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  • 1. Aneja, Arun
    et al.
    Pal, Rudrajeet
    University of Borås, Swedish School of Textiles.
    Militky, Jiri
    Kupka, Karel
    Kremenakova, Dana
    Torstensson, Håkan
    University of Borås, Swedish School of Textiles.
    Textile Thru the Looking Glass: A Novel Perspective2013Conference paper (Other academic)
    Abstract [en]

    Today, textiles and fiber science in US, Europe and Japan from its once lofty perch in the global economy, stands in stark contrast to its preeminent position of few decades ago. Its influence on the society as a whole has eroded enormously. Many of the synthetic fiber products that once fuelled the rapid growth of the industry have become mature commodity products now characterized by low growth and lower profit margins. To add to the current dilemma, organizational ‘health’ and growth processes are constantly threatened in this era of turbulence. Thus the drive for survival and success has translated, in recent times, to quest for resiliency – to survive and thrive in turbulences. On the other hand, most managers and academicians agree that innovation ensures superior organizational performance while recent research has shown that most resilient companies can dynamically orchestrate diverse innovation strategies. Resiliency in such a context has become a prerequisite for a sustained long term business prosperity fuelled by diverse technological innovations. This has intensified the organization’s search for differentiated products and services, processes, business models, technology, strategies etc. pushing firms to gain competitive advantage and also to develop new knowledge and innovation performance to drive sustainable growth. Organizations now follow multiple innovation strategies to pragmatically devise their innovation repertoire for delivering growth, hence, success in turbulent times while emphasizing resiliency. What does the future hold and how can we reverse the trend to achieve and sustain the impressive credentials of the past? To understand the significance of what the future may hold, and to reverse the downward spiral of the industry, we must evaluate the successes and failures of the past and come to grips with rapid global changes and turbulences currently underway. The present article seeks to explore such an inexorable phenomenon of quantifying and correlating innovation and business resiliency over a time line, from the annual financial data of 35 healthy and unhealthy companies along with 5 textile companies over a span of few decades. These are then extrapolated with certain predictive capabilities to suggest future trends and strategies for the textile companies. Learning from these companies, if adopted, will yield capacity to transform the scenario. Assessments and classification of the economic health of a company is typically made based on some quantity derived from selected indices, such as Altman’s Z-score. These methods can describe an instantaneous status, or a “time snap” of an economical subject but lack information about the time-dynamics of the assessment, which is important for investors, shareholders and the management. We suggest using historical data to estimate current trends in the form of the first and second time-derivative of the appropriate quantity in the time domain. This new information is independent on the quantity itself and beside more precise description can be used as new predictor to improve effectiveness of classification of successful and unsuccessful subjects. This approach is further discussed in this paper.

  • 2. Johannesson, Pär
    et al.
    Speckert, Michael
    Dressler, Klaus
    de Maré, Jacques
    Lorén, Sara
    University of Borås, School of Engineering.
    Ruf, Nikolaus
    Rychlik, Igor
    Streit, Anja
    Svensson, Thomas
    Evaluation of Customer Loads2013In: Guide to Load Analysis for Durablity in Vehicle Engineering / [ed] P Johannesson, M Speckert, Wiley , 2013, p. 287-320Chapter in book (Other academic)
    Abstract [en]

    The overall goal of vehicle design is to make a robust and reliable product that meets the demands of the customers and this book treats the topic of analysing and describing customer loads with respect to durability. Guide to Load Analysis for Vehicle and Durability Engineering supplies a variety of methods for load analysis and also explains their proper use in view of the vehicle design process. In Part I, Overview, there are two chapters presenting the scope of the book as well as providing an introduction to the subject. Part II, Methods for Load Analysis, describes useful methods and indicates how and when they should be used. Part III, Load Analysis in view of the Vehicle Design Process, offers strategies for the evaluation of customer loads, in particular characterization of customer populations, which leads to the derivation of design loads, and finally to the verification of systems and components.

  • 3.
    Kupka, Karel
    et al.
    Trilobyte Statistical Software, Ltd..
    Pal, Rudrajeet
    University of Borås, Faculty of Textiles, Engineering and Business.
    Aneja, Arun Pal
    East Carolina State University.
    Militky, Jiri
    Technical University of Liberec.
    Characterizing Business Resilience Using SVM-Based Predictive Modeling2016In: Meeting on Statistics in Business and Industry / [ed] Universitat Politècnica de Catalunya, Department of Statistics and Operations Research, Barcelona, Spain, 2016, p. 39-40Conference paper (Other academic)
    Abstract [en]

    Business resilience has gained prominence, in academia and practice, vis-à-vis the heightened challenges recently faced by organizations, e.g. financial crisis. Developing resilience by thriving or bouncing back from crises yields sound business health in the future.However extant scholarly discussion on predictive modelling of economic resilience is rather limited, while business health studies are mainly limited to bankruptcy failure predictions. These studies mostly utilize financial snapshots (based on only few years data) to construct the predictive models hence are static in nature (Balcaen and Ooghe 2006). Several assumptions underpin these static models, e.g. considering failure as a steady process devoid of organizational history (Appiah et al. 2015, du Jardin and Séverin 2011). Even though, few recent studies (cf. du Jardin and Séverin (2011), Chen et al. (2013) etc.) have designed a “trajectory of corporate collapse” to forecast the changes in firms’ financial health, using various ‘expert systems’ like self-organizing maps (SOM) based upon unsupervised neural network approach, these studies still interpret the findings largely for predicting bankruptcy (a ‘state’) rather than drawing inference on the economic growth or recovery patterns (a ‘trajectory’) of organizations – a key to generate resilience. Neither these studies utilize longitudinal financial data (spanning over many years) to capture the dynamics of corporate history required to build resilience of organizations in reality.In this context, our paper proposes developing a predictive econometric model of business resilience by using ‘expert’ SVM method. The expanded predictor based on financial ratios highlighted by Altman (1968)’s Z-score also takes into consideration the corporate dynamics (first and second derivatives). Historical financial data is gathered from 198 firms representing 26 Dow Jones industrial sectors, and starting from 1960s.Our prediction model achieved comparatively high predictive accuracy of ---- (for a forecasting horizon of ----- years) and is comparable to similar studies. However, the main contribution of the paper is in proposing four archetypical patterns in business health trajectories, derived from the historical hind-sight, defined by tendency-dynamics combinations and is essential to characterize business resilience as follows:

    Business Health (at T = t+1) = Business Health (T = 0 to t) + Resilience function

    These four typical situations range from the most pessimistic case (tendency = Down, dynamics = Down) to the most promising (Up-Up). The four archetypes can be used to explain four resilience functions, viz. (i): up-up as sustainable resilience, (ii) up-down as short-term resilience, till t = T, (iii) down-up as resilience in near-future, at t = T, and (iv) down-down as lack of resilience.

  • 4.
    Lantz, Björn
    University of Borås, School of Engineering.
    Den statistiska undersökningen: grundläggande metodik och typiska problem2011Book (Other academic)
  • 5.
    Lorén, Sara
    et al.
    University of Borås, School of Engineering.
    de Maré, Jacques
    Maintenance for reliability: a case study2015In: Annals of Operations Research, ISSN 0254-5330, E-ISSN 1572-9338, Vol. 224, no 1Article in journal (Refereed)
    Abstract [en]

    The optimal replacement problem for components with stochastic lives has an appealing solution based on the TTT-transform. The issue is revisited for components which are regularly inspected and where statistical uncertainties are taken into account by means of the method of predicted profile likelihood. The ideas are applied on crack growth data on a low pressure nozzle in a jet engine. It turns out that the standard method is not directly applicable and that the effect of uncertainties on the replacement times is not easy to predict.

  • 6.
    Lorén, Sara
    et al.
    University of Borås, School of Engineering.
    Svensson, Thomas
    Second Moment Reliability Evaluation vs. Monte Carlo Simulations for Weld Fatigue Strength2012In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 28, no 8, p. 887-896Article in journal (Refereed)
    Abstract [en]

    Monte Carlo simulations have become very popular in industrial applications as a tool to study variational influences on reliability assessments. The method is appealing because it can be done without any statistical knowledge and produces results that appear very informative. However, in most cases, the information gathered is no more than a complicated transformation of initial guesses because the statistical distributions of the dominating variational influences are unknown. The seemingly informative result may then be highly misleading, in particular, when the user lacks sufficient statistical knowledge. Instead, in cases where the input knowledge of the distributional properties is vague, it may be better to use a reliability method based on the actual knowledge, often not more than second moment characteristics. This can easily be done by using a method, based on variances, covariances, and sensitivity coefficients. Here, a specific problem of fatigue life of a welded structure is studied by (i) a Monte Carlo simulation method and (ii) a second moment method. Both methods are evaluated on a fatigue strain–life approach and use experimental data showing variation in weld geometry and material strength parameters. The two methods are compared and discussed in view of the engineering problem of reliability with respect to fatigue damage.

  • 7.
    Pal, Rudrajeet
    et al.
    University of Borås, Faculty of Textiles, Engineering and Business.
    Kupka, Karel
    TriloByte Statistical Software Ltd., Czech Republic.
    Aneja, Arun
    College of Engineering & Technology, East Carolina State University, USA.
    Statistical models for rating financial performance and health of companies2015In: / [ed] College of Textiles, North Carolina State University, 2015Conference paper (Other academic)
    Abstract [en]

    Authors analyze financial data from a set of almost 200 major US companies from both manufacturing and service. The selection of the firms considered the spectrum of sectors of Dow Jones Industrial composite including manufacturing companies in aerospace & defence, automotive, beverages, footware & apparel, health technology, oil & gas and service-oriented companies like consumer services, discount stores, telecom services, insurance. Short overview of recent development in business health modelling is given. Based on the data and known expert ratings, the recently published Stagewise regression algorithm was employed to identify the most relevant predictors out of all possible financial ratios as based on the data. Support Vector Machine was used subsequently to construct a quantitative probabilistic prediction model for business investment risk evaluation. With this approach, it is possible to build less rigid, more specific models suitable for smaller sectors an shorter periods, thus allowing investors and management to better react to dynamic changes in business environment. Quantitative prediction based on Support Vector Machine (SVM) models provides more information and better decission support than traditional binary prediction (good/bad). The paper provides directly applicable parametric decission models predicting both numerical rating and good/bad classification probability  for manufacturing and service sectors. Short-term and more specific models can be used to characterize not only the business subject themselves, but also to characterize, parametrize and compare business environments.

  • 8.
    Pal, Rudrajeet
    et al.
    University of Borås, Faculty of Textiles, Engineering and Business.
    Kupka, Karel
    Trilobyte Statistical Software Ltd., Czech Republic.
    Aneja, Arun
    College of Technology and Computer Science, East Carolina University, United States.
    Militky, Jiri
    Department of Material Engineering, University of Liberec, Czech Republic.
    From classical business failure prediction models to business financial models for resilience: using advanced statistical methodologies2015In: : ICORS, 2015, p. 39-40Conference paper (Refereed)
    Abstract [en]

    Over the last 35 years business failure prediction using various methods like univariate analysis, multi-variate analysis, credit risk models etc. has become a major research domain within corporate finance (Balcaen and Ooghe 2006). These mathematical models are increasingly accepted by financial institutions, governments and the European Union in the Basel Accords (Basel II/III). However, most classic statistical failure prediction models are developed without comprehensive understanding of the nature of company failure with often arbitrarily variables chosen in an ad-hoc manner (Beaver 1967b, Cybinski 2001). In this context, the paper uses advanced statistical methodology to propose a robust business financial modeling technique. Data on 18 key financial parameters were collected for 198 US-based public companies along with their expert credit rating for 2012-13. Firstly, a correlation study was performed between Altman scores and widely accepted expert rating based on stock exchange activities. Secondly, “stage-wise” regression was conducted to select the statistically most significant candidate ratios (from 153 to 9) (Hastie et al. 2007a). Thirdly, linear regression model was employed to model the credit rating and also to reduce the candidate variables (from 9 predictor ratios to five those were statistically significant). Fourth, the significant variables were used to construct the decision plane for the linear discriminant model using support vector machine classifier (SVM-C) estimation procedure (Scholkopf et al. 1995, Vapnik 1998). Binary response variable was obtained by dividing the ratings into two groups: high rating (or “good companies”) and low rating (or “not so good companies”) by choosing an arbitrary threshold rating value. Finally a logistic regression model helped to define the probability of having high rating (i.e. greater than 5) for a given company. Findings were manifold. The correlation between the Z-score and rating was poor (0.0223 and 0.0133 respectively for manufacturing and service companies). The linear regression models, on the other hand, showed high correlation coefficient (0.64 and 0.71 respectively) between predicted and actual expert ratings. With a few exceptions, in the heavy industry sectors, data was homogeneous (found using predicted residual method). The equation of the new discriminating hyper plane created by the SVM classification model (termed as Investor Inclination Index - I3 model) was proposed which means that expert ratings can be more significantly correlated to a set of candidate financial ratios predicting it. These are: (i) Cost of Goods Sold/Total Operating Expenses, (ii) Earnings Before Interest and Taxes/Total Liabilities, (iii) Earnings Before Interest/Total Revenue, (iv) Retained Earnings/Total Revenue, and (v) Working Capital/Research and Development Expense. The paper contributes by updating the original Altman discriminant model by using a data-driven predictor selection strategy to create a general methodology for building financial models providing economic meaningfulness to the credit rating used for assessing company’s performance and health. A wider use of validated financial models will encourage corporate businesses and even SMEs to evaluate themselves internally thus allowing them to identify possible threats and improve credit rating. Future research aims to provide explicit economic meaningfulness to the individual predictor ratios so that companies can create a strategic resource model (SRM) by interpreting the I3 model to determine how to create a decision support aid for the company’s business management. Also authors aim to extend the contribution by developing a tendency-dynamic status to the financial predictor for incorporating a time-series behavior for pattern recognition. This will provide economic meaningfulness to the financial models; predict financial risk and means to be resilient.

  • 9.
    Wittek, Peter
    University of Borås, Swedish School of Library and Information Science.
    Two-way incremental seriation in the temporal domain with three-dimensional visualization: Making sense of evolving high-dimensional data sets2013In: Computational Statistics & Data Analysis, ISSN 0167-9473, E-ISSN 1872-7352, Vol. 66, p. 193-201Article in journal (Refereed)
    Abstract [en]

    Two-way seriation is a popular technique to analyse groups of similar instances and their features, as well as the connections between the groups themselves. The two-way seriated data may be visualized as a two-dimensional heat map or as a three-dimensional landscape where colour codes or height correspond to the values in the matrix. To achieve a meaningful visualization of high-dimensional data, a compactly supported convolution kernel is introduced, which is similar to filter kernels used in image reconstruction and geostatistics. This filter populates the high-dimensional space with values that interpolate nearby elements, and provides insight into the clustering structure. Ordinary two-way seriation is also extended to deal with updates of both the row and column spaces. Combined with the convolution kernel, a three-dimensional visualization of dynamics is demonstrated on two data sets, a news collection and a set of microarray measurements.

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