Efficient installed base information management and utilization in global after sales service business

This Master´s thesis explores how the a global industrial corporation’s after sales service department should arrange its installed base management practices in order to maintain and utilize the installed base information effectively. Case company has product-related records, such as product’s lifecycle information, service history information and information about product’s performance. Information is collected and organized often case by case, therefore the systematic and effective use of installed base information is difficult also the overview of installed base is missing. The goal of the thesis study was to find out how the case company can improve the installed base maintenance and management practices and improve the installed base information availability and reliability. Installed base information management practices were first examined through the literature. The empirical research was conducted by the interviews and questionnaire survey, targeted to the case company’s service department. The research purpose was to find out the challenges related to case company´s service department’s information management practices. The study also identified the installed base information needs and improvement potential in the availability of information. Based on the empirical research findings, recommendations for improve installed base management practices and information availability were created. Grounding of the recommendations, the case company is suggested the following proposals for action: Service report development, improving the change management process, ensuring the quality of the product documentation in early stages of product life cycle and decision to improve installed base management practices.

Algorithmic Solutions for Combinatorial Problems in Resource Management of Manufacturing Environments

This thesis studies the use of heuristic algorithms in a number of combinatorial problems that occur in various resource constrained environments. Such problems occur, for example, in manufacturing, where a restricted number of resources (tools, machines, feeder slots) are needed to perform some operations. Many of these problems turn out to be computationally intractable, and heuristic algorithms are used to provide efficient, yet sub-optimal solutions. The main goal of the present study is to build upon existing methods to create new heuristics that provide improved solutions for some of these problems. All of these problems occur in practice, and one of the motivations of our study was the request for improvements from industrial sources. We approach three different resource constrained problems. The first is the tool switching and loading problem, and occurs especially in the assembly of printed circuit boards. This problem has to be solved when an efficient, yet small primary storage is used to access resources (tools) from a less efficient (but unlimited) secondary storage area. We study various forms of the problem and provide improved heuristics for its solution. Second, the nozzle assignment problem is concerned with selecting a suitable set of vacuum nozzles for the arms of a robotic assembly machine. It turns out that this is a specialized formulation of the MINMAX resource allocation formulation of the apportionment problem and it can be solved efficiently and optimally. We construct an exact algorithm specialized for the nozzle selection and provide a proof of its optimality. Third, the problem of feeder assignment and component tape construction occurs when electronic components are inserted and certain component types cause tape movement delays that can significantly impact the efficiency of printed circuit board assembly. Here, careful selection of component slots in the feeder improves the tape movement speed. We provide a formal proof that this problem is of the same complexity as the turnpike problem (a well studied geometric optimization problem), and provide a heuristic algorithm for this problem.

Kysynnän ennakointiprosessin kehittäminen leipomoyrityksessä

Tämän työn tarkoituksena on kehittää lyhyen tähtäimen kysynnän ennakointiprosessia VAASAN Oy:ssä, jossa osa tuotteista valmistetaan kysyntäennakoiden perusteella. Valmistettavien tuotteiden luonteesta johtuva varastointimahdollisuuden puuttuminen, korkea toimitusvarmuustavoite sekä tarvittavien ennakoiden suuri määrä asettavat suuret haasteet kysynnän ennakointiprosessille. Työn teoriaosuudessa käsitellään kysynnän ennustamisen tarvetta, ennusteiden käyttökohteita sekä kysynnän ennustamismenetelmiä. Pelkällä kysynnän ennustamisella ei kuitenkaan päästä toimitusketjun kannalta optimaaliseen lopputulokseen, vaan siihen tarvitaan kokonaisvaltaista kysynnän hallintaa. Se on prosessi, jonka tavoitteena on tasapainottaa toimitusketjun kyvykkyydet ja asiakkaiden vaatimukset keskenään mahdollisimman tehokkaasti. Työssä tutkittiin yrityksessä kolmen kuukauden aikana eksponentiaalisen tasoituksen menetelmällä laadittuja ennakoita sekä ennakoijien tekemiä muutoksia niihin. Tutkimuksen perusteella optimaalinen eksponentiaalisen tasoituksen alfa-kerroin on 0,6. Ennakoijien tilastollisiin ennakoihin tekemät muutokset paransivat ennakoiden tarkkuutta ja ne olivat erityisen tehokkaita toimituspuutteiden minimoimisessa. Lisäksi työn tuloksena ennakoijien käyttöön saatiin monia päivittäisiä rutiineja helpottavia ja automatisoivia työkaluja.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

Development of simple and efficient synthetic strategies for production of fine chemicals of pharmaceutical relevance : metal-mediated allylation combined with applied catalysis

Den snart 200 år gamla vetenskapsgrenen organisk synteskemi har starkt bidragit till moderna samhällens välfärd. Ett av flaggskeppen för den organiska synteskemin är utvecklingen och produktionen av nya läkemedel och speciellt de aktiva substanserna däri. Därmed är det viktigt att utveckla nya syntesmetoder, som kan tillämpas vid framställningen av farmaceutiskt relevanta målstrukturer. I detta sammanhang är den ultimata målsättningen dock inte endast en lyckad syntes av målmolekylen, utan det är allt viktigare att utveckla syntesrutter som uppfyller kriterierna för den hållbara utvecklingen. Ett av de centralaste verktygen som en organisk kemist har till förfogande i detta sammanhang är katalys, eller mera specifikt möjligheten att tillämpa olika katalytiska reaktioner vid framställning av komplexa målstrukturer. De motsvarande industriella processerna karakteriseras av hög effektivitet och minimerad avfallsproduktion, vilket naturligtvis gynnar den kemiska industrin samtidigt som de negativa miljöeffekterna minskas avsevärt. I denna doktorsavhandling har nya syntesrutter för produktion av finkemikalier med farmaceutisk relevans utvecklats genom att kombinera förhållandevis enkla transformationer till nya reaktionssekvenser. Alla reaktionssekvenser som diskuteras i denna avhandling påbörjades med en metallförmedlad allylering av utvalda aldehyder eller aldiminer. De erhållna produkterna innehållende en kol-koldubbelbindning med en närliggande hydroxyl- eller aminogrupp modifierades sedan vidare genom att tillämpa välkända katalytiska reaktioner. Alla syntetiserade molekyler som presenteras i denna avhandling karakteriseras som finkemikalier med hög potential vid farmaceutiska tillämpningar. Utöver detta tillämpades en mängd olika katalytiska reaktioner framgångsrikt vid syntes av dessa molekyler, vilket i sin tur förstärker betydelsen för de katalytiska verktygen i organiska kemins verktygslåda.

Generalized Differential Evolution for Global Multi-Objective Optimization with Constraints

The objective of this thesis work is to develop and study the Differential Evolution Algorithm for multi-objective optimization with constraints. Differential Evolution is an evolutionary algorithm that has gained in popularity because of its simplicity and good observed performance. Multi-objective evolutionary algorithms have become popular since they are able to produce a set of compromise solutions during the search process to approximate the Pareto-optimal front. The starting point for this thesis was an idea how Differential Evolution, with simple changes, could be extended for optimization with multiple constraints and objectives. This approach is implemented, experimentally studied, and further developed in the work. Development and study concentrates on the multi-objective optimization aspect. The main outcomes of the work are versions of a method called Generalized Differential Evolution. The versions aim to improve the performance of the method in multi-objective optimization. A diversity preservation technique that is effective and efficient compared to previous diversity preservation techniques is developed. The thesis also studies the influence of control parameters of Differential Evolution in multi-objective optimization. Proposals for initial control parameter value selection are given. Overall, the work contributes to the diversity preservation of solutions in multi-objective optimization.

Tuotannonohjausmuodon vaihtaminen tilausohjautuvasta varasto-ohjautuvaan

Diplomityössä tutkitaan tuotannonohjausmuodon vaihtamista tilausohjautuvasta varasto-ohjautuvaan. Tavoitteena on löytää kohdeyritykselle kustannustehokkain vaihtoehto tuotannonohjaukseen. Tutkimuksessa analysoidaan yrityksen tuotantoprosessi nykytila-analyysilla toiminnan tehokkuuden arvioimiseksi ja tuotannon pullonkaulojen löytämiseksi. Asiakastilauksen kytkentäpisteet ja taloudelliset tuotantoeräkoot määritellään yrityksen valmistamille tuotteille. Työn tuloksissa käydään tuotannon tunnuslukujen avulla läpi tuotannon tehokkuuden kehittymistä projektin aikana. Varasto-ohjautuva tuotannonohjausmuoto osoittautui kannattavimmaksi ohjausmuodoksi tarkasteltavana ajankohtana. Toimitusvarmuus ja kapasiteetin käyttöaste parantuivat ohjausmuodon vaihtamisen myötä. Asetuskustannuksia saatiin vähennettyä ja tuotantoprosessia kehitettyä. Tuotannon läpimenoaikojen lyhennyttyä tilausohjautuvuutta tulee tarkastella uudestaan.

Maahanmuuttajien kotoutumiskoulutuksen moduulimallin ulkoinen arviointi : Hieman hiomalla timantiksi

Kainuun ELY-keskus järjestää yhteistyössä alueen TE-toimistojen kanssa maahanmuuttajien kotoutumiskoulutusta moduulimuotoisena. Kotoutumiskoulutusta järjestetään aikuisille, kotoutumislain piirissä oleville maahanmuuttajille. Koulutus on pääsääntöisesti työvoimapoliittista koulutusta ja sen tarkoituksena on kehittää maahanmuuttajien suomen- tai ruotsinkielentaitoa sekä yhteiskunnan tuntemusta. Tämä raportti on Kainuun ELY-keskuksen hallinnoiman Kansainvälinen työvoima -projektin (ESR) toimeksiannosta tehtävän ulkoisen arvioinnin loppuraportti. Kotoutumiskoulutuksen moduulimallin ulkoisella arvioinnilla tähdätään mallin kehittämiseen ja sitä kautta maahanmuuttajien kotoutumisen ja myös työllistymismahdollisuuksien parantamiseen. Ulkoisen arvioinnin tavoitteena on moduulimallin kehittäminen tuloksellisempaan ja tehokkaampaan suuntaan siten, että se paremmin palvelee maahanmuuttajien kotoutumistavoitteita.

Strategic outsourcing of R&D: The location-specific advantages of India to the pharmaceutical industry

Companies are increasingly under pressure to be more efficient both in terms of costs and overall performance and thus, they seek new ways to develop their products and innovate. For pharmaceutical industry it can take several decades to launch a new drug to the markets. Since pharmaceutical industry is one of the most research-intensive industries, is outsourcing one way to enhance the R&D processes of such companies. It is said that outsourcing to offshore locations is vastly more challenging and complicated than any other exporting activity or inter-company relationship that has evoked a lot of discussion. By outsourcing strategically, companies must also thoroughly focus on transaction costs and core competences. Today, the suppliers are looked for beyond national boundaries and furthermore, the location of the outsourcing activity must also be thoroughly considered. Consequently, the purpose of this study is to analyze what is known of strategic outsourcing of pharmaceutical R&D to India. In order to meet the purpose of the study, this study tries to answer three sub-questions set to it: first, what is strategic outsourcing, second, why pharmaceutical companies utilize strategic outsourcing of R&D and last, why pharmaceutical companies select India as the location for outsourcing their R&D. The study is a qualitative study. The purpose of the study was approached by a literature review with systematic elements and sub-questions were analyzed through different relevant theories, such as theory of transaction costs, core competences and location advantages. Applicable academic journal articles were comprehensively included in the study. The data was collected from electronic journal article databases using key words and almost only peer-reviewed, as new as possible articles were included. Also both the reference list of the included articles and article recommendations from professionals generated more articles for inclusion. The data was analyzed through thematization that resulted in themes that illuminate the purpose of the study and sub-questions. As an outcome of the analysis, each of the theory chapters in the study represents one sub-question. The literature used in this study revealed that strategic outsourcing of R&D is increasingly used in pharmaceutical industry and the major motives to practice it has to do with lowering costs, accessing skilled labor, resources and knowledge and enhancing their quality while speeding up the introduction of new drugs. Mainly for the above-mentioned motives India is frequently chosen as the target location for pharma outsourcers. Still, the literature is somewhat incomplete in this complex phenomenon and more research is needed.

Exploration and Design of Power-Efficient Networked Many-Core Systems

Multiprocessing is a promising solution to meet the requirements of near future applications. To get full benefit from parallel processing, a manycore system needs efficient, on-chip communication architecture. Networkon- Chip (NoC) is a general purpose communication concept that offers highthroughput, reduced power consumption, and keeps complexity in check by a regular composition of basic building blocks. This thesis presents power efficient communication approaches for networked many-core systems. We address a range of issues being important for designing power-efficient manycore systems at two different levels: the network-level and the router-level. From the network-level point of view, exploiting state-of-the-art concepts such as Globally Asynchronous Locally Synchronous (GALS), Voltage/ Frequency Island (VFI), and 3D Networks-on-Chip approaches may be a solution to the excessive power consumption demanded by today’s and future many-core systems. To this end, a low-cost 3D NoC architecture, based on high-speed GALS-based vertical channels, is proposed to mitigate high peak temperatures, power densities, and area footprints of vertical interconnects in 3D ICs. To further exploit the beneficial feature of a negligible inter-layer distance of 3D ICs, we propose a novel hybridization scheme for inter-layer communication. In addition, an efficient adaptive routing algorithm is presented which enables congestion-aware and reliable communication for the hybridized NoC architecture. An integrated monitoring and management platform on top of this architecture is also developed in order to implement more scalable power optimization techniques. From the router-level perspective, four design styles for implementing power-efficient reconfigurable interfaces in VFI-based NoC systems are proposed. To enhance the utilization of virtual channel buffers and to manage their power consumption, a partial virtual channel sharing method for NoC routers is devised and implemented. Extensive experiments with synthetic and real benchmarks show significant power savings and mitigated hotspots with similar performance compared to latest NoC architectures. The thesis concludes that careful codesigned elements from different network levels enable considerable power savings for many-core systems.

Performance and scalability of isolated DC-DC converter topologies in low voltage, high current applications

Fuel cells are a promising alternative for clean and efficient energy production. A fuel cell is probably the most demanding of all distributed generation power sources. It resembles a solar cell in many ways, but sets strict limits to current ripple, common mode voltages and load variations. The typically low output voltage from the fuel cell stack needs to be boosted to a higher voltage level for grid interfacing. Due to the high electrical efficiency of the fuel cell, there is a need for high efficiency power converters, and in the case of low voltage, high current and galvanic isolation, the implementation of such converters is not a trivial task. This thesis presents galvanically isolated DC-DC converter topologies that have favorable characteristics for fuel cell usage and reviews the topologies from the viewpoint of electrical efficiency and cost efficiency. The focus is on evaluating the design issues when considering a single converter module having large current stresses. The dominating loss mechanism in low voltage, high current applications is conduction losses. In the case of MOSFETs, the conduction losses can be efficiently reduced by paralleling, but in the case of diodes, the effectiveness of paralleling depends strongly on the semiconductor material, diode parameters and output configuration. The transformer winding losses can be a major source of losses if the windings are not optimized according to the topology and the operating conditions. Transformer prototyping can be expensive and time consuming, and thus it is preferable to utilize various calculation methods during the design process in order to evaluate the performance of the transformer. This thesis reviews calculation methods for solid wire, litz wire and copper foil winding losses, and in order to evaluate the applicability of the methods, the calculations are compared against measurements and FEM simulations. By selecting a proper calculation method for each winding type, the winding losses can be predicted quite accurately before actually constructing the transformer. The transformer leakage inductance, the amount of which can also be calculated with reasonable accuracy, has a significant impact on the semiconductor switching losses. Therefore, the leakage inductance effects should also be taken into account when considering the overall efficiency of the converter. It is demonstrated in this thesis that although there are some distinctive differences in the loss distributions between the converter topologies, the differences in the overall efficiency can remain within a range of a few percentage points. However, the optimization effort required in order to achieve the high efficiencies is quite different in each topology. In the presence of practical constraints such as manufacturing complexity or cost, the question of topology selection can become crucial.

Measuring effects of strategic utilization of social media in business-to-business marketing: Case Greenlux Finland Oy

Utilization of social media is increasingly common in B2B marketing. Social media is an efficient and cheap marketing and communication channel available for everyone, and thus extremely attractive marketing medium. The more companies get involved in social media the more failures are reported. It is not enough for a company to just be present in social media. Succeeding on it requires hard work, investing time and money, and ability to measure and to monitor performance. With an increasing number of companies failing in utilizing social media, together with lack of research on strategic utilization of social media focusing on B2B marketing, measuring, and monitoring create a purpose for this research. The aim of this research is to discover methods for measuring and monitoring effects of strategic utilization of social media in B2B marketing. Most relevant financial and non-financial indicators are discussed, and the methods by which these can be monitored and measured. In addition, effects of strategic utilization of social media on the case company are measured and analyzed. The research methodology used in this research is a participatory action research, which includes elements of both qualitative and quantitative research methods. The case company examined in the research provides a unique opportunity to follow through all phases of strategic utilization of social media for B2B marketing purposes concluding real effects of social media to the case company, and thus gain a deep understanding about this new marketing medium in the perspective of B2B marketing. Duration of the research period is seven months. During this time, information is collected, measured, and analyzed. Case company does not have any other marketing activities simultaneously which makes it possible to examine social media apart from effects of other visible marketing activities. Effects of strategic utilization of social media can be monitored and measured in many ways. Methods that should be used depend on goals set for social media. Fundamental nature of social media requires multidimensional assessment, and thus effects should be measured, and monitored considering both financial and non-financial indicators. The results implicates that effects of strategic utilization of social media are relatively wide ranged. According to the findings, social media affects positively on brand, number of web page visitors, visitor behavior, and on distribution of awareness. According to investment calculations social media is a legitimate investment for case company. Results also implicate that by using social media case company gains conversation, arouses interest, gets attention, and creates interactivity. In addition and as a side note, winter holiday season appears to have a great effect on social media activity of B2B companies’ representatives.

On Energy Efficient Computing Platforms

In accordance with the Moore's law, the increasing number of on-chip integrated transistors has enabled modern computing platforms with not only higher processing power but also more affordable prices. As a result, these platforms, including portable devices, work stations and data centres, are becoming an inevitable part of the human society. However, with the demand for portability and raising cost of power, energy efficiency has emerged to be a major concern for modern computing platforms. As the complexity of on-chip systems increases, Network-on-Chip (NoC) has been proved as an efficient communication architecture which can further improve system performances and scalability while reducing the design cost. Therefore, in this thesis, we study and propose energy optimization approaches based on NoC architecture, with special focuses on the following aspects. As the architectural trend of future computing platforms, 3D systems have many bene ts including higher integration density, smaller footprint, heterogeneous integration, etc. Moreover, 3D technology can signi cantly improve the network communication and effectively avoid long wirings, and therefore, provide higher system performance and energy efficiency. With the dynamic nature of on-chip communication in large scale NoC based systems, run-time system optimization is of crucial importance in order to achieve higher system reliability and essentially energy efficiency. In this thesis, we propose an agent based system design approach where agents are on-chip components which monitor and control system parameters such as supply voltage, operating frequency, etc. With this approach, we have analysed the implementation alternatives for dynamic voltage and frequency scaling and power gating techniques at different granularity, which reduce both dynamic and leakage energy consumption. Topologies, being one of the key factors for NoCs, are also explored for energy saving purpose. A Honeycomb NoC architecture is proposed in this thesis with turn-model based deadlock-free routing algorithms. Our analysis and simulation based evaluation show that Honeycomb NoCs outperform their Mesh based counterparts in terms of network cost, system performance as well as energy efficiency.