Chia Yen Lee

A More Efficient Algorithm for Convex Nonparametric Least Squares

Convex Nonparametric Least Squares (CNLSs) is a nonparametric regression method that does not require a priori specification of the functional form. The CNLS problem is solved by mathematical programming techniques; however, since the CNLS problem size grows quadratically as a function of the number of observations, standard quadratic programming (QP) and Nonlinear Programming (NLP) algorithms are inadequate for handling large samples, and the computational burdens become significant even for relatively small samples. This study proposes a generic algorithm that improves the computational performance in small samples and is able to solve problems that are currently unattainable. A Monte Carlo simulation is performed to evaluate the performance of six variants of the proposed algorithm. These experimental results indicate that the most effective variant can be identified given the sample size and the dimensionality. The computational benefits of the new algorithm are demonstrated by an empirical application that proved insurmountable for the standard QP and NLP algorithms.

Two-dimensional efficiency decomposition to measure the demand effect in productivity analysis

This paper proposes a two-dimensional efficiency decomposition (2DED) of profitability for a production system to account for the demand effect observed in productivity analysis. The first dimension identifies four components of efficiency: capacity design, demand generation, operations, and demand consumption, using Network Data Envelopment Analysis (Network DEA). The second dimension decomposes the efficiency measures and integrates them into a profitability efficiency framework. Thus, each component’s profitability change can be analyzed based on technical efficiency change, scale efficiency change and allocative efficiency change. An empirical study based on data from 2006 to 2008 for the US airline industry finds that the regress of productivity is mainly caused by a demand fluctuation in 2007–2008 rather than technical regression in production capabilities.

Meta-Data Envelopment Analysis: Finding a Direction Towards Marginal Profit Maximization

This paper discusses a new meta-DEA approach to solve the problem of choosing direction vectors when estimating the directional distance function. The proposed model emphasizes finding the “direction” for productivity improvement rather than estimating the “score” of efficiency; focusing on “planning” over “evaluation”. In fact, the direction towards marginal profit maximization implies a step-by-step improvement and “wait-and-see” decision process, which is more consistent with the practical decision-making process. An empirical study of U.S. coal-fired power plants operating in 2011 validates the proposed model. The results show that the efficiency measure using the proposed direction is consistent with all other indices with the exception of the direction towards the profit-maximized benchmark. We conclude that the marginal profit maximization is a useful guide for determining direction in the directional distance function.

Proactive data envelopment analysis: Effective production and capacity expansion in stochastic environments

Demand fluctuations that cause variations in output levels will affect a firm’s technical inefficiency. To assess this demand effect, a demand-truncated production function is developed and an “effectiveness” measure is proposed. Often a firm can adjust some input resources influencing the output level in an attempt to match demand. We propose a short-run capacity planning method, termed proactive data envelopment analysis, which quantifies the effectiveness of a firm’s production system under demand uncertainty. Using a stochastic programming DEA approach, we improve upon short-run capacity expansion planning models by accounting for the decreasing marginal benefit of inputs and estimating the expected value of effectiveness, given demand. The law of diminishing marginal returns is an important property of production function; however, constant marginal productivity is usually assumed for capacity expansion problems resulting in biased capacity estimates. Applying the proposed model in an empirical study of convenience stores in Japan demonstrates the actionable advice the model provides about the levels of variable inputs in uncertain demand environments. We conclude that the method is most suitable for characterizing production systems with perishable goods or service systems that cannot store inventories.

A decomposition of productivity change in the semiconductor manufacturing industry

This study divides a production system into three components: production design, demand support, and operations. Efficiency is then decomposed via network data envelopment analysis and integrated into the Malmquist Productivity Index framework to develop a more detailed decomposition of productivity change. The proposed model can identify the demand effect and the identity of the root cause of technical regress. Specifically, the demand effect allows the source of technical regress to be attributed to both demand deterioration and technical regress in the production technology. An empirical study using data from 1995 to 2000 for the semiconductor manufacturing industry is presented to demonstrate and validate the proposed method. The result shows that the regress of productivity in 1997–1998 and 1999–2000 is mainly caused by demand fluctuations rather than by technical regression in production capabilities.

Distinguishing Operational Performance in Power Production: A New Measure of Effectiveness by DEA

Measuring the efficiency of power plant systems requires capturing fluctuations in the level of sales to customers as well as accounting for the effects of regulatory caps on emissions. This study proposes a novel effectiveness measure considering desirable outputs and undesirable outputs via data envelopment analysis (DEA). The new measure complements typical efficiency measures. We test the validity of the proposed measure with an empirical case study of the fifty U.S. states and the District of Columbia. We find that the current interregional electricity transmission plan increases 8.56% in effectiveness. For the emissions control, we suggest a 9.8% reduction in electricity generation towards an effective production frontier. We conclude that the proposed effectiveness measures ability to distinguish sales and regulation effects from typical productive efficiency eliminates the bias often found in currently used measures.

Measuring Efficiency in Imperfectly Competitive Markets: An Example of Rational Inefficiency

The standard assumption in the efficiency literature, that firms attempt to produce on the production frontier, may not hold in markets that are not perfectly competitive, where the production decisions of all firms will determine the market price, i.e., an increase in a firm’s output level leads to a lower market clearing price and potentially lower profits. This paper models both the production possibility set and the inverse demand function, and identifies a Nash equilibrium and improvement targets which may not be on the production frontier when some inputs or outputs are fixed. This behavior is referred to as rational inefficiency because the firm reduces its productivity levels in order to increase profits. For a general short-run multiple input/output production process, which allows a firm to adjust its output levels and variable input levels, the existence and the uniqueness of the Nash equilibrium is proven. The estimation of a production frontier extends standard market analysis by allowing benchmark performance to be identified. On-line supplementary materials include all proofs and two additional results; when changes in quantity have a significant influence on price and all input and outputs are adjustable, we observe more benchmark production plans on the increasing returns to scale portion of the frontier. Additionally, a direction for improvement toward the economic efficient production plan is estimated, thus providing a solution to the direction selection issue in a directional distance analysis.

A simulation analysis for evaluating TFT-LCD fab capacity expansion with a distant transportation problem

Capacity planning for large-scale high-tech manufacturing processes such as semiconductor manufacturing and thin film transistor-liquid crystal display (TFT-LCD) using simulation of an entire fabrication facility (fab) requires a large computational effort and thus few studies have been in real settings. To address the needs of a realistic problem, this study aimed to develop an effective approach based on a discrete-event simulation model for evaluating the throughput, cycle time and utilisation in an integrated fab to integrate manufacturing and transportation resources. In particular, we conducted an empirical study in a real TFT-LCD fab expansion facing a difficult capacity planning problem arising from the expectation that one or more bottlenecks may shift to different sites, including the transportation system between the incumbent and the expansion fabs. Different product-mix alternatives and feeding policies are investigated to determine the best fab configuration. The results have shown practical viability of the proposed simulation technique to significantly reduce the computational effort associated with the capacity planning process and derive useful alternatives for supporting capacity expansion decisions.

Operational performance management of the power industry: a distinguishing analysis between effectiveness and efficiency

The trend toward a more competitive electricity market has led to efforts by the electric power industry to develop advanced efficiency evaluation models that adapt to market behavior operations management. The promotion of the operational performance management of the electric power industry plays an important role in China’s efforts toward energy conservation, emission control and sustainable development. Traditional efficiency measures are not able to distinguish sales effects from productive efficiency and thus are not sufficient for measuring the operational performance of an electricity generation system for achieving its specific market behavior operations management goals, such as promoting electricity sales. Effectiveness measures are associated with the capacity of an electricity generation system to adjust its input resources that influence its electricity generation and, thus, the capacity to match the electricity demand. Therefore, the effectiveness measures complement the efficiency measures by capturing the sales effect in the operational performance evaluation. This study applies a newly developed data envelopment analysis-based effectiveness measurement to evaluate the operational performance of the electric power industry in China’s 30 provincial regions during the 2006–2010 periods. Both the efficiency and effectiveness of the electricity generation system in each region are measured, and the associated electricity sales effects and electricity reallocation effects are captured. Based on the results of the effectiveness measures, the alternative operational performance improvement strategies and potentials in terms of input resources savings and electricity generation adjustments are proposed. The empirical results indicate that the current interregional electricity transmission and reallocation efforts are effective in China overall, and a moderate increase in electricity generation with a view to improving the effect on sales is more crucial for improving effectiveness.

Nash-profit efficiency: A measure of changes in market structures

Imperfectly competitive markets can be characterized by endogenous prices, limited or no competition, and the exercise of market power. To address the resulting dysfunctionality, this study proposes an alternative efficiency measure estimated by the directional distance function (DDF) with the direction toward Nash equilibrium, and develops the Nash-profit efficiency (NPE) and its decomposition which complements the typical profit efficiency measure. We model the production possibility set and the price functions of inputs and outputs, and then develop the mixed complementarity problem (MiCP). We validate the model with an empirical study of the oil and natural gas industry in New York State between 1981 and 1989. The results show that before 1984, firms exploited a less competitive market; that between 1984 and 1986, the number of new entrants transformed the market; and that after 1986, no firms could exercise market power due to market restructuring (deregulation) and an unforeseen oil glut. Based on the results, we conclude that the direction toward Nash equilibrium can be justified for efficiency estimation in imperfectly competitive markets, and that NPE is appropriate for investigating changes in market structures.