Kasvustrategia it-alan pk-yritykselle

Pienille ja keskisuurille yrityksille eli pk-yrityksille on olemassa useita kasvustrategioita. Nämä kasvustrategiat tähtäävät joko liiketoiminnan laajentamiseen, nykyisten resurssien hyödyntämiseen, uusien resurssien luotaamiseen tai liiketoiminnan supistamiseen. Valitsemaansa kasvustrategiaa noudattamalla yritys pyrkii pääsemään kasvutavoitteisiinsa. Tämän tutkimuksen tavoitteena on tieteellisen kirjallisuuden pohjalta arvioida kasvustrategioita ja valita niistä pienelle it-alalla toimivalle case-yritykselle sopivin. Sopivimmaksi havaitun kasvustrategian pohjalta case-yritykselle laaditaan räätälöity kasvustrategia. Kasvustrategian laatimiseen käytetään tieteellisen kirjallisuuden lisäksi empiirisenä menetelmänä tapaustutkimusta. Tapaustutkimus tehdään teemahaastatteluina kymmenelle yritykselle, joilla on liike-elämästä saatuja kokemuksia case-yritykselle valitusta kasvustrategiasta. Tutkimuksen tuloksena case-yritykselle sopivin kasvustrategia on verkostokasvustrategia. Verkostokasvustrategiaa noudattamalla case-yritys keskittyy omaan ydinosaamiseensa ja sen kehittämiseen ja hankkii tarvitsemansa muun osaamisen verkostokumppaneilta. Verkostoitumisen hyötyinä case-yritykselle on muun muassa pieni omien työntekijöiden tarve, tietotaidon saaminen, kilpailukyvyn paraneminen, markkinoinnin tehostuminen ja uusille markkinoille pääsyn helpottuminen. Verkostoitumisen haasteina case-yritykselle on sen sijaan muun muassa pienen yrityksen uskottavuusongelma, sopivien verkostokumppaneiden löytäminen ja epäluotettavat verkostokumppanit. Tutkimuksen johtopäätöksenä verkostoitumisesta saatavien hyötyjen nähdään kuitenkin olevan riskejä suuremmat case-yritykselle.

Monte Carlo Methods in Parameter Estimation of Nonlinear Models

Yksi keskeisimmistä tehtävistä matemaattisten mallien tilastollisessa analyysissä on mallien tuntemattomien parametrien estimointi. Tässä diplomityössä ollaan kiinnostuneita tuntemattomien parametrien jakaumista ja niiden muodostamiseen sopivista numeerisista menetelmistä, etenkin tapauksissa, joissa malli on epälineaarinen parametrien suhteen. Erilaisten numeeristen menetelmien osalta pääpaino on Markovin ketju Monte Carlo -menetelmissä (MCMC). Nämä laskentaintensiiviset menetelmät ovat viime aikoina kasvattaneet suosiotaan lähinnä kasvaneen laskentatehon vuoksi. Sekä Markovin ketjujen että Monte Carlo -simuloinnin teoriaa on esitelty työssä siinä määrin, että menetelmien toimivuus saadaan perusteltua. Viime aikoina kehitetyistä menetelmistä tarkastellaan etenkin adaptiivisia MCMC menetelmiä. Työn lähestymistapa on käytännönläheinen ja erilaisia MCMC -menetelmien toteutukseen liittyviä asioita korostetaan. Työn empiirisessä osuudessa tarkastellaan viiden esimerkkimallin tuntemattomien parametrien jakaumaa käyttäen hyväksi teoriaosassa esitettyjä menetelmiä. Mallit kuvaavat kemiallisia reaktioita ja kuvataan tavallisina differentiaaliyhtälöryhminä. Mallit on kerätty kemisteiltä Lappeenrannan teknillisestä yliopistosta ja Åbo Akademista, Turusta.

The Boundaries of the Firm from the Extended Transaction Cost Economics Point of View - Empirical Study

Tutkimuksen tavoitteena oli tutkia yrityksen rajoja laajennetun transaktiokustannusteorian näkökulmasta. Tutkimus oli empiirinen tutkimus, jossa tutkittiin viittä toimialaa. Tutkimuksen tavoitteena oli verrata paperiteollisuutta teräs-, kemian-, ICT- ja energiateollisuuteen. Aineisto empiiriseen osioon kerättiin puolistrukturoiduilla teemahaastatteluilla. Tutkimus osoitti, että laajennettu transaktiokustannusteoria soveltuu hyvinyrityksen rajojen määrittelyyn. Staattinen transaktiokustannusteorian selitysaste ei ole riittävä, joten dynaaminen laajennus on tarpeellinen. Tutkimuksessa ilmeni, että paperiteollisuudella verrattuna muihin toimialoihin on suurimmat haasteet tehokkaiden rajojen määrittämisessä.

Observations on Software Testing Practice

This thesis investigates factors that affect software testing practice. The thesis consists of empirical studies, in which the affecting factors were analyzed and interpreted using quantitative and qualitative methods. First, the Delphi method was used to specify the scope of the thesis. Secondly, for the quantitative analysis 40industry experts from 30 organizational units (OUs) were interviewed. The survey method was used to explore factors that affect software testing practice. Conclusions were derived using correlation and regression analysis. Thirdly, from these 30 OUs, five were further selected for an in-depth case study. The data was collected through 41 semi-structured interviews. The affecting factors and their relationships were interpreted with qualitative analysis using grounded theory as the research method. The practice of software testing was analyzed from the process improvement and knowledge management viewpoints. The qualitative and quantitativeresults were triangulated to increase the validity of the thesis. Results suggested that testing ought to be adjusted according to the business orientation of the OU; the business orientation affects the testing organization and knowledge management strategy, and the business orientation andthe knowledge management strategy affect outsourcing. As a special case, the complex relationship between testing schedules and knowledge transfer is discussed. The results of this thesis can be used in improvingtesting processes and knowledge management in software testing.

Establishing intercompany relationships: Motives and methods for successful collaborative engagement

This study explores the early phases of intercompany relationship building, which is a very important topic for purchasing and business development practitioners as well as for companies' upper management. There is a lot ofevidence that a proper engagement with markets increases a company's potential for achieving business success. Taking full advantage of the market possibilities requires, however, a holistic view of managing related decision-making chain. Most literature as well as the business processes of companies are lacking this holism. Typically they observe the process from the perspective of individual stages and thus lead to discontinuity and sub-optimization. This study contains a comprehensive introduction to and evaluation of literature related to various steps of the decision-making process. It is studied from a holistic perspective ofdetermining a company's vertical integration position within its demand/ supplynetwork context; translating the vertical integration objectives to feasible strategies and objectives; and operationalizing the decisions made through engagement with collaborative intercompany relationships. The empirical part of the research has been conducted in two sections. First the phenomenon of intercompany engagement is studied using two complementary case studies. Secondly a survey hasbeen conducted among the purchasing and business development managers of several electronics manufacturing companies, to analyze the processes, decision-makingcriteria and success factors of engagement for collaboration. The aim has been to identify the reasons why companies and their management act the way they do. As a combination of theoretical and empirical research an analysis has been produced of what would be an ideal way of engaging with markets. Based on the respective findings the study concludes by proposing a holistic framework for successful engagement. The evidence presented throughout the study demonstrates clear gaps, discontinuities and limitations in both current research and in practical purchasing decision-making chains. The most significant discontinuity is the identified disconnection between the supplier selection process and related criteria and the relationship success factors.

The Key Features of Knowledge and Knowledge Management in Product Development Process

Tutkimusongelmana oli kuinka tiedon johtamisella voidaan edesauttaa tuotekehitysprosessia. Mitkä ovat ne avaintekijät tietoympäristössä kuin myös itse tiedossa, joilla on merkitystä erityisesti tuotekehitysprosessin arvon muodostumiseen ja prosessien kehittämiseen? Tutkimus on laadullinen Case-tutkimus. Tutkimusongelmat on ensin selvitetty kirjallisuuden avulla, jonka jälkeen teoreettinen viitekehys on rakennettu tutkimaan rajattua ongelma-aluetta case-yrityksestä. Empiirisen tutkimuksen materiaali koostuu pääasiallisesti henkilökohtaisten teemahaastattelujen aineistosta. Tulokset merkittävimmistä tiedon hyväksikäytön haittatekijöistä, kuten myös parannusehdotukset on lajiteltu teoreettisessa viitekehyksessä esitettyjen oletustekijöiden mukaan. Haastatteluissa saadut vastaukset tukevat kirjallisuudesta ja alan ammattilaiselta saatua käsitystä tärkeimmistä vaikuttavista tekijöistä. Tärkeimmät toimenpiteet ja aloitteet joilla parannettaisiin tiedon muodostumista, koskivat ennnen kaikkea työnteon ulkoisia olosuhteita, eikä niinkään tiedon muodostumisen prosessia itseään. Merkittävimpiä haittatekijöitä olivat kultturiin, fyysiseen ja henkiseen tilaan ja henkilöstöresursseihin liittyvät ongelmat. Ratkaisuja ongelmiin odotettiin saatavan lähinnä tietotekniikan, henkilöstöresurssien ja itse tiedon muokkaamisen avulla. Tuotekehitysprosessin ydin tietovirtojen ja –pääomien luokittelu ja tulkitseminen tiedon muodostusta kuvaavan Learning Spiralin avulla antoi lähinnä teoreettisia viitteitä siitä millaisia keinoja on olemassa tiedon lisäämiseen ja jakamiseen eri tietotyypeittäin. Tulosten perusteella caseyrityksessä pitäisi kiinnittää erityistä huomiota tiedon dokumentointiin ja jakamiseen erityisesti sen tiedon osalta, joka on organisaatiossa vain harvalla ja/tai luonteeltaan hyvin tacitia.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Analyzing customer profitability: an empirical study

The purpose of this study was to define the customer profitability of the case company as well as to specify the factors that explain customer profitability. The study was made with a quantitative research method. The research hypotheses were formulated mainly on the grounds of previous research, and were tested with statistical research methods. The research results showed that customer profitability is not equally distributed among the customers of the case company, and the majority of its customers is profitable. The interpreters for absolute customer profitability were sales volume and the customer’s location region. The interpreters for relative customer profitability were the customer’s location region and the product segment into which a customer can be classified on the basis of the products that were sold to this customer.

Inter organizational learning within and by innovation networks

The thesis deals with the phenomenon of learning between organizations in innovation networks that develop new products, services or processes. Inter organizational learning is studied especially at the level of the network. The role of the network can be seen as twofold: either the network is a context for inter organizational learning, if the learner is something else than the network (organization, group, individual), or the network itself is the learner. Innovations are regarded as a primary source of competitiveness and renewal in organizations. Networking has become increasingly common particularly because of the possibility to extend the resource base of the organization through partnerships and to concentrate on core competencies. Especially in innovation activities, networks provide the possibility to answer the complex needs of the customers faster and to share the costs and risks of the development work. Networked innovation activities are often organized in practice as distributed virtual teams, either within one organization or as cross organizational co operation. The role of technology is considered in the research mainly as an enabling tool for collaboration and learning. Learning has been recognized as one important collaborative process in networks or as a motivation for networking. It is even more important in the innovation context as an enabler of renewal, since the essence of the innovation process is creating new knowledge, processes, products and services. The thesis aims at providing enhanced understanding about the inter organizational learning phenomenon in and by innovation networks, especially concentrating on the network level. The perspectives used in the research are the theoretical viewpoints and concepts, challenges, and solutions for learning. The methods used in the study are literature reviews and empirical research carried out with semi structured interviews analyzed with qualitative content analysis. The empirical research concentrates on two different areas, firstly on the theoretical approaches to learning that are relevant to innovation networks, secondly on learning in virtual innovation teams. As a result, the research identifies insights and implications for learning in innovation networks from several viewpoints on organizational learning. Using multiple perspectives allows drawing a many sided picture of the learning phenomenon that is valuable because of the versatility and complexity of situations and challenges of learning in the context of innovation and networks. The research results also show some of the challenges of learning and possible solutions for supporting especially network level learning.

Determining risk potential through accidents and near-accidents: an empirical study in a steel factory

Fatal and permanently disabling accidents form only one per I cent of all occupational accidents but in many branches of industry they account for more than half the accident costs. Furthermore the human suffering of the victim and his family is greater in severe accidents than in slight ones. For both human and economic reasons the severe accident risks should be identified befor injuries occur. It is for this purpose that different safety analysis methods have been developed . This study shows two new possible approaches to the problem.. The first is the hypothesis that it is possible to estimate the potential severity of accidents independent of the actual severity. The second is the hypothesis that when workers are also asked to report near accidents, they are particularly prone to report potentially severe near accidents on the basis of their own subjective risk assessment. A field study was carried out in a steel factory. The results supported both the hypotheses. The reliability and the validity of post incident estimates of an accident's potential severity were reasonable. About 10 % of accidents were estimated to be potentially critical; they could have led to death or very severe permanent disability. Reported near accidents were significantly more severe, about 60 $ of them were estimated to be critical. Furthermore the validity of workers subjective risk assessment, manifested in the near accident reports, proved to be reasonable. The studied new methods require further development and testing. They could be used both in routine usage in work places and in research for identifying and setting the priorities of accident risks.

Strategic choices and performance – an empirical analysis of largest European and North American energy companies

Energy industry has gone through major changes globally in past two decades. Liberalization of energy markets has led companies to integrate both vertically and horizontally. Growing concern on sustainable development and aims to decrease greenhouse gases in future will increase the portion of renewable energy in total energy production. Purpose of this study was to analyze using statistical methods, what impacts different strategic choices has on biggest European and North American energy companies’ performance. Results show that vertical integration, horizontal integration and use of renewable energy in production had the most impact on profitability. Increase in level of vertical integration decreased companies’ profitability, while increase in horizontal integration improved companies’ profitability. Companies that used renewable energy in production were less profitable than companies not using renewable energy.

Project and customer profitability analysis: an empirical case study

The objective of this case study is to provide a Finnish solution provider company an objective, in-depth analysis of their project based business and especially of project estimation accuracy. A project and customer profitability analysis is conducted as a complementary addition to describe profitability of the Case Company’s core division. The theoretical framework is constructed on project profitability and customer profitability analysis. Project profitability is approached starting from managing projects, continuing to project pricing process and concluding to project success. The empirical part of this study describes the Case Company’s project portfolio, and by means of quantitative analysis, the study describes how the characteristics of a project impact the project’s profitability. The findings indicate that it really makes a difference in project portfolio’s estimated and actual profitability when methods of installation and technical specifications are scrutinized. Implications on profitability are gathered into a risk assessment tool proposal.

Characteristics of credit risk assessment methods of major Nordic banks in allocating capital when extending credits to large corporations

Credit risk assessment is an integral part of banking. Credit risk means that the return will not materialise in case the customer fails to fulfil its obligations. Thus a key component of banking is setting acceptance criteria for granting loans. Theoretical part of the study focuses on key components of credit assessment methods of Banks in the literature when extending credits to large corporations. Main component is Basel II Accord, which sets regulatory requirement for credit risk assessment methods of banks. Empirical part comprises, as primary source, analysis of major Nordic banks’ annual reports and risk management reports. As secondary source complimentary interviews were carried out with senior credit risk assessment personnel. The findings indicate that all major Nordic banks are using combination of quantitative and qualitative information in credit risk assessment model when extending credits to large corporations. The relative input of qualitative information depends on the selected approach to the credit rating, i.e. point-in-time or through-the-cycle.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

Role of Capabilities in Purchasing and Supply Management Performance

The objective of this thesis was to study the role of capabilities in purchasing and supply management. For the pre-understanding of the research topic, purchasing and supply management development and the multidimensional, unstructured and complex nature of purchasing and supply management performance was studied in literature review. In addition, a capability-based purchasing and supply management performance framework were researched and structured for the empirical research. Due to the unstructured nature of the research topic, the empirical research is three-pronged in this study including three different research methods: the Delphi method, semi-structured interview, and case research. As a result, the purchasing and supply management capability assessment tool was structured to measure current level of capabilities and impact of capabilities on purchasing and supply management performance. The final results indicate that capabilities are enablers of purchasing and supply management performance, and therefore critical to purchasing and supply performance.