Abstract: In any production process, every product is aimed to attain a certain standard, but the presence of assignable cause of variability affects our process, thereby leading to low quality of product. The ability to identify and remove this type of variability reduces its overall effect, thereby improving the quality of the product. In case of a univariate control chart signal, it is easy to detect the problem and give a solution since it is related to a single quality characteristic. However, the problems involved in the use of multivariate control chart are the violation of multivariate normal assumption and the difficulty in identifying the quality characteristic(s) that resulted in the out of control signals. The purpose of this paper is to examine the use of non-parametric control chart (the bootstrap approach) for obtaining control limit to overcome the problem of multivariate distributional assumption and the p-value method for detecting out of control signals. Results from a performance study show that the proposed bootstrap method enables the setting of control limit that can enhance the detection of out of control signals when compared, while the p-value method also enhanced in identifying out of control variables.
Abstract: Soy protein is a common ingredient added to processed meats to enhance its functional characteristics. In our study, soybean products (fermented soy Natto and protein hydrolysate) containing hydrolyzed peptides and amino acids, with or without ascorbic acid were added to burger in order to improve its quality characteristics. Results showed that soy additives significantly increased moisture and protein content and reduced (P < 0.05) fat values. Ash content did not affect with Natto additive. Color tools, lightness and yellowness were higher (P
Abstract: When an assignable cause(s) manifests itself to a multivariate process and the process shifts to an out-of-control condition, a root-cause analysis should be initiated by quality engineers to identify and eliminate the assignable cause(s) affected the process. A root-cause analysis in a multivariate process is more complex compared to a univariate process. In the case of a process involved several correlated variables an effective root-cause analysis can be only experienced when it is possible to identify the required knowledge including the out-of-control condition, the change point, and the variable(s) responsible to the out-of-control condition, all simultaneously. Although literature addresses different schemes to monitor multivariate processes, one can find few scientific reports focused on all the required knowledge. To the best of the author’s knowledge this is the first time that a multi task model based on artificial neural network (ANN) is reported to monitor all the required knowledge at the same time for a multivariate process with more than two correlated quality characteristics. The performance of the proposed scheme is evaluated numerically when different step shifts affect the mean vector. Average run length is used to investigate the performance of the proposed multi task model. The simulated results indicate the multi task scheme performs all the required knowledge effectively.
Abstract: This work discusses an innovative methodology for
deployment of service quality characteristics. Four groups of organizational features that may influence the quality of services are identified: human resource, technology, planning, and organizational
relationships. A House of Service Quality (HOSQ) matrix is built to
extract the desired improvement in the service quality characteristics
and to translate them into a hierarchy of important organizational
features. The Mean Square Error (MSE) criterion enables the
pinpointing of the few essential service quality characteristics to be
improved as well as selection of the vital organizational features. The
method was implemented in an engineering supply enterprise and
provides useful information on its vital service dimensions.