Maintenance and construction outsourcing in a public-owned electricity network company

Euroopan sähkösektori on ollut viimeisen vuosikymmenen suurten mullistusten kourissa. Sähkömarkkinoiden avautumisen jälkeen monopoliliiketoimintaa harjoittavien sähköyhtiöiden on ollut pakko parantaa tuottavuuttaan. Ratkaisuksi tähän on etsitty apua huolto- ja rakennustoimintojen ulkoistamisella. Ulkoistaminen on kuitenkin uusi menetelmä tällä sektorilla. Tämän tutkielman tavoitteena on selvittää syyt, jotka tanskalaisella sähköverkkoyhtiöllä oli huolto- ja rakennustoimintojen ulkoistamiseen, sekä löytää siitä saatavat hyödyt ja siihen sisältyvät riskit. Tutkimus suoritetaan käyttäen apuna kirjallisuutta, saatavilla olevia due diligence-, sekä muita raportteja ja analyysejä, sekä tapausta koskettavien tahojen haastatteluja.Lisäksi sähköverkkoalan asiantuntijoiden kanssa käytyjä konsultointia käytetäänselvitykseen. Tutkimus osoittaa, että perimmäiset ajurit huolto- ja rakennustoimintojen ulkoistamiseen tulivat lainmuutosten ja vapautuneiden sähkömarkkinoiden asettamista paineista. Kunnallisessa organisaatiossa parantaa tehokkuutta ulkoistamalla jotain toimintoja yksityisomisteiselle palvelun tuottajalle. Muut ulkoistamisesta odotetut hyödyt olivat alentuneet kustannukset, virtaviivaisempi organisaation ja sähköverkkoyhtiön tehottomista osista eroon pääseminen ennen sen myymistä.

Theoretical background for market emergence framework : Case: Electricity Distribution Industry

Both the competitive environment and the internal structure of an industrial organization are typically included in the processes which describe the strategic management processes of the firm, but less attention has been paid to the interdependence between these views. Therefore, this research focuses on explaining the particular conditions of an industry change, which lead managers to realign the firm in respect of its environment for generating competitive advantage. The research question that directs the development of the theoretical framework is: Why do firms outsource some of their functions? The three general stages of the analysis are related to the following research topics: (i) understanding forces that shape the industry, (ii) estimating the impacts of transforming customer preferences, rivalry, and changing capability bases on the relevance of existing assets and activities, and emergence of new business models, and (iii) developing optional structures for future value chains and understanding general boundaries for market emergence. The defined research setting contributes to the managerial research questions “Why do firms reorganize their value chains?”, “Why and how are decisions made?” Combining Transaction Cost Economics (TCE) and Resource-Based View (RBV) within an integrated framework makes it possible to evaluate the two dimensions of a company’s resources, namely the strategic value and transferability. The final decision of restructuring will be made based on an analysis of the actual business potential of the outsourcing, where benefits and risks are evaluated. The firm focuses on the risk of opportunism, hold-up problems, pricing, and opportunities to reach a complete contract, and finally on the direct benefits and risks for financial performance. The supplier analyzes the business potential of an activity outside the specific customer, the amount of customer-specific investments, the service provider’s competitive position, abilities to revenue gains in generic segments, and long-term dependence on the customer.

Analysis of Patterns in Electricity Spot Market Time Series

Due to its non-storability, electricity must be produced at the same time that it is consumed, as a result prices are determined on an hourly basis and thus analysis becomes more challenging. Moreover, the seasonal fluctuations in demand and supply lead to a seasonal behavior of electricity spot prices. The purpose of this thesis is to seek and remove all causal effects from electricity spot prices and remain with pure prices for modeling purposes. To achieve this we use Qlucore Omics Explorer (QOE) for the visualization and the exploration of the data set and Time Series Decomposition method to estimate and extract the deterministic components from the series. To obtain the target series we use regression based on the background variables (water reservoir and temperature). The result obtained is three price series (for Sweden, Norway and System prices) with no apparent pattern.

On advancing business intelligence in the electricity retail market

In recent decades, business intelligence (BI) has gained momentum in real-world practice. At the same time, business intelligence has evolved as an important research subject of Information Systems (IS) within the decision support domain. Today’s growing competitive pressure in business has led to increased needs for real-time analytics, i.e., so called real-time BI or operational BI. This is especially true with respect to the electricity production, transmission, distribution, and retail business since the law of physics determines that electricity as a commodity is nearly impossible to be stored economically, and therefore demand-supply needs to be constantly in balance. The current power sector is subject to complex changes, innovation opportunities, and technical and regulatory constraints. These range from low carbon transition, renewable energy sources (RES) development, market design to new technologies (e.g., smart metering, smart grids, electric vehicles, etc.), and new independent power producers (e.g., commercial buildings or households with rooftop solar panel installments, a.k.a. Distributed Generation). Among them, the ongoing deployment of Advanced Metering Infrastructure (AMI) has profound impacts on the electricity retail market. From the view point of BI research, the AMI is enabling real-time or near real-time analytics in the electricity retail business. Following Design Science Research (DSR) paradigm in the IS field, this research presents four aspects of BI for efficient pricing in a competitive electricity retail market: (i) visual data-mining based descriptive analytics, namely electricity consumption profiling, for pricing decision-making support; (ii) real-time BI enterprise architecture for enhancing management’s capacity on real-time decision-making; (iii) prescriptive analytics through agent-based modeling for price-responsive demand simulation; (iv) visual data-mining application for electricity distribution benchmarking. Even though this study is from the perspective of the European electricity industry, particularly focused on Finland and Estonia, the BI approaches investigated can: (i) provide managerial implications to support the utility’s pricing decision-making; (ii) add empirical knowledge to the landscape of BI research; (iii) be transferred to a wide body of practice in the power sector and BI research community.

Forecasting financial weather - can we foresee market sentiment? Quantitative analysis of nodal prices in the New Zealand Electricity Spot Market

Time series of hourly electricity spot prices have peculiar properties. Electricity is by its nature difficult to store and has to be available on demand. There are many reasons for wanting to understand correlations in price movements, e.g. risk management purposes. The entire analysis carried out in this thesis has been applied to the New Zealand nodal electricity prices: offer prices (from 29 May 2002 to 31 March 2009) and final prices (from 1 January 1999 to 31 March 2009). In this paper, such natural factors as location of the node and generation type in the node that effects the correlation between nodal prices have been reviewed. It was noticed that the geographical factor affects the correlation between nodes more than others. Therefore, the visualisation of correlated nodes was done. However, for the offer prices the clear separation of correlated and not correlated nodes was not obtained. Finally, it was concluded that location factor most strongly affects correlation of electricity nodal prices; problems in visualisation probably associated with power losses when the power is transmitted over long distance.

Sähkön markkinahintaa selittävät tekijät. Case: Nord Pool 2000-2005

Sähkön markkinahinta on saanut osakseen suurta huomiota viimeaikoina. Sähkömarkkinoiden vapautuminen ja päästökaupan avaaminen Euroopassa onentisestään nostanut sähkömarkkinoita näkyville lehdissä. Tämä tutkielma tutkii erilaisten tekijöiden vaikutusta sähkön markkinahintaan regressioanalyysin avulla. Edellä mainitun päästösopimusten markkinahinnan lisäksi tutkittiin kivihiilen sekä maakaasun markkinahintojen, lämpötilojen, jokien virtaamien, vesivarantojen täyttöasteiden sekä Saksan sähkömarkkinoiden hinnan vaikutusta sähkön markkinahintaan Nord Pool -sähköpörssissä. Työssä luotiin myös sähkön markkinahintaa ennustava malli. Kaikkien selittävien tekijöiden korrelaatiot olivat oletusten mukaiset ja regressioanalyysi onnistui selittämään yli 80 % sähkön markkinahinnan vaih-teluista. Merkittävimpiä selittäviä tekijöitä olivat vesivarannot sekä jokien virtaamat. Ennustavan mallin keskimääräinen suhteellinen virhe oli noin 10 %, joten ennustetarkkuus oli melko hyvä.

Liquid Packaging Board Market in China

Tämän diplomityön tavoitteena on määrittää liiketoimintailmapiiri ja markkinapotentiaali Kiinan nestepakkauskartonkimarkkinoilla. Tutkimus tukee nestepakkauskartonkivalmistajan vientitoimintoja. Pääosin käytännön tiedoista koostuva tutkimus suoritettiin keräämällä sekundaääritietoa ja haastattelemalla alan erikoisasiantuntijoita. Suuri ja kasvava väestö sekä nopeasti kehittyvä talous tukee nestepakkauskartonkimarkkinoiden kasvua Kiinassa. Viimeaikainen globalisaatio haittapuolineen saattaa kuitenkin horjuttaa poliittista ja sosiaalista tasapainoa ja tätä kautta ylellisyys hyödykkeiden kuten kartonkipakattujen tuotteiden kysyntää Kiinassa. Tuontirajoitukset Kiinaan ovat laskemassa valtion tämän hetkisen kansainvälistymispolitiikan seurauksena. Kartonki vahvistaa asemaansa kilpailevien pakkausmateriaalien joukossa. Kiinan kokonaisnestepakkauskartonkimarkkinat kasvavat vuosittain 10,7 % ja ovat vuonna 2001 noin 100 000 tonnia.

Regime Switching Models for Electricity Time Series that Capture Fat Tailed Distributions

In the power market, electricity prices play an important role at the economic level. The behavior of a price trend usually known as a structural break may change over time in terms of its mean value, its volatility, or it may change for a period of time before reverting back to its original behavior or switching to another style of behavior, and the latter is typically termed a regime shift or regime switch. Our task in this thesis is to develop an electricity price time series model that captures fat tailed distributions which can explain this behavior and analyze it for better understanding. For NordPool data used, the obtained Markov Regime-Switching model operates on two regimes: regular and non-regular. Three criteria have been considered price difference criterion, capacity/flow difference criterion and spikes in Finland criterion. The suitability of GARCH modeling to simulate multi-regime modeling is also studied.

A comparison of electricity spot prices simulation using ARMA-GARCH and mean-reverting models

The aim of this work is to compare two families of mathematical models for their respective capability to capture the statistical properties of real electricity spot market time series. The first model family is ARMA-GARCH models and the second model family is mean-reverting Ornstein-Uhlenbeck models. These two models have been applied to two price series of Nordic Nord Pool spot market for electricity namely to the System prices and to the DenmarkW prices. The parameters of both models were calibrated from the real time series. After carrying out simulation with optimal models from both families we conclude that neither ARMA-GARCH models, nor conventional mean-reverting Ornstein-Uhlenbeck models, even when calibrated optimally with real electricity spot market price or return series, capture the statistical characteristics of the real series. But in the case of less spiky behavior (System prices), the mean-reverting Ornstein-Uhlenbeck model could be seen to partially succeeded in this task.

Stress testing and risk evaluation of new capacity payment algorithm proposed by Market Council

In the Russian Wholesale Market, electricity and capacity are traded separately. Capacity is a special good, the sale of which obliges suppliers to keep their generating equipment ready to produce the quantity of electricity indicated by the System Operator. The purpose of the formation of capacity trading was the maintenance of reliable and uninterrupted delivery of electricity in the wholesale market. The price of capacity reflects constant investments in construction, modernization and maintenance of power plants. So, the capacity sale creates favorable conditions to attract investments in the energy sector because it guarantees the investor that his investments will be returned.

Price spike forecasting in a competitive day-ahead energy market

Electricity price forecasting has become an important area of research in the aftermath of the worldwide deregulation of the power industry that launched competitive electricity markets now embracing all market participants including generation and retail companies, transmission network providers, and market managers. Based on the needs of the market, a variety of approaches forecasting day-ahead electricity prices have been proposed over the last decades. However, most of the existing approaches are reasonably effective for normal range prices but disregard price spike events, which are caused by a number of complex factors and occur during periods of market stress. In the early research, price spikes were truncated before application of the forecasting model to reduce the influence of such observations on the estimation of the model parameters; otherwise, a very large forecast error would be generated on price spike occasions. Electricity price spikes, however, are significant for energy market participants to stay competitive in a market. Accurate price spike forecasting is important for generation companies to strategically bid into the market and to optimally manage their assets; for retailer companies, since they cannot pass the spikes onto final customers, and finally, for market managers to provide better management and planning for the energy market. This doctoral thesis aims at deriving a methodology able to accurately predict not only the day-ahead electricity prices within the normal range but also the price spikes. The Finnish day-ahead energy market of Nord Pool Spot is selected as the case market, and its structure is studied in detail. It is almost universally agreed in the forecasting literature that no single method is best in every situation. Since the real-world problems are often complex in nature, no single model is able to capture different patterns equally well. Therefore, a hybrid methodology that enhances the modeling capabilities appears to be a possibly productive strategy for practical use when electricity prices are predicted. The price forecasting methodology is proposed through a hybrid model applied to the price forecasting in the Finnish day-ahead energy market. The iterative search procedure employed within the methodology is developed to tune the model parameters and select the optimal input set of the explanatory variables. The numerical studies show that the proposed methodology has more accurate behavior than all other examined methods most recently applied to case studies of energy markets in different countries. The obtained results can be considered as providing extensive and useful information for participants of the day-ahead energy market, who have limited and uncertain information for price prediction to set up an optimal short-term operation portfolio. Although the focus of this work is primarily on the Finnish price area of Nord Pool Spot, given the result of this work, it is very likely that the same methodology will give good results when forecasting the prices on energy markets of other countries.

Co-creating value with customers as a new approach of entering new market

Global trends associated with development of information technology, globalization, industrial and economic changes are influencing on company and customer domains and thus transforming company-customer relationship. The company centric paradigm with a strong product focus shifts to a customer oriented one with a strong emphasis on customer collaboration. As a result, the customer role changes from a passive observer to an active player. Moreover, global trends contribute to transformation of competitive environment making it tougher and simplifying an access to resources previously considered as unique. All that factors push the companies towards cooperation with customers in order to identify unarticulated needs and finding the best possible solution to existing customer problems. The Master’s Thesis is done for Outotec (Lappeenranta) which considers extension of dewatering business in Russian coal market. Research aims to identify key features of coal preparation and dewatering of fine coal and tailings in Russian preparation plants; analyze the state of Russian coal market and evaluate market potential for Outotec dewatering solutions. The study has a qualitative nature and implements an action research methodology that involves both creation of knowledge and introduction of changes into the system. The base for taking actions is formed by theoretical framework that targets on describing company - customer interaction and has selected co-creation as the most appropriate method of customer involvement. The integration of co-creation approach into an action research cycle allows not only fulfilling the research objectives but also facilitates organizational learning and intraorganizational collaboration, assists in establishing customer contacts and making the first steps into the market, bringing new joint projects to the company and opening real business opportunities.

Entry mode choice of Finnish startups entering Chinese market in tablet industry

Since the implement of opening policy, the overall economy of China has maintained rapid and stable development, which has now makes China become the world's second largest economy. China, it is to become the largest overseas market for many large global enterprises from various industries, this naturally also includes the Tablet PC industry that raised in recent years. The purpose of this thesis is to analyze different internal and external factors that influence the entry mode choices of Finnish SMEs in tablet industry entering Chinese market. The goal is to find out the suitable entry modes for the Finnish tablet or other relevant SMEs entering Chinese market. Qualitative analysis is the main research method in empirical part of this study. The interviews were carried out with the case company and other two Finnish business organizations in China. The result of the study indicated that the internal resource and external business environment affect the entry modes choices much more than other factors for SMES. The exporting mode and sales subsidiary could be a better choice for SMEs entering Chinese market. Furthermore, firms should fully learn the Chinese market combine with its own background before making decisions.