Electronic Business Model for International Software Market

Tämän diplomityön tavoitteena on muodostaa sähköinen liiketoimintamalli kansainvälisen ohjelmistoyrityksen tarpeisiin. Ohjelmiston uusi ominaisuus antaa kolmansille osapuolille mahdollisuuden määritellä itse rakennusmallintamisessa tarvittavia komponentteja, mikä luo mahdollisuuden uuteen liiketoimintaan. Liiketoimintamallien teoria ja asiantuntijoiden haastattelut tulevat osoittamaan, että paras ratkaisu tässä tapauksessa on portaali, joka rakentuu komponenttimarkkinoista, e-kaupasta ja virtuaaliyhteisöstä. Komponenttimarkkinat on jaettu vapaaseen vaihdantaan ja sertifioitujen kehittäjien kaupankäyntiin. Tämä tarjoaa mahdollisuuksia kehittäjille valita sitoutuneisuutensa taso, samoin kuin motivoi heitä osallistumaan. E-kauppa on suunniteltu sovelluksille ja monimutkaisemmille komponenteille. Virtuaaliyhteisön kautta käyttäjät voivat keskustella mielipiteistään ja saada tukea ohjelmiston käyttämiseen sekä komponenttien kehittämiseen.

Analyzing Carbon Trade Permits in Optimization Framework

The threats caused by global warming motivate different stake holders to deal with and control them. This Master's thesis focuses on analyzing carbon trade permits in optimization framework. The studied model determines optimal emission and uncertainty levels which minimize the total cost. Research questions are formulated and answered by using different optimization tools. The model is developed and calibrated by using available consistent data in the area of carbon emission technology and control. Data and some basic modeling assumptions were extracted from reports and existing literatures. The data collected from the countries in the Kyoto treaty are used to estimate the cost functions. Theory and methods of constrained optimization are briefly presented. A two-level optimization problem (individual and between the parties) is analyzed by using several optimization methods. The combined cost optimization between the parties leads into multivariate model and calls for advanced techniques. Lagrangian, Sequential Quadratic Programming and Differential Evolution (DE) algorithm are referred to. The role of inherent measurement uncertainty in the monitoring of emissions is discussed. We briefly investigate an approach where emission uncertainty would be described in stochastic framework. MATLAB software has been used to provide visualizations including the relationship between decision variables and objective function values. Interpretations in the context of carbon trading were briefly presented. Suggestions for future work are given in stochastic modeling, emission trading and coupled analysis of energy prices and carbon permits.

Risk-return trade-off and autocorrelation

A trade-off between return and risk plays a central role in financial economics. The intertemporal capital asset pricing model (ICAPM) proposed by Merton (1973) provides a neoclassical theory for expected returns on risky assets. The model assumes that risk-averse investors (seeking to maximize their expected utility of lifetime consumption) demand compensation for bearing systematic market risk and the risk of unfavorable shifts in the investment opportunity set. Although the ICAPM postulates a positive relation between the conditional expected market return and its conditional variance, the empirical evidence on the sign of the risk-return trade-off is conflicting. In contrast, autocorrelation in stock returns is one of the most consistent and robust findings in empirical finance. While autocorrelation is often interpreted as a violation of market efficiency, it can also reflect factors such as market microstructure or time-varying risk premia. This doctoral thesis investigates a relation between the mixed risk-return trade-off results and autocorrelation in stock returns. The results suggest that, in the case of the US stock market, the relative contribution of the risk-return trade-off and autocorrelation in explaining the aggregate return fluctuates with volatility. This effect is then shown to be even more pronounced in the case of emerging stock markets. During high-volatility periods, expected returns can be described using rational (intertemporal) investors acting to maximize their expected utility. During lowvolatility periods, market-wide persistence in returns increases, leading to a failure of traditional equilibrium-model descriptions for expected returns. Consistent with this finding, traditional models yield conflicting evidence concerning the sign of the risk-return trade-off. The changing relevance of the risk-return trade-off and autocorrelation can be explained by heterogeneous agents or, more generally, by the inadequacy of the neoclassical view on asset pricing with unboundedly rational investors and perfect market efficiency. In the latter case, the empirical results imply that the neoclassical view is valid only under certain market conditions. This offers an economic explanation as to why it has been so difficult to detect a positive tradeoff between the conditional mean and variance of the aggregate stock return. The results highlight the importance, especially in the case of emerging stock markets, of noting both the risk-return trade-off and autocorrelation in applications that require estimates for expected returns.

Scheduling dynamic dataflow graphs with model checking

With the shift towards many-core computer architectures, dataflow programming has been proposed as one potential solution for producing software that scales to a varying number of processor cores. Programming for parallel architectures is considered difficult as the current popular programming languages are inherently sequential and introducing parallelism is typically up to the programmer. Dataflow, however, is inherently parallel, describing an application as a directed graph, where nodes represent calculations and edges represent a data dependency in form of a queue. These queues are the only allowed communication between the nodes, making the dependencies between the nodes explicit and thereby also the parallelism. Once a node have the su cient inputs available, the node can, independently of any other node, perform calculations, consume inputs, and produce outputs. Data ow models have existed for several decades and have become popular for describing signal processing applications as the graph representation is a very natural representation within this eld. Digital lters are typically described with boxes and arrows also in textbooks. Data ow is also becoming more interesting in other domains, and in principle, any application working on an information stream ts the dataflow paradigm. Such applications are, among others, network protocols, cryptography, and multimedia applications. As an example, the MPEG group standardized a dataflow language called RVC-CAL to be use within reconfigurable video coding. Describing a video coder as a data ow network instead of with conventional programming languages, makes the coder more readable as it describes how the video dataflows through the different coding tools. While dataflow provides an intuitive representation for many applications, it also introduces some new problems that need to be solved in order for data ow to be more widely used. The explicit parallelism of a dataflow program is descriptive and enables an improved utilization of available processing units, however, the independent nodes also implies that some kind of scheduling is required. The need for efficient scheduling becomes even more evident when the number of nodes is larger than the number of processing units and several nodes are running concurrently on one processor core. There exist several data ow models of computation, with different trade-offs between expressiveness and analyzability. These vary from rather restricted but statically schedulable, with minimal scheduling overhead, to dynamic where each ring requires a ring rule to evaluated. The model used in this work, namely RVC-CAL, is a very expressive language, and in the general case it requires dynamic scheduling, however, the strong encapsulation of dataflow nodes enables analysis and the scheduling overhead can be reduced by using quasi-static, or piecewise static, scheduling techniques. The scheduling problem is concerned with nding the few scheduling decisions that must be run-time, while most decisions are pre-calculated. The result is then an, as small as possible, set of static schedules that are dynamically scheduled. To identify these dynamic decisions and to find the concrete schedules, this thesis shows how quasi-static scheduling can be represented as a model checking problem. This involves identifying the relevant information to generate a minimal but complete model to be used for model checking. The model must describe everything that may affect scheduling of the application while omitting everything else in order to avoid state space explosion. This kind of simplification is necessary to make the state space analysis feasible. For the model checker to nd the actual schedules, a set of scheduling strategies are de ned which are able to produce quasi-static schedulers for a wide range of applications. The results of this work show that actor composition with quasi-static scheduling can be used to transform data ow programs to t many different computer architecture with different type and number of cores. This in turn, enables dataflow to provide a more platform independent representation as one application can be fitted to a specific processor architecture without changing the actual program representation. Instead, the program representation is in the context of design space exploration optimized by the development tools to fit the target platform. This work focuses on representing the dataflow scheduling problem as a model checking problem and is implemented as part of a compiler infrastructure. The thesis also presents experimental results as evidence of the usefulness of the approach.

Explaining the Shifts of International Trade in Pulp and Paper Industry

The pulp and paper industry is currently facing broad structural changes due to global shifts in demand and supply. These changes have significant impacts on national economies worldwide. In this paper, we describe the recent trends in the pulp and recovered paper (RP) production, and estimate augmented gravity models of bilateral trade for chemical pulp and RP exports with panel data. According to our results, there is some variation in the effects of the traditional gravity-model variables between pulp grades and RP. The results imply also that, in comparison to export supply, import demand plays a larger role in determining the volume of exports. Finally, it is evident that Asia, particularly China, is the most important driver of chemical pulp and RP trade: China is hungry for fiber, and must import to satisfy its growing needs. Moreover, the speed of China’s growth in chemical pulp and RP imports has been driving the increased significance of planted forests in the exports of hardwood pulp (BHKP) as well.

Evaluation of Major E-commerce Companies Business Model

Technological advances have enabled a large spread of online retailers. E-commerce business is challenging the nature of traditional foundation of trade, therefore trade is on transition. The Internet allows consumers to compare and examine online store offerings, regardless of place and time. Digitalization has created new opportunities for creating customer value. The growing trend towards online shopping world has taken place in a very short period of time. Businesses, either use e-commerce as an additional operation, or completely rely, on the Internet business opportunities. The aim of this Master`s thesis is to define and evaluate key business model factors of two major e-commerce companies, Alibaba.com and Amazon.com. These key business model factors have a positive effect on successful e-commerce business. In addition, Alibaba.com and Amazon.com are compared with the help of these identified factors. Moreover, e-commerce business factors provide opportunities to create greater customer value regardless of the industry. Customer value is the core of business and a competitive advantage. Customer value is defined as a ratio between sacrifices and benefits. E-commerce companies should effectively evaluate customer value, so they could customize their offerings to meet better desired customer needs. Even though e-commerce business is a relatively new phenomenon, it has been widely studied

Gearing Towards Omnichannel Grocery Retail Through Business Model Innovation

Technological innovations and the advent of digitalization have led retail business into one of its biggest transformations of all time. Consumer behaviour has changed rapidly and the customers are ever more powerful, demanding, tech-savvy and moving on various plat-forms. These attributes will continue to drive the development and robustly restructure the architecture of value creation in the retail business. The largest retail category, grocery yet awaits for a real disruption, but the signals for major change are already on the horizon. The first wave of online grocery retail was introduced in the mid 1990’s and it throve until millennium. Many overreactions, heavy investments and the burst IT-bubble almost stag-nated the whole industry for a long period of time. The second wave started with a venge-ance around 2010. Some research was carried out during the first wave from a single-viewpoint of online grocery retail, but without a comprehensive approach to online-offline business model integration. Now the accelerating growth of e-business has initiated an increased interest to examine the transformation from traditional business models towards e-business models and their integration on the companies’ traditional business models. This research strove to examine how can we recognize and analyze how digitalization and online channels are affecting the business models of grocery retail, by using business mod-el canvas as an analysis tool. Furthermore business model innovation and omnichannel retail were presented and suggested as potential solutions for these changes. 21 experts in online grocery industry were being interviewed. The thoughts of the informants were being qualitatively analysed by using an analysis tool called the business model canvas. The aim of this research was to portray a holistic view on the Omnichannel grocery retail business model, and the value chain, in which the case company Arina along with its partners are operating. The key conclusions exhibited that online grocery retail business model is not an alterna-tive model nor a substitute for the traditional grocery retail business model, though all of the business model elements are to some extent affected by it, but rather a complementary business model that should be integrated into the prevailing, conventional grocery retail business model. A set of business model elements, such as value proposition and distribu-tion channels were recognized as the most important ones and sources of innovation within these components were being illustrated. Segments for online grocery retail were empiri-cally established as polarized niche markets in contrast of the segmented mass-market of the conventional grocery retail. Business model innovation was proven to be a considera-ble method and a conceptual framework, by which to come across with new value proposi-tions that create competitive advantage for the company in the contemporary, changing business environment. Arina as a retailer can be considered as a industry model innovator, since it has initiated an entire industry in its market area, where other players have later on embarked on, and in which the contributors of the value chain, such as Posti depend on it to a great extent. Consumer behaviour clearly affects and appears everywhere in the digi-talized grocery trade and it drives customers to multiple platforms where retailers need to be present. Omnichannel retail business model was suggested to be the solution, in which the new technologies are being utilized, contemporary consumer behaviour is embedded in decision-making and all of the segments and their value propositions are being served seamlessly across the channels.