Managing critical success strategies for an enterprise resource planning project

This paper develops an innovative objectives-oriented approach with one evaluation model and three optimization models for managing the implementation of a set of critical success strategies (CSSs) for an enterprise resource planning (ERP) project in an organization. To evaluate the CSSs based on their contribution to the organizational objectives, the evaluation model addresses an important issue of measuring the relationship between objectives in a three-level hierarchy involving the organization, its functional departments, and the ERP project. To determine the optimal management priority for implementing the CSSs from the organization’s perspective, the three optimization models maximize their total implementation value by integrating individual departments’ management preferences. An empirical study is conducted to demonstrate how these models work and how their outcomes can provide practical insights and implications in planning and managing the implementation of the CSSs for an ERP project.     

Long-tail longitudinal modeling of insurance company expenses

The insurance industry is known to have high operating expenses in the financial services sector. Insurers, investors and regulators are interested in models to understand the behavior of expenses. However, the current practice ignores skewness, occasional negative values as well as their temporal dependence.Addressing these three features, this paper develops a longitudinal model of insurance company expenses that can be used for prediction, to identify unusual behavior, and to measure firm efficiency. Specifically, we use a three-parameter asymmetric Laplace density for the marginal distribution of insurers’ expenses in each year. Copula functions are employed to accommodate their temporal dependence. As a function of explanatory variables, the location parameter allows us to analyze an insurer’s expenses in light of the firm’s characteristics. Our model can be interpreted as a longitudinal quantile regression.The analysis is performed using property-casualty insurance company data from the National Association of Insurance Commissioners of years 2001-2006. Due to the long-tailed nature of insurers’ expenses, two alternative approaches are proposed to improve the performance of the longitudinal quantile regression model: rescaling and transformation. Predictive densities are derived that allow one to compare the predictions for individual insurers in a hold-out-sample. Both predictive models are shown to be reasonable with the rescaling method outperforming the transformation method. Compared with standard longitudinal models, our model is shown to be superior in identifying insurers’ unusual behavior.     

Advertising strategies in a franchise system

The main question of this research is: Who should undertake promotional and brand-image advertising if the franchisor and franchisees act so as to maximize their respective profits? To address this question, we study a two-stage advertising game between a franchisor and two adjacent franchisees. In the first stage of the game, the franchisor chooses between three advertising models – centralizing or delegating the two types of advertising to the franchisees or delegating only promotional advertising. In the second stage, given the franchisor’s choice of an advertising model, the two franchisees decide whether or not to cooperate. Our main findings are that (1) the franchisees should cooperate if the franchisor delegates either both brand-image and promotional advertising or promotional advertising, although cooperation between franchisees does not necessary improve the franchisor’s profits. (2) The choice of an advertising arrangement critically depends on the margins as well as the costs of performing both promotional and brand-image advertising. We also discuss the conditions under which the three advertising models should be implemented.     

Measuring the efficiency of highway maintenance contracting strategies: A bootstrapped non-parametric meta-frontier approach

Highly deteriorated US road infrastructure, major budgetary restrictions and the significant growth in traffic have led to an emerging need for improving performance of highway maintenance practices. Privatizing some portions of road maintenance operations by state Departments of Transportation (DOTs) under the auspices of performance-based contracts has been one of the innovative initiatives in response to such a need. This paper adapts the non-parametric meta-frontier framework to the two-stage bootstrapping technique to develop an analytical approach for evaluating the relative efficiency of two highway maintenance contracting strategies. The first strategy pertains to the 180 miles of Virginia’s Interstate highways maintained by Virginia DOT using traditional maintenance practices. The second strategy pertains to the 250 miles of Virginia’s Interstate highways maintained via a Public Private Partnership using a performance-based maintenance approach. The meta-frontier approach accounts for the heterogeneity that exists among different types of highway maintenance contracts due to different limitations and regulations. The two-stage bootstrapping technique accounts for the large set of uncontrollable factors that affect the highway deterioration processes. The preliminary findings, based on the historical data for the state of Virginia, suggest that road authorities (counties) that have used traditional contracting for transforming the maintenance expenditures into the improvement of the road conditions seem to be more efficient than road authorities that have used the performance-based contracting. This paper recommends that road authorities use hybrid contracting approaches that include best practices of both traditional and performance-based highway maintenance contracting.     

SSM and technology management: Developing multimethodology through practice

Growing competition and economic recession is driving the need for more rapid redesign of operations enabled by innovative technologies. The acquisition, development and implementation of systems to manage customer complaints and control the quality assurance process is a critical area for engineering and manufacturing companies. Multimethodologies, and especially those that can bridge ‘soft’ and ‘hard’ OR practices, have been seen as a possible means to facilitate rapid problem structuring, the analysis of alternative process design and then the specification through to implementation of systems solutions. Despite the many ‘hard’ and ‘soft’ OR problem structuring and management methods available, there are relatively few detailed empirical research studies of how they can be combined and conducted in practice. This study examines how a multimethodology was developed, and used successfully, in an engineering company to address customer complaints/concerns, both strategically and operationally. The action research study examined and utilised emerging ‘soft’ OR theory to iteratively develop a new framework that encompasses problem structuring through to technology selection and adoption. This was based on combining Soft Systems Methodology (SSM) for problem exploration and structuring, learning theories and methods for problem diagnosis, and technology management for selecting between alternatives and implementing the solution. The results show that, through the use of action research and the development of a contextualised multimethodology, stakeholders within organisations can participate in the design of new systems and more rapidly adopt technology to address the operational problems of customer complaints in more systemic, innovative and informed ways.     

System identification of a pick-and-place mechanism driven by a permanent magnet synchronous motor

The main objective of this study is to utilize two dynamic models: a mathematical model and a simple model, to identify a pick-and-place mechanism (PPM) which is driven by a permanent magnet synchronous motor (PMSM). In this paper, Hamilton’s principle is employed to derive the mathematical model, which is a nonlinear differential equation, while Newton’s second law is utilized to derive the simple linear model. In system identification, we adopt the real-coded genetic algorithm (RGA) to find not only the parameters of the PPM, but also the PMSM simultaneously. From the identification simulations and experimental results, it is demonstrated that the identification results of the mathematical model present the better matching with the experimental results of the system.     

Comparison of three semiparametric methods for estimating dependence parameters in copula models

Three semiparametric methods for estimating dependence parameters in copula models are compared, namely maximum pseudo-likelihood estimation and the two method-of-moment approaches based on the inversion of Spearman’s rho and Kendall’s tau. For each of these three asymptotically normal estimators, an estimator of their asymptotic (co)variance is stated in three different situations, namely the bivariate one-parameter case, the multivariate one-parameter case and the multivariate multiparameter case. An extensive Monte Carlo study is carried out to compare the finite-sample performance of the three estimators under consideration in these three situations. In the one-parameter case, it involves up to six bivariate and four-variate copula families, and up to five levels of dependence. In the multiparameter case, attention is restricted to trivariate and four-variate normal and t copulas. The maximum pseudo-likelihood estimator appears as the best choice in terms of mean square error in all situations except for small and weakly dependent samples. It is followed by the method-of-moment estimator based on Kendall’s tau, which overall appears to be significantly better than its analogue based on Spearman’s rho. The simulation results are complemented by asymptotic relative efficiency calculations. The numerical computation of Spearman’s rho, Kendall’s tau and their derivatives in the case of copula families for which explicit expressions are not available is also investigated.     

Mixture cure models in credit scoring: If and when borrowers default

Mixture cure models were originally proposed in medical statistics to model long-term survival of cancer patients in terms of two distinct subpopulations - those that are cured of the event of interest and will never relapse, along with those that are uncured and are susceptible to the event. In the present paper, we introduce mixture cure models to the area of credit scoring, where, similarly to the medical setting, a large proportion of the dataset may not experience the event of interest during the loan term, i.e. default. We estimate a mixture cure model predicting (time to) default on a UK personal loan portfolio, and compare its performance to the Cox proportional hazards method and standard logistic regression. Results for credit scoring at an account level and prediction of the number of defaults at a portfolio level are presented; model performance is evaluated through cross validation on discrimination and calibration measures. Discrimination performance for all three approaches was found to be high and competitive. Calibration performance for the survival approaches was found to be superior to logistic regression for intermediate time intervals and useful for fixed 12 month time horizon estimates, reinforcing the flexibility of survival analysis as both a risk ranking tool and for providing robust estimates of probability of default over time. Furthermore, the mixture cure model’s ability to distinguish between two subpopulations can offer additional insights by estimating the parameters that determine susceptibility to default in addition to parameters that influence time to default of a borrower.     

Exploring the critical pillars and causal relations within the NRI: An innovative approach

The Global Information Technology Report released by the World Economic Forum (WEF) has employed networked readiness index (NRI) to measure the global competitiveness of a country’s information and communication technologies (ICT) diffusion. The final NRI overall scores were measured by an arithmetic mean aggregation of the composite pillars scores, which implicitly assumed that all the pillars have constant weights. The Report did not explore the critical pillars and causal relations for better decision making. To add values to this Report, the objective of this paper is to propose an innovative approach by using data mining techniques and partial least squares path modeling to scrutinize the critical pillars within the NRI and to further explore the causal relations amongst them. An empirical analysis based on the latest Report (2009-2010) is carried out. The results show that “business usage,” “business readiness,” and “market environment” are the three root drivers—critical pillars to manipulate the NRI overall scores; whereas “government readiness,” which is further mostly affected by the “government usage,” is the foremost enabler to the NRI overall scores. Based on the results, policy makers are suggested to allocate limited resources with priority to the three root drivers and one foremost enabler to frog-leap the global competitiveness of national ICT diffusion.     

Mutual funds performance appraisal using stochastic multicriteria acceptability analysis

Mutual fund investors are concerned with the selection of the best fund in terms of performance among the set of alternative funds. This paper proposes an innovative mutual funds performance evaluation measure in the context of multicriteria decision making. We implement a multicriteria methodology using stochastic multicriteria acceptability analysis, on Greek domestic equity funds for the period 2000-2009. Combining a unique dataset of risk-adjusted returns such as Carhart’s alpha with funds’ cost variables, we obtain a multicriteria performance evaluation and ranking of the mutual funds, by means of an additive value function model. The main conclusion is that among employed variables, the sophisticated Carhart’s alpha plays the most important role in determining fund rankings. On the other hand, funds’ rankings are affected only marginally by operational attributes. We believe that our results could have serious implications either in terms of a fund rating system or for constructing optimal combinations of portfolios.     

Conditional information criteria for selecting variables in linear mixed models

In this paper, we consider the problem of selecting the variables of the fixed effects in the linear mixed models where the random effects are present and the observation vectors have been obtained from many clusters. As the variable selection procedure, here we use the Akaike Information Criterion, AIC. In the context of the mixed linear models, two kinds of AIC have been proposed: marginal AIC and conditional AIC. In this paper, we derive three versions of conditional AIC depending upon different estimators of the regression coefficients and the random effects. Through the simulation studies, it is shown that the proposed conditional AIC’s are superior to the marginal and conditional AIC’s proposed in the literature in the sense of selecting the true model. Finally, the results are extended to the case when the random effects in all the clusters are of the same dimension but have a common unknown covariance matrix.     

Measuring the relative efficiency of quality management practices in Turkish public and private universities

Using data envelopment analysis (DEA) in conjunction with stochastic frontier analysis (SFA), the aim of this study was to measure the relative efficiency of quality management (QM) practices in Turkish public and private universities. Based on the extant literature, a set of nine critical QM factors and seven performance indicators for Turkish universities were identified as input and output variables, respectively. SFA confirmed the existence of significant relationships between QM factors and performance indicators. DEA findings indicated that private universities with higher levels of QM efficiency on stakeholder-focus indicators achieved better performance in terms of fulfilling the expectations of their stakeholders. In contrast, public universities were more successful in managing QM practices for a superior teaching and research performance. Finally, after eliminating the managerial discrepancies, no significant structural efficiency difference was found between these two groups of universities through stakeholder-focus model, though some significant variation was noted in both factor-efficiency and total-efficiency models. As for total-efficiency model, we may infer that the structural differences found in favour of public universities for factor-efficiencies are counterbalanced by private universities which tend to focus more on their stakeholders in managing QM applications.     

基于SPA联系数的大连产业结构演化规律研究

SPA联系数是把研究对象各种测度联系起来加以系统性地描述、计算、分析的一种结构函数,其一般形式为a+bi+cj,用于城市第一产业、第二产业、第三产业相互联系和演化规律的研究有其合理和创新之处,其特点是把城市第一产业、第二产业、第三产业看成一个系统.用于大连产业结构演化规律研究后发现,大连市在1978~1999年20年间经历了稳定——剧烈调整——稳定这样三个阶段.在今后的10~20年间,对于第三产业的投资仍将处在首位.     

Dynamic analysis and system identification of an LCD glass-handling robot driven by a PMSM

In this paper, Hamilton’s principle is employed to derive Lagrange’s equations of an liquid crystal display (LCD) glass-handling robot driven by a permanent magnet synchronous motor (PMSM). The robot has three arms driven by two timing belts. The dynamic formulations can be expressed by one and four independent variables, which are named as the rigid and flexible models, respectively. In order to verify the dynamic formulation is correct, we reduce the flexible model to the rigid one under some assumptions. In this paper, we adopt the real-coded genetic algorithm (RGA) to identify all the parameters of the robot and PMSM simultaneously. It is found that the RGA can identify system parameters which are difficult to be measured in practical problems, for examples, the inductance, stator resistance, motor torque constant, damping coefficient of the motor and timing belts. In numerical simulations, vibrations due to flexibility of the timing belts are investigated for the angular displacements, speeds, accelerations of arms, and the horizontal and vertical displacements of the robot. The angular displacements of the robot arm and the translational positions of the robot end are obtained in the numerical simulations and experimental results. From their comparisons, it is demonstrated that identification results of the dynamic model with four independent variables present the better matching with experimental results of the system.     

An “inverse” relationship between mathematics identities and classroom practices among early career elementary teachers: The impact of accountability

This qualitative case study guided by portraiture examines the relationships between three early career elementary teachers’ beliefs about themselves in relation to mathematics (mathematics identities) and their classroom practices. Through autobiographical inquiry, reflective practice, classroom observations, interviews, and artifacts, findings show that all three second grade teachers appeared to have an “inverse” relationship between their mathematics identities and their classroom practices. In this relationship, as negative as they felt about themselves with regards to mathematics, they expended that much more effort to ensure that their students would have positive experiences with it and not be stigmatized by it as they had been. Accountability to schools, students, and parents, to increase student achievement appeared to play an important role in this relationship. Implications for preservice teacher education, inservice professional development, and research on beliefs and practices are discussed.     

Using Bayesian networks for bankruptcy prediction: Some methodological issues

This study provides operational guidance for building naïve Bayes Bayesian network (BN) models for bankruptcy prediction. First, we suggest a heuristic method that guides the selection of bankruptcy predictors. Based on the correlations and partial correlations among variables, the method aims at eliminating redundant and less relevant variables. A naïve Bayes model is developed using the proposed heuristic method and is found to perform well based on a 10-fold validation analysis. The developed naïve Bayes model consists of eight first-order variables, six of which are continuous. We also provide guidance on building a cascaded model by selecting second-order variables to compensate for missing values of first-order variables. Second, we analyze whether the number of states into which the six continuous variables are discretized has an impact on the model’s performance. Our results show that the model’s performance is the best when the number of states for discretization is either two or three. Starting from four states, the performance starts to deteriorate, probably due to over-fitting. Finally, we experiment whether modeling continuous variables with continuous distributions instead of discretizing them can improve the model’s performance. Our finding suggests that this is not true. One possible reason is that continuous distributions tested by the study do not represent well the underlying distributions of empirical data. Finally, the results of this study could also be applicable to business decision-making contexts other than bankruptcy prediction.     

Consumer environmental awareness and competition in two-stage supply chains

This paper focuses on the impact of competition and consumers’ environmental awareness on key supply chain players. We consider both the production competition between partially substitutable products made by different manufacturers, and the competition between retail stores. We use two-stage Stackelberg game models to investigate the dynamics between the supply chain players given three supply chain network structures. We find that as consumers’ environmental awareness increases, retailers and manufacturers with superior eco-friendly operations will benefit; while the profitability of the inferior eco-friendly firm will tend to increase if the production competition level is low, and will tend to decrease if the production competition level is high. In addition, higher levels of retail competition may make manufacturers with inferior eco-friendly operations more likely to benefit from the increase of consumers’ environmental awareness. Moreover, as production competition intensifies, the profits of the retailers will always increase, while the profits of the manufacturers with inferior eco-friendly operations will always decrease. The profitability of the manufacturers with superior eco-friendly operations will also tend to decrease, unless consumers’ environmental awareness is high and the superior manufacturer has a significant cost advantage related to product environmental improvement.     

Semistrict models of connected 3-types and Tamsamani’s weak 3-groupoids

Homotopy 3-types can be modelled algebraically by Tamsamani’s weak 3-groupoids as well as, in the path-connected case, by cat²-groups. This paper gives a comparison between the two models in the path-connected case. This leads to two different semistrict algebraic models of connected 3-types using Tamsamani’s model. Both are then related to Gray groupoids.     

多层防御模式下武器目标分配决策的群体智能优化算法

武器目标分配(WTA)是军事运筹学中经典的NP完全问题,迄今为止未找到求精确解的多项式时间算法.针对武器数量、布防空间、运行维护成本以及人力资源等多约束下的多层防御WTA问题,采用粒子群优化(PSO)和蚁群优化(ACO)两种群体智能算法求解.给出了PSO和ACO算法实现方案,通过一个算例评估两个算法的性能.结果表明,两种算法都能给出高质量的近似最优解,对求解WTA问题是有效的.PSO在解的质量、算法鲁棒性和计算效率方面均优于ACO.