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With increasing pressure to cut carbon emissions and develop sustainability plans, companies need carbon credits to offset emissions that cannot be eliminated from their operations, new global carbon exchange to launch in Singapore by 2021 end, the high-quality credit can bridge this gap and play an important role in the overall climate change mitigation strategy. The purpose of this research is to use the production data (including carbon emissions) of paper-making companies in Taiwan to establish a circular economy mathematical programming model and the concept based on the activity cost method (ABC) to explore the impact of different carbon emissions costs (such carbon tax, carbon cap-and-trade, etc.) on the company's production structure and profitability impact. The research results show that different carbon emission cost models have different effects on the company's optimal product-mix and profitability. The managerial implication is to combine the extensive application of the carbon emission credit mechanism, which can offset the carbon emissions in the production process and have a significant impact on the company's sustainable competitiveness. In addition, this study can also enable decision makers to understand the impact of different carbon emission cost models on the profitability of the company's product mix, which can be used as a reference for production planning decisions. [ABSTRACT FROM AUTHOR]

Although the business environment is stochastic, deterministic data envelopment analysis (DEA) models are typically used to measure the efficiency of commercial banks for the purpose of simplicity. Bank operations are characterized by a network structure due to the dual role of deposits, which, on the one hand, are the output of the process of borrowing funds from depositors and, on the other hand, are the input of the process of making loans. Since the outputs of the production process of the bank are correlated with its inputs, the model for measuring efficiency in this case is a stochastic program with correlated data. To take the correlation between the inputs and outputs into consideration, in this paper, a standard normal transformation is applied for the correlated data, and a network stochastic model is developed to obtain the distribution of the stochastic efficiency. The model is used to measure the efficiency of twenty-two commercial banks in Taiwan. The results are more reliable, discriminative, and informative than those obtained from the existing models. They also show that the performance of a bank is mainly affected by its loan performance. Different from the stereotype suggesting that private companies usually operate more efficiently than state-owned companies, public banks perform better than private banks in Taiwan. [ABSTRACT FROM AUTHOR]

The specification of a product is frequently changed to satisfy new requirement from market buyers. To maintain market competitiveness, manufacturers must plan innovative production and sale strategies to achieve maximum benefit. This activity is known as innovative maintenance (IM). In this paper, we therefore consider a two-stage production-inventory model with replacement and imperfect component processes during which the components replacement is a common issue of IM. We then find the optimal production and sale strategies that minimize the total cost per unit time. An algorithm is proposed to find these optimal solutions. Finally, we quote two numerical examples from the Taiwan polyurethane industry to illustrate the proposed model and algorithm. Sensitivity analysis is also performed to obtain some managerial insights. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to use the Analytical Hierarchy Process (AHP) method to build the resource and environment efficiency evaluation index system about economic zone on the west side of Taiwan Straits from the following three perspectives: the economic and social factors, resource using efficiency, and environmental pollution degree. It quantifies the weight of each index according to literature review and experts' opinions. Meanwhile, the reliability of the index system is validated from index data availability, standard availability and evaluation operability in order to improve resources and the environment efficiency of the economic zone. Finally, the conclusions can provide references for other regions to build the index system. [ABSTRACT FROM AUTHOR]

Improving operating efficiency is one objective of internal controls (IC). This paper investigates the relationship between IC implementation and operating efficiency of universities. Using data from questionnaire survey and from the field, this study measures IC implementation and applies data envelopment analysis to estimate operating efficiency of 99 universities in Taiwan. The OLS regression results indicate that IC implementation has an insignificant association with teaching-related efficiency, but has a negative and significant association with research-related efficiency. Dividing the sample into public and private universities, the analysis indicates that for public universities, IC implementation has no significant association with any of the two measures of efficiency. But, for private universities, there is a positive and significant association between IC implementation and teaching-related efficiency. The association between IC implementation and research-related efficiency is negative and significant. [ABSTRACT FROM AUTHOR]

In recent years, the need for emergency resources has dramatically increased and it has caused an overcrowding problem for the emergency department (ED). Solving this problem by increasing medical resources is either impractical or infeasible. Thus, this manuscript develops a multi-objective mathematical model to allocate medical resources for the emergency department (ED). The optimal resource allocation is exploited by using some meta-heuristic algorithms, i.e., fast and elitism non-dominated sorting genetic algorithm (NSGA II), non-dominated sorting particle swarm algorithm (NSPSO), and non-dominated sorting differential evolution (NSDE). Thereafter, a dynamic simulation model, which embeds the solutions from the resource allocation model in the simulation process, is constructed. Each feasible solution from the three meta-heuristic algorithms is simulated to estimate the performance of the resources allocated in terms of the average service level and staff utilization. The results show that the performance of the NSGAII, where the average service level and staff utilization for the current resources are 0.844 and 0.751, respectively, is better than those of NSPSO and NSDE. Besides, the number of medical staff gives a significant effect on the service level and utilization while the number of beds only impacts staff utilization. The simulation model can find out that the best combination of the number of staff and the number of beds is from 1 to 10 staffs and 1–6 beds to maximize the utilization and service level. [ABSTRACT FROM AUTHOR]

The purpose of this research is to introduce an innovative decision architecture to assess corporate risks by utilizing accounting narratives and to further examine the association between those risks and operating performance. We run text mining algorithms to identify the types of risks from narratives and incorporate them with a readability measure (i.e., linguistic cue(s)) to conjecture a firm manager's attitude toward corporate risk. We consider a two-stage network data envelopment analysis (TNDEA) with the benefit of opening up the black-box of a production process via considering internal activities. The outcome derived from TNDEA is determined by users who decide "a priori" what the specification of the model should be, without considering any alternatives. The study further incorporates the fuzzy rough set theory (FRST) with TNDEA by considering inclusion/exclusion or different combinations of inputs, intermediates, and outputs so as to realize a corporate's underlying business situation. Decision attributes taken from the outcome of FRST-TNDEA and condition attributes gathered from annual reports are jointly inserted into an artificial intelligence (AI) technique to establish the forecasting model. The integrated circuit (IC) industry has long been viewed as an essential backbone of Taiwan's economy, and therefore it is taken as our research target. The results show that the introduced linguistic cues have positive and considerable impacts on firm performance. Overall, the findings herein provide direct support for recent regulators that require corporates to add a new section on risk factors in accounting narratives so as to prevent users from making biased judgments. [ABSTRACT FROM AUTHOR]

Following the emergence of the Internet, the Internet of things (IOT) brought about another wave of technological and economic revolutions. Through the lens of the production process, this study utilises the dynamic network slack-based measure model in data envelopment analysis to evaluate 32 IOT companies in Taiwan in terms of their innovation efficiency, operational efficiency and overall efficiency for the period of 2007–2017. Empirical results reveal that the average operational and overall efficiencies of IOT companies in Taiwan have been decreasing considerably since 2008. However, their average innovation efficiency remains stable over the sample period owing to government reductions in enterprise research and development (R&D) tax credit incentives. Through the impulse response function method, this study further confirms that the Statute for Industrial Innovation, which was implemented in 2010 and revised and reimplemented in 2016, specifically, policies concerning enterprise R&D tax credits, affect the efficiencies of IOT companies in Taiwan. Overall, this study reveals the performance evaluation process of IOT companies by showing that their innovation capability affects their operational efficiency. Thus, the government is advised to incorporate innovation measures into relevant industrial policies to achieve policy effectiveness. [ABSTRACT FROM AUTHOR]

For mass resonant system, a long-term operation causes the system degrading which ends up with system vibration and affects system control accuracy. A number of model-based feedforward control methods were developed to compensate for such systematic control error. However, the parameters of the complex and nonlinear control systems need to be identified during the degrading process so that the precise speed control can be achieved as well as the health condition of the mechanical system can be extracted. This study proposes an equipment health maintenance framework in a three-mass resonant servo control system with the LuGre friction model, which conducts model-based parameter estimation and feedforward compensation. Since the traditional methods require off-line frequency-sweep which causes the downtime in the production line, we suggest a constrained particle swarm optimization (CPSO) to estimate the mechanical parameters so that the machine can operate simultaneously. More specifically, with embedding the soft equality constraints of the anti-resonance frequency in the mass resonant system, the CPSO enables the feasible region shrank and the vibration suppressed. In particular, we address these constraints by obtaining the feasibility-preserving approach with the dynamic relaxing constraints. An experimental study of a leading electronics manufacturing company in Taiwan has been conducted to validate the proposed approach with a designed experiment of a belt drive system. The results show that the mass resonant system with the LuGre friction model via CPSO successfully reflects the main effects of current variation in the mechanical system. [ABSTRACT FROM AUTHOR]

Effective and thorough credit-risk management is a key factor for lending institutions, as significant financial losses can arise from the borrowers' default. Consequently, machine learning methods can measure and analyze credit risk objectively when at the same time they face increasingly attention. This study analyzes default payment data from a credit cards' portfolio containing some 30,000 clients from Taiwan with twenty-three attributes and with no missing information. We compare prediction accuracy of seven classification methods used, i.e. KNN, Logistic Regression, Naïve Bayes, Decision Trees, Random Forest, SVC, and Linear SVC. The results indicate that only few out of most of the typical variables used can adequately analyze default characteristics in terms of lending decisions. The results provide effective feedback to credit evaluators, lending institutions and business analysts for in-depth analysis. Also, they mention to the importance of the precautionary borrowing techniques to be used to better understand credit-card borrowers' behavior, along with specific accounting, historical and demographical characteristics. [ABSTRACT FROM AUTHOR]

This study aims to investigate the effects of centralized allocation and optimization of railway resources on overall operational efficiency of the railway industry. Its results are intended to help Taiwan railway industry in resource allocation and reduction of organization resistance caused by resource deployment in Taiwan. For this purpose, this study proposes and divides resource reallocation into long-, middle-, and short-term plans, with three resource adjustment programs. These programs consider different geographical ranges and resource conditions of personnel and equipment in resource allocation by using a two-phase centralized data envelopment analysis. Conducted in 2011, the Taiwan railway data analysis indicated that high overall output that was attributed to efficient allocation of resources induced large-scale organizational changes and adjustments (such as staff reduction) in the railway industry. These changes resulted in widespread organization resistance. Nonetheless, this process enabled the railway industry in Taiwan to achieve balance between output and organization resistance under different environments efficiently. [ABSTRACT FROM AUTHOR]