PENERAPAN DIAGRAM KENDALI MAXIMUM MULTIVARIATE CUMULATIVE SUM (MAX-MCUSUM) PADA PENGENDALIAN KUALITAS PRODUK KACANG (Studi Kasus: Produk Kacang Garing di PT XY)

Now, Statistical quality control be a particular concern to large companies.PT XY is one of the largest nut company in Indonesia that has implemented the quality standards of a product. Max-MCUSUM control chart becomes a tool that is graphically used to monitor and evaluate whether the process is under control or nut. Based on Cheng and Thaga (2005), Max-MCUSUM control chart takes precedence over detecting small shift based on average and variability in industry data. The quality characteristic of Kacang Garing will be variables, namely broken nut skin, bean seed 1, and foam nut skin. Max-MCUSUM control chart is controlled with the control limit (h) from ARL (Average Run Length) simulation of 370 is 429,69. ARL is an average of samples that need to be decribed before it goes out of control. The research continued with multivariate capability process with MCp worth 0,905 and MCpk worth 1,355. Those value indicates that Kacang Garing has met the quality specification stipulated by PT XY. Broken nut skin becomes the most dominant cause based on pareto chart and carried out analysis by using fishbone chart so that is known the main factor causing broken nut skin are machine, material, and method.

Design multivariate statistical process control procedure in the case of Ethio cement

PurposeThis study aims to design a multivariate control chart that improves the applicability of the traditional Hotelling T2 chart. This new type of multivariate control chart displays sufficient information about the states and relationships of the variables in the production process. It is used to make better quality control decisions during the production process.Design/methodology/approachMultivariate data are collected at an equal time interval and are represented by nodes of the graph. The edges connecting the nodes represent the sequence of operation. Each node is plotted on the control chart based on their Hotelling T2 statistical distance. The changing behavior of each pair of input and output nodes is studied by the neural network. A case study from the cement industry is conducted to validate the control chart.FindingsThe finding of this paper is that the points and lines in the classic Hotelling T2 chart are effectively substituted by nodes and edges of the graph respectively. Nodes and edges have dimension and color and represent several attributes. As a result, this control chart displays much more information than the traditional Hotelling T2 control chart. The pattern of the plot represents whether the process is normal or not. The effect of the sequence of operation is visible in the control chart. The frequency of the happening of nodes is recognized by the size of nodes. The decision to change the product feature is assisted by finding the shortest path between nodes. Moreover, consecutive nodes have different behaviors, and that behavior change is recognized by neural network.Originality/valueModifying the classical Hotelling T2 control chart by integrating with the concept of graph theory and neural network is new of its kind.

An Eco-Friendly Proposal by Integrating Chromatographic Fingerprinting and Multivariate Control Chart in a Non-Target Analysis to Evaluate Grape Juices from Different Farming Practices

A potential eco-friendly method without organic solvents is presented by integrating a chromatographic fingerprint and multivariate control chart based on Q residuals to differentiate grape juices from different farming practices. The sample preparation was only water dilution, and the mobile phase was water acidified with sulfuric acid, which can be readily neutralized before its disposal. The proposed method is shown to be a simple way to distinguish between organic and non-organic grape juices in a non-target way, successfully evaluating an external validation data set, where organic and non-organic samples were correctly assigned. Through the chromatographic profile, it is possible to suggest that one of the species responsible for this distinction may be from the anthocyanins class.

An Assessment of Imbalanced Control Chart Pattern Recognition by Artificial Neural Networks

Manual detection of abnormality in control data is an annoying work which requires a specialized person. Automatic detection might be simpler and effective. Various methodologies such as ANN, SVM, Fuzzy Logic, etc. have been implemented into the control chart patterns to detect abnormal patterns in real time. In general, control chart data is imbalanced, meaning the rate of minority class (abnormal pattern) is much lower than the rate of normal class (normal pattern). To take this fact into consideration, authors implemented a weighting strategy in conjunction with ANN and investigated the performance of weighted ANN for several abnormal patterns, then compared its performance with regular ANN. This comparison is also made under different conditions, for example, abnormal and normal patterns are separable, partially separable, inseparable and the length of data is fixed as being 10,20, and 30 for each. Based on numerical results, weighting policy can better predict in some of the cases in terms of classifying samples belonging to minority class to the correct class.