The digitalization of our society and the creation of data intense industries are transforming how industrial sales can be made. Large volumes of data are generated when businesses and people use digital products and services which are available in the modern world. Some of this data describes the digital products and services when they are in use, i.e., it is in-service data. Furthermore, data has during the last decade been seen as an asset which can improve decision-making and has made sales activities become increasingly customer specific. The purpose of this study was to explore how knowledge from in-service data can serve B2B selling. To realize this purpose the following three research questions were answered by conducting a single case study of a large company in the information and communications technology (ICT) industry. (RQ1) How does a company in a data intense industry use knowledge from in-service data in the B2B sales process? (RQ2) What opportunities does knowledge from in-service data create in the B2B sales process? (RQ3) What challenges hinder a company from using knowledge from in-service data in the B2B sales process? RQ1: This study has concluded that, in the context of a data intense industry, throughout the steps in the B2B sales process, knowledge from in-service data is actively used by the sales team, however, to varying degrees. In-service data is used in six categories of sales activities: (1) to understand the customer in terms of their technical and strategical needs, which enables lead generation and cross-selling, (2) to make information from in-service data available through data collection, storage, and analyses, (3) to nurture the relationship between buyer and seller by creating understanding, trust and satisfactory offers to the customer, (4) to present solutions with convincing arguments, (5) to solve problems and satisfy the customer’s needs, and (6) to provide post-sale value-adding services. Moreover, three general resources which are used in the activities were identified: An audit report which presents the information of the data, a plan which presents strategic expansions of the solution, and simulations of the solution. Furthermore, four general actors who are performing the activities were identified: the Key Account Manager (KAM) who is responsible for conducting the sales interactions with the customer, the sales team, and the presales team who both support the KAM, and the customer. In addition to the general resources and actors, companies may use step-specific resources and actors. RQ2: Four categories of opportunities were identified: knowledge from in-service data (1) assists KAMs in discovering customer needs, (2) guides the KAM in creating better customer specific solutions, (3) helps the KAM move the sale faster through the sales process, and (4) assists the company in becoming a true partner who provides strategic services, rather than acting as a supplier. RQ3: Finally, four categories of challenges were identified: (1) organizational, (2) technological, (3) cultural, and (4) legal & security. Out of these, obtaining access to the data was identified as the greatest challenge to use in-service data. The opportunities and the challenge to access data are deemed to be general for companies in data intense industries, while the other challenges are depending on the structure, size, and culture of the individual company. The findings of this study contribute to a general understanding of how companies in data intense industries may use knowledge from in-service data, what opportunities this data create for their B2B sales process, and which challenges they face when they pursue activities which use the knowledge from in-service data. To conclude, in-service data serves B2B selling especially as a source of customer knowledge. It is used by salespeople to understand the customer in terms of its technical and strategical needs and salespeople use this knowledge to conduct various customer-oriented sales activities. In-service data creates several opportunities in B2B sales. However, several challenges must be overcome to seize the opportunities. Especially the question of data access.

Enterprise resource planning (ERP) systems are complex software packages that support an integrated real-time setting among the various business functions in an entire organization. ERP systems improve productivity, but only to the extent that employees accept and use the systems extensively to perform their duties. The leaders of many organizations have not been able to realize the expected benefits because of a lack of user acceptance. The purpose of this quantitative cross-sectional survey study was to examine the factors that influence user acceptance of ERP systems in the United States. Davis's technology acceptance model was the theoretical foundation used to relate the independent variables (perceived usefulness and perceived ease of use) to the dependent variable (user acceptance of ERP systems). The focus of the research questions was on the strength of the relationships between each of the independent variables and user acceptance of ERP systems in the United States. Data were from 97 purposively selected ERP system end users in the United States using the survey instrument based on the technology acceptance model. Regression and correlation analyses revealed a positive relationship between perceived usefulness and user acceptance, but no relationship was found between perceived ease of use and user acceptance. The findings indicated difficulties in using ERP systems for end users in the United States, which stakeholders could rectify to improve productivity in organizations. Positive social change implications include improving the standard of living, increasing the literacy rate, and reducing negative externalities to improve human and social conditions in society.

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近幾年來，雲端應用已是非常普遍現象，雲端計算是推動巨量資料( Big Data)分析與與開放資料界面等關鍵服務之營運模式，同時在近來熱門話題如物聯網、智慧城市、工業4.0等扮演著重要角色。 本文研究目的係探討台灣傳統機械業製造廠使用雲端服務的狀況及如何運用雲端服務創造效益；本研究是以F公司為研究個案，研究個案如何透過雲端應用增加企業效益、顧客滿意度,在當前環境下如何以擬訂之策略增加競爭對手門檻及產品區隔市場，並符合現在趨勢，以迎接工業4.0雛型做準備。 本研究透過個案資料收集、相關文獻探討及個案執行人員訪談，探討雲端服務在傳統製造業為公司經營所創造之效益。本研究從「傳統機械業遇到的挑戰與策略」、「個案F公司背景及營運現況(公司、產品、顧客)」、「雲端計算的應用」、「傳統機械業加上雲端應用後的效益」四個面向切入探討。 研究發現，傳統機械業在雲端應用的利益，不僅可以成為公司銷售策略、提升現有服務的品質更能以創新服務協助企業做節能診斷與建議，這樣不僅替客戶節省成本，並且增進顧客滿意度及黏著度。而另一個發現是透過雲端應用，可進一步作供應鏈管理，減少企業備料時間及降低庫存，使企業在激烈的競爭環境中，降低成本、增加毛利並創造企業長期的競爭優勢，同時為未來工業4.0之引進做準備。 關鍵字:雲端應用、傳統機械業、增加企業效益、 創新服務、增進顧客滿意度、 工業4.0. / Cloud applications have become prevalent in recent years. In particular, cloud computing is the operational model in key services for promoting big data analysis and open data interface. It also plays the vital roles in recent popular subjects, such as the internet of things, smart city, and industry 4.0. The objective of this study is to explore the current conditions of how cloud services are utilized in the traditional machine factories in Taiwan and to create benefits. With Company F as the case, this study examines how cloud applications are used to improve corporate benefits and customer satisfaction, as well as how strategies should be formulated for market differentiation to increase competitiveness, connect with current trends, and prepare for the prototype of industry 4.0. Through data collection, literary review, and interview with project implementation staff of the case, the study explores the benefits that cloud applications generate for the traditional manufacturing businesses. There are four focuses of investigation in this study—challenges encountered and strategies formulated by the traditional mechanical industry; case study of company F, including company background and the existing operation concerning the company, product and customers; applications of cloud computing; benefits of cloud applications on traditional mechanical industry. It is indicated in research findings that in the traditional mechanical industry, the benefits of cloud computing will not only become corporate sales strategy and improve existing service quality, but also provide diagnosis and recommendations on energy saving for corporations with innovative services. This will help clients cut costs, and improve customer satisfaction and adhesion. An additional finding is that cloud applications can further manage the supply chain, minimize lead time, and reduce inventory, which enable corporations to reduce costs, increase gross profits, and create long term corporate advantages in a competitive environment to prepare for the upcoming industry 4.0. Keywords: Cloud applications, traditional mechanical industry, improve corporate benefit, innovative service, increase customer satisfaction, industry 4.0.

The focus of this master thesis is the analysis of the financial situation of a particular company from 2008 - 2013. The first part of the thesis provides a theoretical description of the methods that are used in the assessment of the financial situation of the company. The following part of the thesis provides facts about the company. The diploma thesis conducted a strategic analysis of external and internal surroundings. The financial assessment of the company is based on the analysis of financial statements of the company. In the final part of the thesis the findings are evaluated and solutions proposed accordingly. The implementation of the outlined solutions should lead to the gradual improvement of the financial situation of the company.

Marketing, online marketing communication, advertising, marketing research, questionnaire survey

В существующей ситуации высокой непредсказуемости и динамичности рыночного окружения эффективная организация производственного и сбытового процесса становится ключевым фактором успешного функционирования и поддержания долгосрочной конкурентоспособности угольных предприятий на внутреннем и международном рынках. Основой повышения эффективности деятельности угольных компаний может стать применение современного экономико-математического инструментария оптимизации планирования производственной и сбытовой деятельности. Основной целью исследования является совершенствование методического инструментария планирования производственно-сбытовой деятельности угольных предприятий для повышения ее эффективности. В работе представлены результаты исследования особенностей производственно-сбытовой деятельности российских угольных предприятий в современных рыночных условиях: выявлены внешние факторы и внутренние ограничения, оказывающие существенное влияние на эффективность их деятельности, проанализированы существующие методы планирования производственно-сбытовой деятельности, а также обозначены возможные пути повышения эффективности. В магистерской диссертации разработан методический поход к оптимизации планирования производственно-сбытовой деятельности угольных предприятий с применением экономико-математических методов и методов оптимизации плановых решений. / In the current situation of high unpredictability and dynamism of the market environment, the effective organization of the production and marketing process becomes a key factor in the successful functioning and maintenance of long-term competitiveness of coal companies in the domestic and international markets. The basis for improving the efficiency of coal companies can be the use of modern economic and mathematical tools for optimizing the planning of production and marketing activities. The main purpose of the study is to improve the methodical tools for planning the production and marketing activities of coal companies in order to increase its efficiency. The paper presents the results of a study of the features of the production and marketing activities of Russian coal companies in modern market conditions: external factors and internal constraints that have a significant impact on the efficiency of their activities are identified, existing methods of planning production and marketing activities are analyzed, and possible ways to improve efficiency are identified. In the master's thesis, a methodical approach to optimizing the planning of production and marketing activities of coal companies was developed using economic and mathematical methods and methods for optimizing planned decisions.

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Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. Sequential Minimal Optimization (SMO) is a decomposition-based algorithm which breaks this large QP problem into a series of smallest possible QP problems. However, it still costs O(n2) computation time. In our SVM implementation, we can do training with huge data sets in a distributed manner (by breaking the dataset into chunks, then using Message Passing Interface (MPI) to distribute each chunk to a different machine and processing SVM training within each chunk). In addition, we moved the kernel calculation part in SVM classification to a graphics processing unit (GPU) which has zero scheduling overhead to create concurrent threads. In this thesis, we will take advantage of this GPU architecture to improve the classification performance of SVM.