Thesis gives insight into the strategic policy and its prospects in the company Avon Cosmetics, ltd. The aim of thesis is to reveal the barriers that caused a reduction in performance of the company and other development options to restore the former prosperity through processing of detailed strategic analysis. Based on the results of processed strategic analysis and evaluation of strategic decision there are derives strategic recommendations at the end of the work which specify the company's weakenesses, threats and opportunities.

Virtual Power Plants (VPPs) are an emerging form of technology that allows owners of electricity producing appliances, such as electric vehicles, to partake in a pool of producers of sustainable energy. The Swedish electricity grid owner Svenska Kraftnät hosts a platform where VPPs act as intermediaries between energy producing customers and third party buyers. A requirement to participate in these transactions, however, is to post a bid specifying the amount of power that can be produced from a VPP during a given hour at least 48 hours into the future. This is where forecasting comes into the picture. This report compares the accuracy of eight different machine learning models when tasked with forecasting power output using the same training data from an electric vehicle-based VPP. The study also examines which inferences about customer behavior can be drawn from the same data and give strategic recommendations to VPPs based on the findings of the study. Upon evaluating the results, it was found that deep learning models outperformed autoregressive models, which in turn outperformed Random Forest Regression and Support Vector Regression. As for customer behaviors found in the data, a small negative correlation between spot prices and delivered output was found, suggesting that customers limit their charging when spot prices are high. Further, more power is generally produced during nighttime and on weekends. The data also shows an autocorrelation with a lag of 24 hours, suggesting that charging behaviors on a given day influence charging behaviors the subsequent day. / Virtuella kraftverk (VPPs) är en framväxande form av teknologi som tillåter ägare av elproducerande enheter, till exempel elbilar, att delta i ett nätverk av producenter av hållbar energi. Den svenska elnätsägaren Svenska Kraftnät driver en plattform där VPPs agerar mellanhänder mellan energiproducerande kunder och tredjepartsköpare. Ett krav för att delta i budgivningen är dock att som VPP kunna lägga ett bud som specificerar hur stor effekt som kan produceras under en viss timme, minst 48 timmar i framtiden. Här kommer prognoser in i bilden. Denna rapport jämför precisionen för åtta olika maskininlärningsmodeller som har i uppgift att predicera effektproduktion med hjälp av samma data från ett elbilsbaserat VPP. Denna studie undersöker också vilka slutsatser som kan dras angående kundbeteenden från given data och ger strategiska rekommendationer baserat på studiens resultat. Efter utvärdering av resultaten kunde det konstateras att Deep Learning-modeller överträffade autoregressiva modeller, som i sin tur överträffade Random Forest Regression och Support Vector Regression. Vad gäller kundbeteenden i given data, kan sägas att en låg negativ korrelation fanns mellan spotpriser och effektproduktion, vilket tyder på att kunder begränsar laddning av elbilar när spotpriserna är höga. Vidare kan sägas att mer effekt generellt sett produceras på kvällar och helger. Studiens data visar också på en autokorrelation med en eftersläpning (lag) på 24 timmar, vilket tyder på att laddningsmönster under en given dag influerar laddningsmönster under nästkommande dag.

Målet med detta projekt var att optimera produktionslinjen för att maximera antalet färdigställda artiklar som produceras. Projektet påbörjades med en planering och en undersökning av de verktygen som skulle användas så som simulering, lean och information relaterat till produktionslinjen. Därefter modellerades den i simulerings-verktyget utifrån den faktainsamlingen som utfördes och simuleringsmetoden. När modellen var klar utfördes experimentella tester för att undersökningar hur specifika faktorer så som strategiskt placerade buffertar och antalet operatörer påverkade effektiviteten på produktionslinjens bearbetning. Utifrån resultaten av dessa tester bestäms en optimal nivå för faktorerna som skall användas i full factorial design testerna. Med dessa tester utförda kunde det noteras att ingen optimering kunde utföras på produktionslinjen då simuleringen uppnådde endast 72% när målet för produktionslinjen är 75%. Istället gavs andra strategiska rekommendationer på vad företaget kunde göra för att öka effektiviteten på produktionslinjen. Dessa rekommendationer inkluderar, en tillfällig buffert, vänta ut de tillfälliga stoppen, åtgärda avvikande processteg och minska kassationerna. / The goal of this project was to optimize the production line to maximize the number of completed items produced. The project started with a planning and research of the tools which will be used such as simulation, lean principles, and information related to the production line. Thereafter, it was modelled in the simulation tool based on the data collection that was carried out and the simulation method. Once the model was ready, experimental tests were conducted to investigate how specific factors, such as strategically placed buffers and the number of operators, affected the efficiency of the production line's processing. Based on the results of these tests, an optimal level for the factors to be used in the full factorial design tests was determined. With these tests performed, it was noted that no optimization could be carried out on the production line as the simulation achieved only 72% efficiency when the target for the production line is 75%. Instead, other strategic recommendations were provided on what the company could do to increase the efficiency of the production line. These recommendations include, a temporary buffer, waiting out temporary stoppages, addressing deviant process steps, and reducing rejections.

The impementation of the Strategic Analysis of Bonatrans, a. s. is the main object of the Diploma Thesis. The firm is operating in the market as a supplier of the railway components. Due to the worldwide scope of activities of the firm, the Thesis is focused on the External analysis with the emphasis on the opportunities and threats seeking, arising from the increasing globalization, the high degree of international integration and the changes in the Railway Indusry Sector. The Thesis comprises The International Business Sector Analyis, Competitive Analysis and it takes into account the population growth, urbanization and the impact of climatic change and technological revolution. The practical part of the Thesis is fully based on the theoretical part. After determining the Business Factors of Success and Cmpetitive advantage the Strategic Recommendations for further development of the company are established.