Utilizing analytic modelling process in industrial retail context

Nykypäivän monimutkaisessa ja epävakaassa liiketoimintaympäristössä yritykset, jotka kykenevät muuttamaan tuottamansa operatiivisen datan tietovarastoiksi, voivat saavuttaa merkittävää kilpailuetua. Ennustavan analytiikan hyödyntäminen tulevien trendien ennakointiin mahdollistaa yritysten tunnistavan avaintekijöitä, joiden avulla he pystyvät erottumaan kilpailijoistaan. Ennustavan analytiikan hyödyntäminen osana päätöksentekoprosessia mahdollistaa ketterämmän, reaaliaikaisen päätöksenteon. Tämän diplomityön tarkoituksena on koota teoreettinen viitekehys analytiikan mallintamisesta liike-elämän loppukäyttäjän näkökulmasta ja hyödyntää tätä mallinnusprosessia diplomityön tapaustutkimuksen yritykseen. Teoreettista mallia hyödynnettiin asiakkuuksien mallintamisessa sekä tunnistamalla ennakoivia tekijöitä myynnin ennustamiseen. Työ suoritettiin suomalaiseen teollisten suodattimien tukkukauppaan, jolla on liiketoimintaa Suomessa, Venäjällä ja Balteissa. Tämä tutkimus on määrällinen tapaustutkimus, jossa tärkeimpänä tiedonkeruumenetelmänä käytettiin tapausyrityksen transaktiodataa. Data työhön saatiin yrityksen toiminnanohjausjärjestelmästä.

Heat Demand Forecasting Models’ Development: Use of Data Mining Tools in SQL Server Analysis Services

This thesis introduces heat demand forecasting models which are generated by using data mining algorithms. The forecast spans one full day and this forecast can be used in regulating heat consumption of buildings. For training the data mining models, two years of heat consumption data from a case building and weather measurement data from Finnish Meteorological Institute are used. The thesis utilizes Microsoft SQL Server Analysis Services data mining tools in generating the data mining models and CRISP-DM process framework to implement the research. Results show that the built models can predict heat demand at best with mean average percentage errors of 3.8% for 24-h profile and 5.9% for full day. A deployment model for integrating the generated data mining models into an existing building energy management system is also discussed.

Troponin I serum levels predict the need of dialysis in incident sepsis patients with acute kidney injury in the intensive care unit

Abstract Introduction: Sepsis, an extremely prevalent condition in the intensive care unit, is usually associated with organ dysfunction, which can affect heart and kidney. Objective: To determine whether the cardiac dysfunction and the Troponin I forecast the occurrence of acute renal failure in sepsis. Methods: Cardiac dysfunction was assessed by echocardiography and by the serum troponin I levels, and renal impairment by AKIN criteria and the need of dialysis. Twenty-nine patients with incident sepsis without previous cardiac or renal dysfunction were enrolled. Results and Discussion: Patients averaged 75.3 ± 17.3 years old and 55% were male. Median APACHE II severity score at ICU admission was 16 (9.7 - 24.2) and mortality rate in 30 days was 45%. On the fifth day, 59% had ventricular dysfunction. Troponin serum levels on day 1 in the affected patients were 1.02 ± 0.6 ng/mL compared with 0.23 ± 0.18 ng/mL in patients without heart dysfunction (p = 0.01). Eighteen out of 29 patients (62%) underwent renal replacement therapy (RRT) and the percent of patients with ventricular dysfunction who required dialysis was higher (94% vs. 16%, p = 0.0001). Values of troponin at day 1 were used to develop a ROC curve to determine their ability to predict the need of dialysis. The area under the curve was 0.89 and the cutoff value was 0.4 ng/mL. Conclusion: We found that an elevation in serum troponin levels, while guarding a relationship with ventricular dysfunction, can be a precious tool to predict the need for dialysis in sepsis patients.

Modelling of changes in electricity end-use and their impacts on electricity distribution

The electricity distribution sector will face significant changes in the future. Increasing reliability demands will call for major network investments. At the same time, electricity end-use is undergoing profound changes. The changes include future energy technologies and other advances in the field. New technologies such as microgeneration and electric vehicles will have different kinds of impacts on electricity distribution network loads. In addition, smart metering provides more accurate electricity consumption data and opportunities to develop sophisticated load modelling and forecasting approaches. Thus, there are both demands and opportunities to develop a new type of long-term forecasting methodology for electricity distribution. The work concentrates on the technical and economic perspectives of electricity distribution. The doctoral dissertation proposes a methodology to forecast electricity consumption in the distribution networks. The forecasting process consists of a spatial analysis, clustering, end-use modelling, scenarios and simulation methods, and the load forecasts are based on the application of automatic meter reading (AMR) data. The developed long-term forecasting process produces power-based load forecasts. By applying these results, it is possible to forecast the impacts of changes on electrical energy in the network, and further, on the distribution system operator’s revenue. These results are applicable to distribution network and business planning. This doctoral dissertation includes a case study, which tests the forecasting process in practice. For the case study, the most prominent future energy technologies are chosen, and their impacts on the electrical energy and power on the network are analysed. The most relevant topics related to changes in the operating environment, namely energy efficiency, microgeneration, electric vehicles, energy storages and demand response, are discussed in more detail. The study shows that changes in electricity end-use may have radical impacts both on electrical energy and power in the distribution networks and on the distribution revenue. These changes will probably pose challenges for distribution system operators. The study suggests solutions for the distribution system operators on how they can prepare for the changing conditions. It is concluded that a new type of load forecasting methodology is needed, because the previous methods are no longer able to produce adequate forecasts.

Erikoistilanteiden huomiointi päivittäistavarakaupan myymälätäydennyksessä

Päivittäistavarakauppa toimialana vaatii hektisyytensä, volyymien suurten vaihtelujen ja tuotteiden ominaispiirteiden takia nopeaa reagointikykyä toimitusketjun sopeuttamisessa. Tällöin pienikin tarkkuuden parantaminen myyntiennusteessa voi aiheuttaa merkittäviä positiivisia kerrannaisvaikutuksia koko ketjussa. Tässä diplomityössä tutkittiin kahta teemaa: Säätilan ja vähittäismyynnin välistä korrelaatiota, sekä tuotteen kampanjassa olon aiheuttamaa kannibalisointivaikutusta muiden tuotteiden menekkiin. Tutkimus toteutettiin työn tilaajan kannalta merkittäväksi mielletyillä tavararyhmillä historialliseen myynti –, sää- ja kampanjadataan perustuen. Tutkimuksen tuloksena todettiin lämpötilan olevan yksittäinen merkittävin tuotteiden menekkiin vaikuttava sääparametri. Kampanjan aiheuttaman myynnin kannibalisointivaikutuksen havaittiin olevan merkittävintä saman tuotesegmentin sisällä, erityisesti lyhyissä kampanjoissa. Työssä luotiin toimintamallit molempiin tutkittuihin teemoihin ennusteperusteisen tarvesuunnittelujärjestelmän ennustetarkkuuden parantamisen työkaluiksi.

The Dynamic Relationship between Finnish Stock Market and Commodities

Since different stock markets have become more integrated during 2000s, investors need new asset classes in order to gain diversification benefits. Commodities have become popular to invest in and thus it is important to examine whether the investors should use commodities as a part for portfolio diversification. This master’s thesis examines the dynamic relationship between Finnish stock market and commodities. The methodology is based on Vector Autoregressive models (VAR). The long-run relationship between Finnish stock market and commodities is examined with Johansen cointegration while short-run relationship is examined with VAR models and Granger causality test. In addition, impulse response test and forecast error variance decomposition are employed to strengthen the results of short-run relationship. The dynamic relationships might change under different market conditions. Thus, the sample period is divided into two sub-samples in order to reveal whether the dynamic relationship varies under different market conditions. The results show that Finnish stock market has stable long-run relationship with industrial metals, indicating that there would not be diversification benefits among the industrial metals. The long-run relationship between Finnish stock market and energy commodities is not as stable as the long-run relationship between Finnish stock market and industrial metals. Long-run relationship was found in the full sample period and first sub-sample which indicate less room for diversification. However, the long-run relationship disappeared in the second sub-sample which indicates diversification benefits. Long-run relationship between Finnish stock market and agricultural commodities was not found in the full sample period which indicates diversification benefits between the variables. However, long-run relationship was found from both sub-samples. The best diversification benefits would be achieved if investor invested in precious metals. No long-run relationship was found from either sample. In the full sample period OMX Helsinki had short-run relationship with most of the energy commodities and industrial metals and the causality was mostly running from equities to commodities. During the first sub period the number of short-run relationships and causality shrunk but during the crisis period the number of short-run relationships and causality increased. The most notable result found was unidirectional causality from gold to OMX Helsinki during the crisis period.

Forecasting with social media – Using Facebook likes to predict the results of the 2015 Finnish Parliamentary elections

Already one-third of the human population uses social media on a daily basis. The biggest social networking site Facebook has over billion monthly users. As a result, social media services are now recording unprecedented amount of data on human behavior. The phenomenon has certainly caught the attention of scholars, businesses and governments alike. Organizations around the globe are trying to explore new ways to benefit from the massive databases. One emerging field of research is the use of social media in forecasting. The goal is to use data gathered from online services to predict offline phenomena. Predicting the results of elections is a prominent example of forecasting with social media, but regardless of the numerous attempts, no reliable technique has been established. The objective of the research is to analyze how accurately the results of parliament elections can be forecasted using social media. The research examines whether Facebook “likes” can be effectively used for predicting the outcome of the Finnish parliament elections that took place in April 2015. First a tool for gathering data from Facebook was created. Then the data was used to create an electoral forecast. Finally, the forecast was compared with the official results of the elections. The data used in the research was gathered from the Facebook walls of all the candidates that were running for the parliament elections and had a valid Facebook page. The final sample represents 1131 candidates and over 750000 Facebook “likes”. The results indicate that creating a forecast solely based on Facebook “likes” is not accurate. The forecast model predicted very dramatic changes to the Finnish political landscape while the official results of the elections were rather moderate. However, a clear statistical relationship between “likes” and votes was discovered. In conclusion, it is apparent that citizens and other key actors of the society are using social media in an increasing rate. However, the volume of the data does not directly increase the quality of the forecast. In addition, the study faced several other limitations that should be addressed in future research. Nonetheless, discovering the positive correlation between “likes” and votes is valuable information that can be used in future studies. Finally, it is evident that Facebook “likes” are not accurate enough and a meaningful forecast would require additional parameters.

Helsinki hub – new traffic flows and business networks

This thesis is the Logistics Development Forum's assignment and the work dealing with the development of the Port of Helsinki as part of Helsinki hub. The Forum aims to develop logistics efficiency through public-private co-operation and development of the port is clearly dependent on both factors. Freight volumes in the Port of Helsinki are the biggest single factor in hub and, therefore, the role of the port of the entire hub development is strong. The aim is to look at how the port will develop as a result of changes in the foreign trade of Finland and the Northern European logistics trends in 25 years time period. Work includes the current state analysis and scenario work. The analyses are intended to find out, which trends are the most important in the port volume development. The change and effect of trends is examined through scenarios based on current state. Based on the work, the structure of Finnish export industry and international demand are in the key role in the port volume development. There is significant difference between demands of Finnish exporting products in different export markets and the development between the markets has different impacts on the port volumes by mass and cargo type. On the other hand, the Finnish economy is stuck in a prolonged recession and competition between ports has become a significant factor in the individual port's volume development. Ecological valuesand regulations have changed the competitive landscape and maritime transport emissions reductions has become an important competitive factor for short routes in the Baltic Sea, such as in the link between Helsinki and Tallinn.

Modelling the exothermic heat and the demand of cooling in paper and pulp industry biological wastewater treatment

This master thesis presents a study on the requisite cooling of an activated sludge process in paper and pulp industry. The energy consumption of paper and pulp industry and it’s wastewater treatment plant in particular is relatively high. It is therefore useful to understand the wastewater treatment process of such industries. The activated sludge process is a biological mechanism which degrades carbonaceous compounds that are present in waste. The modified activated sludge model constructed here aims to imitate the bio-kinetics of an activated sludge process. However, due to the complicated non-linear behavior of the biological process, modelling this system is laborious and intriguing. We attempt to find a system solution first using steady-state modelling of Activated Sludge Model number 1 (ASM1), approached by Euler’s method and an ordinary differential equation solver. Furthermore, an enthalpy study of paper and pulp industry’s vital pollutants was carried out and applied to revise the temperature shift over a period of time to formulate the operation of cooling water. This finding will lead to a forecast of the plant process execution in a cost-effective manner and management of effluent efficiency. The final stage of the thesis was achieved by optimizing the steady state of ASM1.

Keskikokoisen maatilan biokaasu- ja aurinkosähköratkaisujen kannattavuus Etelä-Savossa

Tässä kandidaatintyössä perehdytään biokaasun syntyprosessiin ja sen hyödyntämismahdollisuuksiin, sekä vertaillaan biokaasun tuotannon määrää Suomessa ja Saksassa. Työssä tarkastellaan biokaasuvoimalan kannattavuutta keskikokoisen maatilan yhteydessä Etelä-Savossa ja käydään läpi biokaasuvoimalalle Suomessa myönnettäviä tukimuotoja. Tukimuotojen lisäksi käydään läpi erilaisia lupia ja hyväksyntöjä, joita maatilan yhteyteen rakennettava biokaasu-voimalaitos tarvitsee. Työn toisessa osassa käydään läpi aurinkoenergian hyödyntämismahdollisuuksia, aurinkosähköjärjestelmän komponentteja, sekä perehdytään aurinkopaneelin toimintaperiaatteeseen. Tarkastellaan biokaasuvoimalan lisäksi myös aurinkovoimalan kannattavuutta maatilan yhteydessä ja vertaillaan biokaasu- ja aurinkovoimalan ominaisuuksia keskenään. Lisäksi vertaillaan aurinkosähkön tuotantoa Suomessa ja Saksassa. Työn tavoitteena on selvittää biokaasu- ja aurinkosähkövoimalan kannattavuus esimerkkimaatilalla. Biokaasulaitoksen hinta-arvio saatiin vastauksena tarjouspyyntöön ja aurinkosähköjärjestelmän hinta arvioitiin kotimaisten toimittajien aurinkosähköpakettien hintojen avulla. Biokaasuvoimalan sähköntuottoennuste sekä huolto- ja käyttökustannukset perustuvat kirjallisuudesta saatuihin arvoihin. Aurinkovoimalan sähköntuottoennuste ja paneelien suuntauksen vaikutusta tuotantoon laskettiin PVGIS:n laskurilla sekä HOMER-ohjelmistolla. Kannattavuuslaskelmien perusteella kumpikaan voimalaitostyyppi ei tutkituilla voimalaitosten suuruuksilla ole kannattava 20 tai edes 30 vuoden pitoajalla esimerkkimaatilalla nykyisellä sähkönhinnalla ja tukitasolla. Aurinkosähköjärjestelmälle saadaan kuitenkin 20 vuoden takaisinmaksuaika, jos se hankitaan ilman lainarahaa. Tällöin voidaan ajatella, että laitos on kannattava. Biokaasulaitoksen kannattavuutta parantaisivat tukien ja sähkön hinnan nousun ohella kaasun ja lämmön myyntimahdollisuudet, joita esimerkkimaatilalla ei ole. Aurinkovoimalan kannattavuutta parantaisivat puolestaan tukien ja korkeamman sähkön hinnan lisäksi paremmin paneelien tuotantoa seuraava kulutus, jolloin pienempi osuus sähköstä päätyisi myyntiin.

Tuotannon ja varastonhallinnan kehittäminen kysyntäennusteiden avulla

Diplomityön tavoitteena on tutkia kysyntäennusteiden hyödyntämistä valmistavan teollisuusyrityksen tuotannossa ja varastonhallinnassa. Työn alussa esitellään kysynnän tuntemiseen ja ennustamiseen liittyvään teoriaan, jonka jälkeen tutkitaan teorian tarjoamia mahdollisuuksia linkittää kysyntäennusteet tuotannon ja varastonhallinnan avuksi. Työn empiria osassa kuvataan ensin Peikko Group Oy:n kysyntäennusteiden nykytilanne. Tämän jälkeen vertaillaan kahden eri lähestymistavan soveltuvuutta, joilla kohdeyritys voisi mahdollisesti rakentaa tuotannolle ja varastonhallinnalle tarpeellisia kysyntäennusteita päätöksenteon tueksi. Tuotannon kannalta työn keskeisin tulos on kysyntäennusteiden pohjalta muodostettu kuormaennuste ja varastonhallinnan kannalta tarvittavan ennustetarkkuuden määrittäminen, jotta ennusteita voitaisiin hyödyntää varastonohjausparametrien määrityksessä.

Forecasting the Yield Curve of Government Bonds: A Comparative Study

For the past 20 years, researchers have applied the Kalman filter to the modeling and forecasting the term structure of interest rates. Despite its impressive performance in in-sample fitting yield curves, little research has focused on the out-of-sample forecast of yield curves using the Kalman filter. The goal of this thesis is to develop a unified dynamic model based on Diebold and Li (2006) and Nelson and Siegel’s (1987) three-factor model, and estimate this dynamic model using the Kalman filter. We compare both in-sample and out-of-sample performance of our dynamic methods with various other models in the literature. We find that our dynamic model dominates existing models in medium- and long-horizon yield curve predictions. However, the dynamic model should be used with caution when forecasting short maturity yields

Empirical Assessment of an Intertemporal Option Pricing Model with Latent variables

This paper assesses the empirical performance of an intertemporal option pricing model with latent variables which generalizes the Hull-White stochastic volatility formula. Using this generalized formula in an ad-hoc fashion to extract two implicit parameters and forecast next day S&P 500 option prices, we obtain similar pricing errors than with implied volatility alone as in the Hull-White case. When we specialize this model to an equilibrium recursive utility model, we show through simulations that option prices are more informative than stock prices about the structural parameters of the model. We also show that a simple method of moments with a panel of option prices provides good estimates of the parameters of the model. This lays the ground for an empirical assessment of this equilibrium model with S&P 500 option prices in terms of pricing errors.

De la légitimité du recours à l'action déclaratoire dans les litiges du commerce international

Comme son titre l'indique, ce mémoire traite de la légitimité du recours à l'action déclaratoire en droit international privé québécois. L'action déclaratoire, qu'elle soit introduite par déclaration ou par requête, a pour but de faire prononcer un tribunal sur l'existence ou l'inexistence de droits et obligations des parties. Bien que très ancienne, l'action déclaratoire n'était que peu utilisée au Québec jusqu'à l'avènement en 1966 de la requête en jugement déclaratoire dans notre Code de procédure civile. Aujourd'hui, cette action est largement utilisée en droit public dans le cadre du pouvoir de surveillance et de contrôle de la Cour supérieure, mais aussi dans le contexte du droit international privé comme une stratégie de défense, ou parfois d'attaque, dans le cadre d'un litige international. Fondamentalement, la finalité de cette action est d'offrir un mécanisme de protection judiciaire des droits d'un individu lorsque les autres recours ne sont pas disponibles ou accessibles, et de permettre un recours efficace hors du cadre traditionnel de la procédure ordinaire. Dès lors, il semble contestable d'utiliser en droit international privé l'action en jugement déclaratoire pour bloquer les procédures ordinaires autrement applicables. L'objet de cette étude est ainsi de démontrer que bien que le recours à l'action déclaratoire soit légitime en droit international privé, son utilisation actuelle à des fins stratégiques en présence, ou en prévision, d'une action ordinaire intentée dans une autre juridiction, paraît difficilement justifiable. Ainsi, la première partie de ce mémoire est consacrée à l'étude de la légitimité de l'action déclaratoire en droit international privé québécois, et la seconde partie s'intéresse aux effets d'une requête en jugement déclaratoire étrangère sur la procédure internationale au Québec.

Dissent in monetary policy decisions

Voting records indicate that dissents in monetary policy committees are frequent and predictability regressions show that they help forecast future policy decisions. In order to study whether the latter relation is causal, we construct a model of committee decision making and dissent where members' decisions are not a function of past dissents. The model is estimated using voting data from the Bank of England and the Riksbank. Stochastic simulations show that the decision-making frictions in our model help account for the predictive power of current dissents. The effect of insti- tutional characteristics and structural parameters on dissent rates is examined using simulations as well.