Demand forecasting process design and methods in medium sized enterprise

The purpose of this thesis was to study the design of demand forecasting processes and management of demand. In literature review were different processes found and forecasting methods and techniques interviewed. Also role of bullwhip effect in supply chain was identified and how to manage it with information sharing operations. In the empirical part of study is at first described current situation and challenges in case company. After that will new way to handle demand introduced with target budget creation and how information sharing with 5 products and a few customers would bring benefits to company. Also the new S&OP process created within this study and organization for it.

Low levels of sex hormone-binding globulin and hyperproinsulinemia as markers of increased pancreatic ß-cell demand in men

Low levels of sex hormone-binding globulin (SHBG) are considered to be an indirect index of hyperinsulinemia, predicting the later onset of diabetes mellitus type 2. In the insulin resistance state and in the presence of an increased pancreatic ß-cell demand (e.g. obesity) both absolute and relative increases in proinsulin secretion occur. In the present study we investigated the correlation between SHBG and pancreatic ß-cell secretion in men with different body compositions. Eighteen young men (30.0 ± 2.4 years) with normal glucose tolerance and body mass indexes (BMI) ranging from 22.6 to 43.2 kg/m2 were submitted to an oral glucose tolerance test (75 g) and baseline and 120-min blood samples were used to determine insulin, proinsulin and C-peptide by specific immunoassays. Baseline SHBG values were significantly correlated with baseline insulin (r = -0.58, P<0.05), proinsulin (r = -0.47, P<0.05), C-peptide (r = -0.55, P<0.05) and also with proinsulin at 120 min after glucose load (r = -0.58, P<0.05). Stepwise regression analysis revealed that proinsulin values at 120 min were the strongest predictor of SHBG (r = -0.58, P<0.05). When subjects were divided into obese (BMI >28 kg/m2, N = 8) and nonobese (BMI £25 kg/m2, N = 10) groups, significantly lower levels of SHBG were found in the obese subjects. The obese group had significantly higher baseline proinsulin, C-peptide and 120-min proinsulin and insulin levels. For the first time using a specific assay for insulin determination, a strong inverse correlation between insulinemia and SHBG levels was confirmed. The finding of a strong negative correlation between SHBG levels and pancreatic ß-cell secretion, mainly for the 120-min post-glucose load proinsulin levels, reinforces the concept that low SHBG levels are a suitable marker of increased pancreatic ß-cell demand.

The demand-supply balancing process in supply chain management - case mining & metals industry equipment

The aim of this thesis is to search how to match the demand and supply effectively in industrial and project-oriented business environment. The demand-supply balancing process is searched through three different phases: the demand planning and forecasting, synchronization of demand and supply and measurement of the results. The thesis contains a single case study that has been implemented in a company called Outotec. In the case study the demand is planned and forecasted with qualitative (judgmental) forecasting method. The quantitative forecasting methods are searched further to support the demand forecast and long term planning. The sales and operations planning process is used in the synchronization of the demand and supply. The demand forecast is applied in the management of a supply chain of critical unit of elemental analyzer. Different meters on operational and strategic level are proposed for the measurement of performance.

Utilization of AMI-Technology in a Multilayered Demand Response Service Environment

This thesis studied the performance of Advanced metering infrastructure systems in a challenging Demand Response environment. The aim was to find out what kind of challenges and bottlenecks could be met when utilizing AMI-systems in challenging Demand Response tasks. To find out the challenges and bottlenecks, a multilayered demand response service concept was formed. The service consists of seven different market layers which consist of Nordic electricity market and the reserve markets of Fingrid. In the simulations the AMI-systems were benchmarked against these seven market layers. It was found out, that the current generation AMI-systems were capable of delivering Demand Response on the most challenging market layers, when observed from time critical viewpoint. Additionally, it was found out, that to enable wide scale Demand Response there are three major challenges to be acknowledged. The challenges hindering the utilization of wide scale Demand Response were related to poor standardization of the systems in use, possible problems in data connectivity solutions and the current electricity market regulation model.

Accuracy improvement of a demand forecasting model

This research concerns different statistical methods that assist to increase the demand forecasting accuracy of company X’s forecasting model. Current forecasting process was analyzed in details. As a result, graphical scheme of logical algorithm was developed. Based on the analysis of the algorithm and forecasting errors, all the potential directions for model future improvements in context of its accuracy were gathered into the complete list. Three improvement directions were chosen for further practical research, on their basis, three test models were created and verified. Novelty of this work lies in the methodological approach of the original analysis of the model, which identified its critical points, as well as the uniqueness of the developed test models. Results of the study formed the basis of the grant of the Government of St. Petersburg.

Evaluating and improving demand forecast practices for B2B and B2C sectors

Demand forecasting is one of the fundamental managerial tasks. Most companies do not know their future demands, so they have to make plans based on demand forecasts. The literature offers many methods and approaches for producing forecasts. Former literature points out that even though many forecasting methods and approaches are available, selecting a suitable approach and implementing and managing it is a complex cross-functional matter. However, it’s relatively rare that researches are focused on the differences in forecasting between consumer and industrial companies. The aim of this thesis is to investigate the potential of improving demand forecasting practices for B2B and B2C sectors in the global supply chains. Business to business (B2B) sector produces products for other manufacturing companies. On the other hand, consumer (B2C) sector provides goods for individual buyers. Usually industrial sector have a lower number of customers and closer relationships with them. The research questions of this thesis are: 1) What are the main differences and similarities in demand planning between B2B and B2C sectors? 2) How the forecast performance for industrial and consumer companies can be improved? The main methodological approach in this study is design science, where the main objective is to develop tentative solutions to real-life problems. The research data has been collected from a case company. Evaluation and improving in organizing demand forecasting can be found in three interlinked areas: 1) demand planning operational environment, 2) demand forecasting techniques, 3) demand information sharing scenarios. In this research current B2B and B2C demand practices are presented with further comparison between those two sectors. It was found that B2B and B2C sectors have significant differences in demand practices. This research partly filled the theoretical gap in understanding the difference in forecasting in consumer and industrial sectors. In all these areas, examples of managerial problems are described, and approaches for mitigating these problems are outlined.

Addressing conflicts of interest in the research paper: a societal demand in contemporary science?

In the last decade, dialogue between science and society has found a forum in an increasing number of publications on topics such as public engagement with science and public trust in science. Concerning the latter, issues that include cases of research misconduct, accountability in research, and conflicts of interest (COIs) have shaped global discussions on the communication of science. In the publication setting, the perception that hiding COIs and/or not managing them well may affect public trust in the research record has grown among editors. We conducted a search for editorials addressing COIs between 1989 and 2011, using four major databases: Medline/PubMed, Embase, Scopus, and Web of Knowledge. We explored the content of these editorials and the relationship they established between COIs and the public trust in science. Our results demonstrate that the relationship between disclosure of COIs and public trust in science has become a major concern among editors. We, thus, argue that COIs should be discussed more openly and frequently in graduate courses in the sciences, around the globe, not only in biomedical but also in non-biomedical areas. This is a critical issue in contemporary science, as graduate students are the future voices and decision-makers of the research community. Therefore, COIs, especially in the broader context of science and society, merit closer attention from policymakers, researchers, and educators. At times of great expectations for public engagement with science, mishandling of COIs may have undesirable consequences for public engagement with science and confidence in the scientific endeavor.

Households’ willingness to engage in demand response in the Finnish retail electricity market: an empirical study

If electricity users adjusted their consumption patterns according to time-variable electricity prices or other signals about the state of the power system, generation and network assets could be used more efficiently, and matching intermittent renewable power generation with electricity demand would be facilitated. This kind of adjustment of electricity consumption, or demand response, may be based on consumers’ decisions to shift or reduce electricity use in response to time-variable electricity prices or on the remote control of consumers’ electric appliances. However, while demand response is suggested as a solution to many issues in power systems, actual experiences from demand response programs with residential customers are mainly limited to short pilots with a small number of voluntary participants, and information about what kinds of changes consumers are willing and able to make and what motivates these changes is scarce. This doctoral dissertation contributes to the knowledge about what kinds of factors impact on residential consumers’ willingness and ability to take part in demand response. Saving opportunities calculated with actual price data from the Finnish retail electricity market are compared with the occurred supplier switching to generate a first estimate about how large savings could trigger action also in the case of demand response. Residential consumers’ motives to participate in demand response are also studied by a web-based survey with 2103 responses. Further, experiences of households with electricity consumption monitoring systems are discussed to increase knowledge about consumers’ interest in getting more information on their electricity use and adjusting their behavior based on it. Impacts of information on willingness to participate in demand response programs are also approached by a survey for experts of their willingness to engage in demand response activities. Residential customers seem ready to allow remote control of electric appliances that does not require changes in their everyday routines. Based on residents’ own activity, the electricity consuming activities that are considered shiftable are very limited. In both cases, the savings in electricity costs required to allow remote control or to engage in demand response activities are relatively high. Nonmonetary incentives appeal to fewer households.

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.

Sähkönmyyntiyhtiöiden riskienhallinnan haasteet ja kehittäminen

Diplomityön tavoitteena on esitellä sähkökaupan ja erityisesti sähköyhtiöiden kokemia sähkönmyynnin riskejä sekä kuvata sähkönmyyntiin liittyvää riskienhallinnan problematiikkaa. Tarkastelun näkökulmana on tietojärjestelmien ja saatavissa olevan tiedon hyödyntäminen energiayhtiöiden riskienhallinnassa. Toinen päätavoitteista on tutkia, kuinka saatavilla olevaa tiedon hyödyntämistä voidaan kehittää sähkönmyynnin hinnoittelussa sekä suojausten suunnittelussa. Työ toteutettiin työskentelemällä asiantuntijana energia-alaan keskittyneessä ohjelmistoyrityksessä sekä haastattelemalla yhdeksän suomalaisen sähkönmyyntiyhtiön henkilöitä riskienhallinnan haasteiden sekä tietojärjestelmien näkökulmasta. Saatavilla olevien tietojen nykyistä parempi hyödyntäminen ja automatisointi voivat auttaa pienentämään yhtiöiden riskitasoa ja parantaa menestymisen edellytyksiä sähkönmyynnin vähittäismarkkinoilla. Lisäksi kulloiseenkin markkinatilanteeseen sopivat sähkön hankintahinnan suojausstrategiat sekä monipuoliset dynaamiset hinnoittelumallit auttavat pienentämään yhtiön kokemia riskejä tai niiden vaikutuksia. Näiden hyödyntäminen vaatii laajaa ymmärrystä sähkö- ja johdannaismarkkinoiden toiminnasta sekä usein myös nykyisten tietojärjestelmien kehittämistä. Tulevaisuudessa yhä yleistyvä hajautettu tuotanto sekä kysynnän jousto asettavat tietojärjestelmille uusia vaatimuksia, jotka toteutuessaan mahdollistavat uudenlaisten palveluiden käyttöönoton sekä voivat tuoda tilaa myös alan uusille toimijoille. Työssä käsitellään energiayhtiöiden kokemia riskejä sähkönmyynnin näkökulmasta, esitellään alan yleisimmät riskit sekä keinot ja työkalut niiltä suojautumiseen. Työn lopuksi tarkastellaan sähkönmyynnin ja –hankinnan oleellisimpia prosesseja riskienhallinnan kehittämisen näkökulmasta.

Prospects of Bitcoin diffusion

Currency is something people deal with every day in their lives. The contemporary society is very much revolving around currencies. Even though technological development has been rapid, the principle of currency has stayed relatively unchanged for a long time. Bitcoin is a digital currency that introduced an alternative to other digital currencies, and to the traditional physical currencies. Bitcoin is peer-to-peer, open source, and it erases the need of a third party in transactions. Bitcoin has since inception gained certain fame, but it has not established itself as a common currency in the world. The purpose of this study was to analyse what kind of potential does Bitcoin have to become a widely accepted currency in day-to-day transactions. The main research question was divided into three sub questions: • What kind of a process is the diffusion of new innovations? • What kinds of factors speak for the wider adoption of Bitcoin? • What kinds of factors speak against the wider adoption of Bitcoin? The purpose of the study was approached by having diffusion of innovations as the theoretical framework. The four elements in diffusion of innovations are, innovation, communication, time, and social system. The theoretical framework is applied to Bitcoin, and the research questions answered by analysing Bitcoin’s potential diffusion prospects. The body of research data consisted of media texts and statistics. In this study, content analysis was the research method. The main findings of the study are that Bitcoin has clear strengths, but it faces a large amount of uncertainty. Bitcoin’s strong areas are the transactions. They are fast, easy, and cheap. From the innovation diffusion perspective Bitcoin is still relatively unknown, and the general public’s attitudes towards it are sceptical. The research findings purport that Bitcoin has potential demand especially when the financial system of a region is dysfunctional, or when there is a financial crisis. Bitcoin is not very trusted, and the majority of people do not see a reason to start using Bitcoin in the future. A large number of people associate it with illegal activities. In general people are largely unaware of what Bitcoin is or what are the strengths and weaknesses. Bitcoin is an innovative alternative currency. However, unless people see a major need for Bitcoin due to a financial crisis, or dysfunctionality in the financial system, Bitcoin will not become much more widespread as it is today. Bitcoin’s underlying technology can be harnessed to multiple uses. Developments in that field in the future are something that future researchers could look into.