Silko-Scalese Machining Corporation

2017 ◽  
pp. 1-10
Author(s):  
Naren K. Gursahaney ◽  
Elliott N. Weiss
Keyword(s):  
Process Control ◽  
Decision Tree ◽  
Statistical Process Control ◽  
Cash Flow ◽  
Decision Tree Analysis ◽  
Discounted Cash Flow ◽  
Statistical Process ◽  
Large Computer ◽  
Tree Analysis

Alan Silko must decide whether to invest in seven statistical-process-control (SPC) stations in order to increase his chances of becoming a “select supplier” for a large computer company. The student must do a discounted-cash-flow/decision-tree analysis of the option. The student is also given the opportunity to construct x-bar and range charts and to do an SPC analysis.

scholarly journals Diagnosis of Out-of-control Signals in Multivariate Statistical Process Control Based on Bagging and Decision Tree

2017 ◽  
Vol 2 (2) ◽  
pp. 1 ◽  
Author(s):  
Jing Jiang ◽  
Hua-Ming Song
Keyword(s):  
Process Control ◽  
Decision Tree ◽  
Statistical Process Control ◽  
Ensemble Learning ◽  
Experimental Results ◽  
Multivariate Statistical Process Control ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Multiple Classifiers ◽  
Control Signals

In this paper, we propose an ensemble method based on bagging and decision tree to resolve the problem of diagnosing out-of-control signals in multivariate statistical process control. To classify the out-of-control signals, we obtain a series of classifiers through ensemble learning on decision tree. Then we will integrate the classification results of multiple classifiers to determine the final classification. The experimental results show that our method could improve the accuracy of classification and is superior to other methods in terms of diagnosing out-of-control signals in multivariate statistical process control.

scholarly journals Porcelain Product Quality Analysis in PT XYZ

2015 ◽  
Vol 6 (4) ◽  
pp. 580
Author(s):  
Ho Hwi Chie ◽  
Januar Nasution ◽  
Ketut Gita Ayu ◽  
Nike Septivani ◽  
Yualfin Renaldi
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Management Tools ◽  
Statistical Process ◽  
Tree Analysis ◽  
Porcelain Product ◽  
Product Defect

PT. XYZ is a company engaged in manufacturing porcelain dinner ware such as plates, cups, teapot, bowl, etc Porcelain product is safe for use and product defect will only affect the aesthetic not the functional side. The company always maintain the quality of the products produced as by maintaining a good product, in terms of visuals, will keep customers interested in the product. Good quality products characterized by quality A / B and C, and the product defect characterized by the quality of D, Lost, and BU. Concepts and methods used to analyze is a statistical process control (SPC) which includes Pareto diagram, fraction nonconformities, flow charts and fishbone diagrams and management tools (fault tree analysis). Statistical Process Control (SPC) is one of the methods, which includes Pareto charts, fraction nonconformities, flow chart, and fishbone diagram and also management tools (fault tree analysis). SPC is useful to find the facts from the problems and factors that affect the quality of the products, while fault tree analysis is useful to analyze each of the production process.

Real-time pattern recognition in statistical process control: A hybrid neural network/decision tree-based approach

2005 ◽  
Vol 219 (3) ◽  
pp. 283-298 ◽  
Author(s):  
Ruey-Shiang Guh
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Process Control ◽  
Decision Tree ◽  
Real Time ◽  
Statistical Process Control ◽  
Hybrid System ◽  
Control Charts ◽  
Statistical Process ◽  
Fine Classification

Pattern recognition is an important issue in statistical process control because unnatural patterns displayed by control charts can be associated with specific causes that adversely impact on the manufacturing process. A common problem of existing approaches to control chart pattern (CCP) recognition is false classification between different types of CCP that share similar features in a real-time process-monitoring scenario, in which only limited pattern points are available for recognition. This study proposes a hybrid learning-based system that integrates neural networks and decision tree learning to overcome the classification problem in a real-time CCP recognition scheme. This hybrid system consists of three sequential modules, namely feature extraction, coarse classification, and fine classification. The coarse-classification model employs a four-layer back propagation network to detect and classify unnatural CCPs. The fine-classification module contains four decision trees used in a simple heuristic algorithm for further classifying the detected CCPs. Simulation experiments demonstrate that the false recognition problem has been effectively addressed by the proposed hybrid system. Compared with conventional control chart approaches, the proposed system has better performance in terms of recognition speed and also can accurately identify the type of unnatural CCP. Although a real-time CCP recognizer for the individual's (X) chart is the specific application presented here, the proposed hybrid methodology based on neural networks and decision trees can be applied to other control charts.

Optimizing Investment Decisions Using DCF, Decision Tree Analysis, and Real Options Analysis

2017 ◽  
pp. 98-114
Author(s):  
Ramya Rajajagadeesan Aroul
Keyword(s):  
Decision Tree ◽  
Real Options ◽  
Large Scale ◽  
Investment Decisions ◽  
Call Option ◽  
Decision Tree Analysis ◽  
Discounted Cash Flow ◽  
Tree Analysis ◽  
Real Options Analysis ◽  
Distinct Approach

Large scale infrastructure expansions in hotels are exposed to uncertainty. Since the costs involved in these expansion projects are high and often irreversible, hotels would benefit from analyses that incorporate uncertainty along with traditional valuation techniques like the discounted cash flow (DCF) method. Decision tree analysis (DTA) and real options analysis (ROA) have been in use for the past couple of decades to handle uncertainties and optimize investment decisions. DTA provides a distinct approach to strategic investments that quantitatively takes into account the uncertainties involved in the investments. Under uncertainty, the decision about whether to expand is analogous to the decision about whether to exercise an American call option. By using ROA to the hotel expansion scenario, managers can incorporate and quantify, flexibility and timing in their analysis. The objective of this chapter is to detail the DCF, DTA and ROA methodologies and their applications specific to hotel expansion investments.

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