Application of statistical data and methods to establish RPN ratings of FMEA method for construction projects

The Failure Mode and Effects Analysis (FMEA) is paramount for analytical skills of reliability design in dynamic prevention. The FMEA model is a significant method which can simultaneously reduce the operating errors or delays as well as improve the construction quality. In particular, the Risk Priority Number (RPN) in the FMEA model is a vital tool which helps construction managers prioritize problem-solving. As the Internet of Things and big data analytical skills have become progressively widespread and mature, among the three risk indicators of RPN, the number of operating errors or delays per unit time can be estimated by the data collected from the analysis of statistical methods and regarded as the basis of 10-level classification. In addition, when the loss is larger, then the severity is higher. This paper proposed three evaluation criteria, including Occurrence, Severity, and Detection of RPN in construction engineering, and a 10-level classification model. To assist the construction managers, priority for construction improvement can be identified based on RPN calculations

Attribute Service Performance Index Based on Poisson Process

The purpose of a shop enhancing customer satisfaction is to raise its total revenue as the rate of customer purchases in the shop increases. Some studies have pointed out that the amount of customer arrival at a shop is a Poisson process. A simple and easy-to-use evaluation index proposed for the Poisson process with the attribute characteristic will help various shops evaluate their business performance. In addition, developing an excellent and practical service performance evaluation method will be beneficial to the advancement of shop service quality as well as corporate image, thereby increasing the profitability and competitiveness of the shop. As the surroundings of the internet of things (IoT) are becoming gradually common and mature, various commercial data measurement and collection technologies are constantly being refined to form a huge amount of production data. Efficient data analysis and application can assist enterprises in making wise and efficient decisions within a short time. Thus, following the simple and easy-to-use principle, this paper proposes an attribute service performance index based on a Poisson process. Since the index had unknown parameters, this paper subsequently figured out the best estimator and used the central limit theorem to derive the confidence interval of the service efficiency index based on random samples. Then, we constructed the membership function based on the α-cuts of the triangular shaped fuzzy number. Finally, we came up with a fuzzy testing model based on the membership function to improve the accuracy of the test when the sample size is small in order to meet enterprises’ needs for quick responses as well as reducing the evaluation cost

Green Outsourcer Selection Model Based on Confidence Interval of PCI for SMT Process

Taiwan’s electronics industry usually outsources most of its important components for production to enhance market competitiveness and operational flexibility. The quality of all component products is important to ensure the quality of the final product. In electronic assembly, printed circuit boards (PCBs) are key components that carry other electronic components to provide a stable circuit working environment. Surface Mounted Technology (SMT) is the mainstream technology in electronic assembly plants. Obviously, good SMT process quality is relatively important to the final product quality. The process capability index (PCI) is the most widely used process quality evaluation tool in the industry. Therefore, this paper used the PCI representing quality as the green outsourcer selection tool for the SMT process, derived the confidence interval of PCI to develop a quality evaluation model of green outsourcers, and considered the model as the green outsourcer selection model. Meanwhile, this model can be provided to enterprises, outsourcers, or suppliers to evaluate and improve the process quality of components to ensure the quality of components and final products. Since the selection model is based on confidence intervals, it can reduce the risk of misjudgment due to sampling error

Renewable Power Output Forecasting Using Least-Squares Support Vector Regression and Google Data

Sustainable and green technologies include renewable energy sources such as solar power, wind power, and hydroelectric power. Renewable power output forecasting is an essential contributor to energy technology and strategy analysis. This study attempts to develop a novel least-squares support vector regression with a Google (LSSVR-G) model to accurately forecast power output with renewable power, thermal power, and nuclear power outputs in Taiwan. This study integrates a Google application programming interface (API), least-squares support vector regression (LSSVR), and a genetic algorithm (GA) to develop a novel LSSVR-G model for accurately forecasting power output from various power outputs in Taiwan. Material price and the search volume via Google’s search engine for keywords, which is used for various power outputs and is collected by Google APIs, are used as input data. The forecasting model uses LSSVR. Furthermore, the LSSVR employs a GA to find the optimal parameters for the LSSVR. Real-world annual power output datasets collected from Taiwan were used to demonstrate the forecasting performance of the model. The empirical results reveal that the proposed LSSVR-G model is superior to all other considered models both in terms of accuracy and stability, and, thus, can be a useful tool for renewable power forecasting. Moreover, the accuracy forecasting thermal power and nuclear power could effectively assist in understanding the future trend of renewable power output in Taiwan. The accurately forecasting result could effectively provide basic information for renewable power, thermal power, and nuclear power planning and policy making in Taiwan

Quality-Based Supplier Selection Model for Products with Multiple Quality Characteristics

The concept of Industry 4.0 was first proposed by the German government in 2011. As the Internet of Things (IoT) becomes more prevalent and big data analysis technology becomes more mature, it is beneficial for the manufacturing industry to integrate and apply the related technologies to pursue the goal of smart manufacturing. Taiwan’s machine tool industry and downstream machine-tool purchasers, who are scattered around the world, have formed a machine-tool industry chain. To help the machine-tool industry and the suppliers of important components boost their process capabilities, ensure the final product quality of machine tools and improve the process capabilities of the entire industry chain, this study used radar charts to present the statistical testing information of the process capabilities of all quality characteristics, so that managers could have more complete information when evaluating and selecting appropriate suppliers. As noted in many studies, improving product quality and availability can reduce not only the rate of reworking and scrappage during production but also the frequency of maintenance or replacement of components after purchase. As a result, the loss of costs caused by reworking, scrappage, and maintenance can be diminished, carbon emissions can be lowered, and environmental pollution can be reduced as well, which will help to achieve sustainable operation in the entire machine tool industry chain

A Fuzzy Evaluation Model Aimed at Smaller-the-Better-Type Quality Characteristics

Numerous key components of tool machines possess critical smaller-the-better-type quality characteristics. Under the assumption of normality, a one-to-one mathematical relationship exists between the process quality index and the process yield. Therefore, this paper utilized the index to produce a quality fuzzy evaluation model aimed at the small-the-better-type quality characteristics and adopted the model as a decision-making basis for improvement. First, we derived the 100(1 −α)% confidence region of the process mean and process standard deviation. Next, we obtained the 100(1 −α)% confidence interval of the quality index using the mathematical programming method. Furthermore, a one-tailed fuzzy testing method based on this confidence interval was proposed, aiming to assess the process quality. In addition, enterprises’ pursuit of rapid response often results in small sample sizes. Since the evaluation model is built on the basis of the confidence interval, not only can it diminish the risk of wrong judgment due to sampling errors, but it also can enhance the accuracy of evaluations for small sample sizes

Renewable Power Output Forecasting Using Least-Squares Support Vector Regression and Google Data

[[abstract]]Sustainable and green technologies include renewable energy sources such as solar power, wind power, and hydroelectric power. Renewable power output forecasting is an essential contributor to energy technology and strategy analysis. This study attempts to develop a novel least-squares support vector regression with a Google (LSSVR-G) model to accurately forecast power output with renewable power, thermal power, and nuclear power outputs in Taiwan. This study integrates a Google application programming interface (API), least-squares support vector regression (LSSVR), and a genetic algorithm (GA) to develop a novel LSSVR-G model for accurately forecasting power output from various power outputs in Taiwan. Material price and the search volume via Google’s search engine for keywords, which is used for various power outputs and is collected by Google APIs, are used as input data. The forecasting model uses LSSVR. Furthermore, the LSSVR employs a GA to find the optimal parameters for the LSSVR. Real-world annual power output datasets collected from Taiwan were used to demonstrate the forecasting performance of the model. The empirical results reveal that the proposed LSSVR-G model is superior to all other considered models both in terms of accuracy and stability, and, thus, can be a useful tool for renewable power forecasting. Moreover, the accuracy forecasting thermal power and nuclear power could effectively assist in understanding the future trend of renewable power output in Taiwan. The accurately forecasting result could effectively provide basic information for renewable power, thermal power, and nuclear power planning and policy making in Taiwan

Developing a performance index with a Poisson process and an exponential distribution for operations management and continuous improvement

[[abstract]]The adoption of performance measurements is becoming increasingly widespread and represents the key success factor for companies seeking to gain a sustainable competitive advantage. Consequently, there is a need to develop a systematic method for companies to be more performance-measurement focused. This study presents an operating performance index (OPI) based on the concept of customers arriving at a store from a Poisson process and an exponential distribution. To support the efficient use of the proposed method, the statistical properties of OPI are given and a step-by-step operating procedure is constructed. The proposed method can not only evaluate and determine whether the current performance meets the level of Six Sigma, but also improve the precision when estimating a parameter. To validate the application and viability of the proposed method, it is applied to a realistic case study for operating performance measurement and improvement. The results reveal that the proposed method provides a more effective way to achieve Six Sigma, and that it can easily be implemented in practice for operations management and continuous improvement

Developing one-sided specification six-sigma fuzzy quality index and testing model to measure the process performance of fuzzy information

[[abstract]]Depending on the quality characteristic, a process capability index (PCI) can be used for one-sided specifications or for bilateral specifications. A number of researchers have investigated the statistical properties of one-sided specification indices and proposed methods for applications. The later introduction of the Six Sigma approach also assisted many firms in effectively enhancing their production capacities, reducing waste, and increasing effectiveness. Chen et al. (2017a) modified the PCI for one-sided specifications and proposed the Six Sigma Quality Index (SSQI), which coincidently equals the quality level and has a one-to-one relationship with yield. However, uncertainty in quality characteristic measurements is common in practice, which can lead to judgment errors in conventional process capability assessment methods. This study therefore developed an SSQI for one-sided specifications based on the fuzzy testing method created by Buckley (2005) and developed a Six Sigma fuzzy evaluation index and testing model. In addition to having a simpler calculation procedure, the model takes the process capability and Six Sigma quality level into consideration and can process the uncertainties in the data to make it more convenient for the industry to solve engineering issues. Finally, we presented a practical example to demonstrate the applications. The model proposed in this study can provide the industry with a practical approach to assess process quality in a fuzzy environment

Lifetime performance evaluation model based on quick response thinking

In practice, lifetime performance index CL has been a method commonly applied to the evaluation of quality performance. L is the upper or lower limit of the specification. The product lifetime distribution is mostly abnormal distribution. This study explored that the lifetime of commodities comes from exponential distribution. Complete data collection is the primary goal of analysis. However, the censoring type is one of the most commonly used methods due to considerations of manpower and material cost or the timeliness of product launch. This study adopted Type-II right censoring to find out the uniformly minimum variance unbiased (UMVU) estimator of the lifetime performance index CL and its probability density function. Afterward this study obtained the 100×(1-α)% confidence interval of the lifetime performance index CL as well as created the uniformly most powerful (UMP) test and the power of the test for the product lifetime performance index. Last, this study came up with a numerical example to demonstrate the suggested method as well as the application of the model