Enhancing decisions with life cycle analysis for risk management

Although corporate financial distress is an infrequent occurrence, it has an extremely debilitating effect on the stability of a firm when it does occur. For this reason, an accurate risk assessment mechanism is needed in numerous industry sectors, particularly in financial institutions and banking. Based on corporation life cycle theory and risk management, this study develops a risk pre-warning model, namely the RSVMDT model, to eliminate serious financial punching and to examine the effectiveness of transparency and the full disclosure index (TFDI) during each life cycle stage. The RSVMDT model includes three techniques: random forest (RF), support vector machines (SVMs), and decision trees (DTs). The RF is used to determine the essential attributes of firms and therefore decrease the computational complexity of financial analysis and improve the classification accuracy. The SVM is employed as a classifier to identify corporations in financial distress. Finally, the DT is utilized as a rule generator that allows decision makers to adjust the financial structures of firms at specific life cycle stages. Together, these three techniques can increase the probability of corporate survival in a highly competitive environment. Additionally, the study further evaluates the importance of the TFDI during a turbulent economy. The public sectors can benefit from this evaluation by formulating future policies based on the rules derived from the developed RSVMDT model.     

MCELCCh-FDP: Financial distress prediction with classifier ensembles based on firm life cycle and Choquet integral

Financial distress prediction (FDP) has always been an important issue in the business and financial management. This research proposed a novel multiple classifier ensemble model based on firm life cycle and Choquet integral for FDP, named MCELCCh-FDP, as a new approach to tackle with financial distress. Empirical study based on Chinese listed companies’ real data is conducted, and the results show that the proposed MCELCCh-FDP model has higher prediction accuracy than single classifiers. In order to verify the prediction capability of firm life cycle and Choquet integral in FDP model, comparative analysis is conducted. The experiment results indicate that the introduction of firm life cycle and Choquet integral in FDP can greatly enhance prediction accuracy.     

集成化企业建模系统体系结构与实施方法研究

提出一种集成化的企业建模方法,给出了建模框架体系和基于工作流的过程视图建模方法,并设计了基于ＣＯＲＢＡ软件总线的集成化企业建模与仿真优化系统,将企业建模的生命周期加以扩展,提出４阶段生命周期的建模方法,并以工作流模型作为企业模型的核心,提出了实现企业模型到实施系统转化的两种可行方法。     

基于产品生命周期信息集成的研究

随着计算机水平和信息技术的不断提高,化工企业的各个部门采用了大量的软件和技术来提高各自的自动化水平和工作效率,但是由于各个部门的任务不同而应用不同形式的软件和文档管理,由于数据交换格式不统一,信息模型不一致,在产品生命周期不同阶段之间出现了的“信息孤岛”。针对这种现象,本文采用STEP标准来统一底层产品数据信息,用XML作为高层工艺过程文件交换标准实现数据通信。提出了面向产品生命周期的两层信息集成理论框架。将产品生命周期中各个阶段的产品模型集成在一起,达到数据共享和充分交流,以期实现企业在整个产品生命周期各个阶段的共同协作,实现更低的成本、更高的质量,同时缩短产品开发和生产周期。最后通过实例验证表明该框架的实用性和有效性。     

SFFS-PC-NN optimized by genetic algorithm for dynamic prediction of financial distress with longitudinal data streams

Recently, research of financial distress prediction has become increasingly urgent. However, existing static models for financial distress prediction are not able to adapt to the situation that the sample data flows constantly with the lapse of time. Financial distress prediction with static models does not meet the demand of the dynamic nature of business operations. This article explores the theoretical and empirical research of dynamic modeling on financial distress prediction with longitudinal data streams from the view of individual enterprise. Based on enterprise’s longitudinal data streams, dynamic financial distress prediction model is constructed by integrating financial indicator selection by using sequential floating forward selection method, dynamic evaluation of enterprise’s financial situation by using principal component analysis at each longitudinal time point, and dynamic prediction of financial distress by using back-propagation neural network optimized by genetic algorithm. This model’s ex-ante prediction efficiently combines its ex-post evaluation. In empirical study, three listed companies’ half-year longitudinal data streams are used as the sample set. Results of dynamic financial distress prediction show that the longitudinal and dynamic model of enterprise’s financial distress prediction is more effective and feasible than static model.     

软件生命周期质量评价方法研究

针对软件质量现存评估标准一方面笼统抽象、主观性强,另外一方面缺乏对软件某个特定阶段质量评估模型的问题,通过对软件生命周期进行研究,对需求分析、软件设计、软件编码和软件测试四个阶段,从bug引入阶段、bug发现阶段、bug缺陷等级、bug数量、bug产生原因、bug修正代价六个方面进行分析,采用了改进加权模糊熵权法确定度量元权重系数,建立软件生命周期不同阶段质量评价模型及软件总体质量评价模型;通过工程实践结果证明,该生命周期质量评价模型能够有效地对软件不同阶段进行评价,为量化软件生命周期不同阶段质量水平提供了一种新方法,减少了软件质量评价中的主观性。     

Financial distress early warning based on group decision making

Financial distress early warning is important for business bankruptcy prevention, and various quantitative prediction methods based on financial ratios have been proposed. However, little attention has been paid to the important role of experts’ experiential knowledge and non-financial information. From this point of view, the article puts forward a group decision-making approach based on experts’ knowledge and all kinds of financial or non-financial information to diagnose business financial distress. Based on the risk factors of enterprise financial distress, a qualitative attribute set and its scoring criteria are designed. A method integrating linguistic label and interval value is adopted for decision makers to express their preference on attributes, and a multi-expert negotiation mechanism is designed for weighting attributes. Diagnosis on business financial distress is made through the grey evaluation method, which also tries to find out the potential risks that may cause financial distress. Case study of a real world company is carried out to validate the proposed financial distress early warning method based on group decision making.     

Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors

Financial distress prediction including bankruptcy prediction has called broad attention since 1960s. Various techniques have been employed in this area, ranging from statistical ones such as multiple discriminate analysis (MDA), Logit, etc. to machine learning ones like neural networks (NN), support vector machine (SVM), etc. Case-based reasoning (CBR), which is one of the key methodologies for problem-solving, has not won enough focus in financial distress prediction since 1996. In this study, outranking relations (OR), including strict difference, weak difference, and indifference, between cases on each feature are introduced to build up a new feature-based similarity measure mechanism in the principle of k-nearest neighbors. It is different from traditional distance-based similarity mechanisms and those based on NN, fuzzy set theory, decision tree (DT), etc. Accuracy of the CBR prediction method based on OR, which is called as OR-CBR, is determined directly by such four types of parameters as difference parameter, indifference parameter, veto parameter, and neighbor parameter. It is described in detail that what the model of OR-CBR is from various aspects such as its developed background, formalization of the specific model, and implementation of corresponding algorithm. With three year’s real-world data from Chinese listed companies, experimental results indicate that OR-CBR outperforms MDA, Logit, NN, SVM, DT, Basic CBR, and Grey CBR in financial distress prediction, under the assessment of leave-one-out cross-validation and the process of Max normalization. It means that OR-CBR may be a preferred model dealing with financial distress prediction in China.     

Predicting corporate financial distress based on integration of decision tree classification and logistic regression

Lately, stock and derivative securities markets continuously and rapidly evolve in the world. As quick market developments, enterprise operating status will be disclosed periodically on financial statement. Unfortunately, if executives of firms intentionally dress financial statements up, it will not be observed any financial distress possibility in the short or long run. Recently, there were occurred many financial crises in the international marketing, such as Enron, Kmart, Global Crossing, WorldCom and Lehman Brothers events. How these financial events affect world’s business, especially for the financial service industry or investors has been public’s concern. To improve the accuracy of the financial distress prediction model, this paper referred to the operating rules of the Taiwan Stock Exchange Corporation (TSEC) and collected 100 listed companies as the initial samples. Moreover, the empirical experiment with a total of 37 ratios which composed of financial and other non-financial ratios and used principle component analysis (PCA) to extract suitable variables. The decision tree (DT) classification methods (C5.0, CART, and CHAID) and logistic regression (LR) techniques were used to implement the financial distress prediction model. Finally, the experiments acquired a satisfying result, which testifies for the possibility and validity of our proposed methods for the financial distress prediction of listed companies.This paper makes four critical contributions: (1) the more PCA we used, the less accuracy we obtained by the DT classification approach. However, the LR approach has no significant impact with PCA; (2) the closer we get to the actual occurrence of financial distress, the higher the accuracy we obtain in DT classification approach, with an 97.01% correct percentage for 2 seasons prior to the occurrence of financial distress; (3) our empirical results show that PCA increases the error of classifying companies that are in a financial crisis as normal companies; and (4) the DT classification approach obtains better prediction accuracy than the LR approach in short run (less one year). On the contrary, the LR approach gets better prediction accuracy in long run (above one and half year). Therefore, this paper proposes that the artificial intelligent (AI) approach could be a more suitable methodology than traditional statistics for predicting the potential financial distress of a company in short run.     

基于GA-SVM的企业财务困境预测

通过遗传算法结合支持向量机算法中期望风险边界,对我国上市公司财务数据进行特征提取,并优化构造广义最优分类超平面,从而获得具有较好整体预测性能的联合模型。数值实验表明,该方法可以降低特征空间维数,具有较好的分类准确率。实证结果表明,GA-SVM联合预测模型具有可靠的预测财务困境能力,有着良好的应用前景。     

支持业务流程分析的工作流仿真研究

业务流程管理生命周期中的流程分析环节是保证流程模型有效性和提高流程设计质量的关键．比较计算分析而言，仿真分析方法能更真实、更方便地获得业务流程全面的性能数据．首先探讨了仿真分析的基本过程，并应用资源约束随机工作流网理论来构建仿真模型，给出了仿真结果的关键性能指标．然后以一个实际的业务流程为例，在ExSpect平台上实现了仿真分析的全过程．     

大规模定制下的汽车销售订单生命周期管理

在分析大规模定制生产特点的基础上,结合产品设计制造过程,提出了销售订单生命周期管理的概念,描述了销售订单的信息模型,划分了订单生命周期的4个阶段与8个状态,建立了销售订单同产品生命周期的五域映射模型,规划了基于订单的产品全息模型。将该系统在典型企业进行了应用验证,结果表明订单的生命周期管理为大规模定制生产模式提供了更为准确的产品制造信息,并为产品服役结束后的回收再利用提供了信息支持。     

基于新闻文本的上市公司财务困境组合预测模型

已有上市公司财务困境预测模型主要是基于结构化数据进行研究,为进一步提高上市公司财务困境预测模型准确率,本文将非结构化数据引入上市公司财务困境预测问题中,研究了基于新闻文本分类的上市公司财务困境预测模型,结合新闻文本信息和财务信息提出上市公司财务困境组合预测模型。本文首先将新闻数据进行预处理,然后基于新闻文本数据通过支持向量机（SVM）进行财务困境预测,同时基于财务数据通过Logistic模型进行财务困境预测,最后采用阈值表决集成策略整合两种模型的预测结果,实验结果证明了模型的有效性。     

企业信息化绩效评估体系及其评价方法

通过对企业信息化实施过程全生命周期的分析研究，建立了一套可按实施阶段进行动态评估的信息化绩效评估体系。并在AHP法的基础上提出了目标递进的层次结构，实现了对企业信息化的阶段性和综合性评估。通过对百余家企业的实际评估与考察，得到了理想的效果。反映了该评估体系的科学性和合理性，且具有更强的实际操作性，为企业提供了及时有效的反馈和改进信息。     

Model continuity in cyber-physical systems: A control-centered methodology based on agents

A Cyber Physical System (CPS) is given by the integration of cyber and physical components, usually with feedback loops, where physical processes affect computations and vice versa. Design and implementation of complex CPSs is a multidisciplinary and demanding task. Challenges arise especially for the exploitation of heterogeneous and different models during the various phases of system life cycle. This paper proposes an agent-based and control-centric methodology which is well suited for the development of complex CPSs. The approach is novel and supports model continuity which enables the use of a unique model along all the development stages of a system ranging from analysis, by simulation, down to real-time implementation and execution. In the paper, basic concepts of the methodology are provided together with implementation details. Effectivenesses of the approach is demonstrated through a case study concerning a prototyped CPS devoted to the optimization of power consumption in a smart micro-grid automation system.     

Evaluating environmental strategies in a textile printing and dyeing enterprise by an agent-based simulation model

To improve the capabilities of saving energy and reducing pollutant emission of textile printing and dyeing (PD) industry, this article presents a novel agent-based simulation model for assessing the impacts of environmental strategies on a PD enterprise. Two typical PD enterprises in China are simulated with different modelling granularities: one is at a module level, while the other is at an enterprise level. The module-level simulation model depicts detailed production processes in a PD enterprise and evaluates five candidate strategies on their capabilities of improving energy usage and waste emission. The enterprise-level simulation model views a PD enterprise as an agent and assesses three tax strategies for waste discharge. The simulation results show that the proposed general model could be a valuable tool to explore potential solutions to saving energy and reducing waste emission in PD enterprises, after being calibrated to a real case.     

过程企业信息建模与集成研究

提出了过程企业多维信息模型,从功能层次、空间层次和生命周期维的角度对过程企业信息的特点进行分析;在此基础上,介绍了过程企业信息集成的目标、内容、特点和关键支持技术;最后,提出了基于XML的过程企业信息集成框架。     

Towards measuring the effectiveness of a facilities layout

Performance measurement models are essential to support various decision making problems that may arise during life cycle of a facilities layout. Available models are only suitable for early stages in the design phase of life cycle. However, measurement models have a great consequence in other phases also such as production planning, control and when modification to be incorporated due to the changes in market demand, which happens very often in today's global competition. In addition, the existing models have considered only material handling cost as the performance measurement factor. Nevertheless, the empty travel of material handling equipment, layout flexibility and area utilisation have a significant contribution towards the layout effectiveness. It is therefore necessary to have a measurement model to determine the facilities layout's effectiveness by considering all significant factors. A measurement model considering a set of three layout effectiveness factors—facilities layout flexibility (FLF), productive area utilisation (PAU) and closeness gap (CG)—is developed in our research. The proposed model will enable the decision-maker of a manufacturing enterprise to analyse a layout in three different aspects, based on which they can make decision towards productivity improvement. This paper mainly discusses about the measurement of the CG. The CG is developed with respect to the objective of bringing closer the highly interactive facilities/departments. The CG presented in this paper extends other related works by incorporating numerous aspects of layout that include empty travel of material handling equipment, information flow, personnel flow and equipment flow.     

Using competencies in performance estimation: From the activity to the process

Performance estimation of enterprise processes generally involves models focused on a particular viewpoint (temporal, financial, etc.). This estimation rarely takes the impact of the human factor into account. It is now fully understood that it is important to include this factor when modelling and analyzing enterprise processes. In this paper, a conceptual approach to human resource modelling is proposed on the basis of an analysis of human integration in enterprise process models. The human entity is described through individual and collective competencies, which are needed to implement a process activity and assess its performance. The proposed formally built model provides a new approach to correlating some qualitative and quantitative aspects of the human influence on industrial performance. This model formalizes and generalizes performance estimation by linking human capacities and process performance. This approach is illustrated on an industrial process example.     

Combining cluster analysis with classifier ensembles to predict financial distress

The ability to accurately predict business failure is a very important issue in financial decision-making. Incorrect decision-making in financial institutions is very likely to cause financial crises and distress. Bankruptcy prediction and credit scoring are two important problems facing financial decision support. As many related studies develop financial distress models by some machine learning techniques, more advanced machine learning techniques, such as classifier ensembles and hybrid classifiers, have not been fully assessed. The aim of this paper is to develop a novel hybrid financial distress model based on combining the clustering technique and classifier ensembles. In addition, single baseline classifiers, hybrid classifiers, and classifier ensembles are developed for comparisons. In particular, two clustering techniques, Self-Organizing Maps (SOMs) and k-means and three classification techniques, logistic regression, multilayer-perceptron (MLP) neural network, and decision trees, are used to develop these four different types of bankruptcy prediction models. As a result, 21 different models are compared in terms of average prediction accuracy and Type I & II errors. By using five related datasets, combining Self-Organizing Maps (SOMs) with MLP classifier ensembles performs the best, which provides higher predication accuracy and lower Type I & II errors.