the manufacturing system; the working operations within the CNC cells are completely automated, thus the workers tasks are restricted only to the setup operations. The effect of reducing the human resource capacity on scheduling of parts within cells, and on several process performance measures, is investigated through an extensive design of experiment involving different process scenarios. The process activities and resources dynamics are simulated and optimized by means of an integrated simulation framework, consisting of an object-oriented simulator interfaced with a heuristic procedure, designed within the eM-Plant™ software environment. The obtained results show how the human resource can be maintained significantly lower than the number of cells without affecting the output of the manufacturing system. [ABSTRACT FROM AUTHOR]