Value added logistics in supply and demand chain : COSTFIX : New operation model for maintenance material logistics in paper industry

This research report illustrates and examines new operation models for decreasing fixed costs and transforming them into variable costs in the field of paper industry. The report illustrates two cases – a new operation model for material logistics in maintenance and an examination of forklift truck fleet outsourcing solutions. Conventional material logistics in maintenance operation is illustrated and some problems related to conventional operation are identified. A new operation model that solves some of these problems is presented including descriptions of procurement and service contracts and sources of added value. Forklift truck fleet outsourcing solutions are examined by illustrating the responsibilities of a host company and a service provider both before and after outsourcing. The customer buys outsourcing services in order to improve its investment productivity. The mechanism of how these services affect the customer company’s investment productivity is illustrated.

Säilöntämenetelmien parantaminen höyryvoimaprosessissa

Lahti Energia Oy:n kivihiiltä polttavan Kymijärvi I voimalaitoksen vuotuiset käyttötunnit ovat vähentyneet. Syy vähennykseen on ollut uuden kaasutusvoimalaitoksen käyttöönotto vuonna 2012, jolloin vanhan voimalaitoksen käytölle ei ole ollut tarvetta kesäkuukausina. Seisonta-aikana voimalaitoksen höyryvoimaprosessi tulee säilöä. Säilönnällä tarkoitetaan toimenpiteitä, joilla estetään korroosio-olosuhteiden syntyminen voimalaitoksen laitteisiin. Säilöntätavasta riippuen estetään joko hapen tai kosteuden esiintyminen. Tässä diplomityössä tutkitaan eri säilöntämenetelmien ominaisuuksia tavoitteena löytää sopivin vaihtoehto vanhalle kivihiilivoimalaitokselle. Työssä perehdytään teorian kautta kolmeen menetelmään voimalaitoksen kattilan säilönnässä. Typpi- ja märkäsäilönnässä estetään hapen ja kuivailmasäilönnässä kosteuden esiintyminen. Työssä on myös tutkittu kattilan säilöntämenetelmien vaikutuksia muihin laitteistoihin ja järjestelmiin. Tuloksien perusteella voidaan todeta, että kuivailmasäilöntä on toimivin ratkaisu voimalaitoksen viimeisille käyttövuosille. Voimalaitoksen jäädessä varavoimalaitokseksi on typpisäilöntä kustannustehokkain vaihtoehto.

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.

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.

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.

Apply of energy storages in electricity distribution networks and reserve capacity applications

The Thesis is dedicated to development of an operative tool to support decision making of battery energy storages implementation in distribution networks. The basics of various battery technologies, their perspectives and challenges are represented in the Thesis. Mathematical equations that describe economic effect from battery energy storage installation are offered. The main factors that influence profitability of battery settings have been explored and mathematically defined. Mathematical model and principal trends of battery storage profitability under an impact of the major factors are determined. The meaning of annual net value was introduced to show the difference between savings and required costs. The model gives a clear vision for dependencies between annual net value and main factors. Proposals for optimal network and battery characteristics are suggested.

Yritysten sähkön kysyntäjouston tuotteistaminen

Muuttuva sähköntuotantorakenne yhdessä Euroopan yhdentyvien sähkömarkkinoiden kanssa muuttaa energiayhtiöiden toimintaympäristöä tulevaisuudessa. Markkinoilta poistu-va säätökykyinen lauhdevoima ja lisääntyvä säiden mukaan vaihteleva tuuli- ja aurin-koenergia lisäävät säätövoiman tarvetta energiajärjestelmässä. Osa lisääntyvästä säätövoi-man tarpeesta voidaan kattaa kulutuksen kysyntäjoustoa lisäämällä. Tässä työssä tavoitteena on kehittää Savon Voima Oyj:lle kysyntäjoutopalvelu yritysasiak-kaille. Työssä kartoitetaan kysyntäjouston eri markkinapaikkojen mahdollisuuksia kysyntä-jouston toteuttamiselle, sekä käydään läpi niiden asettamia rajoitteita kysyntäjouston to-teuttamisen kannalta. Työn osana kehitettyä kysyntäjouston potentiaalin laskentatyökalua käytetään arvioitaessa valittujen pilottiasiakkaiden potentiaalia Elspot-markkinoille osallistuvan kysyntäjouston osalta. Laskennan tulosten perusteella kysyntäjouston toteuttaminen Elspot-markkinoille on kannattavaa valittujen pilottiasiakkaiden kohdalla. Työssä käydään läpi kysyntäjoustopalvelun prosessia, sen vaiheita sekä huomioon otettavia asioita tarjottaessa kysyntäjoustopalvelua yrityksille. Lopuksi esitetään liiketoimintamalli, jolla palvelua lähdetään yritysasiakkaille tarjoamaan. Liiketoimintamallissa esitetään koh-deryhmät ja markkinat kysyntäjouston toteuttamiselle.

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.