Exploring the performance of small- and medium-sized enterprises through the credit crunch

Small- and medium-sized enterprises (SMEs) are major contributors to most western economies. They are often supported by government policies, and in the UK the government encourages banks to lend to them. It is generally believed that the credit crunch has had an impact on the performance of SMEs. This study looks at the impact of the crunch using large samples from 2007 through to 2010. It looks at performance by region, age and industrial sector (SIC code). It then proceeds to explore the modelling of default over the years, with a focus on young businesses. It was found that there is a degree of stability within the models, though the level of default varies across years. Young businesses, as has been found before, are shown to be more vulnerable.     

Implementation of Lean Six Sigma in small- and medium-sized manufacturing enterprises in the Netherlands

In this paper we provide an exploration and analysis of Lean Six Sigma (LSS) implementation in Dutch manufacturing/engineering small- and medium-sized enterprises (SMEs). Critical success factors (CSFs) and impeding factors are identified and analysed. Exploratory empirical evidence about LSS implementation in Dutch SMEs was collected from a survey study on Dutch SMEs. Statistical testing was applied to validate the ranking of the CSFs. To deepen insight in how organizations translate CSFs into practice and cope with impeding factors, additional in-depth qualitative information was gathered from six case studies. Linking to customer, vision and plan statement, communication and management involvement and participation are the highest ranked CSFs. Internal resistance, the availability of resources, changing business focus and lack of leadership are the strongest impeding factors. The case studies confirmed the importance of the CSFs and revealed three new CSFs: personal LSS-experience of Top management, development of the project leader's soft skills and supply chain focus. SMEs in the Netherlands make no distinct separation between lean manufacturing and Six Sigma, but rather apply both approaches intertwined.     

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.     

Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis

In this paper, we present a general framework for understanding the role of artificial neural networks (ANNs) in bankruptcy prediction. We give a comprehensive review of neural network applications in this area and illustrate the link between neural networks and traditional Bayesian classification theory. The method of cross-validation is used to examine the between-sample variation of neural networks for bankruptcy prediction. Based on a matched sample of 220 firms, our findings indicate that neural networks are significantly better than logistic regression models in prediction as well as classification rate estimation. In addition, neural networks are robust to sampling variations in overall classification performance.     

Adding value to bank branch performance evaluation using cognitive maps and MCDA: a case study

Bank branch performance evaluation is a difficult endeavour. Some of the main reasons for this difficulty are the complexity inherent in the variety of aspects considered in the evaluation, and the multiple and conflicting interests of the different stakeholders involved. In this paper, we show how cognitive mapping and measuring attractiveness by a categorical-based evaluation technique can be used to support the evaluation of bank branches through the development of multidimensional performance evaluation systems, and to deal explicitly with the trade-offs between the different dimensions of performance and interests of different stakeholders. A case study is discussed where these techniques are used in a constructive way, making the learning activity easier and introducing transparency in the decision-making process. The strengths and weaknesses of the integrated use of these two operational research techniques in this context are also discussed.     

Dynamic structural percolation model of loss distribution for cyber risk of small and medium-sized enterprises for tree-based LAN topology

Cyber risk due to breach can be seen as a risk of a financial loss due to breach of an institution’s IT infrastructure by unauthorized parties and exploiting, taking possession of, or disclosing data assets, thus creating financial and/or reputation damage. In this paper, as a primary contribution to the existing body of actuarial literature, we propose a structural model of aggregate loss distribution for cyber risk of small and medium-sized enterprises under the assumption of a tree-based LAN topology. Up to our knowledge, there exist no theoretical models of an aggregate loss distribution for cyber risk in this setting. To achieve our goal, we contextualize the problem in the probabilistic graph-theoretical framework using percolation models. We assume that the IT network topology is represented by a random graph allowing for heterogeneous loss topology and providing instructive numerical examples.     

A comparison of Bayesian,Hazard, and Mixed Logit model of bankruptcy prediction

Computational Management Science - The purpose of this study is to examine the impact of the choice of cut-off points, sampling procedures, and business cycles on the forecasting accuracy of...     

An information technologies and information systems industry-based classification in small and medium-sized enterprises: An institutional view

IT/IS assimilation in organizations has been analyzed mainly by large companies, where its greatest adoption is observed. However, studies that analyze the effects IT/IS have on SMEs (small and medium-sized enterprises) have also begun to appear. The institutional theory offers an approach to understanding IT/IS diffusion and the adoption process caused by isomorphism within the institutional environment, mainly industry. One of its main postulates is the institutionalization of organizations can be an answer to the pressures that organizations receive to be similar. With the purpose of analyzing this postulate, we have identified an IT/IS adoption typology through a sample of organizations coming from main industries, using multivariant analysis. This typology has allowed us to evaluate IT/IS institutionalization in SMEs, and to analyze the explanatory potential of the institutional theory in order to evaluate IT/IS assimilation in organizations.     

MCDA and preference disaggregation in group decision support systems

Within the frame of decision aid literature, group decision making has drawn the attention of researchers from a wide spectrum of disciplines. Group Decision Support Systems (GDSS) can play a critical role, in decision situations with multiple individuals, each having his/her own private point of view on the handling of the decision problem. In such an environment, the conflict between the members of the group is not a seldom situation. Multiple criteria decision aid (MCDA) methods can be proven as invaluable tools in handling such interpersonal conflicts where the aim is to achieve consensus between the group members or at least reduce the amount of conflict among participating individuals. This paper reviews some of the past approaches in the multiple criteria–multiple decision makers context.     

Formalizing and solving the problem of clustering in MCDA

The topic of clustering has been widely studied in the field of Data Analysis, where it is defined as an unsupervised process of grouping objects together based on notions of similarity. Clustering in the field of Multi-Criteria Decision Aid (MCDA) has seen a few adaptations of methods from Data Analysis, most of them however using concepts native to that field, such as the notions of similarity and distance measures. As in MCDA we model the preferences of a decision maker over a set of decision alternatives, we can find more diverse ways of comparing them than in Data Analysis. As a result, these alternatives may also be arranged into different potential structures. In this paper we wish to formally define the problem of clustering in MCDA using notions that are native to this field alone, and highlight the different structures which we may try to uncover through this process. Following this we propose a method for finding these structures. As in any clustering problem, finding the optimal result in an exact manner is impractical, and so we propose a stochastic heuristic approach, which we validate through tests on a large set of artificially generated benchmarks.     

Combined MCDA–IP Approach for Project Selection in the Electricity Market

The European policy target of substantially increasing the share of renewable energy in electricity generation, in combination with national subsidization support schemes, has strongly motivated private investors towards this business sector. In Greece, this interest is particularly apparent in the case of wind energy exploitation, due to a favourable legislative framework and the important wind potential available in several Greek regions. In such endowed regions, a very high number of applications were submitted to the competent authority, most of which compete for the same most attractive (windy) sites. The selection among these applications is a multicriteria problem that has been solved with the support of a Decision-Aid tool combining the multicriteria classification method ELECTRE-TRI with Integer Linear Programming. The developed approach takes into account both, the performances of the applications to the evaluation criteria, as well as a number of technical and policy constraints.     

Lean principles,practices, and impacts: a study on small and medium-sized enterprises (SMEs)

Lean as a business strategy is used to improve quality and service, eliminate waste, reduce time and costs, and enhance overall organizational effectiveness. Heightening challenges in competition in recent years have prompted many small and medium-sized enterprises (SMEs) to adopt lean to enhance firms’ competitiveness. This paper attempts to present an all-inclusive study and it examines various factors associated with the implementation of lean in SMEs in the U.S. The findings suggest that most of SMEs have a relatively accurate understanding of lean concept and philosophy. The primary reasons to implement lean are mainly internal, including reduce cost, improve profit margin, improve utilization of plant/facility, and maintain competitive position. A hierarchical cluster analysis was conducted to investigate lean status. It was discovered that both advanced adopters and beginners of lean are discovered. ANOVA test results show that there exist quite significant differences in terms of the degrees of lean implementation in SMEs. Varied lean tools and programs have been applied and they are positively related with firms’ performance. Lastly, the paper provides evidences that major lean barriers are encountered by SMEs regarding management or people related factors as well as key knowledge and know-how.     

Extending the use of scenario planning and MCDA for the evaluation of strategic options

Multi-criteria decision analysis (MCDA) is well equipped to deal with conflicting, qualitative objectives when evaluating strategic options. Scenario planning provides a framework for confronting uncertainty, which MCDA lacks. Integration of these methods offers various advantages, yet its effective application in evaluating strategic options would benefit from scenarios that reflect a larger number of wide-ranging scenarios developed in a time-efficient manner, as well as incorporation of MCDA measures that inform within and across scenario comparison of options. The main contribution of this paper is to illustrate how a more diverse set of scenarios could be developed quickly, and to investigate how regret could be used to facilitate comparison of options. First, the reasons for these two areas of development are elaborated with respect to existing techniques. The impacts of applying the proposed method in practice are then assessed through a case study involving food security in Trinidad and Tobago. The paper concludes with a discussion of findings and areas for further research.     

Using fuzzy cognitive mapping techniques to model situation awareness for army infantry platoon leaders

This paper describes work on the development of an actionable model of situation awareness for Army infantry platoon leaders using fuzzy cognitive mapping techniques. Developing this model based on the formal representation of the platoon leader provided by the Goal-Directed Task Analysis (GDTA) methodology advances current cognitive models because it provides valuable insight on how to effectively support human cognition within the decision-making process. We describe the modeling design approach and discuss validating the model using the VBS2 simulation environment.     

An MCDA approach for the selection of bike projects based on structuring and appraising activities

This paper presents an MCDA approach for the structuring and appraising activities of a large and complex decision problem. More specifically, the paper makes use of the three-step structuring process for decision analysis proposed by von Winterfeldt and Edwards: (1) identifying the problem; (2) selecting an appropriate analytic approach; and (3) developing a detailed analytic structure. For illustration of the approach a case study dealing with the assessment task of prioritising and selecting initiatives and projects from a public pool with limited funds is examined throughout the paper. The process is embedded in a Decision Support System (DSS) making use of the REMBRANDT technique for pair wise comparisons to determine project rankings. A procedure for limiting the number of pair wise comparisons to be made in the process is in this connection presented. Finally, strengths and weaknesses in the approach are discussed and conclusions are made.     

A carbon risk prediction model for Chinese heavy-polluting industrial enterprises based on support vector machine

Chinese heavy-polluting industrial enterprises, especially petrochemical or chemical industry, labeled low carbon efficiency and high emission load, are facing the tremendous pressure of emission reduction under the background of global shortage of energy supply and constrain of carbon emission. However, due to the limited amount of theoretic and practical research in this field, problems like lacking prediction indicators or models, and the quantified standard of carbon risk remain unsolved. In this paper, the connotation of carbon risk and an assessment index system for Chinese heavy-polluting industrial enterprises (eg. coal enterprise, petrochemical enterprises, chemical enterprises et al.) based on support vector machine are presented. By using several heavy-polluting industrial enterprises’ related data, SVM model is trained to predict the carbon risk level of a specific enterprise, which allows the enterprise to identify and manage its carbon risks. The result shows that this method can predict enterprise’s carbon risk level in an efficient, accurate way with high practical application and generalization value.     

Switching tax structure and payouts in endogenous bankruptcy models

In the spirit of Leland (H.E. Leland, Corporate Debt Value, Bond Covenant, and Optimal Capital Structure, J. Finance 49 (1994), pp. 1213–1252), we consider a structural credit risk model with tax provisions under the assumption of a positive payout rate. By defining a more general tax structure than in (Leland, 1994), we introduce a general switching corporate tax rate function and analytically derive the value of the tax benefits claim, the whole capital structure and the smooth pasting condition. In this set-up, the endogenous failure level is derived and both the singular and joint effect of the two introduced risk factors (payouts and tax asymmetry) on optimal managerial financing decisions are studied. Results show a quantitatively significant impact on optimal debt issuance and leverage ratios, bringing them to values more in line with historical norms and providing a way to explain differences in observed leverage across firms.     

Large deviations for generalized compound Poisson risk models and its bankruptcy moments

We extend the classical compound Poisson risk model to the case where the premium income process, based on a Poisson process, is no longer a linear function. For this more realistic risk model, Lundberg type limiting results on the finite time ruin probabilities are derived. Asymptotic behaviour of the tail probabilities of the claim surplus process is also investigated.     

Bankruptcy prediction of small and medium enterprises using a flexible binary generalized extreme value model

We introduce a binary regression accounting-based model for bankruptcy prediction of small and medium enterprises (SMEs). The main advantage of the model lies in its predictive performance in identifying defaulted SMEs. Another advantage, which is especially relevant for banks, is that the relationship between the accounting characteristics of SMEs and response is not assumed a priori (eg, linear, quadratic or cubic) and can be determined from the data. The proposed approach uses the quantile function of the generalized extreme value distribution as link function as well as smooth functions of accounting characteristics to flexibly model covariate effects. Therefore, the usual assumptions in scoring models of symmetric link function and linear or pre-specified covariate-response relationships are relaxed. Out-of-sample and out-of-time validation on Italian data shows that our proposal outperforms the commonly used (logistic) scoring model for different default horizons.