Financier’s credit risk: A Performance-Based Classification Model

The purpose of this study is to view credit risk from the financier’s point of view in a theoretical framework. Results and aspects of the previous studies regarding measuring credit risk with accounting based scoring models are also examined. The theoretical framework and previous studies are then used to support the empirical analysis which aims to develop a credit risk measure for a bank’s internal use or a risk management tool for a company to indicate its credit risk to the financier. The study covers a sample of Finnish companies from 12 different industries and four different company categories and employs their accounting information from 2004 to 2008. The empirical analysis consists of six stage methodology process which uses measures of profitability, liquidity, capital structure and cash flow to determine financier’s credit risk, define five significant risk classes and produce risk classification model. The study is confidential until 15.10.2012.

Classification and inventory management of abnormal demand items

The main objective of the study is to form a framework that provides tools to recognise and classify items whose demand is not smooth but varies highly on size and/or frequency. The framework will then be combined with two other classification methods in order to form a three-dimensional classification model. Forecasting and inventory control of these abnormal demand items is difficult. Therefore another object of this study is to find out which statistical forecasting method is most suitable for forecasting of abnormal demand items. The accuracy of different methods is measured by comparing the forecast to the actual demand. Moreover, the study also aims at finding proper alternatives to the inventory control of abnormal demand items. The study is quantitative and the methodology is a case study. The research methods consist of theory, numerical data, current state analysis and testing of the framework in case company. The results of the study show that the framework makes it possible to recognise and classify the abnormal demand items. It is also noticed that the inventory performance of abnormal demand items differs significantly from the performance of smoothly demanded items. This makes the recognition of abnormal demand items very important.

Asiakkuuksien strateginen arvottaminen ja luokittelu

Tämän tutkimuksen kohdeorganisaatio on suuren teollisuusyrityksen sisäinen raaka-aineen hankkija ja toimittaja. Tutkimuksessa selvitetään, mistä kohdeorganisaation hankinta-asiakkuuksien arvo muodostuu ja kuinka olemassa olevan liiketoimintadatan perusteella voidaan tutkia, arvioida ja luokitella kauppojen ja asiakkuuksien arvokkuutta aikaan sitomatta, objektiivisesti ja luotettavasti. Tutkimuksen teoriaosiossa esitellään lähestymistapoja ja menetelmiä, joiden avulla voidaan jalostaa olemassa olevasta datasta uutta sidosryhmätietämystä liiketoiminnan käyttöön, sekä tarkastellaan asiakaskannattavuusanalyysin, portfolioanalyysin, sekä asiakassegmentoinnin perusteita ja malleja. Näiden teorioiden ja mallien pohjalta rakennetaan kohdeorganisaatiolle räätälöity, indeksoituihin hinta-, määrä- ja kauppojen toistuvuus-muuttujiin perustuva, asiakkuuksien arvottamis- ja luokittelumalli. Arvottamis- ja luokittelumalli testataan vuosien 2003–2007 liiketoimintadatasta muodostetulla 389 336 kaupparivin otoksella, joka sisältää 42 186 arvioitavaa asiakkuussuhdetta. Merkittävin esille nouseva havainto on noin 5 000:n keskimääräistä selkeästi kalliimman asiakkuuden ryhmä. Aineisto ja sen poikkeavuudet testataan tilastollisin menetelmin, jotta saadaan selville asiakkuuden arvoon vaikuttavat ja arvoa selittävät tekijät. Lopuksi pohditaan arvottamismallin merkitystä analyyttisemman ostotoiminnan ja asiakkuudenhallinnan välineenä, sekä esitetään muutamia parannusehdotuksia.

A Business Incubator, Accelerator, or Coworking Space? Case Health Innovation Village at GE

Health Innovation Village at GE is one of the new communities targeted for startup and growth-oriented companies. It has been established at the premises of a multinational conglomerate that will promote networking and growth of startup companies. The concept combines features from traditional business incubators, accelerators, and coworking spaces. This research compares Health Innovation Village to these concepts regarding its goals, target clients, source of income, organization, facilities, management, and success factors. In addition, a new incubator classification model is introduced. On the other hand, Health Innovation Village is examined from its tenants’ perspective and improvements are suggested. The work was implemented as a qualitative case study by interviewing GE staff with connections to Health Innovation Village as well as startup entrepreneurs and employees’ working there. The most evident features of Health Innovation Village correspond to those of business incubators although it is atypical as a non-profit corporate business incubator. Strong network orientation and connections to venture capitalists are common characteristics of these new types of accelerators. The design of the premises conforms to the principles of coworking spaces, but the services provided to the startup companies are considerably more versatile than the services offered by coworking spaces. The advantages of Health Innovation Village are that there are first-class premises and exceptionally good networking possibilities that other types of incubators or accelerators are not able to offer. A conglomerate can also provide multifaceted special knowledge for young firms. In addition, both GE and the startups gained considerable publicity through their cooperation, indeed a characteristic that benefits both parties. Most of the expectations of the entrepreneurs were exceeded. However, communication and the scope of cooperation remain challenges. Micro companies spend their time developing and marketing their products and acquiring financing. Therefore, communication should be as clear as possible and accessible everywhere. The startups would prefer to cooperate significantly more, but few have the time available to assume the responsibility of leadership. The entrepreneurs also expected to have more possibilities for cooperation with GE. Wider collaboration might be accomplished by curation in the same way as it is used in the well-functioning coworking spaces where curators take care of practicalities and promote cooperation. Communication issues could be alleviated if the community had its own Intranet pages where all information could be concentrated. In particular, a common calendar and a room reservation system could be useful. In addition, it could be beneficial to have a section of the Intranet open for both the GE staff and the startups so that those willing to share their knowledge and those having project offers could use it for advertising.

CREATING A MODEL FOR INVENTORY MANAGEMENT SYSTEM

Tutkielman tarkoituksena oli mallintaa varastonhallintajärjestelmä, joka olisi sopiva case yritykselle. Tutkimus aloitettiin case yrityksen varastonhallinan nykytilan kartoituksella, jonka jälkeen tutkittiin varastonhallinnan eri osa-alueisiin. Varastonhallinnan osa-alueista käsiteltiin varastotyyppejä, motiiveja, tavoitteita, kysynnän ennustamista sekä erilaisia varastonhallinnan työkaluja. Sen lisäksi tutkittiin erilaisia varaston täydennysmalleja. Teoriaosuudessa käsiteltiin lisäksi kolmea erilaista tietojärjestelmätyyppiä: toiminnanohjausjärjestelmää, sähköisen kaupankäynnin järjestelmää sekä räätälöityä järjestelmää. Tutkimussuunnitelmassa nämä kolme järjestelmää rajattiin vaihtoehdoiksi, joista jokin valittaisiin case yrityksen varastonhallintajärjestelmäksi. Teorian ja nykytilan pohjalta tehtiin viitekehys, jossa esiteltiin varastonhallintajärjestelmän tieto- ja toiminnallisuusominaisuuksia. Nämä ominaisuudet priorisoitiin neljään eri luokkaan ominaisuuden kriittisyyden mukaan. Järjestelmävaihtoehdot arvioitiin viitekehyksen kriteerien mukaisesti, miten helposti ominaisuudet olisivat toteutettavissa eri vaihtoehdoissa. Tulokset laskettiin näiden arviointien perusteella, jonka jälkeen tulosten analysoinnissa huomattiin, että toiminnanohjausjärjestelmä sopisi parhaiten case yrityksen varastonhallintajärjestelmäksi.

On Topological Solitons in The Faddeev-Skyrme Model and its Extensions

The topological solitons of two classical field theories, the Faddeev-Skyrme model and the Ginzburg-Landau model are studied numerically and analytically in this work. The aim is to gain information on the existence and properties of these topological solitons, their structure and behaviour under relaxation. First, the conditions and mechanisms leading to the possibility of topological solitons are explored from the field theoretical point of view. This leads one to consider continuous deformations of the solutions of the equations of motion. The results of algebraic topology necessary for the systematic treatment of such deformations are reviewed and methods of determining the homotopy classes of topological solitons are presented. The Faddeev-Skyrme and Ginzburg-Landau models are presented, some earlier results reviewed and the numerical methods used in this work are described. The topological solitons of the Faddeev-Skyrme model, Hopfions, are found to follow the same mechanisms of relaxation in three different domains with three different topological classifications. For two of the domains, the necessary but unusual topological classification is presented. Finite size topological solitons are not found in the Ginzburg-Landau model and a scaling argument is used to suggest that there are indeed none unless a certain modification to the model, due to R. S. Ward, is made. In that case, the Hopfions of the Faddeev-Skyrme model are seen to be present for some parameter values. A boundary in the parameter space separating the region where the Hopfions exist and the area where they do not exist is found and the behaviour of the Hopfion energy on this boundary is studied.

Classification, comorbidity, heredity, and risk factors of female sexual dysfunctions

Female sexual dysfunctions, including desire, arousal, orgasm and pain problems, have been shown to be highly prevalent among women around the world. The etiology of these dysfunctions is unclear but associations with health, age, psychological problems, and relationship factors have been identified. Genetic effects explain individual variation in orgasm function to some extent but until now quantitative behavior genetic analyses have not been applied to other sexual functions. In addition, behavior genetics can be applied to exploring the cause of any observed comorbidity between the dysfunctions. Discovering more about the etiology of the dysfunctions may further improve the classification systems which are currently under intense debate. The aims of the present thesis were to evaluate the psychometric properties of a Finnish-language version of a commonly used questionnaire for measuring female sexual function, the Female Sexual Function Index (FSFI), in order to investigate prevalence, comorbidity, and classification, and to explore the balance of genetic and environmental factors in the etiology as well as the associations of a number of biopsychosocial factors with female sexual functions. Female sexual functions were studied through survey methods in a population based sample of Finnish twins and their female siblings. There were two waves of data collection. The first data collection targeted 5,000 female twins aged 33–43 years and the second 7,680 female twins aged 18–33 and their over 18–year-old female siblings (n = 3,983). There was no overlap between the data collections. The combined overall response rate for both data collections was 53% (n = 8,868), with a better response rate in the second (57%) compared to the first (45%). In order to measure female sexual function, the FSFI was used. It includes 19 items which measure female sexual function during the previous four weeks in six subdomains; desire, subjective arousal, lubrication, orgasm, sexual satisfaction, and pain. In line with earlier research in clinical populations, a six factor solution of the Finnish-language version of the FSFI received supported. The internal consistencies of the scales were good to excellent. Some questions about how to avoid overestimating the prevalence of extreme dysfunctions due to women being allocated the score of zero if they had had no sexual activity during the preceding four weeks were raised. The prevalence of female sexual dysfunctions per se ranged from 11% for lubrication dysfunction to 55% for desire dysfunction. The prevalence rates for sexual dysfunction with concomitant sexual distress, in other words, sexual disorders were notably lower ranging from 7% for lubrication disorder to 23% for desire disorder. The comorbidity between the dysfunctions was substantial most notably between arousal and lubrication dysfunction even if these two dysfunctions showed distinct patterns of associations with the other dysfunctions. Genetic influences on individual variation in the six subdomains of FSFI were modest but significant ranging from 3–11% for additive genetic effects and 5–18% for nonadditive genetic effects. The rest of the variation in sexual functions was explained by nonshared environmental influences. A correlated factor model, including additive and nonadditive genetic effects and nonshared environmental effects had the best fit. All in all, every correlation between the genetic factors was significant except between lubrication and pain. All correlations between the nonshared environment factors were significant showing that there is a substantial overlap in genetic and nonshared environmental influences between the dysfunctions. In general, psychological problems, poor satisfaction with the relationship, sexual distress, and poor partner compatibility were associated with more sexual dysfunctions. Age was confounded with relationship length but had over and above relationship length a negative effect on desire and sexual satisfaction and a positive effect on orgasm and pain functions. Alcohol consumption in general was associated with better desire, arousal, lubrication, and orgasm function. Women pregnant with their first child had fewer pain problems than nulliparous nonpregnant women. Multiparous pregnant women had more orgasm problems compared to multiparous nonpregnant women. Having children was associated with less orgasm and pain problems. The conclusions were that desire, subjective arousal, lubrication, orgasm, sexual satisfaction, and pain are separate entities that have distinct associations with a number of different biopsychosocial factors. However, there is also considerable comorbidity between the dysfunctions which are explained by overlap in additive genetic, nonadditive genetic and nonshared environmental influences. Sexual dysfunctions are highly prevalent and are not always associated with sexual distress and this relationship might be moderated by a good relationship and compatibility with partner. Regarding classification, the results supports separate diagnoses for subjective arousal and genital arousal as well as the inclusion of pain under sexual dysfunctions.

Service offering classification for logistics services

This thesis studies the development of service offering model that creates added-value for customers in the field of logistics services. The study focusses on offering classification and structures of model. The purpose of model is to provide value-added solutions for customers and enable superior service experience. The aim of thesis is to define what customers expect from logistics solution provider and what value customers appreciate so greatly that they could invest in value-added services. Value propositions, costs structures of offerings and appropriate pricing methods are studied. First, literature review of creating solution business model and customer value is conducted. Customer value is found out with customer interviews and qualitative empiric data is used. To exploit expertise knowledge of logistics, innovation workshop tool is utilized. Customers and experts are involved in the design process of model. As a result of thesis, three-level value-added service offering model is created based on empiric and theoretical data. Offerings with value propositions are proposed and the level of model reflects the deepness of customer-provider relationship and the amount of added value. Performance efficiency improvements and cost savings create the most added value for customers. Value-based pricing methods, such as performance-based models are suggested to apply. Results indicate the interest of benefitting networks and partnership in field of logistics services. Networks development is proposed to be investigated further.

Open innovation model for a large company in the ICT industry: Case Ericsson LMF

Open innovation paradigm states that the boundaries of the firm have become permeable, allowing knowledge to flow inwards and outwards to accelerate internal innovations and take unused knowledge to the external environment; respectively. The successful implementation of open innovation practices in firms like Procter & Gamble, IBM, and Xerox, among others; suggest that it is a sustainable trend which could provide basis for achieving competitive advantage. However, implementing open innovation could be a complex process which involves several domains of management; and whose term, classification, and practices have not totally been agreed upon. Thus, with many possible ways to address open innovation, the following research question was formulated: How could Ericsson LMF assess which open innovation mode to select depending on the attributes of the project at hand? The research followed the constructive research approach which has the following steps: find a practical relevant problem, obtain general understanding of the topic, innovate the solution, demonstrate the solution works, show theoretical contributions, and examine the scope of applicability of the solution. The research involved three phases of data collection and analysis: Extensive literature review of open innovation, strategy, business model, innovation, and knowledge management; direct observation of the environment of the case company through participative observation; and semi-structured interviews based of six cases involving multiple and heterogeneous open innovation initiatives. Results from the cases suggest that the selection of modes depend on multiple reasons, with a stronger influence of factors related to strategy, business models, and resources gaps. Based on these and others factors found in the literature review and observations; it was possible to construct a model that supports approaching open innovation. The model integrates perspectives from multiple domains of the literature review, observations inside the case company, and factors from the six open innovation cases. It provides steps, guidelines, and tools to approach open innovation and assess the selection of modes. Measuring the impact of open innovation could take years; thus, implementing and testing entirely the model was not possible due time limitation. Nevertheless, it was possible to validate the core elements of the model with empirical data gathered from the cases. In addition to constructing the model, this research contributed to the literature by increasing the understanding of open innovation, providing suggestions to the case company, and proposing future steps.

Differential evolution based classification with pool of distances and aggregation operators

The objective of this thesis is to develop and generalize further the differential evolution based data classification method. For many years, evolutionary algorithms have been successfully applied to many classification tasks. Evolution algorithms are population based, stochastic search algorithms that mimic natural selection and genetics. Differential evolution is an evolutionary algorithm that has gained popularity because of its simplicity and good observed performance. In this thesis a differential evolution classifier with pool of distances is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, to determine the optimal values for all free parameters of the classifier model during the training phase of the classifier. The differential evolution classifier applies the individually optimized distance measure for each new data set to be classified is generalized to cover a pool of distances. Instead of optimizing a single distance measure for the given data set, the selection of the optimal distance measure from a predefined pool of alternative measures is attempted systematically and automatically. Furthermore, instead of only selecting the optimal distance measure from a set of alternatives, an attempt is made to optimize the values of the possible control parameters related with the selected distance measure. Specifically, a pool of alternative distance measures is first created and then the differential evolution algorithm is applied to select the optimal distance measure that yields the highest classification accuracy with the current data. After determining the optimal distance measures for the given data set together with their optimal parameters, all determined distance measures are aggregated to form a single total distance measure. The total distance measure is applied to the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; a sample belongs to the class represented by the nearest prototype vector when measured with the optimized total distance measure. During the training process the differential evolution algorithm determines the optimal class vectors, selects optimal distance metrics, and determines the optimal values for the free parameters of each selected distance measure. The results obtained with the above method confirm that the choice of distance measure is one of the most crucial factors for obtaining higher classification accuracy. The results also demonstrate that it is possible to build a classifier that is able to select the optimal distance measure for the given data set automatically and systematically. After finding optimal distance measures together with optimal parameters from the particular distance measure results are then aggregated to form a total distance, which will be used to form the deviation between the class vectors and samples and thus classify the samples. This thesis also discusses two types of aggregation operators, namely, ordered weighted averaging (OWA) based multi-distances and generalized ordered weighted averaging (GOWA). These aggregation operators were applied in this work to the aggregation of the normalized distance values. The results demonstrate that a proper combination of aggregation operator and weight generation scheme play an important role in obtaining good classification accuracy. The main outcomes of the work are the six new generalized versions of previous method called differential evolution classifier. All these DE classifier demonstrated good results in the classification tasks.