Kansainvälinen markkinavalinta ohjelmistoalan mikroyrityksessä

Tutkielman tutkimusongelmana on, miten kansainvälinen kohdemarkkinoiden valinta pitäisi suorittaa ohjelmistoalan mikroyrityksessä. Tutkielma on kvalitatiivinen, yksittäinen tapaustutkimus. Teoriaosassa tutkimusongelmaa lähestytään tarkastelemalla kirjallisuutta, joka käsittelee kansainväliseen kohdemarkkinavalintaan vaikuttavia tekijöitä sekä olemassa olevia markkinavalintamalleja. Vanhojen mallien pohjalta kehitetään uusi käytännönläheinen ohjelmistoalan mikroyritykselle sopiva maavalintamalli. Kehitetty maavalintamalli koostuu eri vaiheista, joita ovat 1) sisäisten tekijöiden kartoittaminen, 2) alkukarsinta, 3) yrityksen kilpailukyvyn ja markkinoiden houkuttelevuuden mittaaminen matriisin avulla. Kehitettyä mallia sovelletaan tapausyritykseen tekemällä kansainvälinen markkinavalinta perustuen työpöytätutkimukseen, jossa käytetään Internetistä saatavaa sekundääridataa ja markkinatietoa. Työn lopussa esitetään markkinavalinnan tulokset ja toimenpidesuositukset yrityksen johdolle. Tutkimus osoittaa, että resurssipulasta huolimatta systemaattinen ja loogisesti etenevä maavalintaprosessi on mahdollista toteuttaa myös mikroyrityksessä. Ennalta määrätyt numeeriset rajat valintakriteereille mahdollistavat markkinoiden tarkastelun objektiivisuuden. Suurimpana haasteena ohjelmistoalan mikroyrityksen kansainvälisessä kohdemarkkinavalinnassa on yrityksen kilpailukyvyn mittaaminen eri markkinoilla. Tämäjohtuu osin ohjelmistoalan dynaamisesta luonteesta sekä kilpailija-analyysien subjektiivisuudesta.

Finnish buyers’ perceptions of Indian textile and clothing suppliers: Country of origin in focus

Choosing the right supplier is crucial for long-term business prospects and profitability. Thus organizational buyers are naturally very interested in how they can select the right supplier for their needs. Likewise, suppliers are interested in knowing how their customers make purchasing decisions in order to effectively sell and market to them. From the point of view of the textile and clothing (T&C) industry, regulatory changes and increasing low-cost and globalization pressures have led to the rise of low-cost production locations India and China as the world’s largest T&C producers. This thesis will examine T&C trade between Finland and India specifically in the context of non-industrial T&C products. Its main research problem asks: what perceptions do Finnish T&C industry buyers hold of India and Indian suppliers? B2B buyers use various supplier selection models and criteria in making their purchase decisions. A significant amount of research has been done into supplier selection practices, and in the context of international trade, country of origin (COO) perceptions specifically have garnered much attention. This thesis uses a mixed methods approach (online questionnaire and in-depth interviews) to evaluate Finnish T&C buyers’ supplier selection criteria, COO perceptions of India and experiences of Indian suppliers. It was found that the most important supplier selection criteria used by Finnish T&C buyers are quality, reliability and cost. COO perceptions were not found to be influential in purchasing process. Indian T&C suppliers’ strengths were found to be low cost, flexibility and a history of traditional T&C expertise. Their weaknesses include product quality and unreliable delivery times. Overall, the main challenges that need to be overcome by Indian T&C companies are logistical difficulties and the cost vs. quality trade-off. Despite positive perceptions of India for cost, the overall value offered by Indian T&C products was perceived to be low due to poor quality. Unreliable delivery time experiences also affected buyer’s reliability perceptions of Indian suppliers. The main limiting factors of this thesis relate to the small sample size used in the research. This limits the generalizability of results and the ability to evaluate the reliability and validity of some of the research instruments.

IT-yrityksen etabloituminen Keski-Eurooppaan

Kansainvälistyminen on yksi avaintekijä pienelle kasvua tavoittelevalle IT-yritykselle. Tämän diplomityön tavoitteena oli tehdä case -yritykselle kansainvälistymissuunnitelma ensimmäisten ulkomaan toimintojen aloittamiseksi. Tutkimuksessa selvitetään kansainvälistymismalleja, kohdemarkkinan valintamalleja sekä operaatiomuotoja pk-yrityksen näkökulmasta. Tässä työssä käsitellään malleja, joilla pk-yrityksen tulisi valita kohdemarkkinansa ja operaatiomuoto huomioiden pk-yrityksen usein rajalliset resurssit. Tutkimuksen ensimmäisessä vaiheessa tutustuttiin eri kansainvälistymismalleihin, kansainvälistymisen motiiveihin ja esteisiin. Toisessa vaiheessa käytiin läpi kohdemarkkinan valintamalleja sekä eri operaatiomuotoja. Tutkimuksen viimeisessä vaiheessa vertailtiin annettuja kohdemarkkinoita ja tehtiin case-yritykselle kansainvälistymissuunnitelma pohjautuen aiempaan tutkimukseen. Tutkimuksen tuloksena syntyneessä kansainvälistymissuunnitelmassa suositellaan potentiaalisinta kohdemarkkinaa ja operaatiomuotoa, sekä lasketaan investointikustannus etabloitumiselle.

Automated formulation of optimisation models for steel beam structures

Over 70% of the total costs of an end product are consequences of decisions that are made during the design process. A search for optimal cross-sections will often have only a marginal effect on the amount of material used if the geometry of a structure is fixed and if the cross-sectional characteristics of its elements are property designed by conventional methods. In recent years, optimalgeometry has become a central area of research in the automated design of structures. It is generally accepted that no single optimisation algorithm is suitable for all engineering design problems. An appropriate algorithm, therefore, mustbe selected individually for each optimisation situation. Modelling is the mosttime consuming phase in the optimisation of steel and metal structures. In thisresearch, the goal was to develop a method and computer program, which reduces the modelling and optimisation time for structural design. The program needed anoptimisation algorithm that is suitable for various engineering design problems. Because Finite Element modelling is commonly used in the design of steel and metal structures, the interaction between a finite element tool and optimisation tool needed a practical solution. The developed method and computer programs were tested with standard optimisation tests and practical design optimisation cases. Three generations of computer programs are developed. The programs combine anoptimisation problem modelling tool and FE-modelling program using three alternate methdos. The modelling and optimisation was demonstrated in the design of a new boom construction and steel structures of flat and ridge roofs. This thesis demonstrates that the most time consuming modelling time is significantly reduced. Modelling errors are reduced and the results are more reliable. A new selection rule for the evolution algorithm, which eliminates the need for constraint weight factors is tested with optimisation cases of the steel structures that include hundreds of constraints. It is seen that the tested algorithm can be used nearly as a black box without parameter settings and penalty factors of the constraints.

Adaptive MCMC methods with applications in environmental and geophysical models

This work presents new, efficient Markov chain Monte Carlo (MCMC) simulation methods for statistical analysis in various modelling applications. When using MCMC methods, the model is simulated repeatedly to explore the probability distribution describing the uncertainties in model parameters and predictions. In adaptive MCMC methods based on the Metropolis-Hastings algorithm, the proposal distribution needed by the algorithm learns from the target distribution as the simulation proceeds. Adaptive MCMC methods have been subject of intensive research lately, as they open a way for essentially easier use of the methodology. The lack of user-friendly computer programs has been a main obstacle for wider acceptance of the methods. This work provides two new adaptive MCMC methods: DRAM and AARJ. The DRAM method has been built especially to work in high dimensional and non-linear problems. The AARJ method is an extension to DRAM for model selection problems, where the mathematical formulation of the model is uncertain and we want simultaneously to fit several different models to the same observations. The methods were developed while keeping in mind the needs of modelling applications typical in environmental sciences. The development work has been pursued while working with several application projects. The applications presented in this work are: a winter time oxygen concentration model for Lake Tuusulanjärvi and adaptive control of the aerator; a nutrition model for Lake Pyhäjärvi and lake management planning; validation of the algorithms of the GOMOS ozone remote sensing instrument on board the Envisat satellite of European Space Agency and the study of the effects of aerosol model selection on the GOMOS algorithm.

Reuse of information for selection of methods and operating conditions for drying of pharmaceutical materials

Drying is a major step in the manufacturing process in pharmaceutical industries, and the selection of dryer and operating conditions are sometimes a bottleneck. In spite of difficulties, the bottlenecks are taken care of with utmost care due to good manufacturing practices (GMP) and industries' image in the global market. The purpose of this work is to research the use of existing knowledge for the selection of dryer and its operating conditions for drying of pharmaceutical materials with the help of methods like case-based reasoning and decision trees to reduce time and expenditure for research. The work consisted of two major parts as follows: Literature survey on the theories of spray dying, case-based reasoning and decision trees; working part includes data acquisition and testing of the models based on existing and upgraded data. Testing resulted in a combination of two models, case-based reasoning and decision trees, leading to more specific results when compared to conventional methods.

Mechanistic population Models in Biology: Model Derivation and Application

In general, models of ecological systems can be broadly categorized as ’top-down’ or ’bottom-up’ models, based on the hierarchical level that the model processes are formulated on. The structure of a top-down, also known as phenomenological, population model can be interpreted in terms of population characteristics, but it typically lacks an interpretation on a more basic level. In contrast, bottom-up, also known as mechanistic, population models are derived from assumptions and processes on a more basic level, which allows interpretation of the model parameters in terms of individual behavior. Both approaches, phenomenological and mechanistic modelling, can have their advantages and disadvantages in different situations. However, mechanistically derived models might be better at capturing the properties of the system at hand, and thus give more accurate predictions. In particular, when models are used for evolutionary studies, mechanistic models are more appropriate, since natural selection takes place on the individual level, and in mechanistic models the direct connection between model parameters and individual properties has already been established. The purpose of this thesis is twofold. Firstly, a systematical way to derive mechanistic discrete-time population models is presented. The derivation is based on combining explicitly modelled, continuous processes on the individual level within a reproductive period with a discrete-time maturation process between reproductive periods. Secondly, as an example of how evolutionary studies can be carried out in mechanistic models, the evolution of the timing of reproduction is investigated. Thus, these two lines of research, derivation of mechanistic population models and evolutionary studies, are complementary to each other.

Methods and Models for Accelerating Dynamic Simulation of Fluid Power Circuits

The objective of this dissertation is to improve the dynamic simulation of fluid power circuits. A fluid power circuit is a typical way to implement power transmission in mobile working machines, e.g. cranes, excavators etc. Dynamic simulation is an essential tool in developing controllability and energy-efficient solutions for mobile machines. Efficient dynamic simulation is the basic requirement for the real-time simulation. In the real-time simulation of fluid power circuits there exist numerical problems due to the software and methods used for modelling and integration. A simulation model of a fluid power circuit is typically created using differential and algebraic equations. Efficient numerical methods are required since differential equations must be solved in real time. Unfortunately, simulation software packages offer only a limited selection of numerical solvers. Numerical problems cause noise to the results, which in many cases leads the simulation run to fail. Mathematically the fluid power circuit models are stiff systems of ordinary differential equations. Numerical solution of the stiff systems can be improved by two alternative approaches. The first is to develop numerical solvers suitable for solving stiff systems. The second is to decrease the model stiffness itself by introducing models and algorithms that either decrease the highest eigenvalues or neglect them by introducing steady-state solutions of the stiff parts of the models. The thesis proposes novel methods using the latter approach. The study aims to develop practical methods usable in dynamic simulation of fluid power circuits using explicit fixed-step integration algorithms. In this thesis, twomechanisms whichmake the systemstiff are studied. These are the pressure drop approaching zero in the turbulent orifice model and the volume approaching zero in the equation of pressure build-up. These are the critical areas to which alternative methods for modelling and numerical simulation are proposed. Generally, in hydraulic power transmission systems the orifice flow is clearly in the turbulent area. The flow becomes laminar as the pressure drop over the orifice approaches zero only in rare situations. These are e.g. when a valve is closed, or an actuator is driven against an end stopper, or external force makes actuator to switch its direction during operation. This means that in terms of accuracy, the description of laminar flow is not necessary. But, unfortunately, when a purely turbulent description of the orifice is used, numerical problems occur when the pressure drop comes close to zero since the first derivative of flow with respect to the pressure drop approaches infinity when the pressure drop approaches zero. Furthermore, the second derivative becomes discontinuous, which causes numerical noise and an infinitely small integration step when a variable step integrator is used. A numerically efficient model for the orifice flow is proposed using a cubic spline function to describe the flow in the laminar and transition areas. Parameters for the cubic spline function are selected such that its first derivative is equal to the first derivative of the pure turbulent orifice flow model in the boundary condition. In the dynamic simulation of fluid power circuits, a tradeoff exists between accuracy and calculation speed. This investigation is made for the two-regime flow orifice model. Especially inside of many types of valves, as well as between them, there exist very small volumes. The integration of pressures in small fluid volumes causes numerical problems in fluid power circuit simulation. Particularly in realtime simulation, these numerical problems are a great weakness. The system stiffness approaches infinity as the fluid volume approaches zero. If fixed step explicit algorithms for solving ordinary differential equations (ODE) are used, the system stability would easily be lost when integrating pressures in small volumes. To solve the problem caused by small fluid volumes, a pseudo-dynamic solver is proposed. Instead of integration of the pressure in a small volume, the pressure is solved as a steady-state pressure created in a separate cascade loop by numerical integration. The hydraulic capacitance V/Be of the parts of the circuit whose pressures are solved by the pseudo-dynamic method should be orders of magnitude smaller than that of those partswhose pressures are integrated. The key advantage of this novel method is that the numerical problems caused by the small volumes are completely avoided. Also, the method is freely applicable regardless of the integration routine applied. The superiority of both above-mentioned methods is that they are suited for use together with the semi-empirical modelling method which necessarily does not require any geometrical data of the valves and actuators to be modelled. In this modelling method, most of the needed component information can be taken from the manufacturer’s nominal graphs. This thesis introduces the methods and shows several numerical examples to demonstrate how the proposed methods improve the dynamic simulation of various hydraulic circuits.

Use of preclinical and early clinical data for dose selection of a selective estrogen receptor modulator toremifene in treatment of breast cancer

In development of human medicines, it is important to predict early and accurately enough the disease and patient population to be treated as well as the effective and safe dose range of the studied medicine. This is pursued by using preclinical research models, clinical pharmacology and early clinical studies with small sample sizes. When successful, this enables effective development of medicines and reduces unnecessary exposure of healthy subjects and patients to ineffectice or harmfull doses of experimental compounds. Toremifene is a selective estrogen receptor modulator (SERM) used for treatment of breast cancer. Its development was initiated in 1980s when selection of treatment indications and doses were based on research in cell and animal models and on noncomparative clinical studies including small number of patients. Since the early development phase, the treatment indication, the patient population and the dose range were confirmed in large comparative clinical studies in patients. Based on the currently available large and long term clinical study data the aim of this study was to investigate how the early phase studies were able to predict the treatment indication, patient population and the dose range of the SERM. As a conclusion and based on the estrogen receptor mediated mechanism of action early studies were able to predict the treatment indication, target patient population and a dose range to be studied in confirmatory clinical studies. However, comparative clinical studies are needed to optimize dose selection of the SERM in treatment of breast cancer.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.