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PurposeIn profile monitoring, which is a growing research area in the field of statistical process control, the relationship between response and explanatory variables is monitored over time. The purpose of this paper is to focus on the process capability analysis of linear profiles. Process capability indices give a quick indication of the capability of a manufacturing process.Design/methodology/approachIn this paper, the proportion of the non‐conformance criteria is employed to estimate process capability index. The paper has considered the cases where specification limits is constant or is a function of explanatory variable X. Moreover, cases where both equal and random design schemes in profile data acquisition is required (as the explanatory variable) is considered. Profiles with the assumption of deterministic design points are usually used in the calibration applications. However, there are other applications where design points within a profile would be i.i.d. random variables from a given distribution.FindingsSimulation studies using simple linear profile processes for both fixed and random explanatory variable with constant and functional specification limits are considered to assess the efficacy of the proposed method.Originality/valueThere are many cases in industries such as semiconductor industries where quality characteristics are in form of profiles. There is no method in the literature to analyze process capability for theses processes, however recently quite a few methods have been presented in monitoring profiles. Proposed methods provide a framework for quality engineers and production engineers to evaluate and analyze capability of the profile processes.

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Red blood cells (RBCs) and platelets are examples of perishable items with a fixedshelf life. Recent studies show that transfusing fresh RBCs may lead to an improvement of patient outcomes. In addition, to better manage their inventory, hospitals prefer to receive fresh RBCs and platelets. Therefore, as well as minimizing outdates and shortages, reducing the average age of issue is a key performance criterion for blood banks. The issuing policy in a perishable inventory system has a substantial impact on the age of issue and outdate and shortage rates. Although several studies have compared the last in first out (LIFO) and the first in first out (FIFO) policies for perishable products, only a few studies have considered the situation of blood banks where replenishment is not controllable. In this study, we examine various issuing policies for a perishable inventory system with uncontrollable replenishment, and outline a modified FIFO policy. Our proposed modified FIFO policy partitions the inventory into two parts such that the first part holds the items with age less than a threshold. It then applies the FIFO policy in each part and the LIFO policy between the parts. We present two approximation techniques to estimate the average age of issue, the average time between successive outdates and the average time between successive shortages of the modified FIFO policy. Our analysis shows in several cases that where the objective function is a single economic function, or it is formulated as a multiobjective model, the modified FIFO policy outperforms the FIFO and LIFO policies.

When the distribution of a process characterized by a profile is non normal, process capability analysis using normal assumption often leads to erroneous interpretations of the process performance. Profile monitoring is a relatively new set of techniques in quality control that is used in situations where the state of product or process is represented by a function of two or more quality characteristics. Such profiles can be modeled using linear or nonlinear regression models. In some applications, it is assumed that the quality characteristics follow a normal distribution; however, in certain applications this assumption may fail to hold and may yield misleading results. In this article, we consider process capability analysis of non normal linear profiles. We investigate and compare five methods to estimate non normal process capability index (PCI) in profiles. In three of the methods, an estimation of the cumulative distribution function (cdf) of the process is required to analyze process capability in profiles. In order to estimate cdf of the process, we use a Burr XII distribution as well as empirical distributions. However, the resulted PCI with estimating cdf of the process is sometimes far from its true value. So, here we apply artificial neural network with supervised learning which allows the estimation of PCIs in profiles without the need to estimate cdf of the process. Box-Cox transformation technique is also developed to deal with non normal situations. Finally, a comparison study is performed through the simulation of Gamma, Weibull, Lognormal, Beta and student-t data.

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